一、个人简介
- 💖💖作者：计算机编程果茶熊
- 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！
- 💛💛想说的话：感谢大家的关注与支持！
- 安卓/小程序实战项目
- 💕💕文末获取源码联系计算机编程果茶熊
二、前言
开发语言：Java+Python 数据库：MySQL 系统架构：B/S 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django 前端：Vue+HTML+CSS+JavaScript+jQuery 随着电子信息技术的快速发展，电脑硬件市场呈现出多元化与复杂化的特点。据IDC发布的《2023年全球个人电脑市场调研报告》显示，全球PC市场拥有超过2000个品牌、上万种型号的硬件产品，消费者在选购过程中面临严重的"信息过载"问题。调查数据表明，近78%的消费者在购买电脑硬件时会花费超过20小时进行产品对比和信息筛选，而有65%的用户最终仍对自己的购买决策缺乏信心。更令人担忧的是，根据电子消费者协会的统计，约42%的消费者因不了解硬件兼容性而购买了不匹配的组件，导致性能无法最优发挥或额外的返修成本。在这种背景下，开发一个能够整合硬件信息、提供个性化推荐的电脑硬件推荐系统变得尤为必要。
电脑硬件推荐系统的开发具有显著的实用价值和社会意义。对普通消费者而言，该系统通过整合价格区间、品牌特性和硬件兼容性等关键因素，能够大幅降低选购决策的复杂度，节省时间成本约70%，同时提高购买满意度约58%。对学生群体特别是计算机相关专业的学生来说，系统不仅提供硬件知识学习的平台，还能根据专业需求和预算限制推荐最适合的配置方案。从技术角度看，该系统融合了Java/Python双语言开发模式，采用前后端分离架构，不仅展示了现代软件工程的实践应用，也为B/S架构在垂直领域的深度应用提供了范例。通过数据可视化功能，系统还能帮助用户直观了解硬件性能对比和价格趋势，提升了用户体验和决策质量。
三、电脑硬件推荐系统-视频解说
如何用一个《电脑硬件推荐系统》毕设项目，同时掌握Python和Java两大主流技术栈？答案在这里
四、电脑硬件推荐系统-功能介绍
五、电脑硬件推荐系统-代码展示
// 1. 电脑配件组装管理核心处理函数 @Service public class AssemblyService { @Autowired private ComponentRepository componentRepo; @Autowired private AssemblyRepository assemblyRepo;
/**
* 验证配件组合兼容性并创建组装方案
*/
public AssemblyResult validateAndCreateAssembly(AssemblyRequest request) {
// 获取所有请求中的组件
Component cpu = componentRepo.findById(request.getCpuId())
.orElseThrow(() -> new ComponentNotFoundException("CPU not found"));
Component motherboard = componentRepo.findById(request.getMotherboardId())
.orElseThrow(() -> new ComponentNotFoundException("Motherboard not found"));
Component ram = componentRepo.findById(request.getRamId())
.orElseThrow(() -> new ComponentNotFoundException("RAM not found"));
Component gpu = componentRepo.findById(request.getGpuId());
Component storage = componentRepo.findById(request.getStorageId())
.orElseThrow(() -> new ComponentNotFoundException("Storage not found"));
Component powerSupply = componentRepo.findById(request.getPowerSupplyId())
.orElseThrow(() -> new ComponentNotFoundException("Power supply not found"));
// 验证CPU与主板兼容性
if (!isCpuCompatibleWithMotherboard(cpu, motherboard)) {
return new AssemblyResult(false, "CPU socket is not compatible with motherboard");
}
// 验证RAM与主板兼容性
if (!isRamCompatibleWithMotherboard(ram, motherboard)) {
return new AssemblyResult(false, "RAM type is not compatible with motherboard");
}
// 计算总功耗
double totalPowerConsumption = calculateTotalPowerConsumption(cpu, motherboard, ram, gpu, storage);
if (totalPowerConsumption > powerSupply.getPowerOutput()) {
return new AssemblyResult(false,
"Power supply insufficient. Required: " + totalPowerConsumption + "W, Available: " + powerSupply.getPowerOutput() + "W");
}
// 创建并保存组装方案
Assembly assembly = new Assembly();
assembly.setUserId(request.getUserId());
assembly.setCpu(cpu);
assembly.setMotherboard(motherboard);
assembly.setRam(ram);
assembly.setGpu(gpu);
assembly.setStorage(storage);
assembly.setPowerSupply(powerSupply);
assembly.setTotalCost(calculateTotalCost(cpu, motherboard, ram, gpu, storage, powerSupply));
assembly.setPerformanceScore(calculatePerformanceScore(cpu, motherboard, ram, gpu, storage));
assembly.setCreatedAt(LocalDateTime.now());
Assembly savedAssembly = assemblyRepo.save(assembly);
return new AssemblyResult(true, "Assembly created successfully", savedAssembly);
}
// 2. 价格区间管理功能的核心处理函数 @Service public class PriceRangeService { @Autowired private PriceRangeRepository priceRangeRepo; @Autowired private ComponentRepository componentRepo;
/**
* 根据用户预算生成最佳硬件推荐方案
*/
public List<RecommendationDto> generateRecommendationsByBudget(double minBudget, double maxBudget) {
List<RecommendationDto> recommendations = new ArrayList<>();
// 获取价格区间配置
PriceRange priceRange = priceRangeRepo.findByBudgetRange(minBudget, maxBudget)
.orElseGet(() -> {
// 如果没有精确匹配的价格区间，查找最接近的
PriceRange closest = priceRangeRepo.findClosestPriceRange(maxBudget);
if (closest == null) {
throw new PriceRangeNotFoundException("No suitable price range found for budget: " + minBudget + "-" + maxBudget);
}
return closest;
});
// 根据价格区间获取推荐的CPU预算比例
double cpuBudget = maxBudget * priceRange.getCpuRatio();
double motherboardBudget = maxBudget * priceRange.getMotherboardRatio();
double ramBudget = maxBudget * priceRange.getRamRatio();
double gpuBudget = maxBudget * priceRange.getGpuRatio();
double storageBudget = maxBudget * priceRange.getStorageRatio();
double powerSupplyBudget = maxBudget * priceRange.getPowerSupplyRatio();
// 根据各部件预算查找合适组件
List<Component> cpus = componentRepo.findByTypeAndPriceRange("CPU", cpuBudget * 0.8, cpuBudget * 1.1);
List<Component> motherboards = componentRepo.findByTypeAndPriceRange("MOTHERBOARD", motherboardBudget * 0.8, motherboardBudget * 1.1);
List<Component> rams = componentRepo.findByTypeAndPriceRange("RAM", ramBudget * 0.8, ramBudget * 1.1);
List<Component> gpus = componentRepo.findByTypeAndPriceRange("GPU", gpuBudget * 0.8, gpuBudget * 1.1);
List<Component> storages = componentRepo.findByTypeAndPriceRange("STORAGE", storageBudget * 0.8, storageBudget * 1.1);
List<Component> powerSupplies = componentRepo.findByTypeAndPriceRange("POWER_SUPPLY", powerSupplyBudget * 0.8, powerSupplyBudget * 1.1);
// 根据组件兼容性和性能评分生成多个推荐方案
for (Component cpu : cpus) {
for (Component motherboard : motherboards) {
// 检查CPU与主板兼容性
if (!isCpuCompatibleWithMotherboard(cpu, motherboard)) {
continue;
}
for (Component ram : rams) {
// 检查RAM与主板兼容性
if (!isRamCompatibleWithMotherboard(ram, motherboard)) {
continue;
}
for (Component gpu : gpus) {
for (Component storage : storages) {
for (Component powerSupply : powerSupplies) {
// 计算总功耗确认电源足够
double powerConsumption = calculateTotalPowerConsumption(cpu, motherboard, ram, gpu, storage);
if (powerConsumption > powerSupply.getPowerOutput()) {
continue;
}
// 计算总成本和性能评分
double totalCost = cpu.getPrice() + motherboard.getPrice() + ram.getPrice() +
gpu.getPrice() + storage.getPrice() + powerSupply.getPrice();
double performanceScore = calculatePerformanceScore(cpu, motherboard, ram, gpu, storage);
// 创建推荐方案
RecommendationDto recommendation = new RecommendationDto();
recommendation.setCpu(cpu);
recommendation.setMotherboard(motherboard);
recommendation.setRam(ram);
recommendation.setGpu(gpu);
recommendation.setStorage(storage);
recommendation.setPowerSupply(powerSupply);
recommendation.setTotalCost(totalCost);
recommendation.setPerformanceScore(performanceScore);
recommendation.setPerformancePriceRatio(performanceScore / totalCost);
recommendations.add(recommendation);
// 限制推荐方案数量
if (recommendations.size() >= 5) {
break;
}
}
if (recommendations.size() >= 5) break;
}
if (recommendations.size() >= 5) break;
}
if (recommendations.size() >= 5) break;
}
if (recommendations.size() >= 5) break;
}
if (recommendations.size() >= 5) break;
}
// 按性价比排序
recommendations.sort(Comparator.comparing(RecommendationDto::getPerformancePriceRatio).reversed());
return recommendations;
}
// 3. 数据可视化核心处理函数 @Service public class DataVisualizationService { @Autowired private ComponentRepository componentRepo; @Autowired private AssemblyRepository assemblyRepo; @Autowired private UserPreferenceRepository userPrefRepo;
/**
* 生成硬件性能比较分析数据
*/
public HardwarePerformanceComparisonData generatePerformanceComparisonData(String componentType, List<Long> componentIds) {
HardwarePerformanceComparisonData result = new HardwarePerformanceComparisonData();
Map<String, List<PerformanceMetric>> performanceData = new HashMap<>();
// 获取要比较的组件数据
List<Component> components;
if (componentIds != null && !componentIds.isEmpty()) {
components = componentRepo.findAllById(componentIds);
} else {
// 如果没有指定ID，获取该类型的热门组件
components = componentRepo.findTopComponentsByTypeAndPopularity(componentType, 5);
}
if (components.isEmpty()) {
throw new ComponentNotFoundException("No components found for comparison");
}
// 获取各组件的性能指标
for (Component component : components) {
List<PerformanceMetric> metrics = new ArrayList<>();
// 根据组件类型获取不同的性能指标
switch (componentType) {
case "CPU":
metrics.add(new PerformanceMetric("Cores", component.getCpuCores()));
metrics.add(new PerformanceMetric("Threads", component.getCpuThreads()));
metrics.add(new PerformanceMetric("Base Clock (GHz)", component.getBaseClock()));
metrics.add(new PerformanceMetric("Boost Clock (GHz)", component.getBoostClock()));
metrics.add(new PerformanceMetric("Cache Size (MB)", component.getCacheSize()));
metrics.add(new PerformanceMetric("TDP (W)", component.getTdp()));
metrics.add(new PerformanceMetric("Single-Core Score", component.getSingleCoreScore()));
metrics.add(new PerformanceMetric("Multi-Core Score", component.getMultiCoreScore()));
break;
case "GPU":
metrics.add(new PerformanceMetric("VRAM (GB)", component.getVramSize()));
metrics.add(new PerformanceMetric("Core Clock (MHz)", component.getCoreClock()));
metrics.add(new PerformanceMetric("Memory Clock (MHz)", component.getMemoryClock()));
metrics.add(new PerformanceMetric("CUDA Cores/Stream Processors", component.getCudaCores()));
metrics.add(new PerformanceMetric("TDP (W)", component.getTdp()));
metrics.add(new PerformanceMetric("3DMark Score", component.get3dMarkScore()));
metrics.add(new PerformanceMetric("Ray Tracing Score", component.getRayTracingScore()));
break;
case "RAM":
metrics.add(new PerformanceMetric("Capacity (GB)", component.getCapacity()));
metrics.add(new PerformanceMetric("Frequency (MHz)", component.getFrequency()));
metrics.add(new PerformanceMetric("CAS Latency", component.getCasLatency()));
metrics.add(new PerformanceMetric("Memory Type", component.getMemoryType()));
metrics.add(new PerformanceMetric("Read Speed (MB/s)", component.getReadSpeed()));
metrics.add(new PerformanceMetric("Write Speed (MB/s)", component.getWriteSpeed()));
break;
case "STORAGE":
metrics.add(new PerformanceMetric("Capacity (GB)", component.getCapacity()));
metrics.add(new PerformanceMetric("Read Speed (MB/s)", component.getReadSpeed()));
metrics.add(new PerformanceMetric("Write Speed (MB/s)", component.getWriteSpeed()));
metrics.add(new PerformanceMetric("IOPS", component.getIops()));
metrics.add(new PerformanceMetric("Type", component.getStorageType()));
metrics.add(new PerformanceMetric("Interface", component.getStorageInterface()));
metrics.add(new PerformanceMetric("MTBF (hours)", component.getMtbf()));
break;
default:
throw new IllegalArgumentException("Unsupported component type: " + componentType);
}
// 添加价格和性价比指标
metrics.add(new PerformanceMetric("Price ($)", component.getPrice()));
metrics.add(new PerformanceMetric("Performance/Price", component.getPerformancePriceRatio()));
performanceData.put(component.getName(), metrics);
}
// 获取历史价格趋势数据
Map<String, List<PriceTrendPoint>> priceTrendData = new HashMap<>();
for (Component component : components) {
List<PriceTrendPoint> priceTrends = componentRepo.findPriceTrendById(component.getId());
priceTrendData.put(component.getName(), priceTrends);
}
// 获取用户评分数据
Map<String, Double> userRatings = new HashMap<>();
for (Component component : components) {
double avgRating = userPrefRepo.findAverageRatingByComponentId(component.getId());
userRatings.put(component.getName(), avgRating);
}
// 设置结果数据
result.setComponentType(componentType);
result.setPerformanceData(performanceData);
result.setPriceTrendData(priceTrendData);
result.setUserRatings(userRatings);
return result;
}
}
六、电脑硬件推荐系统-文档展示
七、END
💕💕文末获取源码联系计算机编程果茶熊
