402 lines
15 KiB
PHP
402 lines
15 KiB
PHP
<?php
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namespace app\cunkebao\controller;
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use think\Db;
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use app\store\model\TrafficOrderModel;
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use app\common\model\TrafficSource;
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use app\store\model\WechatFriendModel;
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/**
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* RFM 客户价值评分控制器
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* 基于 RFM 客户价值评分体系技术实施文档实现
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*/
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class RFMController extends BaseController
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{
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// 默认配置参数
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const DEFAULT_CYCLE_DAYS = 180; // 默认统计周期（天）
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const DEFAULT_WEIGHT_R = 0.4; // R维度权重
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const DEFAULT_WEIGHT_F = 0.3; // F维度权重
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const DEFAULT_WEIGHT_M = 0.3; // M维度权重
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const DEFAULT_ABNORMAL_MONEY_RATIO = 3.0; // 异常金额阈值倍数
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const DEFAULT_SCORE_SCALE = 5; // 默认5分制
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/**
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* 从 traffic_order 表计算客户 RFM 评分
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*
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* @param string|null $identifier 流量池用户标识
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* @param string|null $ownerWechatId 微信ID，为空则统计所有数据
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* @param array $config 配置参数
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* - cycle_days: 统计周期（天），默认180
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* - weight_R: R维度权重，默认0.4
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* - weight_F: F维度权重，默认0.3
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* - weight_M: M维度权重，默认0.3
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* - abnormal_money_ratio: 异常金额阈值倍数，默认3.0
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* - score_scale: 评分分制（5或100），默认5
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* - missing_strategy: 缺失值处理策略（'score_1'或'exclude'），默认'score_1'
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* @return array
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*/
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public function calculateRfmFromTrafficOrder($identifier = null, $ownerWechatId = null, $config = [])
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{
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try {
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// 合并配置参数
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$cycleDays = isset($config['cycle_days']) ? (int)$config['cycle_days'] : self::DEFAULT_CYCLE_DAYS;
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$weightR = isset($config['weight_R']) ? (float)$config['weight_R'] : self::DEFAULT_WEIGHT_R;
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$weightF = isset($config['weight_F']) ? (float)$config['weight_F'] : self::DEFAULT_WEIGHT_F;
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$weightM = isset($config['weight_M']) ? (float)$config['weight_M'] : self::DEFAULT_WEIGHT_M;
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$abnormalMoneyRatio = isset($config['abnormal_money_ratio']) ? (float)$config['abnormal_money_ratio'] : self::DEFAULT_ABNORMAL_MONEY_RATIO;
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$scoreScale = isset($config['score_scale']) ? (int)$config['score_scale'] : self::DEFAULT_SCORE_SCALE;
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$missingStrategy = isset($config['missing_strategy']) ? $config['missing_strategy'] : 'score_1';
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// 权重归一化处理
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$weightSum = $weightR + $weightF + $weightM;
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if ($weightSum != 1.0) {
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$weightR = $weightR / $weightSum;
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$weightF = $weightF / $weightSum;
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$weightM = $weightM / $weightSum;
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}
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// 计算时间范围
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$endTime = time(); // 统计截止时间（当前时间）
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$startTime = $endTime - ($cycleDays * 24 * 3600); // 统计起始时间
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// 构建查询条件
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$where = [
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['isDel', '=', 0],
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['createTime', '>=', $startTime],
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['createTime', '<', $endTime],
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];
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// identifier 条件
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if (!empty($identifier)) {
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$where[] = ['identifier', '=', $identifier];
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}
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// ownerWechatId 条件
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if (!empty($ownerWechatId)) {
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$where[] = ['ownerWechatId', '=', $ownerWechatId];
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}
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// 1. 数据过滤和聚合 - 获取每个客户的R、F、M原始值
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$orderModel = new TrafficOrderModel();
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$customers = $orderModel
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->where($where)
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->where(function ($query) {
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// 只统计有效订单（actualPay大于0）
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$query->where('actualPay', '>', 0);
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})
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->field('identifier, MAX(createTime) as lastOrderTime, COUNT(DISTINCT id) as orderCount, SUM(CAST(actualPay AS DECIMAL(18,2))) as totalAmount')
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->group('identifier')
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->select();
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if (empty($customers)) {
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return [
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'code' => 200,
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'msg' => '暂无数据',
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'data' => []
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];
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}
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// 2. 计算每个客户的R值（最近消费天数）
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$customerData = [];
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foreach ($customers as $customer) {
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$recencyDays = floor(($endTime - $customer['lastOrderTime']) / (24 * 3600));
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$customerData[] = [
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'identifier' => $customer['identifier'],
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'R' => $recencyDays,
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'F' => (int)$customer['orderCount'],
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'M' => (float)$customer['totalAmount'],
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];
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}
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// 3. 异常值处理 - 剔除大额异常订单
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$mValues = array_column($customerData, 'M');
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if (!empty($mValues)) {
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sort($mValues);
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$m99Percentile = $this->percentile($mValues, 0.99);
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$abnormalThreshold = $m99Percentile * $abnormalMoneyRatio;
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// 标记异常客户（但不删除，仅在计算M维度区间时考虑）
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foreach ($customerData as &$customer) {
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$customer['isAbnormal'] = $customer['M'] > $abnormalThreshold;
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}
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}
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// 4. 使用五分位法计算各维度的区间阈值
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$rThresholds = $this->calculatePercentiles(array_column($customerData, 'R'), true); // R是反向的
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$fThresholds = $this->calculatePercentiles(array_column($customerData, 'F'), false);
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// M维度排除异常值计算区间
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$mValuesForPercentile = array_filter(array_column($customerData, 'M'), function($m) use ($abnormalThreshold) {
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return isset($abnormalThreshold) ? $m <= $abnormalThreshold : true;
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});
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$mThresholds = $this->calculatePercentiles(array_values($mValuesForPercentile), false);
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// 5. 计算每个客户的RFM分项得分
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$results = [];
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foreach ($customerData as $customer) {
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$rScore = $this->scoreByPercentile($customer['R'], $rThresholds, true); // R是反向的
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$fScore = $this->scoreByPercentile($customer['F'], $fThresholds, false);
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$mScore = $customer['isAbnormal'] ? 5 : $this->scoreByPercentile($customer['M'], $mThresholds, false); // 异常值给最高分
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// 计算RFM总分（加权求和）
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$rfmScore = $rScore * $weightR + $fScore * $weightF + $mScore * $weightM;
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// 可选：标准化为1-100分
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$standardScore = null;
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if ($scoreScale == 100) {
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$rfmMin = $weightR * 1 + $weightF * 1 + $weightM * 1;
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$rfmMax = $weightR * 5 + $weightF * 5 + $weightM * 5;
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$standardScore = (int)round(($rfmScore - $rfmMin) / ($rfmMax - $rfmMin) * 99 + 1);
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}
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$results[] = [
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'identifier' => $customer['identifier'],
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'R_raw' => $customer['R'],
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'R_score' => $rScore,
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'F_raw' => $customer['F'],
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'F_score' => $fScore,
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'M_raw' => round($customer['M'], 2),
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'M_score' => $mScore,
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'RFM_score' => round($rfmScore, 2),
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'RFM_standard_score' => $standardScore,
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'cycle_start' => date('Y-m-d H:i:s', $startTime),
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'cycle_end' => date('Y-m-d H:i:s', $endTime),
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'calculate_time' => date('Y-m-d H:i:s'),
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];
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}
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// 按RFM总分降序排序
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usort($results, function($a, $b) {
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return $b['RFM_score'] <=> $a['RFM_score'];
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});
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// 6. 更新 ck_traffic_source 和 s2_wechat_friend 表的RFM值
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$this->updateRfmToTables($results, $ownerWechatId);
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return [
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'code' => 200,
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'msg' => '计算成功',
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'data' => [
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'results' => $results,
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'config' => [
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'cycle_days' => $cycleDays,
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'weight_R' => $weightR,
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'weight_F' => $weightF,
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'weight_M' => $weightM,
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'score_scale' => $scoreScale,
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],
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'statistics' => [
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'total_customers' => count($results),
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'avg_rfm_score' => round(array_sum(array_column($results, 'RFM_score')) / count($results), 2),
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]
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]
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];
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} catch (\Exception $e) {
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return [
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'code' => 500,
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'msg' => '计算失败：' . $e->getMessage(),
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'data' => []
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];
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}
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}
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/**
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* 计算 RFM 评分（兼容旧方法，使用固定阈值）
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* @param int|null $recencyDays 最近购买天数
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* @param int $frequency 购买次数
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* @param float $monetary 购买金额
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* @return array{R:int,F:int,M:int}
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*/
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public static function calcRfmScores($recencyDays = 30, $frequency, $monetary)
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{
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$recencyDays = is_numeric($recencyDays) ? (int)$recencyDays : 9999;
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$frequency = max(0, (int)$frequency);
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$monetary = max(0, (float)$monetary);
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return [
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'R' => self::scoreR_Default($recencyDays),
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'F' => self::scoreF_Default($frequency),
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'M' => self::scoreM_Default($monetary),
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];
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}
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/**
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* 使用固定阈值计算R得分（保留兼容性）
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*/
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protected static function scoreR_Default(int $days): int
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{
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if ($days <= 30) return 5;
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if ($days <= 60) return 4;
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if ($days <= 90) return 3;
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if ($days <= 120) return 2;
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return 1;
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}
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/**
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* 使用固定阈值计算F得分（保留兼容性）
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*/
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protected static function scoreF_Default(int $times): int
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{
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if ($times >= 10) return 5;
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if ($times >= 6) return 4;
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if ($times >= 3) return 3;
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if ($times >= 2) return 2;
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if ($times >= 1) return 1;
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return 0;
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}
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/**
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* 使用固定阈值计算M得分（保留兼容性）
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*/
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protected static function scoreM_Default(float $amount): int
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{
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if ($amount >= 2000) return 5;
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if ($amount >= 1000) return 4;
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if ($amount >= 500) return 3;
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if ($amount >= 200) return 2;
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if ($amount > 0) return 1;
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return 0;
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}
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/**
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* 计算百分位数（五分位法）
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* @param array $values 数值数组
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* @param bool $reverse 是否反向（R维度需要反向，值越小得分越高）
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* @return array 返回[0.2, 0.4, 0.6, 0.8]分位数的阈值数组
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*/
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private function calculatePercentiles($values, $reverse = false)
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{
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if (empty($values)) {
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return [0, 0, 0, 0];
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}
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// 去重并排序
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$uniqueValues = array_unique($values);
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sort($uniqueValues);
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// 如果所有值相同，强制均分5个区间
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if (count($uniqueValues) == 1) {
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$singleValue = $uniqueValues[0];
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if ($reverse) {
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return [$singleValue, $singleValue, $singleValue, $singleValue];
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} else {
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return [$singleValue, $singleValue, $singleValue, $singleValue];
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}
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}
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$percentiles = [0.2, 0.4, 0.6, 0.8];
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$thresholds = [];
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foreach ($percentiles as $p) {
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$thresholds[] = $this->percentile($uniqueValues, $p);
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}
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return $thresholds;
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}
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/**
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* 计算百分位数
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* @param array $sortedArray 已排序的数组
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* @param float $percentile 百分位数（0-1之间）
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* @return float
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*/
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private function percentile($sortedArray, $percentile)
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{
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if (empty($sortedArray)) {
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return 0;
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}
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$count = count($sortedArray);
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$index = ($count - 1) * $percentile;
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$floor = floor($index);
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$ceil = ceil($index);
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if ($floor == $ceil) {
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return $sortedArray[(int)$index];
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}
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$weight = $index - $floor;
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return $sortedArray[(int)$floor] * (1 - $weight) + $sortedArray[(int)$ceil] * $weight;
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}
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/**
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* 根据五分位法阈值计算得分
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* @param float $value 当前值
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* @param array $thresholds 阈值数组[T1, T2, T3, T4]
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* @param bool $reverse 是否反向（R维度反向：值越小得分越高）
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* @return int 得分1-5
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*/
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private function scoreByPercentile($value, $thresholds, $reverse = false)
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{
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if (empty($thresholds) || count($thresholds) < 4) {
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return 1;
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}
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list($t1, $t2, $t3, $t4) = $thresholds;
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if ($reverse) {
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// R维度：值越小得分越高
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if ($value <= $t1) return 5;
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if ($value <= $t2) return 4;
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if ($value <= $t3) return 3;
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if ($value <= $t4) return 2;
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return 1;
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} else {
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// F和M维度：值越大得分越高
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if ($value >= $t4) return 5;
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if ($value >= $t3) return 4;
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if ($value >= $t2) return 3;
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if ($value >= $t1) return 2;
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return 1;
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}
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}
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/**
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* 更新RFM值到 ck_traffic_source 和 s2_wechat_friend 表
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*
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* @param array $results RFM计算结果数组
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* @param string|null $ownerWechatId 微信ID，用于过滤更新范围
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*/
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private function updateRfmToTables($results, $ownerWechatId = null)
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{
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try {
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foreach ($results as $result) {
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$identifier = $result['identifier'];
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$rScore = (string)$result['R_score'];
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$fScore = (string)$result['F_score'];
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$mScore = (string)$result['M_score'];
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// 更新 ck_traffic_source 表
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// 根据 identifier 更新所有匹配的记录
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$trafficSourceUpdate = [
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'R' => $rScore,
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'F' => $fScore,
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'M' => $mScore,
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'updateTime' => time()
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];
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TrafficSource::where('identifier', $identifier)->update($trafficSourceUpdate);
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// 更新 s2_wechat_friend 表
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// wechatId 对应 identifier
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$wechatFriendUpdate = [
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'R' => $rScore,
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'F' => $fScore,
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'M' => $mScore,
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'updateTime' => time()
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];
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$wechatFriendWhere = ['wechatId' => $identifier];
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if (!empty($ownerWechatId)) {
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$wechatFriendWhere['ownerWechatId'] = $ownerWechatId;
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}
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WechatFriendModel::where($wechatFriendWhere)->update($wechatFriendUpdate);
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}
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} catch (\Exception $e) {
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// 记录错误但不影响主流程
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\think\Log::error('更新RFM值失败：' . $e->getMessage());
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}
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}
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}
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