dianshang-qianduan/src/views/portraitModel/components/rfmAnalysis.vue

<template>
  <div class="content">
    <div class="import" @click="openKnowledge">
      <img src="@/assets/images/导入.png" alt="" />
      <span style="color: #fff">任务知识导入</span>
    </div>
    <!-- <div v-html="knowledge" v-if="knowledgeFlag"></div> -->
    <div>
      <div class="caseBack">
        <div class="title">
          <img src="@/assets/images/caseIcon.png" alt="" />
          <span>案例背景：</span>
        </div>
        <span style="line-height: 25px; font-size: 14px">现有以下原始数据，需要采用RFM分析方法，对客户进行分类：</span>
        <div style="margin-top: 10px; display: flex">
          <el-table
            :data="fileTable1"
            style="width: 550px"
            border
            :header-cell-style="{
              color: '#fff',
              fontSize: '14px',
              backgroundColor: '#6A7DE9!important',
            }"
          >
            <el-table-column label="用户ID" align="center" prop="userId"></el-table-column>
            <el-table-column label="用户名称" align="center" prop="userName"></el-table-column>
            <el-table-column label="消费时间" align="center" prop="time"></el-table-column>
            <el-table-column label="消费金额" align="center" prop="amount"></el-table-column>
          </el-table>
          <div class="download" @click="downloadData">下载数据</div>
        </div>
      </div>
      <div class="line"></div>
      <div class="caseBack">
        <div class="title">
          <img src="@/assets/images/步骤.png" alt="" />
          <span>第一步，确定“近一段时间”的时间间隔</span>
        </div>
        <span style="line-height: 25px; font-size: 14px">计划分析最近30天的用户，假设现在是2025.05.30，需要对数据进行筛选，筛选出统计样本</span>
        <div style="margin-top: 10px; display: flex">
          <el-table
            :data="fileTable1"
            style="width: 650px"
            border
            :header-cell-style="{
              color: '#fff',
              fontSize: '14px',
              backgroundColor: '#6A7DE9!important',
            }"
          >
            <el-table-column label="用户ID" align="center" prop="userId"></el-table-column>
            <el-table-column label="用户名称" align="center" prop="userName"></el-table-column>
            <el-table-column label="消费时间" align="center" prop="time"></el-table-column>
            <el-table-column label="消费金额" align="center" prop="amount"></el-table-column>
            <el-table-column label="操作" align="center" prop="opr">
              <template #default="scope">
                <div style="display: flex; align-items: center">
                  <el-select v-model="scope.row.params" placeholder="请选择" style="width: 100px">
                    <el-option :label="'剔除'" :value="'剔除'" />
                    <el-option :label="'纳入'" :value="'纳入'" />
                  </el-select>
                  <div v-if="showAnswerFlag" style="margin-left: 3px">
                    <span v-if="scope.row.isError" style="color: red">X</span>
                    <span v-if="!scope.row.isError" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
          </el-table>
          <div class="submit" @click="step1Submit">提交</div>
        </div>
      </div>
      <div class="line"></div>
      <div class="caseBack">
        <div class="title">
          <img src="@/assets/images/步骤.png" alt="" />
          <span>第二步，计算每个用户的RFM值</span>
        </div>
        <span style="line-height: 25px; font-size: 14px">以用户为统计对象，计算出每个用户的R、F、M这3个指标</span>
        <div style="margin-top: 10px; display: flex">
          <el-table
            :data="step2Data"
            style="width: 720px"
            border
            :header-cell-style="{
              color: '#fff',
              fontSize: '14px',
              backgroundColor: '#6A7DE9!important',
            }"
          >
            <el-table-column label="用户ID" align="center" prop="userId"></el-table-column>
            <el-table-column label="用户名称" align="center" prop="userName"></el-table-column>
            <el-table-column label="最近一次消费时间间隔（R）" align="center" width="210">
              <template #default="scope">
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-input v-model="scope.row.params1" placeholder="请输入" style="width: 100px"></el-input>
                  <div v-if="showAnswerFlag" style="margin-left: 3px">
                    <span v-if="scope.row.isError1" style="color: red">X</span>
                    <span v-if="!scope.row.isError1" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
            <el-table-column label="消费频率（F）" align="center">
              <template #default="scope">
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-input v-model="scope.row.params2" placeholder="请输入" style="width: 100px"></el-input>
                  <div v-if="showAnswerFlag" style="margin-left: 3px">
                    <span v-if="scope.row.isError2" style="color: red">X</span>
                    <span v-if="!scope.row.isError2" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
            <el-table-column label="消费金额（M）" align="center">
              <template #default="scope">
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-input v-model="scope.row.params3" placeholder="请输入" style="width: 100px"></el-input>
                  <div v-if="showAnswerFlag" style="margin-left: 3px">
                    <span v-if="scope.row.isError3" style="color: red">X</span>
                    <span v-if="!scope.row.isError3" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
          </el-table>
          <div class="submit" @click="step2Submit">提交</div>
        </div>
      </div>
      <div class="line"></div>
      <div class="caseBack">
        <div class="title">
          <img src="@/assets/images/步骤.png" alt="" />
          <span>第三步，制定R、F、M评分规则</span>
        </div>
        <span style="line-height: 25px; font-size: 14px">
          最近一次消费时间间隔（R），上一次消费离得越近，也就是R的值越小，用户价值越高。
          <br />
          消费频率（F），购买频率越高，也就是F值越大，用户价值越高。
          <br />
          消费金额（M），消费金额越高，也就是M值越大，用户价值越高。
          <br />
          具体实际业务中，如何定义打分的范围，要根据具体的业务来灵活定，没有统一的标准。将R、F、M3个指标分别按价值从小到大分为1-5分。如下表所示：
        </span>
        <div style="margin-top: 10px; display: flex">
          <el-table
            :data="step3Data"
            style="width: 650px"
            border
            :header-cell-style="{
              color: '#fff',
              fontSize: '14px',
              backgroundColor: '#6A7DE9!important',
            }"
          >
            <el-table-column label="按价值打分" align="center" prop="data1"></el-table-column>
            <el-table-column label="最近一次消费时间间隔(R)" align="center" prop="data2" width="210"></el-table-column>
            <el-table-column label="消费频率(F)" align="center" prop="data3"></el-table-column>
            <el-table-column label="消费金额(M)" align="center" prop="data4"></el-table-column>
          </el-table>
        </div>
      </div>
      <div class="line"></div>
      <div class="caseBack">
        <div class="title">
          <img src="@/assets/images/步骤.png" alt="" />
          <span>第四步，给R、F、M按价值打分</span>
        </div>
        <span style="line-height: 25px; font-size: 14px">按照第二步统计的数值，结合第三步的评估规则，对用户R、F、M值进行评分</span>
        <div style="margin-top: 10px; display: flex">
          <el-table
            :data="step4Data"
            style="width: 1080px"
            border
            :header-cell-style="{
              color: '#fff',
              fontSize: '14px',
              backgroundColor: '#6A7DE9!important',
            }"
          >
            <el-table-column label="用户ID" align="center" prop="data1" width="70"></el-table-column>
            <el-table-column label="用户名称" align="center" prop="data2"></el-table-column>
            <el-table-column label="最近一次消费时间间隔（R）" align="center" prop="data3" width="210"></el-table-column>
            <el-table-column label="消费频率（F）" align="center" prop="data4"></el-table-column>
            <el-table-column label="消费金额（M）" align="center" prop="data5"></el-table-column>
            <el-table-column label="R值打分" align="center" prop="data4">
              <template #default="scope">
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-input v-model="scope.row.params1" placeholder="请输入" style="width: 100px"></el-input>
                  <div v-if="showAnswerFlag" style="margin-left: 3px">
                    <span v-if="scope.row.isError1" style="color: red">X</span>
                    <span v-if="!scope.row.isError1" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
            <el-table-column label="F值打分" align="center" prop="data4">
              <template #default="scope">
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-input v-model="scope.row.params2" placeholder="请输入" style="width: 100px"></el-input>
                  <div v-if="showAnswerFlag" style="margin-left: 3px">
                    <span v-if="scope.row.isError2" style="color: red">X</span>
                    <span v-if="!scope.row.isError2" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
            <el-table-column label="M值打分" align="center" prop="data4">
              <template #default="scope">
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-input v-model="scope.row.params3" placeholder="请输入" style="width: 100px"></el-input>
                  <div v-if="showAnswerFlag" style="margin-left: 3px">
                    <span v-if="scope.row.isError3" style="color: red">X</span>
                    <span v-if="!scope.row.isError3" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
          </el-table>
          <div class="submit" @click="step4Submit">提交</div>
        </div>
      </div>
      <div class="line"></div>
      <div class="caseBack">
        <div class="title">
          <img src="@/assets/images/步骤.png" alt="" />
          <span>第五步，计算所有用户R、F、M打分值的平均数</span>
        </div>
        <span style="line-height: 25px; font-size: 14px">按照第四步给各个用户R、F、M的打分值，分别计算各自的平均数（小数点后保留两位小数）</span>
        <div style="margin-top: 10px; display: flex">
          <el-table
            :data="step5Data"
            style="width: 680px"
            border
            :header-cell-style="{
              color: '#fff',
              fontSize: '14px',
              backgroundColor: '#6A7DE9!important',
            }"
          >
            <el-table-column label="" align="center" prop="title" />
            <el-table-column label="R值打分" align="center">
              <template #default="scope">
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-input v-model="scope.row.params1" placeholder="请输入" style="width: 100px"></el-input>
                  <div v-if="showAnswerFlag" style="margin-left: 3px">
                    <span v-if="scope.row.isError1" style="color: red">X</span>
                    <span v-if="!scope.row.isError1" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
            <el-table-column label="F值打分" align="center">
              <template #default="scope">
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-input v-model="scope.row.params2" placeholder="请输入" style="width: 100px"></el-input>
                  <div v-if="showAnswerFlag" style="margin-left: 3px">
                    <span v-if="scope.row.isError2" style="color: red">X</span>
                    <span v-if="!scope.row.isError2" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
            <el-table-column label="M值打分" align="center">
              <template #default="scope">
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-input v-model="scope.row.params3" placeholder="请输入" style="width: 100px"></el-input>
                  <div v-if="showAnswerFlag" style="margin-left: 3px">
                    <span v-if="scope.row.isError3" style="color: red">X</span>
                    <span v-if="!scope.row.isError3" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
          </el-table>
          <div class="submit" @click="step5Submit">提交</div>
        </div>
      </div>
      <div class="line"></div>
      <div class="caseBack">
        <div class="title">
          <img src="@/assets/images/步骤.png" alt="" />
          <span>第六步，计算所有用户R、F、M打分值与平均数的高低情况</span>
        </div>
        <span style="line-height: 25px; font-size: 14px">判断每个用户的价值值R、F、M3个值是高于平均值，还是低于平均值。如果一行里的R值打分大于平均值，就在R值高低列里记录为“高”，否则记录为“低”</span>
        <div style="margin-top: 10px; display: flex">
          <el-table
            :data="step6Data"
            :span-method="tableSpanMethod"
            style="width: 980px"
            border
            :header-cell-style="{
              color: '#fff',
              fontSize: '14px',
              backgroundColor: '#6A7DE9!important',
            }"
          >
            <el-table-column label="用户ID" align="center" prop="data1" />
            <el-table-column label="用户名称" align="center" prop="data2" />
            <el-table-column label="R值打分" align="center" prop="data3"></el-table-column>
            <el-table-column label="F值打分" align="center" prop="data4"></el-table-column>
            <el-table-column label="M值打分" align="center" prop="data5"></el-table-column>
            <el-table-column label="R值高低" align="center" width="130">
              <template #default="scope">
                <!-- <el-input
                  v-model="scope.row.params1"
                  placeholder="请输入"
                  style="width: 100px"
                  v-if="scope.$index != 3"
                >
                </el-input> -->
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-select v-model="scope.row.params1" placeholder="请选择" style="width: 100px" v-if="scope.$index != 3">
                    <el-option :label="'高'" :value="'高'" />
                    <el-option :label="'低'" :value="'低'" />
                  </el-select>
                  <div v-if="showAnswerFlag & (scope.$index != 3)" style="margin-left: 3px">
                    <span v-if="scope.row.isError1" style="color: red">X</span>
                    <span v-if="!scope.row.isError1" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
            <el-table-column label="F值高低" align="center" width="130">
              <template #default="scope">
                <!-- <el-input
                  v-model="scope.row.params2"
                  placeholder="请输入"
                  style="width: 100px"
                  v-if="scope.$index != 3"
                >
                </el-input> -->
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-select v-model="scope.row.params2" placeholder="请选择" style="width: 100px" v-if="scope.$index != 3">
                    <el-option :label="'高'" :value="'高'" />
                    <el-option :label="'低'" :value="'低'" />
                  </el-select>
                  <div v-if="showAnswerFlag & (scope.$index != 3)" style="margin-left: 3px">
                    <span v-if="scope.row.isError2" style="color: red">X</span>
                    <span v-if="!scope.row.isError2" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
            <el-table-column label="M值高低" align="center" width="130">
              <template #default="scope">
                <!-- <el-input
                  v-model="scope.row.params3"
                  placeholder="请输入"
                  style="width: 100px"
                  v-if="scope.$index != 3"
                >
                </el-input> -->
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-select v-model="scope.row.params3" placeholder="请选择" style="width: 100px" v-if="scope.$index != 3">
                    <el-option :label="'高'" :value="'高'" />
                    <el-option :label="'低'" :value="'低'" />
                  </el-select>
                  <div v-if="showAnswerFlag & (scope.$index != 3)" style="margin-left: 3px">
                    <span v-if="scope.row.isError3" style="color: red">X</span>
                    <span v-if="!scope.row.isError3" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
          </el-table>
          <div class="submit" @click="step6Submit">提交</div>
        </div>
      </div>
      <div class="line"></div>
      <div class="caseBack">
        <div class="title">
          <img src="@/assets/images/步骤.png" alt="" />
          <span>第七步，用户分类</span>
        </div>
        <span style="line-height: 25px; font-size: 14px">按照用户分类表格里定义的规则进行比较，就可以得出用户属于哪种类。</span>
        <div style="text-align: center; width: 800px">
          <p>用户分类规则</p>
          <el-table
            :data="rulesTable"
            style="width: 800px"
            border
            :header-cell-style="{
              color: '#fff',
              fontSize: '14px',
              backgroundColor: '#6A7DE9!important',
            }"
          >
            <el-table-column label="用户分类" align="center" prop="data1" />
            <el-table-column label="最近一次消费时间间隔（R）" align="center" prop="data2" width="220" />
            <el-table-column label="消费频率（F）" align="center" prop="data3" />
            <el-table-column label="消费金额（M）" align="center" prop="data4" />
          </el-table>
        </div>
        <div style="margin-top: 10px; display: flex">
          <el-table
            :data="step7Data"
            style="width: 980px"
            border
            :header-cell-style="{
              color: '#fff',
              fontSize: '14px',
              backgroundColor: '#6A7DE9!important',
            }"
          >
            <el-table-column label="用户ID" align="center" prop="data1" />
            <el-table-column label="用户名称" align="center" prop="data2" />
            <el-table-column label="R值高低" align="center" prop="data3"></el-table-column>
            <el-table-column label="F值高低" align="center" prop="data4"></el-table-column>
            <el-table-column label="M值高低" align="center" prop="data5"></el-table-column>
            <el-table-column label="用户分类" align="center" width="200">
              <template #default="scope">
                <div style="display: flex; align-items: center; justify-content: center">
                  <el-select v-model="scope.row.params" placeholder="请选择" style="width: 170px">
                    <el-option :label="item.label" :value="item.value" v-for="item in classifyList" :key="item.value" />
                  </el-select>
                  <div v-if="showAnswerFlag & (scope.$index != 3)" style="margin-left: 3px">
                    <span v-if="scope.row.isError1" style="color: red">X</span>
                    <span v-if="!scope.row.isError1" style="color: #00bb00">√</span>
                  </div>
                </div>
              </template>
            </el-table-column>
          </el-table>
          <div class="submit" @click="step7Submit">提交</div>
        </div>
      </div>
      <div class="line"></div>
      <div class="caseBack">
        <div class="title">
          <img src="@/assets/images/步骤.png" alt="" />
          <span>第八步，精细化运营</span>
        </div>
        <span style="line-height: 25px; font-size: 14px">对用户分类以后要做什么呢？那就是针对每类用户如何制定运营策略，这个具体公司业务不同，方法也不一样。这里举例说明前4类用户。</span>
        <div class="customerType">
          <img src="@/assets/images/icon001.png" alt="" />
          <span>重要价值用户，RFM三个值都很高，要提供VIP服务</span>
        </div>
        <div class="customerType">
          <img src="@/assets/images/icon002.png" alt="" />
          <span>重要发展用户，消费频率低，但是其他两个值很高，就要想办法提高他的消费频率。</span>
        </div>
        <div class="customerType">
          <img src="@/assets/images/icon003.png" alt="" />
          <span>重要保持用户，最近消费距离现在时间较远，也就是F值低，但是消费频次和消费金额高。这种用户，是一段时间没来的忠实客户。应该主动和他保持联系，提高复购率。</span>
        </div>
        <div class="customerType">
          <img src="@/assets/images/icon004.png" alt="" />
          <span>重要挽留客户，最近消费时间距离现在较远、消费频率低，但消费金额高。这种用户，即将流失，要主动联系用户，调查清楚哪里出了问题，并想办法挽回。</span>
        </div>
      </div>
    </div>
    <el-dialog v-model="dialogVisible" title="任务知识导入" style="width: 1108px; height: 798px" custom-class="gdialog" @close="handlClose">
      <div style="color: #fff; margin-top: -35px; padding: 10px; padding-left: 20px">
        <el-scrollbar height="650px">
          <div v-html="knowledge"></div>
        </el-scrollbar>
      </div>
      <!-- <template #footer>
        <div class="dialog-footer">
          <el-button @click="dialogVisible = false">确定</el-button>
          <el-button type="primary" @click="dialogVisible = false">
            返回
          </el-button>
        </div>
      </template> -->
    </el-dialog>
  </div>
</template>

<script setup>
import { getUserInfo } from "@/utils/auth";
import * as portraitModel from "@/api/portraitModel";
import { downBlobFile } from "@/utils/index.js";
import { onBeforeUnmount, onMounted } from "vue";
const baseUrl = import.meta.env.VITE_APP_BASE_API;
const dialogVisible = ref(false);
const { proxy } = getCurrentInstance();
const timer = ref(null);
const timeCount = ref(0);
const errorNum = ref(0); //总错误次数
const stepError1 = ref(0);
const stepError2 = ref(0);
const step3Error = ref(0);
const step4Error = ref(0);
const step5Error = ref(0);
const step6Error = ref(0);
const step7Error = ref(0);
//判断每一步是否提交
const step1AnswerFlag = ref(false);
const step2AnswerFlag = ref(false);
const step4AnswerFlag = ref(false);
const step5AnswerFlag = ref(false);
const step6AnswerFlag = ref(false);
const step7AnswerFlag = ref(false);
const showAnswerFlag = ref(false);
const knowledgeFlag = ref(false);
const knowledge = ref(
  '<p style="text-align: start;"><strong>1.什么是RFM分析方法？</strong></p><p style="text-align: start;"><img src="data:image/png;base64,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" alt="18c201b733cb5541c64e36a6cbb28af.png" data-href="" style=""/></p><p style="text-align: start;">RFM是3个指标的缩写，最近一次消费时间间隔（Recency），消费频率（Frequency），消费金额（Monetary）。通过这3个指标对用户分类。</p><p style="text-align: start;">这里举个例子来说明这3个指标是什么意思。</p><p style="text-align: start;">比如我有一家店铺，小明是这家店铺的用户。现在是这个月的30号。</p><p style="text-align: start;">1）最近1次消费时间间隔（R）是指用户最近一次消费距离现在多长时间了。</p><p style="text-align: start;">小明最近1次在这就店铺买东西是这个月25号，上一次消费距离现在过去了5天，所以小明的最近1次消费时间间隔是5天。</p><p style="text-align: start;">2）消费频率（F）是指用户一段时间内消费了多少次。</p><p style="text-align: start;">比如“一段时间”定义是最近30天，发现小明最近30天在店铺消费了3次。</p><p style="text-align: start;">3）消费金额（M）是指用户一段时间内的消费金额。</p><p style="text-align: start;">比如“一段时间”是最近30天，发现小明最近30天总共在店铺消费了1000元。</p><p style="text-align: start;">这3个指标业务不同，定义也不同，要根据业务来灵活定义。</p><p style="text-align: start;">最近一次消费时间间隔（R），上一次消费离得越近，也就是R的值越小，用户价值越高。</p><p style="text-align: start;">消费频率（F），购买频率越高，也就是F的值越大，用户价值越高。</p><p style="text-align: start;">消费金额（M），消费金额越高，也就是M的值越大，用户价值越高。</p><p style="text-align: start;">我们把这3个指标按价值从低到高排序，并把这3个指标作为XYZ坐标轴，就可以把空间分为8部分，这样就可以把用户分为下图的8类。</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/FBq54G3akgrMEnC.png" /></p><p style="text-align: start;"><br></p><p style="text-align: start;">我们把这个图里对应的RFM这3个值对应的价值是高还是低，对应到下面这张表里就得到了用户分类的规则。</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/A2Cbolx7eDn5LQa.png" /></p><p style="text-align: start;"><br></p><p style="text-align: start;"><strong>2.RFM分析方法有什么用？</strong></p><p style="text-align: start;">小明的店铺某个月收入大幅下跌。他赶快分析数据，发行原来几个重要的用户被竞争对手挖走了，而这几个用户贡献了店铺80%的收入。</p><p style="text-align: start;">出现这个问题，是因为小明没有对用户分类，对全部用户采取的都是一样的运营决策。怎么对用户分类，识别出有价值的用户呢？</p><p style="text-align: start;">这时候就可以用RFM分析方法把用户分为8类，这样就可以对不同价值用户使用不同的营销决策，把公司有限的资源发挥最大的效果，这就是我们常常听到的精细化运营。比如第1类是重要价值用户，这类用户最近一次消费较近，消费频率也搞，消费金额也搞，要提供vip服务。</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/ehiFpdrPa6tSBqH.png" /></p><p style="text-align: start;">我们日常生活中接触到的会员服务就是这方面的经典案例。根据企鹅智库《2017中国会员经济数据报告》数据显示，用户对信用卡、酒店、航空公司的会员体系最满意。</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/kCyUQMHf8Y1jVIE.png" /></p><p style="text-align: start;"><br></p><p style="text-align: start;"><strong>3.如何使用RFM分析方法？</strong></p><p style="text-align: start;">前面我们说RFM分析方法把用户分为8类，具体是如何做到的呢？</p><p style="text-align: start;"><strong>第1步）计算R、F、M值</strong></p><p style="text-align: start;">要得到R、F、M这3个指标，我们一般需要数据的3个字段：用户ID或者用户名称、消费时间、消费金额。从这3个字段我们可以计算出R、F、M这3个指标。</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/l6TcvGrs5VmxFzu.png" /></p><p style="text-align: start;">以图片中的原始数据为例，现在比如是2020年1月30日，分析最近30天的用户。</p><p style="text-align: start;">小明最近一次消费是（2020.1.26）与现在时间的间隔是4天。在最近30天消费了2次，总共消费金额是5000元。</p><p style="text-align: start;">用这个方法，假如我们这个案例里计算出的是下面表格里的R、F、M值，并在表格里加了3列，用于后面对计算出的R、F、M3个值打分。</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/Wtzb3vLhUc5FE19.png" /></p><p style="text-align: start;"><br></p><p style="text-align: start;"><strong>第2步）给R、F、M按价值打分</strong></p><p style="text-align: start;">注意这里是按指标的价值打分，不是按指标数值大小打分。最近一次消费时间间隔（R），上一次消费离得越近，也就是R的值越小，用户价值越高。</p><p style="text-align: start;">将R、F、M3个指标分别按价值从小到大分为1-5分。</p><p style="text-align: start;">这个案例里，将最近消费时间间隔（R）距离越近价值越大，大于20天的打1分，10-20天的打2分，5-10天打2分，3-5天打4分，3天以内打5分。</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/xnvNu4yhZReOUjM.png" /></p><p style="text-align: start;"><br></p><p style="text-align: start;">我们把这3个指标分别是如何打分的，整理到下面这个表里。</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/G4pZF5awcnDdsgW.png" /></p><p style="text-align: start;">最近一次消费时间间隔（R），上一次消费离得越近，也就是R的值越小，用户价值越高。</p><p style="text-align: start;">消费频率（F），购买频率越高，也就是F值越大，用户价值越高。</p><p style="text-align: start;">消费金额（M），消费金额越高，也就是M值越大，用户价值越高。</p><p style="text-align: start;">具体实际业务中，如何定义打分的范围，要根据具体的业务来灵活定，没有统一的标准。</p><p style="text-align: start;">根据这个打分规则，我们可以把前面计算出的R、F、M值表格里的R值分类、F值分类、M值分类这3列填上上对应的RFM值的打分值：</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/gItQqF3OPzmDf5L.png" /></p><p style="text-align: start;"><br></p><p style="text-align: start;"><strong>第3步）计算价值平均值</strong></p><p style="text-align: start;">分别计算出R值打分、F值打分、M值打分这3列的平均值</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/4PAETJgv96Le3YN.png" /></p><p style="text-align: start;"><br></p><p style="text-align: start;"><strong>第4步）用户分类</strong></p><p style="text-align: start;">在表格里增加3列，分别用于记录R、F、M3个值是高于平均值，还是低于平均值。</p><p style="text-align: start;">如果一行里的R值打分大于平均值，就在R值高低列里记录为“高”，否则记录为“低”。其他F值，M值也这样比较。最终得到了下面表格里的值</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/3HSAr5KgZ17EUwb.png" alt="" data-href="" style="width: 690px;height: auto;"></p><p style="text-align: start;">然后和用户分类表格里定义的规则进行比较，就可以得出用户属于哪种类</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/Kcf7vHjJ3a4NX2d.png" alt="" data-href="" style="width: 690px;height: auto;"></p><p style="text-align: start;"><br></p><p style="text-align: start;"><strong>我们总结下前面的内容：如何用RFM对用户分类？</strong></p><p style="text-align: start;">1）使用原始数据计算出R、F、M值</p><p style="text-align: start;">2）给R、F、M按价值打分，比如按价值从低到高分为1-5分</p><p style="text-align: start;">3）计算价值的平均值，如果某个指标的得分比价值的平均值低，标记为低。如果某个指标的得分比价值的平均值高，标记为高</p><p style="text-align: start;">4）用户分类规则表比较，得出用户分类</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/jaFqWfY8NIlVhnK.png" alt="" data-href="" style="width: 690px;height: auto;"></p><p style="text-align: start;">我们再回过头看前面这个分类图，你就会恍然大悟。在坐标轴的中心，可以理解为某个指标价值的平均值。</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/cWhwdipAkzKXb6L.png" alt="" data-href="" style="width: 690px;height: auto;"></p><p style="text-align: start;"><br></p><p style="text-align: start;"><strong>用户分类后，如何精细化运营呢？</strong></p><p style="text-align: start;">对用户分类以后要做什么呢？那就是针对每类用户如何制定运营策略，这个具体公司业务不同，方法也不一样。这里举例说明前4类用户。</p><p style="text-align: start;">1）重要价值用户，RFM三个值都很高，要提供vip服务</p><p style="text-align: start;">2）重要发展用户，消费频率低，但是其他两个值很高，就要想办法提高他的消费频率</p><p style="text-align: start;">3） 重要保持用户，最近消费距离现在时间较远，也就是F值低，但是消费频次和消费金额高。这种用户，是一段时间没来的忠实客户。应该主动和他保持联系，提高复购率</p><p style="text-align: start;">4） 重要挽留客户，最近消费时间距离现在较远、消费频率低，但消费金额高。这种用户，即将流失，要主动联系用户，调查清楚哪里出了问题，并想办法挽回。</p><p style="text-align: start;">这样通过RFM分析方法来分析用户，对用户进行精细化运营。不断将用户转化为重要价值用户。</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/lKj3ky8UBcXqF2b.png" alt="" data-href="" style="width: 690px;height: auto;"></p><p style="text-align: start;"><br></p><p style="text-align: start;"><strong>4.使用RFM分析方法需要注意什么？</strong></p><p style="text-align: start;">第1个问题：什么是？ 我们学习了，RFM是3个指标的缩写，最近一次消费的时间间隔（R），消费频率（F），消费金额（M）。RFM分析方法可以把用户分为8类。</p><p style="text-align: start;">第2个问题：有什么用？我们学习了，通RFM分析方法，我们可以把用户分为8类，这样就可以对不同用户使用不同的营销策略。比如信用卡的会员服务。</p><p style="text-align: start;">第3个问题：如何用？通过一个案例学会了如何用RFM分析方法对用户分类，了解了RFM背后的原理。现在有很多工具，比如excel，sql，pytho等都可以实现RFM。工作里用到的时候，可以再用搜索引擎去搜对应的实现工具，就可以快速实现了，现在把基本原理掌握了就可以。</p><p style="text-align: start;">现在我们来看<strong>第4个问题：有哪些注意事项？</strong></p><p style="text-align: start;"><strong>（1）第1个注意事项：R、F、M指标定义，不同业务定义不同。要根据具体业务灵活应用。</strong></p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/aKO6ivUlSVTxeIZ.png" alt="" data-href="" style="width: 690px;height: auto;"></p><p style="text-align: start;">这里举个例子。</p><p style="text-align: start;">很多人会有这样的疑问，朋友之间不经常联系，慢慢就淡了。那么，如何管理人脉关系呢？</p><p style="text-align: start;">我们还可以用RFM分析方法来分析你的人脉，针对不同的人，制定不同的联系决策。</p><p style="text-align: start;">最近一次消费时间间隔（R）可以定义为最近一次见面距离现在的时间。</p><p style="text-align: start;">消费频率（F）可以定义为最近1年见面的频率。</p><p style="text-align: start;">消费金额（M）可以定义为最近1年见面深度，比如见面是只打个招呼，还是一起吃个饭，还是畅谈人生。</p><p style="text-align: start;">按照这个定义，我们可以重点关注前3类人脉关系。</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/aAZ13FOPU4KyYro.png" alt="" data-href="" style="width: 690px;height: auto;"></p><p style="text-align: start;">按照这个定义，我们可以重点关注前3类人脉关系</p><p style="text-align: start;">1）重要价值关系：对你的生活和工作非常有价值。他几乎是你最亲密的亲戚、朋友、客户。面对这些人，你应该经常联系，彼此帮助，时不时约出来聊聊天。</p><p style="text-align: start;">2）重要发展关系：联系比较多、一起做过点事，但是聊天是有一句没一句的，这种要重点发展关系。比如把你的困惑或者小秘密和他分享，产生情感连接。</p><p style="text-align: start;">3）重要保持的关系：所谓的熟人，也就是打起电话来记得住这个人，而且也大概了解他的背景，可能很长时间都没有见的那种“朋友”。这种要主动联系，利用节假日登门拜访、共同的朋友持续保持沟通。</p><p style="text-align: start;"><br></p><p style="text-align: start;">冯仑讲过一段话：在正常情况下，人一生交往的关系是三个数：10、30和60。这句话是什么意思呢？其实正好对应我们刚才讲的三种人脉关系。</p><p style="text-align: start;">你遇到危难的时候，能借钱的对象不超过10个人。就算是把亲戚、朋友、父母全算上，你到时候能张口借钱的对象不会超过10个人。</p><p style="text-align: start;">再一层就是属于朋友，经常打交道的、做过点事的，大概不超过30个人。这30人还包括前面说的那10个人。</p><p style="text-align: start;">最外一圈是所谓的熟人，也就是打起电话来记得住这个人，而且也大概了解他的背景，可能很长时间都没有见的那种“朋友”，最多也就是60个。这60个人还包括了前面说的30人。</p><p style="text-align: start;">所以，人这一生，其实不需要太多的关系就能应付得了。需要花精力去了解的人，其实很少，不会很多，不会超过60个。只要把这60个人的名单每天都盘好，能够用一生了。</p><p style="text-align: start;">我有个朋友是这方面高手，他把经常要联系的人记下来，每个月翻出来看看，“这个朋友有3个月没联系了，要约出来聊聊天”。</p><p style="text-align: start;">你看，在关系中，如果两个都被动，那只能慢慢断了联系。所以，保持关系的秘密就是需要其中的一方主动。</p><p style="text-align: start;">人脉比你想象的重要，为了保持有效关系，你必须把自己变成一个在这方面主动的人。</p><p style="text-align: start;"><br></p><p style="text-align: start;"><strong>（2）第2个注意事项：R、F、M按价值如何确定打分的规则？</strong></p><p style="text-align: start;">一般分为1-5分，也可以根据具体业务灵活来调整。</p><p style="text-align: start;">每个分数的范围是多少？这要根据具体业务来定范围的阈值，这就好比你在开车，车速控制在哪个范围内，可以根据路况灵活把握。</p><p style="text-align: start;">同样的，FRM打分的规则可以与业务部门沟通，进行头脑风暴。或者使用聚类的方法对R、F、M的值进行分类，然后给每个类别打分。</p><p style="text-align: start;"><br></p><p style="text-align: start;"><strong>5.总结</strong></p><p style="text-align: start;">我把前面的知识点总结到下面这张图里</p><p style="text-align: start;"><img src="https://s2.loli.net/2024/08/27/wjudWSbksYBrElp.png" alt="" data-href="" style=""></p>'
);
const fileTable1 = ref([
  {
    userId: "1",
    userName: "小明",
    time: "2024.04.28",
    amount: "800",
  },
  {
    userId: "2",
    userName: "小红",
    time: "2024.05.03",
    amount: "300",
  },
  {
    userId: "1",
    userName: "小明",
    time: "2024.05.06",
    amount: "3000",
  },
  {
    userId: "1",
    userName: "小明",
    time: "2024.05.26",
    amount: "2000",
  },
  {
    userId: "2",
    userName: "小红",
    time: "2024.05.27",
    amount: "300",
  },
  {
    userId: "2",
    userName: "小红",
    time: "2024.05.28",
    amount: "400",
  },
  {
    userId: "3",
    userName: "小李",
    time: "2024.05.29",
    amount: "500",
  },
]);
const step2Data = ref([
  {
    userId: "1",
    userName: "小明",
  },
  { userId: "2", userName: "小红" },
  { userId: "3", userName: "小李" },
]);
const step3Data = ref([
  {
    data1: "1",
    data2: "20天以上",
    data3: "2次以内",
    data4: "1000元以内",
  },
  {
    data1: "2",
    data2: "10-20天",
    data3: "2-6次",
    data4: "1000-1500元",
  },
  {
    data1: "3",
    data2: "5-10天",
    data3: "6-8次",
    data4: "1500-3000元",
  },

  {
    data1: "4",
    data2: "3-5天",
    data3: "10-20次",
    data4: "3000-5000元",
  },
  {
    data1: "5",
    data2: "3天以内",
    data3: "20次以上",
    data4: "5000元以上",
  },
]);
const step4Data = ref([
  {
    data1: "1",
    data2: "小明",
    data3: "4",
    data4: "2",
    data5: "5000",
  },
  {
    data1: "2",
    data2: "小红",
    data3: "2",
    data4: "3",
    data5: "1000",
  },
  {
    data1: "3",
    data2: "小李",
    data3: "1",
    data4: "1",
    data5: "500",
  },
]);
const step5Data = ref([
  {
    title: "平均值",
  },
]);
const step6Data = ref([
  {
    data1: "1",
    data2: "小明",
    data3: "",
    data4: "",
    data5: "",
  },
  {
    data1: "2",
    data2: "小红",
    data3: "",
    data4: "",
    data5: "",
  },
  {
    data1: "3",
    data2: "小李",
    data3: "",
    data4: "",
    data5: "",
  },
  {
    data1: "平均值",
    data2: "",
    data3: "",
    data4: "",
    data5: "",
  },
]);
const step7Data = ref([
  {
    data1: "1",
    data2: "小明",
    data3: "",
    data4: "",
    data5: "",
  },
  {
    data1: "2",
    data2: "小红",
    data3: "",
    data4: "",
    data5: "",
  },
  {
    data1: "3",
    data2: "小李",
    data3: "",
    data4: "",
    data5: "",
  },
]);
const rulesTable = ref([
  {
    data1: "1.重要价值用户",
    data2: "高",
    data3: "高",
    data4: "高",
  },
  {
    data1: "2.重要发展用户",
    data2: "高",
    data3: "低",
    data4: "高",
  },
  {
    data1: "3.重要保持用户",
    data2: "低",
    data3: "高",
    data4: "高",
  },
  {
    data1: "4.重要挽留用户",
    data2: "低",
    data3: "低",
    data4: "高",
  },
  {
    data1: "5.一般价值用户",
    data2: "高",
    data3: "高",
    data4: "低",
  },
  {
    data1: "6.一般发展用户",
    data2: "高",
    data3: "低",
    data4: "低",
  },
  {
    data1: "7.一般保持用户",
    data2: "低",
    data3: "高",
    data4: "低",
  },
  {
    data1: "6.一般挽留用户",
    data2: "低",
    data3: "低",
    data4: "低",
  },
]);
const classifyList = ref([
  {
    value: "重要价值用户",
    label: "1.重要价值用户",
  },
  {
    value: "重要发展用户",
    label: "2.重要发展用户",
  },
  {
    value: "重要保持用户",
    label: "3.重要保持用户",
  },
  {
    value: "重要挽留用户",
    label: "4.重要挽留用户",
  },
  {
    value: "一般价值用户",
    label: "5.一般价值用户",
  },
  {
    value: "一般发展用户",
    label: "6.一般发展用户",
  },
  {
    value: "一般保持用户",
    label: "7.一般保持用户",
  },
  {
    value: "一般挽留用户",
    label: "8.一般挽留用户",
  },
]);
onBeforeUnmount(() => {
  saveData();
});
onMounted(() => {
  getData();
});
const downloadData = () => {
  // portraitModel
  //   .downloadDataByRFM({})
  //   .then((res) => {
  //     // console.log(res,'--');
  //     const blob = new Blob([res], {
  //       type: "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
  //     });
  //     downBlobFile(`案例数据-RFM分析`, blob);
  //   })
  //   .catch((error) => {});
  window.open(baseUrl + "/api/downDataByUserPortrait/analysisByRfmDown");
};
const step1Submit = () => {
  // errorNum.value = 0;
  stepError1.value = 0;
  for (let i in fileTable1.value) {
    // console.log(fileTable1.value[i]);
    if (!fileTable1.value[i].params) {
      proxy.$modal.msgError("请先完成所有填空");
      return;
    }
    if (i == 0) {
      if (fileTable1.value[0].params && fileTable1.value[0].params == "纳入") {
        stepError1.value++;
        fileTable1.value[0].isError = true;
      } else {
        fileTable1.value[0].isError = false;
      }
      // console.log(fileTable1.value[0].params);
    } else {
      if (fileTable1.value[i].params && fileTable1.value[i].params == "剔除") {
        stepError1.value++;
        fileTable1.value[i].isError = true;
      } else {
        fileTable1.value[i].isError = false;
      }
    }
  }
  console.log(fileTable1.value, "000");
  proxy.$modal.msgSuccess("提交成功");
  console.log(stepError1.value, "错误次数");
  step1AnswerFlag.value = true;
};
const step2Submit = () => {
  stepError2.value = 0;
  for (let i in step2Data.value) {
    // console.log(fileTable1.value[i]);
    if (!step2Data.value[i].params1) {
      proxy.$modal.msgError("请先完成所有填空");
      return;
    }
  }
  // let stepError2 = 0;
  console.log(555);
  if (step2Data.value[0].params1 && step2Data.value[0].params1 !== "4") {
    stepError2.value++;
    step2Data.value[0].isError1 = true;
  } else {
    step2Data.value[0].isError1 = false;
  }
  if (step2Data.value[0].params2 && step2Data.value[0].params2 !== "2") {
    stepError2.value++;
    step2Data.value[0].isError2 = true;
  } else {
    step2Data.value[0].isError2 = false;
  }
  if (step2Data.value[0].params3 && step2Data.value[0].params3 !== "5000") {
    stepError2.value++;
    step2Data.value[0].isError3 = true;
  } else {
    step2Data.value[0].isError3 = false;
  }
  if (step2Data.value[1].params1 && step2Data.value[1].params1 !== "2") {
    stepError2.value++;
    step2Data.value[1].isError1 = true;
  } else {
    step2Data.value[1].isError1 = false;
  }
  if (step2Data.value[1].params2 && step2Data.value[1].params2 !== "3") {
    stepError2.value++;
    step2Data.value[1].isError2 = true;
  } else {
    step2Data.value[1].isError2 = false;
  }
  if (step2Data.value[1].params3 && step2Data.value[1].params3 !== "1000") {
    stepError2.value++;
    step2Data.value[1].isError3 = true;
  } else {
    step2Data.value[1].isError3 = false;
  }
  if (step2Data.value[2].params1 && step2Data.value[2].params1 !== "1") {
    stepError2.value++;
    step2Data.value[2].isError1 = true;
  } else {
    step2Data.value[2].isError1 = false;
  }
  if (step2Data.value[2].params2 && step2Data.value[2].params2 !== "1") {
    stepError2.value++;
    step2Data.value[2].isError2 = true;
  } else {
    step2Data.value[2].isError2 = false;
  }
  if (step2Data.value[2].params3 && step2Data.value[2].params3 !== "500") {
    stepError2.value++;
    step2Data.value[2].isError3 = true;
  } else {
    step2Data.value[2].isError3 = false;
  }
  console.log(stepError2.value, "错误数");
  proxy.$modal.msgSuccess("提交成功");
  step2AnswerFlag.value = true;
  //将所填值同步到第四步表格
  step4Data.value[0].data3 = step2Data.value[0].params1;
  step4Data.value[0].data4 = step2Data.value[0].params2;
  step4Data.value[0].data5 = step2Data.value[0].params3;
  step4Data.value[1].data3 = step2Data.value[1].params1;
  step4Data.value[1].data4 = step2Data.value[1].params2;
  step4Data.value[1].data5 = step2Data.value[1].params3;
  step4Data.value[2].data3 = step2Data.value[2].params1;
  step4Data.value[2].data4 = step2Data.value[2].params2;
  step4Data.value[2].data5 = step2Data.value[2].params3;
};
const step4Submit = () => {
  // console.log(6454546);
  step4Error.value = 0;
  for (let i in step4Data.value) {
    if (!step4Data.value[i].params1) {
      proxy.$modal.msgError("请先完成所有填空");
      return;
    }
  }
  // let step4Error = 0;
  if (step4Data.value[0].params1 && step4Data.value[0].params1 !== "4") {
    step4Error.value++;
    step4Data.value[0].isError1 = true;
  } else {
    step4Data.value[0].isError1 = false;
  }
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    step4Error.value++;
    step4Data.value[0].isError2 = true;
  } else {
    step4Data.value[0].isError2 = false;
  }
  if (step4Data.value[0].params3 && step4Data.value[0].params3 !== "4") {
    step4Error.value++;
    step4Data.value[0].isError3 = true;
  } else {
    step4Data.value[0].isError3 = false;
  }
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    step4Error.value++;
    step4Data.value[1].isError1 = true;
  } else {
    step4Data.value[1].isError1 = false;
  }
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    step4Error.value++;
    step4Data.value[1].isError2 = true;
  } else {
    step4Data.value[1].isError2 = false;
  }
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    step4Error.value++;
    step4Data.value[1].isError3 = true;
  } else {
    step4Data.value[1].isError3 = false;
  }
  if (step4Data.value[2].params1 && step4Data.value[2].params1 !== "5") {
    step4Error.value++;
    step4Data.value[2].isError1 = true;
  } else {
    step4Data.value[2].isError1 = false;
  }
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    step4Error.value++;
    step4Data.value[2].isError2 = true;
  } else {
    step4Data.value[2].isError2 = false;
  }
  if (step4Data.value[2].params3 && step4Data.value[2].params3 !== "1") {
    step4Error.value++;
    step4Data.value[2].isError3 = true;
  } else {
    step4Data.value[2].isError3 = false;
  }
  console.log(step4Error.value, "错误数");
  proxy.$modal.msgSuccess("提交成功");
  step4AnswerFlag.value = true;
  //将所填值同步到第六步表格
  step6Data.value[0].data3 = step4Data.value[0].params1;
  step6Data.value[0].data4 = step4Data.value[0].params2;
  step6Data.value[0].data5 = step4Data.value[0].params3;
  step6Data.value[1].data3 = step4Data.value[1].params1;
  step6Data.value[1].data4 = step4Data.value[1].params2;
  step6Data.value[1].data5 = step4Data.value[1].params3;
  step6Data.value[2].data3 = step4Data.value[2].params1;
  step6Data.value[2].data4 = step4Data.value[2].params2;
  step6Data.value[2].data5 = step4Data.value[2].params3;
};
const step5Submit = () => {
  // console.log(6333);
  step5Error.value = 0;
  for (let i in step5Data.value) {
    if (!step5Data.value[i].params1) {
      proxy.$modal.msgError("请先完成所有填空");
      return;
    }
  }
  // let step5Error = 0;
  if (step5Data.value[0].params1 && step5Data.value[0].params1 !== "4.67") {
    step5Error.value++;
    step5Data.value[0].isError1 = true;
  } else {
    step5Data.value[0].isError1 = false;
  }
  if (step5Data.value[0].params2 && step5Data.value[0].params2 !== "1.67") {
    step5Error.value++;
    step5Data.value[0].isError2 = true;
  } else {
    step5Data.value[0].isError2 = false;
  }
  if (step5Data.value[0].params3 && step5Data.value[0].params3 !== "2.33") {
    step5Error.value++;
    step5Data.value[0].isError3 = true;
  } else {
    step5Data.value[0].isError3 = false;
  }
  console.log(step5Error.value, "错误数");
  proxy.$modal.msgSuccess("提交成功");
  step5AnswerFlag.value = true;
  //将所填值同步到第六步表格
  step6Data.value[3].data3 = step5Data.value[0].params1;
  step6Data.value[3].data4 = step5Data.value[0].params2;
  step6Data.value[3].data5 = step5Data.value[0].params3;
};
const step6Submit = () => {
  // console.log(555);
  step6Error.value = 0;
  for (let i in step6Data.value.slice(0, step6Data.value.length - 1)) {
    if (!step6Data.value[i].params1 || !step6Data.value[i].params2 || !step6Data.value[i].params3) {
      proxy.$modal.msgError("请先完成所有填空");
      return;
    }
  }
  // let step6Error = 0;
  if (step6Data.value[0].params1 && step6Data.value[0].params1 !== "低") {
    step6Error.value++;
    step6Data.value[0].isError1 = true;
  } else {
    step6Data.value[0].isError1 = false;
  }
  if (step6Data.value[0].params2 && step6Data.value[0].params2 !== "高") {
    step6Error.value++;
    step6Data.value[0].isError2 = true;
  } else {
    step6Data.value[0].isError2 = false;
  }
  if (step6Data.value[0].params3 && step6Data.value[0].params3 !== "高") {
    step6Error.value++;
    step6Data.value[0].isError3 = true;
  } else {
    step6Data.value[0].isError3 = false;
  }
  if (step6Data.value[1].params1 && step6Data.value[1].params1 !== "高") {
    step6Error.value++;
    step6Data.value[1].isError1 = true;
  } else {
    step6Data.value[1].isError1 = false;
  }
  if (step6Data.value[1].params2 && step6Data.value[1].params2 !== "高") {
    step6Error.value++;
    step6Data.value[1].isError2 = true;
  } else {
    step6Data.value[1].isError2 = false;
  }
  if (step6Data.value[1].params3 && step6Data.value[1].params3 !== "高") {
    step6Error.value++;
    step6Data.value[1].isError3 = true;
  } else {
    step6Data.value[1].isError3 = false;
  }
  if (step6Data.value[2].params1 && step6Data.value[2].params1 !== "高") {
    step6Error.value++;
    step6Data.value[2].isError1 = true;
  } else {
    step6Data.value[2].isError1 = false;
  }
  if (step6Data.value[2].params2 && step6Data.value[2].params2 !== "低") {
    step6Error.value++;
    step6Data.value[2].isError2 = true;
  } else {
    step6Data.value[2].isError2 = false;
  }
  if (step6Data.value[2].params3 && step6Data.value[2].params3 !== "低") {
    step6Error.value++;
    step6Data.value[2].isError3 = true;
  } else {
    step6Data.value[2].isError3 = false;
  }
  console.log(step6Error.value, "错误数");
  proxy.$modal.msgSuccess("提交成功");
  step6AnswerFlag.value = true;
  //将所填值同步到第六步表格
  step7Data.value[0].data3 = step6Data.value[0].params1;
  step7Data.value[0].data4 = step6Data.value[0].params2;
  step7Data.value[0].data5 = step6Data.value[0].params3;
  step7Data.value[1].data3 = step6Data.value[1].params1;
  step7Data.value[1].data4 = step6Data.value[1].params2;
  step7Data.value[1].data5 = step6Data.value[1].params3;
  step7Data.value[2].data3 = step6Data.value[2].params1;
  step7Data.value[2].data4 = step6Data.value[2].params2;
  step7Data.value[2].data5 = step6Data.value[2].params3;
};
const step7Submit = () => {
  // console.log(111);
  step7Error.value = 0;
  for (let i in step7Data.value) {
    if (!step7Data.value[i].params) {
      proxy.$modal.msgError("请先完成所有填空");
      return;
    }
  }
  // let step7Error = 0;
  if (step7Data.value[0].params && step7Data.value[0].params !== "重要保持用户") {
    step7Error.value++;
    step7Data.value[0].isError1 = true;
  } else {
    step7Data.value[0].isError1 = false;
  }
  if (step7Data.value[1].params && step7Data.value[1].params !== "重要价值用户") {
    step7Error.value++;
    step7Data.value[1].isError1 = true;
  } else {
    step7Data.value[1].isError1 = false;
  }
  if (step7Data.value[2].params && step7Data.value[2].params !== "一般发展用户") {
    step7Error.value++;
    step7Data.value[2].isError1 = true;
  } else {
    step7Data.value[2].isError1 = false;
  }
  console.log(step7Error.value, "错误数");
  proxy.$modal.msgSuccess("提交成功");
  step7AnswerFlag.value = true;
};
const submitData = () => {
  if (!step1AnswerFlag.value || !step2AnswerFlag.value || !step4AnswerFlag.value || !step5AnswerFlag.value || !step6AnswerFlag.value || !step7AnswerFlag.value) {
    proxy.$modal.msgError("请先完成所有步骤！");
    return;
  }
  errorNum.value = stepError1.value + stepError2.value + step4Error.value + step5Error.value + step6Error.value + step7Error.value;
  console.log(errorNum.value, "总错误次数");
  if (errorNum.value > 5) {
    errorNum.value = 5;
  }
  portraitModel
    .submit({
      userId: JSON.parse(getUserInfo()).userId,
      taskName: "RFM分析",
      numberOfErrors: errorNum.value,
      subState: 1,
    })
    .then((res) => {
      proxy.$modal.msgSuccess("提交成功");
      showAnswerFlag.value = true;
      saveDataSubmit();
    })
    .catch((error) => {});
};
const saveData = () => {
  const params = {
    stepFiveA: fileTable1.value[0].params,
    stepFiveB: fileTable1.value[1].params,
    stepFiveC: fileTable1.value[2].params,
    stepFiveD: fileTable1.value[3].params,
    stepSixA: fileTable1.value[4].params,
    stepSixB: fileTable1.value[5].params,
    stepSixC: fileTable1.value[6].params,
    stepSixD: step2Data.value[0].params1,
    stepSevenA: step2Data.value[0].params2,
    stepSevenB: step2Data.value[0].params3,
    stepSevenC: step2Data.value[1].params1,
    stepSevenD: step2Data.value[1].params2,
    stepEightA: step2Data.value[1].params3,
    stepEightB: step2Data.value[2].params1,
    stepEightC: step2Data.value[2].params2,
    stepEightD: step2Data.value[2].params3,
    stepNineA: step4Data.value[0].params1,
    stepNineB: step4Data.value[0].params2,
    stepNineC: step4Data.value[0].params3,
    stepNineD: step4Data.value[1].params1,
    stepTenA: step4Data.value[1].params2,
    stepTenB: step4Data.value[1].params3,
    stepTenC: step4Data.value[2].params1,
    stepTenD: step4Data.value[2].params2,
    stepElevenA: step4Data.value[2].params3,
    stepOneA: step5Data.value[0].params1,
    stepOneB: step5Data.value[0].params2,
    stepOneC: step5Data.value[0].params3,
    stepOneD: step6Data.value[0].params1,
    stepTwoA: step6Data.value[0].params2,
    stepTwoB: step6Data.value[0].params3,
    stepTwoC: step6Data.value[1].params1,
    stepThreeA: step6Data.value[1].params2,
    stepThreeB: step6Data.value[1].params3,
    stepThreeC: step6Data.value[2].params1,
    stepThreeD: step6Data.value[2].params2,
    stepFourA: step6Data.value[2].params3,
    stepFourB: step7Data.value[0].params,
    stepFourC: step7Data.value[1].params,
    stepFourD: step7Data.value[2].params,
  };
  portraitModel
    .saveData({
      userId: JSON.parse(getUserInfo()).userId,
      module: "RFM分析",
      ...params,
    })
    .then((res) => {})
    .catch((error) => {});
};
const saveDataSubmit = () => {
  const params = {
    stepFiveA: fileTable1.value[0].params,
    stepFiveB: fileTable1.value[1].params,
    stepFiveC: fileTable1.value[2].params,
    stepFiveD: fileTable1.value[3].params,
    stepSixA: fileTable1.value[4].params,
    stepSixB: fileTable1.value[5].params,
    stepSixC: fileTable1.value[6].params,
    stepSixD: step2Data.value[0].params1,
    stepSevenA: step2Data.value[0].params2,
    stepSevenB: step2Data.value[0].params3,
    stepSevenC: step2Data.value[1].params1,
    stepSevenD: step2Data.value[1].params2,
    stepEightA: step2Data.value[1].params3,
    stepEightB: step2Data.value[2].params1,
    stepEightC: step2Data.value[2].params2,
    stepEightD: step2Data.value[2].params3,
    stepNineA: step4Data.value[0].params1,
    stepNineB: step4Data.value[0].params2,
    stepNineC: step4Data.value[0].params3,
    stepNineD: step4Data.value[1].params1,
    stepTenA: step4Data.value[1].params2,
    stepTenB: step4Data.value[1].params3,
    stepTenC: step4Data.value[2].params1,
    stepTenD: step4Data.value[2].params2,
    stepElevenA: step4Data.value[2].params3,
    stepOneA: step5Data.value[0].params1,
    stepOneB: step5Data.value[0].params2,
    stepOneC: step5Data.value[0].params3,
    stepOneD: step6Data.value[0].params1,
    stepTwoA: step6Data.value[0].params2,
    stepTwoB: step6Data.value[0].params3,
    stepTwoC: step6Data.value[1].params1,
    stepThreeA: step6Data.value[1].params2,
    stepThreeB: step6Data.value[1].params3,
    stepThreeC: step6Data.value[2].params1,
    stepThreeD: step6Data.value[2].params2,
    stepFourA: step6Data.value[2].params3,
    stepFourB: step7Data.value[0].params,
    stepFourC: step7Data.value[1].params,
    stepFourD: step7Data.value[2].params,
  };
  portraitModel
    .saveData({
      userId: JSON.parse(getUserInfo()).userId,
      module: "RFM分析",
      ...params,
      subState: 1,
    })
    .then((res) => {})
    .catch((error) => {});
};
const getData = () => {
  portraitModel
    .getData({
      userId: JSON.parse(getUserInfo()).userId,
      module: "RFM分析",
    })
    .then((res) => {
      fileTable1.value[0].params = res.data.stepFiveA;
      fileTable1.value[1].params = res.data.stepFiveB;
      fileTable1.value[2].params = res.data.stepFiveC;
      fileTable1.value[3].params = res.data.stepFiveD;
      fileTable1.value[4].params = res.data.stepSixA;
      fileTable1.value[5].params = res.data.stepSixB;
      fileTable1.value[6].params = res.data.stepSixC;
      step2Data.value[0].params1 = res.data.stepSixD;
      step2Data.value[0].params2 = res.data.stepSevenA;
      step2Data.value[0].params3 = res.data.stepSevenB;
      step2Data.value[1].params1 = res.data.stepSevenC;
      step2Data.value[1].params2 = res.data.stepSevenD;
      step2Data.value[1].params3 = res.data.stepEightA;
      step2Data.value[2].params1 = res.data.stepEightB;
      step2Data.value[2].params2 = res.data.stepEightC;
      step2Data.value[2].params3 = res.data.stepEightD;
      step4Data.value[0].params1 = res.data.stepNineA;
      step4Data.value[0].params2 = res.data.stepNineB;
      step4Data.value[0].params3 = res.data.stepNineC;
      step4Data.value[1].params1 = res.data.stepNineD;
      step4Data.value[1].params2 = res.data.stepTenA;
      step4Data.value[1].params3 = res.data.stepTenB;
      step4Data.value[2].params1 = res.data.stepTenC;
      step4Data.value[2].params2 = res.data.stepTenD;
      step4Data.value[2].params3 = res.data.stepElevenA;
      step5Data.value[0].params1 = res.data.stepOneA;
      step5Data.value[0].params2 = res.data.stepOneB;
      step5Data.value[0].params3 = res.data.stepOneC;
      step6Data.value[0].params1 = res.data.stepOneD;
      step6Data.value[0].params2 = res.data.stepTwoA;
      step6Data.value[0].params3 = res.data.stepTwoB;
      step6Data.value[1].params1 = res.data.stepTwoC;
      step6Data.value[1].params2 = res.data.stepThreeA;
      step6Data.value[1].params3 = res.data.stepThreeB;
      step6Data.value[2].params1 = res.data.stepThreeC;
      step6Data.value[2].params2 = res.data.stepThreeD;
      step6Data.value[2].params3 = res.data.stepFourA;
      step7Data.value[0].params = res.data.stepFourB;
      step7Data.value[1].params = res.data.stepFourC;
      step7Data.value[2].params = res.data.stepFourD;

      //将所填值同步到第七步表格
      step7Data.value[0].data3 = step6Data.value[0].params1;
      step7Data.value[0].data4 = step6Data.value[0].params2;
      step7Data.value[0].data5 = step6Data.value[0].params3;
      step7Data.value[1].data3 = step6Data.value[1].params1;
      step7Data.value[1].data4 = step6Data.value[1].params2;
      step7Data.value[1].data5 = step6Data.value[1].params3;
      step7Data.value[2].data3 = step6Data.value[2].params1;
      step7Data.value[2].data4 = step6Data.value[2].params2;
      step7Data.value[2].data5 = step6Data.value[2].params3;

      //将所填值同步到第六步表格
      step6Data.value[0].data3 = step4Data.value[0].params1;
      step6Data.value[0].data4 = step4Data.value[0].params2;
      step6Data.value[0].data5 = step4Data.value[0].params3;
      step6Data.value[1].data3 = step4Data.value[1].params1;
      step6Data.value[1].data4 = step4Data.value[1].params2;
      step6Data.value[1].data5 = step4Data.value[1].params3;
      step6Data.value[2].data3 = step4Data.value[2].params1;
      step6Data.value[2].data4 = step4Data.value[2].params2;
      step6Data.value[2].data5 = step4Data.value[2].params3;

      //将所填值同步到第六步表格
      step6Data.value[3].data3 = step5Data.value[0].params1;
      step6Data.value[3].data4 = step5Data.value[0].params2;
      step6Data.value[3].data5 = step5Data.value[0].params3;
      //回显答案
      if (res.data.subState & (res.data.subState == 1)) {
        showAnswerFlag.value = true;
        for (let i in fileTable1.value) {
          if (i == 0) {
            if (fileTable1.value[0].params && fileTable1.value[0].params == "纳入") {
              fileTable1.value[0].isError = true;
            } else {
              fileTable1.value[0].isError = false;
            }
            // console.log(fileTable1.value[0].params);
          } else {
            if (fileTable1.value[i].params && fileTable1.value[i].params == "剔除") {
              fileTable1.value[i].isError = true;
            } else {
              fileTable1.value[i].isError = false;
            }
          }
        }
        if (step2Data.value[0].params1 && step2Data.value[0].params1 !== "4") {
          step2Data.value[0].isError1 = true;
        } else {
          step2Data.value[0].isError1 = false;
        }
        if (step2Data.value[0].params2 && step2Data.value[0].params2 !== "2") {
          step2Data.value[0].isError2 = true;
        } else {
          step2Data.value[0].isError2 = false;
        }
        if (step2Data.value[0].params3 && step2Data.value[0].params3 !== "5000") {
          step2Data.value[0].isError3 = true;
        } else {
          step2Data.value[0].isError3 = false;
        }
        if (step2Data.value[1].params1 && step2Data.value[1].params1 !== "2") {
          step2Data.value[1].isError1 = true;
        } else {
          step2Data.value[1].isError1 = false;
        }
        if (step2Data.value[1].params2 && step2Data.value[1].params2 !== "3") {
          step2Data.value[1].isError2 = true;
        } else {
          step2Data.value[1].isError2 = false;
        }
        if (step2Data.value[1].params3 && step2Data.value[1].params3 !== "1000") {
          step2Data.value[1].isError3 = true;
        } else {
          step2Data.value[1].isError3 = false;
        }
        if (step2Data.value[2].params1 && step2Data.value[2].params1 !== "1") {
          step2Data.value[2].isError1 = true;
        } else {
          step2Data.value[2].isError1 = false;
        }
        if (step2Data.value[2].params2 && step2Data.value[2].params2 !== "1") {
          step2Data.value[2].isError2 = true;
        } else {
          step2Data.value[2].isError2 = false;
        }
        if (step2Data.value[2].params3 && step2Data.value[2].params3 !== "500") {
          step2Data.value[2].isError3 = true;
        } else {
          step2Data.value[2].isError3 = false;
        }

        if (step4Data.value[0].params1 && step4Data.value[0].params1 !== "4") {
          step4Data.value[0].isError1 = true;
        } else {
          step4Data.value[0].isError1 = false;
        }
        if (step4Data.value[0].params2 && step4Data.value[0].params2 !== "2") {
          step4Data.value[0].isError2 = true;
        } else {
          step4Data.value[0].isError2 = false;
        }
        if (step4Data.value[0].params3 && step4Data.value[0].params3 !== "4") {
          step4Data.value[0].isError3 = true;
        } else {
          step4Data.value[0].isError3 = false;
        }
        if (step4Data.value[1].params1 && step4Data.value[1].params1 !== "5") {
          step4Data.value[1].isError1 = true;
        } else {
          step4Data.value[1].isError1 = false;
        }
        if (step4Data.value[1].params2 && step4Data.value[1].params2 !== "2") {
          step4Data.value[1].isError2 = true;
        } else {
          step4Data.value[1].isError2 = false;
        }
        if (step4Data.value[1].params3 && step4Data.value[1].params3 !== "2") {
          step4Data.value[1].isError3 = true;
        } else {
          step4Data.value[1].isError3 = false;
        }
        if (step4Data.value[2].params1 && step4Data.value[2].params1 !== "5") {
          step4Data.value[2].isError1 = true;
        } else {
          step4Data.value[2].isError1 = false;
        }
        if (step4Data.value[2].params2 && step4Data.value[2].params2 !== "1") {
          step4Data.value[2].isError2 = true;
        } else {
          step4Data.value[2].isError2 = false;
        }
        if (step4Data.value[2].params3 && step4Data.value[2].params3 !== "1") {
          step4Data.value[2].isError3 = true;
        } else {
          step4Data.value[2].isError3 = false;
        }

        if (step5Data.value[0].params1 && step5Data.value[0].params1 !== "4.67") {
          step5Data.value[0].isError1 = true;
        } else {
          step5Data.value[0].isError1 = false;
        }
        if (step5Data.value[0].params2 && step5Data.value[0].params2 !== "1.67") {
          step5Data.value[0].isError2 = true;
        } else {
          step5Data.value[0].isError2 = false;
        }
        if (step5Data.value[0].params3 && step5Data.value[0].params3 !== "2.33") {
          step5Data.value[0].isError3 = true;
        } else {
          step5Data.value[0].isError3 = false;
        }

        if (step6Data.value[0].params1 && step6Data.value[0].params1 !== "低") {
          step6Data.value[0].isError1 = true;
        } else {
          step6Data.value[0].isError1 = false;
        }
        if (step6Data.value[0].params2 && step6Data.value[0].params2 !== "高") {
          step6Data.value[0].isError2 = true;
        } else {
          step6Data.value[0].isError2 = false;
        }
        if (step6Data.value[0].params3 && step6Data.value[0].params3 !== "高") {
          step6Data.value[0].isError3 = true;
        } else {
          step6Data.value[0].isError3 = false;
        }
        if (step6Data.value[1].params1 && step6Data.value[1].params1 !== "高") {
          step6Data.value[1].isError1 = true;
        } else {
          step6Data.value[1].isError1 = false;
        }
        if (step6Data.value[1].params2 && step6Data.value[1].params2 !== "高") {
          step6Data.value[1].isError2 = true;
        } else {
          step6Data.value[1].isError2 = false;
        }
        if (step6Data.value[1].params3 && step6Data.value[1].params3 !== "高") {
          step6Data.value[1].isError3 = true;
        } else {
          step6Data.value[1].isError3 = false;
        }
        if (step6Data.value[2].params1 && step6Data.value[2].params1 !== "高") {
          step6Data.value[2].isError1 = true;
        } else {
          step6Data.value[2].isError1 = false;
        }
        if (step6Data.value[2].params2 && step6Data.value[2].params2 !== "低") {
          step6Data.value[2].isError2 = true;
        } else {
          step6Data.value[2].isError2 = false;
        }
        if (step6Data.value[2].params3 && step6Data.value[2].params3 !== "低") {
          step6Data.value[2].isError3 = true;
        } else {
          step6Data.value[2].isError3 = false;
        }

        if (step7Data.value[0].params && step7Data.value[0].params !== "重要保持用户") {
          step7Data.value[0].isError1 = true;
        } else {
          step7Data.value[0].isError1 = false;
        }
        if (step7Data.value[1].params && step7Data.value[1].params !== "重要价值用户") {
          step7Data.value[1].isError1 = true;
        } else {
          step7Data.value[1].isError1 = false;
        }
        if (step7Data.value[2].params && step7Data.value[2].params !== "一般发展用户") {
          step7Data.value[2].isError1 = true;
        } else {
          step7Data.value[2].isError1 = false;
        }
      }
    })
    .catch((error) => {});
};
const retrain = () => {
  showAnswerFlag.value = false;
  // portraitModel
  //   .retrain({
  //     userId: JSON.parse(getUserInfo()).userId,
  //     module: "RFM分析",
  //   })
  //   .then((res) => {
  proxy.$modal.msgSuccess("重新实训成功");
  for (let i in fileTable1.value) {
    fileTable1.value[i].params = "";
  }
  for (let i in step2Data.value) {
    step2Data.value[i].params1 = "";
    step2Data.value[i].params2 = "";
    step2Data.value[i].params3 = "";
  }
  for (let i in step4Data.value) {
    step4Data.value[i].params1 = "";
    step4Data.value[i].params2 = "";
    step4Data.value[i].params3 = "";
  }
  step5Data.value[0].params1 = "";
  step5Data.value[0].params2 = "";
  step5Data.value[0].params3 = "";
  for (let i in step6Data.value) {
    step6Data.value[i].params1 = "";
    step6Data.value[i].params2 = "";
    step6Data.value[i].params3 = "";
  }
  for (let i in step7Data.value) {
    step7Data.value[i].params = "";
  }
  // })
  // .catch((error) => {});
};
defineExpose({
  submitData,
  retrain,
  // practicalTraining,
});
const tableSpanMethod = ({ row, column, rowIndex, columnIndex }) => {
  if (rowIndex === 3) {
    if (columnIndex === 0) {
      return [1, 2];
    } else if (columnIndex === 1) {
      return [0, 0];
    }
  }
};
const openKnowledge = () => {
  dialogVisible.value = true;
  startCount();
};
const handlClose = () => {
  console.log(111);
  if (timer.value) {
    clearInterval(timer.value);
    timer.value = null;
  }
  console.log("阅读时长", timeCount.value + "s");
  submitTimeCount();
  timeCount.value = 0;
};
const startCount = () => {
  timer.value = setInterval(() => {
    timeCount.value++;
  }, 1000);
};
const submitTimeCount = () => {
  portraitModel
    .timeCountSubmit({
      stuScoreDetailsDTO: {
        userId: JSON.parse(getUserInfo()).userId,
        viewingTime: timeCount.value,
      },
    })
    .then((res) => {})
    .catch((error) => {});
};
</script>

<style scoped lang="scss">
.content {
  width: 100%;
  // background: rgba(0, 29, 64, 0.5);
  background: #fff;
  border: 1px solid #0452c6;
  padding: 10px;
  color: #000;
  //   opacity: 0.5;

  .import {
    display: flex;
    font-size: 15px;
    font-weight: bold;
    width: 150px;
    height: 50px;
    background-image: url("@/assets/images/taskImport.png");
    background-size: 150px 50px;
    text-align: center;
    align-items: center;
    line-height: 50px;
    cursor: pointer;
    img {
      margin-left: 20px;
      margin-right: 8px;
    }
  }
  .caseBack {
    .title {
      font-weight: bold;
      margin-top: 10px;
      display: flex;
      align-items: center;
      span {
        margin-left: 10px;
      }
    }
    .download {
      cursor: pointer;
      margin-left: 20px;
      color: #000;
      padding-left: 10px;
      font-size: 14px;
      text-align: center;
      line-height: 31px;
      width: 89px;
      height: 31px;
      background: url("@/assets/images/downloadIcon.png");
    }
    .submit {
      cursor: pointer;
      margin-left: 20px;
      color: #000;
      padding-left: 10px;
      font-size: 14px;
      text-align: center;
      line-height: 31px;
      width: 99px;
      height: 31px;
      background: url("@/assets/images/提交任务.png");
    }
    .customerType {
      padding-left: 10px;
      display: flex;
      align-items: center;
      width: 945px;
      height: 26px;
      // background: #003b75;
      background: #fff8ee;
      border-radius: 10px;
      // border: 1px solid #0052a3;
      font-size: 12px;
      margin-top: 10px;
      img {
        margin-right: 10px;
      }
    }
  }
  .line {
    width: 1400px;
    height: 1px;
    border: 1px solid #0452c6;
    opacity: 0.5;
    margin-top: 20px;
    margin-bottom: 20px;
  }
}
::v-deep .el-table tr {
  background-color: #fff !important;
  color: #000;
}

/* 将悬停背景色改为红色 */
::v-deep .el-table--enable-row-hover .el-table__body tr:hover > td {
  background-color: #f5f7fa !important;
}

::v-deep .el-table td {
  padding-top: 4px;
  padding-bottom: 4px;
}
</style>