length,width,height,weight,fragile,shelf_type
50,40,30,5,false,small_shelf
120,80,150,120,false,floor_stack
30,20,10,1,false,small_shelf
200,60,40,80,true,high_rack
10,10,5,0.5,false,small_shelf
300,120,80,300,false,floor_stack
80,60,180,50,false,high_rack
25,20,15,2,true,small_shelf
150,100,200,150,false,floor_stack
60,40,160,40,false,high_rack

<properties>
    <maven.compiler.source>17</maven.compiler.source>
    <maven.compiler.target>17</maven.compiler.target>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <satoken.version>1.37.0</satoken.version>
    <spring-ai.version>1.0.0-M6</spring-ai.version>
    <spring-ai-alibaba.version>1.0.0-M6.1</spring-ai-alibaba.version>
</properties>
<parent>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-parent</artifactId>
    <version>3.2.2</version>
    <relativePath/>
</parent>
<dependencies>
    <!-- For handling CSV files -->
    <dependency>
        <groupId>org.apache.commons</groupId>
        <artifactId>commons-csv</artifactId>
        <version>1.9.0</version>
    </dependency>
    <dependency>
        <groupId>nz.ac.waikato.cms.weka</groupId>
        <artifactId>weka-stable</artifactId>
        <version>3.8.6</version>
    </dependency>
    <dependency>
        <groupId>com.alibaba</groupId>
        <artifactId>fastjson</artifactId>
        <version>1.2.83</version>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-aop</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <!-- MySQL連接驅(qū)動 -->
    <dependency>
        <groupId>mysql</groupId>
        <artifactId>mysql-connector-java</artifactId>
        <version>8.0.28</version>
    </dependency>
    <dependency>
        <groupId>org.projectlombok</groupId>
        <artifactId>lombok</artifactId>
    </dependency>
    <dependency>
        <groupId>com.alibaba.cloud.ai</groupId>
        <artifactId>spring-ai-alibaba-starter</artifactId>
        <version>${spring-ai-alibaba.version}</version>
    </dependency>
</dependencies>
<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>org.springframework.ai</groupId>
            <artifactId>spring-ai-bom</artifactId>
            <version>${spring-ai.version}</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>
<repositories>
    <repository>
        <name>Central Portal Snapshots</name>
        <id>central-portal-snapshots</id>
        <url>https://central.sonatype.com/repository/maven-snapshots/</url>
        <releases>
            <enabled>false</enabled>
        </releases>
        <snapshots>
            <enabled>true</enabled>
        </snapshots>
    </repository>
    <repository>
        <id>spring-milestones</id>
        <name>Spring Milestones</name>
        <url>https://repo.spring.io/milestone</url>
        <snapshots>
            <enabled>false</enabled>
        </snapshots>
    </repository>
    <repository>
        <id>spring-snapshots</id>
        <name>Spring Snapshots</name>
        <url>https://repo.spring.io/snapshot</url>
        <releases>
            <enabled>false</enabled>
        </releases>
    </repository>
</repositories>

<dependency>
    <groupId>org.apache.commons</groupId>
    <artifactId>commons-csv</artifactId>
    <version>1.9.0</version>
</dependency>
<dependency>
    <groupId>nz.ac.waikato.cms.weka</groupId>
    <artifactId>weka-stable</artifactId>
    <version>3.8.6</version>
</dependency>

package com.congge.command.v3;
import weka.classifiers.trees.J48;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
/**
 * 基于本地的訓(xùn)練文件樣本數(shù)據(jù)進行預(yù)測
 */
public class ShelfRecommendationSystemV3 {
    private J48 decisionTree;
    private Instances dataStructure;
    /**
     * 加載訓(xùn)練數(shù)據(jù)并構(gòu)建模型
     */
    public void trainModel(String csvFilePath) throws Exception {
        // 1. 加載CSV數(shù)據(jù)
        DataSource source = new DataSource(csvFilePath);
        Instances data = source.getDataSet();
        // 2. 設(shè)置類別屬性（貨架類型）
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }
        // 3. 保存數(shù)據(jù)結(jié)構(gòu)用于后續(xù)預(yù)測
        this.dataStructure = new Instances(data, 0);
        // 4. 構(gòu)建決策樹模型
        decisionTree = new J48();
        decisionTree.buildClassifier(data);
        System.out.println("模型訓(xùn)練完成！");
    }
    /**
     * 預(yù)測新貨物的推薦貨架類型
     */
    public String predictShelfType(double length, double width, double height,
                                   double weight, boolean fragile) throws Exception {
        if (decisionTree == null || dataStructure == null) {
            throw new IllegalStateException("模型尚未訓(xùn)練，請先調(diào)用trainModel方法");
        }
        // 創(chuàng)建新實例
        weka.core.Instance instance = new weka.core.DenseInstance(6);
        instance.setDataset(dataStructure);
        // 設(shè)置屬性值
        instance.setValue(0, length);
        instance.setValue(1, width);
        instance.setValue(2, height);
        instance.setValue(3, weight);
        instance.setValue(4, fragile ? "true" : "false");
        // 進行預(yù)測
        double prediction = decisionTree.classifyInstance(instance);
        return dataStructure.classAttribute().value((int) prediction);
    }
    /**
     * 評估模型準(zhǔn)確率（使用訓(xùn)練數(shù)據(jù)簡單評估）
     */
    public void evaluateModel(String csvFilePath) throws Exception {
        DataSource source = new DataSource(csvFilePath);
        Instances data = source.getDataSet();
        if (data.classIndex() == -1) {
            data.setClassIndex(data.numAttributes() - 1);
        }
        // 簡單評估（實際應(yīng)用中應(yīng)該使用交叉驗證）
        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.evaluateModel(decisionTree, data);
        System.out.println("\n模型評估結(jié)果:");
        System.out.println(eval.toSummaryString());
        System.out.println(eval.toClassDetailsString());
        System.out.println(eval.toMatrixString());
    }
    public static void main(String[] args) {
        ShelfRecommendationSystemV3 system = new ShelfRecommendationSystemV3();
        try {
            // 1. 訓(xùn)練模型
            String csvPath = "data/shelf_data.csv";
            system.trainModel(csvPath);
            // 2. 評估模型（可選）
            system.evaluateModel(csvPath);
            // 3. 測試預(yù)測
            System.out.println("\n測試預(yù)測:");
            // 測試用例1: 小而輕的貨物
            String recommendation1 = system.predictShelfType(30, 20, 10, 1, false);
            System.out.printf("貨物(30x20x10cm, 1kg, 非易碎) 推薦貨架: %s%n", recommendation1);
            // 測試用例2: 大而重的貨物
            String recommendation2 = system.predictShelfType(200, 100, 180, 200, false);
            System.out.printf("貨物(200x100x180cm, 200kg, 非易碎) 推薦貨架: %s%n", recommendation2);
            // 測試用例3: 易碎的中等大小貨物
            String recommendation3 = system.predictShelfType(60, 40, 30, 5, true);
            System.out.printf("貨物(60x40x30cm, 5kg, 易碎) 推薦貨架: %s%n", recommendation3);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

CREATE TABLE product_shelf_mapping (
    id INT AUTO_INCREMENT PRIMARY KEY,
    product_name VARCHAR(100),
    length DOUBLE NOT NULL COMMENT '長度(cm)',
    width DOUBLE NOT NULL COMMENT '寬度(cm)',
    height DOUBLE NOT NULL COMMENT '高度(cm)',
    weight DOUBLE NOT NULL COMMENT '重量(kg)',
    shelf_type ENUM('small_shelf', 'high_shelf', 'floor_stack') NOT NULL COMMENT '貨架類型',
    last_updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
)ENGINE=InnoDB AUTO_INCREMENT=1 DEFAULT CHARSET=utf8mb4;
-- 清空表（測試時使用，生產(chǎn)環(huán)境請移除）
TRUNCATE TABLE product_shelf_mapping;
-- 插入測試數(shù)據(jù)
INSERT INTO product_shelf_mapping (product_name, length, width, height, weight, shelf_type) VALUES
-- 小貨架（體積小、重量輕）
('小型電子元件', 15, 10, 5, 0.2, 'small_shelf'),
('辦公文具套裝', 30, 20, 8, 0.5, 'small_shelf'),
('手機配件盒', 25, 15, 5, 0.3, 'small_shelf'),
('化妝品樣品', 12, 8, 6, 0.1, 'small_shelf'),
('LED燈泡', 10, 10, 15, 0.15, 'small_shelf'),
-- 高位貨架（中等體積、中等重量，可堆疊）
('書籍禮盒', 40, 30, 20, 2.5, 'high_shelf'),
('廚房調(diào)料套裝', 35, 25, 15, 1.8, 'high_shelf'),
('玩具模型箱', 50, 40, 30, 3.0, 'high_shelf'),
('家庭清潔用品', 45, 35, 25, 2.2, 'high_shelf'),
('寵物食品袋', 55, 40, 20, 4.0, 'high_shelf'),
-- 地堆貨架（超長、超重或異形貨物）
('大型家具部件', 200, 80, 30, 25.0, 'floor_stack'),
('健身器材包裝', 180, 60, 40, 30.0, 'floor_stack'),
('工業(yè)設(shè)備箱', 150, 100, 50, 50.0, 'floor_stack'),
('自行車包裝箱', 160, 70, 20, 18.0, 'floor_stack'),
('長條形管材', 300, 10, 10, 12.0, 'floor_stack'),
-- 邊界值測試（接近分類閾值的貨物）
('中型電器箱', 60, 50, 40, 8.0, 'high_shelf'),  -- 接近地堆但重量稍輕
('超重小物件', 20, 20, 10, 15.0, 'floor_stack'), -- 重量大但體積小
('細長物品', 150, 5, 5, 3.0, 'floor_stack'),    -- 超長但重量輕
('扁平大件', 120, 90, 2, 5.0, 'floor_stack');    -- 面積大但高度低

<!-- MySQL連接驅(qū)動 -->
<dependency>
    <groupId>mysql</groupId>
    <artifactId>mysql-connector-java</artifactId>
    <version>8.0.28</version>
</dependency>

package com.congge.command.v2;
import lombok.Data;
import weka.classifiers.trees.J48;
import weka.core.*;
import java.sql.*;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
/**
 * 基于數(shù)據(jù)庫的樣本數(shù)據(jù)進行預(yù)測
 */
public class ShelfRecommendationSystem {
    // MySQL數(shù)據(jù)庫配置
    private static final String DB_URL = "jdbc:mysql://rm-bp15bb46ti34x4glgdo.mysql.rds.aliyuncs.com:3306/gh_log";
    private static final String DB_USER = "root";
    private static final String DB_PASSWORD = "Dkf5381200";
    public static void main(String[] args) {
        try {
            // 1. 從MySQL加載訓(xùn)練數(shù)據(jù)
            List<Product> trainingData = loadTrainingDataFromMySQL();
            // 2. 轉(zhuǎn)換為Weka Instances格式
            Instances instances = createInstances(trainingData);
            // 3. 訓(xùn)練決策樹模型
            J48 tree = trainDecisionTree(instances);
            // 4. 測試模型
            testModel(tree, instances);
            // 5. 示例：對新產(chǎn)品進行貨架推薦
            Product newProduct = new Product();
            newProduct.setLength(120);
            newProduct.setWidth(80);
            newProduct.setHeight(30);
            newProduct.setWeight(25);
            String recommendedShelf = recommendShelf(tree, newProduct);
            System.out.println("\n推薦貨架類型: " + recommendedShelf);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
    /**
     * 從MySQL數(shù)據(jù)庫加載訓(xùn)練數(shù)據(jù)
     */
    private static List<Product> loadTrainingDataFromMySQL() throws SQLException {
        List<Product> products = new ArrayList<>();
        try (Connection conn = DriverManager.getConnection(DB_URL, DB_USER, DB_PASSWORD)) {
            String query = "SELECT length, width, height, weight, shelf_type FROM product_shelf_mapping";
            try (Statement stmt = conn.createStatement();
                 ResultSet rs = stmt.executeQuery(query)) {
                while (rs.next()) {
                    Product product = new Product();
                    product.setLength(rs.getDouble("length"));
                    product.setWidth(rs.getDouble("width"));
                    product.setHeight(rs.getDouble("height"));
                    product.setWeight(rs.getDouble("weight"));
                    product.setShelfType(rs.getString("shelf_type"));
                    products.add(product);
                }
            }
        }
        System.out.println("從MySQL加載了 " + products.size() + " 條訓(xùn)練數(shù)據(jù)");
        return products;
    }
    /**
     * 創(chuàng)建Weka Instances對象
     */
    private static Instances createInstances(List<Product> products) {
        // 定義屬性
        ArrayList<Attribute> attributes = new ArrayList<>();
        // 數(shù)值型屬性：長、寬、高、重
        attributes.add(new Attribute("length"));
        attributes.add(new Attribute("width"));
        attributes.add(new Attribute("height"));
        attributes.add(new Attribute("weight"));
        //  nominal屬性：貨架類型
        ArrayList<String> shelfTypes = new ArrayList<>();
        shelfTypes.add("small_shelf");
        shelfTypes.add("high_shelf");
        shelfTypes.add("floor_stack");
        attributes.add(new Attribute("shelf_type", shelfTypes));
        // 創(chuàng)建Instances對象
        Instances instances = new Instances("ProductShelfRelation", attributes, 0);
        instances.setClassIndex(instances.numAttributes() - 1); // 設(shè)置分類屬性
        // 添加數(shù)據(jù)
        for (Product product : products) {
            double[] values = new double[instances.numAttributes()];
            values[0] = product.getLength();
            values[1] = product.getWidth();
            values[2] = product.getHeight();
            values[3] = product.getWeight();
            // 設(shè)置分類屬性值
            switch (product.getShelfType()) {
                case "small_shelf": values[4] = 0; break;
                case "high_shelf": values[4] = 1; break;
                case "floor_stack": values[4] = 2; break;
                //default: values[4] = Instance.missingValue();
                default: values[4] = Utils.missingValue();
            }
            instances.add(new DenseInstance(1.0, values));
        }
        return instances;
    }
    /**
     * 訓(xùn)練決策樹模型
     */
    private static J48 trainDecisionTree(Instances instances) throws Exception {
        // 設(shè)置分類器選項
        String[] options = new String[1];
        options[0] = "-U"; // 使用未修剪的樹
        J48 tree = new J48();
        tree.setOptions(options);
        // 訓(xùn)練模型
        tree.buildClassifier(instances);
        // 輸出模型
        System.out.println("\n訓(xùn)練好的決策樹模型:");
        System.out.println(tree);
        return tree;
    }
    /**
     * 測試模型準(zhǔn)確率
     */
    private static void testModel(J48 tree, Instances instances) throws Exception {
        // 隨機劃分訓(xùn)練集和測試集 (70%訓(xùn)練, 30%測試)
        instances.randomize(new Random(0));
        int trainSize = (int) Math.round(instances.numInstances() * 0.7);
        int testSize = instances.numInstances() - trainSize;
        Instances train = new Instances(instances, 0, trainSize);
        Instances test = new Instances(instances, trainSize, testSize);
        // 重新訓(xùn)練模型
        tree.buildClassifier(train);
        // 測試模型
        int correct = 0;
        for (int i = 0; i < test.numInstances(); i++) {
            Instance inst = test.instance(i);
            double predicted = tree.classifyInstance(inst);
            double actual = inst.classValue();
            if (predicted == actual) {
                correct++;
            }
        }
        double accuracy = (double) correct / test.numInstances() * 100;
        System.out.printf("\n模型準(zhǔn)確率: %.2f%% (%d/%d)\n", accuracy, correct, test.numInstances());
    }
    /**
     * 為新產(chǎn)品推薦貨架類型
     */
    public static String recommendShelf(J48 tree, Product product) throws Exception {
        // 創(chuàng)建屬性列表 (必須與訓(xùn)練數(shù)據(jù)相同)
        ArrayList<Attribute> attributes = new ArrayList<>();
        attributes.add(new Attribute("length"));
        attributes.add(new Attribute("width"));
        attributes.add(new Attribute("height"));
        attributes.add(new Attribute("weight"));
        ArrayList<String> shelfTypes = new ArrayList<>();
        shelfTypes.add("small_shelf");
        shelfTypes.add("high_shelf");
        shelfTypes.add("floor_stack");
        attributes.add(new Attribute("shelf_type", shelfTypes));
        // 創(chuàng)建Instances對象
        Instances instances = new Instances("Temp", attributes, 0);
        instances.setClassIndex(instances.numAttributes() - 1);
        // 創(chuàng)建新產(chǎn)品實例
        double[] values = new double[instances.numAttributes()];
        values[0] = product.getLength();
        values[1] = product.getWidth();
        values[2] = product.getHeight();
        values[3] = product.getWeight();
        //values[4] = Instance.missingValue(); // 分類屬性設(shè)為缺失值
        values[4] = Utils.missingValue();
        instances.add(new DenseInstance(1.0, values));
        // 進行預(yù)測
        double prediction = tree.classifyInstance(instances.instance(0));
        // 返回預(yù)測結(jié)果
        return shelfTypes.get((int) prediction);
    }
}
@Data
class Product {
    private double length;
    private double width;
    private double height;
    private double weight;
    private String shelfType;
}

spring:
  # 增加ai的配置
  ai:
    dashscope:
      api-key: 個人apikey
      chat:
        options:
          model: qwen-max

package com.congge.command.v5;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestBody;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;
@RestController
@RequestMapping("/client/ai")
public class ChatController {
    private final ChatClient chatClient;
    public ChatController(ChatClient.Builder chatClientBuilder) {
        this.chatClient = chatClientBuilder
                .build();
    }
    //localhost:8082/client/ai/chat?message=今天北京的天氣如何
    //localhost:8082/client/ai/chat?message=你是誰
    //localhost:8082/client/ai/chat?message=當(dāng)前時間是多少
    @PostMapping("/chat")
    public String chat(@RequestBody Product product){
        Prompt prompt = new Prompt(new UserMessage(buildPrompt(product)));
        String content = chatClient.prompt(prompt).call().content();
        return content;
    }
    private static String buildPrompt(Product product) {
        return String.format("""
            你是一名倉庫管理專家，需要根據(jù)商品尺寸和重量推薦合適的貨架類型。
            貨架類型定義：
            1. small_shelf: 體積小于5000cm3且重量小于1kg的商品
            2. high_shelf: 體積5000-60000cm3且重量1-10kg的商品
            3. floor_stack: 體積大于60000cm3或重量大于10kg或形狀特殊的商品（如超長、超寬）
            商品信息：
            名稱: %s
            尺寸: %.1fcm x %.1fcm x %.1fcm
            重量: %.2fkg
            體積: %.1fcm3
            密度: %.2fg/cm3
            請根據(jù)以上信息，只返回推薦貨架類型，格式為：推薦貨架類型: [類型]
            """,
                product.getProductName(),
                product.getLength(),
                product.getWidth(),
                product.getHeight(),
                product.getWeight(),
                product.getVolume(),
                product.getDensity());
    }
}

package com.congge.command.v5;
import lombok.Data;
@Data
public class Product {
    private Long id;
    private String productName;
    private double length;
    private double width;
    private double height;
    private double weight;
    private String shelfType;
    // 計算體積
    public double getVolume() {
        return length * width * height;
    }
    // 計算密度
    public double getDensity() {
        return weight / getVolume();
    }
}