From e34887fd6fd58cc5a450cfe944c3a8537b69825a Mon Sep 17 00:00:00 2001 From: liujiang <569804566@qq.com> Date: Sun, 9 Nov 2025 20:19:10 +0800 Subject: [PATCH] =?UTF-8?q?master=EF=BC=9A=E5=95=86=E5=93=81=E6=9D=83?= =?UTF-8?q?=E9=87=8D=E9=80=BB=E8=BE=91=E4=BC=98=E5=8C=96=EF=BC=9B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../java/com/ruoyi/quartz/task/XktTask.java | 57 ++++++++++++------- 1 file changed, 37 insertions(+), 20 deletions(-) diff --git a/ruoyi-quartz/src/main/java/com/ruoyi/quartz/task/XktTask.java b/ruoyi-quartz/src/main/java/com/ruoyi/quartz/task/XktTask.java index 0b0a03253..fa010d28f 100644 --- a/ruoyi-quartz/src/main/java/com/ruoyi/quartz/task/XktTask.java +++ b/ruoyi-quartz/src/main/java/com/ruoyi/quartz/task/XktTask.java @@ -521,34 +521,51 @@ public class XktTask { if (CollectionUtils.isEmpty(storeProdList)) { return; } - // 获取 商品销售、商品浏览量、商品收藏量、商品下载量 + Set updateProdIdSet = new HashSet<>(); + final Date now = java.sql.Date.valueOf(LocalDate.now()); + final Date fifteenDaysAgo = java.sql.Date.valueOf(LocalDate.now().minusDays(15)); + final Date twoMonthAgo = java.sql.Date.valueOf(LocalDate.now().minusMonths(2)); + // 获取 商品销售、商品浏览量、商品收藏量、商品下载量 最近2月 List statisticsList = this.storeProdStatMapper.selectList(new LambdaQueryWrapper() - .eq(StoreProductStatistics::getDelFlag, Constants.UNDELETED)); - // 商品浏览量、下载量 - Map prodStatMap = statisticsList.stream().collect(Collectors.toMap(StoreProductStatistics::getStoreProdId, Function.identity())); + .eq(StoreProductStatistics::getDelFlag, Constants.UNDELETED).between(StoreProductStatistics::getVoucherDate, twoMonthAgo, now)); + CollectionUtils.addAll(updateProdIdSet, statisticsList.stream().map(StoreProductStatistics::getStoreProdId).collect(Collectors.toSet())); + // 商品浏览量 + Map viewCountMap = statisticsList.stream().collect(Collectors.groupingBy(StoreProductStatistics::getStoreProdId, Collectors + .summingLong(x -> ObjectUtils.defaultIfNull(x.getViewCount(), 0L)))); + // 商品下载量 + Map downCountMap = statisticsList.stream().collect(Collectors.groupingBy(StoreProductStatistics::getStoreProdId, Collectors + .summingLong(x -> ObjectUtils.defaultIfNull(x.getDownloadCount(), 0L)))); // 商品收藏量 List userFavList = this.userFavMapper.selectList(new LambdaQueryWrapper() .eq(UserFavorites::getDelFlag, Constants.UNDELETED)); + CollectionUtils.addAll(updateProdIdSet, userFavList.stream().map(UserFavorites::getStoreProdId).collect(Collectors.toSet())); Map userFavMap = userFavList.stream().collect(Collectors.groupingBy(UserFavorites::getStoreProdId, Collectors.summingLong(UserFavorites::getId))); - // 商品销售量 + // 商品销售量 最近15天 List saleDetailList = this.saleDetailMapper.selectList(new LambdaQueryWrapper() - .eq(StoreSaleDetail::getDelFlag, Constants.UNDELETED).eq(StoreSaleDetail::getSaleType, SaleType.GENERAL_SALE.getValue())); + .eq(StoreSaleDetail::getDelFlag, Constants.UNDELETED).eq(StoreSaleDetail::getSaleType, SaleType.GENERAL_SALE.getValue()) + .between(StoreSaleDetail::getVoucherDate, fifteenDaysAgo, now)); Map saleMap = saleDetailList.stream().collect(Collectors.groupingBy(StoreSaleDetail::getStoreProdId, Collectors.summingLong(StoreSaleDetail::getQuantity))); - storeProdList.forEach(x -> { - final long viewCount = ObjectUtils.isEmpty(prodStatMap.get(x.getId())) ? 0L : prodStatMap.get(x.getId()).getViewCount(); - final long downloadCount = ObjectUtils.isEmpty(prodStatMap.get(x.getId())) ? 0L : prodStatMap.get(x.getId()).getDownloadCount(); - final long favCount = ObjectUtils.isEmpty(userFavMap.get(x.getId())) ? 0L : userFavMap.get(x.getId()); - final long saleCount = ObjectUtils.isEmpty(saleMap.get(x.getId())) ? 0L : saleMap.get(x.getId()); - // 计算推荐权重，权重计算公式为：(浏览次数 * 0.2 + 下载次数 * 0.3 + 收藏次数 * 0.2 + 销售量 * 0.3) - final long recommendWeight = (long) (viewCount * 0.2 + downloadCount * 0.3 + favCount * 0.2 + saleCount * 0.3); - // 根据销售数量计算销售权重，权重计算公式为：(销售量 * 0.7 + 下载次数 * 0.1 + 收藏次数 * 0.1 + 浏览次数 * 0.1) - final long saleWeight = (long) (saleCount * 0.7 + downloadCount * 0.1 + favCount * 0.1 + viewCount * 0.1); - // 计算人气权重，权重计算公式为：(浏览次数 * 0.3 + 下载次数 * 0.2 + 收藏次数 * 0.2 + 销售量 * 0.3) - final long popularityWeight = (long) (viewCount * 0.3 + downloadCount * 0.2 + favCount * 0.2 + saleCount * 0.3); - x.setRecommendWeight(recommendWeight).setSaleWeight(saleWeight).setPopularityWeight(popularityWeight); - }); - this.storeProdMapper.updateById(storeProdList); + CollectionUtils.addAll(updateProdIdSet, saleMap.keySet()); + List updateList = storeProdList.stream() + .filter(x -> CollectionUtils.isEmpty(updateProdIdSet) || updateProdIdSet.contains(x.getId())) + .map(x -> { + final long viewCount = viewCountMap.getOrDefault(x.getId(), 0L); + final long downloadCount = downCountMap.getOrDefault(x.getId(), 0L); + final long favCount = userFavMap.getOrDefault(x.getId(), 0L); + final long saleCount = saleMap.getOrDefault(x.getId(), 0L); + // 计算推荐权重，权重计算公式为：(浏览次数 * 0.2 + 下载次数 * 0.3 + 收藏次数 * 0.2 + 销售量 * 0.3) + final long recommendWeight = (long) (viewCount * 0.2 + downloadCount * 0.3 + favCount * 0.2 + saleCount * 0.3); + // 根据销售数量计算销售权重，权重计算公式为：(销售量 * 0.7 + 下载次数 * 0.1 + 收藏次数 * 0.1 + 浏览次数 * 0.1) + final long saleWeight = (long) (saleCount * 0.7 + downloadCount * 0.1 + favCount * 0.1 + viewCount * 0.1); + // 计算人气权重，权重计算公式为：(浏览次数 * 0.3 + 下载次数 * 0.2 + 收藏次数 * 0.2 + 销售量 * 0.3) + final long popularityWeight = (long) (viewCount * 0.3 + downloadCount * 0.2 + favCount * 0.2 + saleCount * 0.3); + return x.setRecommendWeight(recommendWeight).setSaleWeight(saleWeight).setPopularityWeight(popularityWeight); + }).collect(Collectors.toList()); + if (CollectionUtils.isEmpty(updateList)) { + return; + } + this.storeProdMapper.updateById(updateList); } /**