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ciecc-agent
Commit bbed8548 authored by wangning's avatar wangning
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fix 智能体知识库 提示词

parent 6c04f4ff
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.idea/workspace.xml
View file @ bbed8548
......@@ -4,17 +4,17 @@
<option name="autoReloadType" value="SELECTIVE" />
</component>
<component name="ChangeListManager">
<list default="true" id="26f8285c-12a3-40dc-b957-23c37b8f3c67" name="Changes" comment="fix">
<change afterPath="$PROJECT_DIR$/src/main/resources/testcases/algo_knowledge/algo_knowledge4.jsonl" afterDir="false" />
<list default="true" id="26f8285c-12a3-40dc-b957-23c37b8f3c67" name="Changes" comment="fix 智能体知识库 问题">
<change afterPath="$PROJECT_DIR$/src/main/resources/promptwords/algosuggest/algo_suggest.txt" afterDir="false" />
<change afterPath="$PROJECT_DIR$/src/main/resources/testcases/algo_knowledge/algo_knowledge5.jsonl" afterDir="false" />
<change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/java/pro/spss/server/agent/domain/constant/ChatConstants.java" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/java/pro/spss/server/agent/domain/constant/ChatConstants.java" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/java/pro/spss/server/agent/service/chatService/ChatServiceImpl.java" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/java/pro/spss/server/agent/service/chatService/ChatServiceImpl.java" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/java/pro/spss/server/agent/service/sessionService/ChatSessionManager.java" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/java/pro/spss/server/agent/service/sessionService/ChatSessionManager.java" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/java/pro/spss/server/agent/utils/DataSummaryUtil.java" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/java/pro/spss/server/agent/utils/DataSummaryUtil.java" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/resources/application-wn.yml" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/resources/application-wn.yml" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/resources/testcases/algo_knowledge1.jsonl" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/resources/testcases/algo_knowledge/algo_knowledge1.jsonl" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/resources/testcases/algo_knowledge2.jsonl" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/resources/testcases/algo_knowledge/algo_knowledge2.jsonl" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/resources/testcases/algo_knowledge3.jsonl" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/resources/testcases/algo_knowledge/algo_knowledge3.jsonl" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/resources/testcases/suggest_algorithm.jsonl" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/resources/testcases/algo_suggest_request/suggest_algorithm.jsonl" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/resources/testcases/suggest_algorithm_bak.jsonl" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/resources/testcases/algo_suggest_request/suggest_algorithm_bak.jsonl" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/resources/promptwords/algo_suggest" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/resources/promptwords/algosuggest/algo_suggest_260121.txt" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/resources/promptwords/algo_suggest_bak260118" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/resources/promptwords/algosuggest/algo_suggest_260118.txt" afterDir="false" />
<change beforePath="$PROJECT_DIR$/src/main/resources/promptwords/algo_suggest_bak260119" beforeDir="false" afterPath="$PROJECT_DIR$/src/main/resources/promptwords/algosuggest/algo_suggest_260119.txt" afterDir="false" />
</list>
<option name="SHOW_DIALOG" value="false" />
<option name="HIGHLIGHT_CONFLICTS" value="true" />
......@@ -43,7 +43,7 @@
"Spring Boot.Application.executor": "Debug",
"git-widget-placeholder": "master",
"kotlin-language-version-configured": "true",
"last_opened_file_path": "D:/projects/ciecc-agent/src/main/resources/testcases/algo_suggest_request",
"last_opened_file_path": "D:/projects/ciecc-agent/src/main/resources/testcases/algo_knowledge",
"project.structure.last.edited": "Project",
"project.structure.proportion": "0.15",
"project.structure.side.proportion": "0.21954022",
......@@ -52,13 +52,14 @@
}]]></component>
<component name="RecentsManager">
<key name="CopyFile.RECENT_KEYS">
<recent name="D:\projects\ciecc-agent\src\main\resources\testcases\algo_knowledge" />
<recent name="D:\projects\ciecc-agent\src\main\resources\promptwords\algosuggest" />
<recent name="D:\projects\ciecc-agent\src\main\resources\testcases\algo_suggest_request" />
<recent name="D:\projects\ciecc-agent\src\main\resources\testcases" />
<recent name="D:\projects\ciecc-agent\src\main\resources\testcases\test_result" />
<recent name="D:\projects\ciecc-agent\src\main\resources\promptwords" />
<recent name="D:\projects\ciecc-agent\src\main\resources" />
</key>
<key name="MoveFile.RECENT_KEYS">
<recent name="D:\projects\ciecc-agent\src\main\resources\promptwords" />
<recent name="D:\projects\ciecc-agent\src\main\resources\testcases\algo_suggest_request" />
<recent name="D:\projects\ciecc-agent\src\main\resources\testcases\algo_knowledge" />
<recent name="D:\projects\ciecc-agent\src\main\resources\testcases" />
......@@ -109,12 +110,21 @@
<option name="project" value="LOCAL" />
<updated>1768958069929</updated>
</task>
<option name="localTasksCounter" value="2" />
<task id="LOCAL-00002" summary="fix 智能体知识库 问题">
<option name="closed" value="true" />
<created>1768978168054</created>
<option name="number" value="00002" />
<option name="presentableId" value="LOCAL-00002" />
<option name="project" value="LOCAL" />
<updated>1768978168054</updated>
</task>
<option name="localTasksCounter" value="3" />
<servers />
</component>
<component name="VcsManagerConfiguration">
<MESSAGE value="fix" />
<option name="LAST_COMMIT_MESSAGE" value="fix" />
<MESSAGE value="fix 智能体知识库 问题" />
<option name="LAST_COMMIT_MESSAGE" value="fix 智能体知识库 问题" />
</component>
<component name="XDebuggerManager">
<watches-manager>
......
src/main/java/pro/spss/server/agent/domain/constant/ChatConstants.java
View file @ bbed8548
......@@ -25,7 +25,7 @@ public class ChatConstants {
public static final String MESSAGE = "message";
public static final String USER_PROMPT = "\n\n【用户需求】\n";
public static final String USER_PROMPT = "\n【用户需求】\n";
public static final String DATA_STATUS = "【当前状态】";
......
src/main/java/pro/spss/server/agent/service/chatService/ChatServiceImpl.java
View file @ bbed8548
......@@ -131,7 +131,11 @@ public class ChatServiceImpl implements BaseChatService {
private ResponseMessage executeTool(UserChatMessage userChatMessage, RequestParams requestParams, String toolName, long startTimestamp) {
String sessionId = userChatMessage.getUserId();
JSONArray historyCopy = chatSessionManager.getMessages(sessionId);
String prompt = userChatMessage.getPrompt();
String userPrompt = ChatConstants.USER_PROMPT+userChatMessage.getPrompt();
String dataPrompt = requestParams.getDataSummary();
String prompt = userPrompt + '\n' +dataPrompt;
log.debug("==================prompt=================");
log.debug(prompt);
ResponseMessage responseMessage = conversationHandler.toolExecutor(requestParams, userChatMessage, toolName, historyCopy, prompt);
responseMessage.setStartTimestamp(startTimestamp);
responseMessage.setEndTimestamp(System.currentTimeMillis());
......
src/main/java/pro/spss/server/agent/utils/DataSummaryUtil.java
View file @ bbed8548
......@@ -14,7 +14,7 @@ import java.util.regex.Pattern;
public class DataSummaryUtil {
private static final Pattern NUMERIC_PATTERN = Pattern.compile("^-?\\d+(\\.\\d+)?$");
private static final int MAX_SAMPLE_VALUES_PER_COLUMN = 3;
private static final int MAX_SAMPLE_VALUES_PER_COLUMN = 10;
/**
* 将 AgentFileReader 返回的样本原文转换为简明的数据概要文本。
......@@ -54,7 +54,7 @@ public class DataSummaryUtil {
// 生成概要文本
StringBuilder sb = new StringBuilder();
sb.append("【数据概要】\n");
sb.append("\n【数据概要】\n");
sb.append("列数:").append(colCount).append(" 样本行:").append(dataRowCount).append("\n");
sb.append("列信息:\n");
for (int i = 0; i < colCount; i++) {
......
src/main/resources/promptwords/algosuggest/algo_suggest.txt 0 → 100644
View file @ bbed8548
一、角色与目标
1. 你的角色
你是一个【算法推荐引擎】。
2. 你的任务
根据用户的自然语言需求：
* 从固定算法库中筛选算法
* 推荐最多 10 个最合适的算法
* 只能用 JSON 输出结果
二、绝对规则（强约束，必须严格遵守）
1. 算法名称必须严格来自算法库的 name 字段
2. 不允许改写、翻译、简化或创造算法名
3. 不在算法库中的算法必须直接删除
4. 按 name 全局去重
5. 不允许输出空字符串算法名
6. 不允许输出重复算法名
7. 除 JSON 外不允许输出任何文字
8. 无论需求是否清晰，输出必须是合法 JSON
9. 推荐算法数量 ≤ 10 个
三、推荐流程
（一）解析用户输入
读取：
1. 用户的自然语言需求
2. 数据概要信息（字段、类型、样本量等）
（二）构建用户标签
根据用户输入的【用户需求】和【数据概要】，自动构建“用户需求标签”。
需抽取并判断以下六类标签：
1. 任务类型（task）（可为一个或多个）
取值：预测 / 回归 / 分类 / 聚类 / 相关 / 检验 / 降维 / 评价 / 排序 / 效率 / 决策
规则：根据用户自然语言语义匹配；若包含多个目标，输出多个任务类型。
2. 数据类型（data）
取值：时间序列 / 非时间序列
规则：数据中包含明确时间字段（年、月、日、季度、时间戳等）→ 时间序列；否则为非时间序列。
3. 关系类型（relation）
取值：线性 / 非线性 / 不限
规则：用户明确说明则按其指定；否则设为“不限”。
4. 样本规模（sample）
取值：小样本 / 偏小样本 / 不限
规则：用户明确说明则按其指定；否则默认：不限。
5. 复杂度（complexity）
取值：简单 / 复杂
规则：
* 用户强调可解释、简单、易理解 → 简单
* 用户强调高精度、复杂模型、深度学习 → 复杂
* 否则按数据特征判断：
* 样本很小且特征少 → 简单
* 特征多或明显非线性 → 复杂
* 无法判断 → 默认简单
6. 模型特性偏好（property）（可选，多选）
取值来自算法库的 property 字段。
规则：用户有明确偏好则映射；无偏好则设为：不限。
输出要求：
仅输出结果，不输出过程。格式必须为：
【用户需求标签】
{"task":xxx,"data":xxx,"relation":xxx,"sample":xxx,"complexity":xxx,"property":xxx}
（三）加载算法知识库
加载算法库中所有算法及其标签，用于与“用户需求标签”进行匹配。
每个算法需包含以下字段：
* task / data / relation / sample / complexity / property
规范：
1. 字段含义与用户需求标签完全一致
2. 加载结果应可直接用于规则或相似度匹配
（四）初筛过滤
按硬条件筛选算法：
1. 算法 task 与用户 task 有交集
2. 算法 data 与用户 data 一致
不满足任一条件的算法直接剔除。
（五）匹配评分与过滤（六标签）
在通过初筛的算法上计算匹配分数。
总分 = task分 + data分 + relation分 + sample分 + complexity分 + property分
评分规则：
1. task 分：
* 算法 task == 用户 task → 3
* 有交集 → 2
2. data 分：
* 算法 data == 用户 data → 2
3. relation 分：
* 用户 relation = 不限 → 1
* 否则算法 relation = 用户 relation → 2
* 否则 → 0
4. sample 分：
* 算法 sample 覆盖或等于 用户 sample → 2
* 否则 → 0
5. complexity 分：
* 算法 complexity = 用户 complexity → 2
* 否则 → 0
6. property 分：
* 用户 property = 不限 → 0
* 否则 = |算法 property ∩ 用户 property|
过滤规则：
* 若总分 < 5，则剔除该算法
（六）结果输出
1. 若有推荐结果：按“正常推荐”格式输出
2. 若无算法满足条件：按“模糊需求”格式输出
3. 只输出合法 JSON，不输出任何多余文字
四、输出规范
（一）正常推荐格式
{
"response": "请从以下算法中选择一种：",
"options": ["算法1", "算法2", "算法3"],
"params": []
}
（二）模糊需求格式
{
"response": "您的需求不够明确，请补充您的分析目标是什么",
"options": [],
"params": []
}
算法知识库：
[
{"name":"最小二乘回归","task":["回归","预测"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["可解释性强","对多重共线性敏感"]},
{"name":"岭回归","task":["回归","预测"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["抗多重共线性","模型稳定"]},
{"name":"套索回归","task":["回归","预测"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["特征选择","模型稀疏"]},
{"name":"多项式回归","task":["回归","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["拟合弯曲关系","易过拟合"]},
{"name":"支持向量机回归","task":["回归","预测"],"data":"非时间序列","relation":"非线性","sample":"偏小样本","complexity":"复杂","property":["小样本表现好","对参数敏感"]},
{"name":"BP神经网络","task":["回归","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["表达能力强","可解释性弱"]},
{"name":"梯度提升树","task":["回归","分类","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["精度高","对噪声敏感"]},
{"name":"移动平均法","task":["预测"],"data":"时间序列","relation":"不限","sample":"偏小样本","complexity":"简单","property":["平滑波动","对突变反应慢"]},
{"name":"指数平滑法","task":["预测"],"data":"时间序列","relation":"不限","sample":"偏小样本","complexity":"简单","property":["重视近期数据","适合短期预测"]},
{"name":"自回归模型（AR）","task":["预测"],"data":"时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["依赖历史值","要求平稳"]},
{"name":"滑动平均模型（MA）","task":["预测"],"data":"时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["刻画随机扰动","短期效果好"]},
{"name":"自回归滑动平均模型","task":["预测"],"data":"时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["综合AR和MA","预测能力强"]},
{"name":"差分自回归移动平均模型","task":["预测"],"data":"时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["处理非平稳序列","建模步骤多"]},
{"name":"灰色模型","task":["预测"],"data":"时间序列","relation":"线性","sample":"小样本","complexity":"简单","property":["小样本适用","抗随机性弱"]},
{"name":"决策树","task":["分类","决策"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"简单","property":["结构直观","易过拟合"]},
{"name":"随机森林","task":["分类","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["稳定性高","计算量大"]},
{"name":"自适应增强算法","task":["分类","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["关注难样本","对噪声敏感"]},
{"name":"判别分析","task":["分类"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["模型简洁","分布假设强"]},
{"name":"二分类逻辑回归","task":["分类","回归"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["概率输出","可解释性好"]},
{"name":"皮尔逊相关分析","task":["相关"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["衡量线性关系","对异常值敏感"]},
{"name":"斯皮尔曼相关分析","task":["相关"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["基于秩次","对异常值不敏感"]},
{"name":"独立样本t检验","task":["检验"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["比较均值差异","要求正态性"]},
{"name":"正态检验","task":["检验"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["检验分布形态","对样本量敏感"]},
{"name":"K-均值聚类","task":["聚类"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["速度快","对初始中心敏感"]},
{"name":"层次聚类","task":["聚类"],"data":"非时间序列","relation":"不限","sample":"小样本","complexity":"复杂","property":["结构直观","计算量大"]},
{"name":"因子分析","task":["降维","评价"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["提取潜在因子","解释性强"]},
{"name":"主成分分析","task":["降维"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["信息压缩","可解释性弱"]},
{"name":"优劣解距离法","task":["排序","评价"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["结果直观","依赖权重"]},
{"name":"秩和比评价法","task":["排序","评价"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["计算简单","信息损失多"]},
{"name":"模糊综合评价","task":["评价","排序"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"复杂","property":["处理模糊信息","主观性强"]},
{"name":"数据包络分析（CCR）","task":["效率","评价"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["规模报酬不变","适合同规模对象"]},
{"name":"数据包络分析（BCC）","task":["效率","评价"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["规模报酬可变","区分技术效率"]},
{"name":"柯布-道格拉斯生产函数","task":["效率","回归"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["形式简单","函数假设强"]}
\ No newline at end of file
src/main/resources/testcases/algo_knowledge/algo_knowledge5.jsonl 0 → 100644
View file @ bbed8548
[
{"name":"最小二乘回归","task":["回归","预测"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["可解释性强","对多重共线性敏感"]},
{"name":"岭回归","task":["回归","预测"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["抗多重共线性","模型稳定"]},
{"name":"套索回归","task":["回归","预测"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["特征选择","模型稀疏"]},
{"name":"多项式回归","task":["回归","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["拟合弯曲关系","易过拟合"]},
{"name":"支持向量机回归","task":["回归","预测"],"data":"非时间序列","relation":"非线性","sample":"偏小样本","complexity":"复杂","property":["小样本表现好","对参数敏感"]},
{"name":"BP神经网络","task":["回归","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["表达能力强","可解释性弱"]},
{"name":"梯度提升树","task":["回归","分类","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["精度高","对噪声敏感"]},
{"name":"移动平均法","task":["预测"],"data":"时间序列","relation":"不限","sample":"偏小样本","complexity":"简单","property":["平滑波动","对突变反应慢"]},
{"name":"指数平滑法","task":["预测"],"data":"时间序列","relation":"不限","sample":"偏小样本","complexity":"简单","property":["重视近期数据","适合短期预测"]},
{"name":"自回归模型（AR）","task":["预测"],"data":"时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["依赖历史值","要求平稳"]},
{"name":"滑动平均模型（MA）","task":["预测"],"data":"时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["刻画随机扰动","短期效果好"]},
{"name":"自回归滑动平均模型","task":["预测"],"data":"时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["综合AR和MA","预测能力强"]},
{"name":"差分自回归移动平均模型","task":["预测"],"data":"时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["处理非平稳序列","建模步骤多"]},
{"name":"灰色模型","task":["预测"],"data":"时间序列","relation":"线性","sample":"小样本","complexity":"简单","property":["小样本适用","抗随机性弱"]},
{"name":"决策树","task":["分类","决策"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"简单","property":["结构直观","易过拟合"]},
{"name":"随机森林","task":["分类","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["稳定性高","计算量大"]},
{"name":"自适应增强算法","task":["分类","预测"],"data":"非时间序列","relation":"非线性","sample":"不限","complexity":"复杂","property":["关注难样本","对噪声敏感"]},
{"name":"判别分析","task":["分类"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["模型简洁","分布假设强"]},
{"name":"二分类逻辑回归","task":["分类","回归"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["概率输出","可解释性好"]},
{"name":"皮尔逊相关分析","task":["相关"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["衡量线性关系","对异常值敏感"]},
{"name":"斯皮尔曼相关分析","task":["相关"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["基于秩次","对异常值不敏感"]},
{"name":"独立样本t检验","task":["检验"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["比较均值差异","要求正态性"]},
{"name":"正态检验","task":["检验"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["检验分布形态","对样本量敏感"]},
{"name":"K-均值聚类","task":["聚类"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["速度快","对初始中心敏感"]},
{"name":"层次聚类","task":["聚类"],"data":"非时间序列","relation":"不限","sample":"小样本","complexity":"复杂","property":["结构直观","计算量大"]},
{"name":"因子分析","task":["降维","评价"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["提取潜在因子","解释性强"]},
{"name":"主成分分析","task":["降维"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["信息压缩","可解释性弱"]},
{"name":"优劣解距离法","task":["排序","评价"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["结果直观","依赖权重"]},
{"name":"秩和比评价法","task":["排序","评价"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"简单","property":["计算简单","信息损失多"]},
{"name":"模糊综合评价","task":["评价","排序"],"data":"非时间序列","relation":"不限","sample":"不限","complexity":"复杂","property":["处理模糊信息","主观性强"]},
{"name":"数据包络分析（CCR）","task":["效率","评价"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["规模报酬不变","适合同规模对象"]},
{"name":"数据包络分析（BCC）","task":["效率","评价"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"复杂","property":["规模报酬可变","区分技术效率"]},
{"name":"柯布-道格拉斯生产函数","task":["效率","回归"],"data":"非时间序列","relation":"线性","sample":"不限","complexity":"简单","property":["形式简单","函数假设强"]}
]
算法标签体系简要说明
通过标签对算法的适用场景和特性进行结构化描述，用于支持算法自动推荐、条件筛选和规则匹配。
任务类型：说明算法能解决的问题，如预测、回归、分类、排序、聚类、相关、检验、降维、评价、效率等。
数据类型：区分是否为时间序列数据。
关系形式：描述模型刻画关系的方式，包括线性、非线性或不限。
样本规模：表示算法对样本数量的适应程度，如小样本或不限。
复杂度：表示算法实现和计算难度，分为简单或复杂。
通过这些标签，可以将用户需求与算法能力快速匹配，实现智能推荐与筛选。
样本很小、特征少 → 优先简单模型
特征多、关系明显非线性 → 偏向复杂模型
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