/* * Copyright (c) 2025, TP-Link Global Inc. All rights reserved. */ package com.tplink.cdd.demo.mongo.po; import lombok.Data; import org.springframework.data.annotation.Id; import org.springframework.data.mongodb.core.index.CompoundIndex; import org.springframework.data.mongodb.core.index.CompoundIndexes; import org.springframework.data.mongodb.core.mapping.Document; import org.springframework.data.mongodb.core.mapping.Field; import org.springframework.data.mongodb.core.mapping.Sharded; /** * @author zhaobingqian * @version 1.0 * @since 2025/3/11 */ @Data @Document("smart_data_frame") @CompoundIndexes({ @CompoundIndex(name = "index_user_dev_channel_time", def = "{'user_id':1, 'dev_id':1, 'channel':1, 'time': 1}"), }) @Sharded(shardKey = "user_id") public class SmartDataFramePO { @Id private String id; @Field(name = "user_id") private String userId; @Field(name = "dev_id") private String devId; @Field(name = "channel") private Short channel; @Field(name = "timestamp") private Long timestamp; @Field(name = "object_id") private Integer objectId; @Field(name = "type") private Integer type; @Field(name = "shape") private Byte shape; @Field(name = "box_tlx") private Integer boxTlX; @Field(name = "box_tly") private Integer boxTlY; @Field(name = "box_w") private Integer boxW; @Field(name = "box_h") private Integer boxH; @Field(name = "confidence") private Byte confidence; @Field(name = "fg_ratio") private Byte fgRatio; @Field(name = "is_fake") private Byte isFake; @Field(name = "is_normal") private Byte isNormal; @Field(name = "point_3dvalid") private Byte point3dValid; @Field(name = "point_3dx") private Short point3dX; @Field(name = "point_3dy") private Short point3dY; @Field(name = "point_3dz") private Short point3dZ; } 这个是mongodb数据表,在starrocks怎么建表

时间: 2025-07-09 16:44:48 浏览: 3
<think>好的,我现在需要帮助用户解决如何将MongoDB中的这个SmartDataFramePO类对应的数据表同步到StarRocks中,并且建表的问题。首先,我需要仔细分析用户提供的Java类,了解MongoDB中的数据结构,然后根据之前的经验和StarRocks的特性来设计建表语句。 首先,用户提供的Java类使用了Spring Data MongoDB的注解,比如@Document、@Field、@Sharded等。这表明对应的MongoDB集合名为“smart_data_frame”,并且分片键是“user_id”。类中的各个字段都有对应的MongoDB字段名,例如userId对应“user_id”,devId对应“dev_id”等。此外,还有组合索引和分片键的信息,但在StarRocks中分片键的处理方式不同,可能需要用到分布式键。 接下来,我需要确定每个字段在MongoDB中的数据类型,并映射到StarRocks中的合适类型。例如,Java中的String类型对应VARCHAR,Short对应SMALLINT,Long对应BIGINT,Byte对应TINYINT,Integer对应INT,Boolean可能对应TINYINT(1)或者BIT,但这里使用的是Byte类型来表示布尔值,所以应该是TINYINT。 然后,用户之前的问题是关于如何建表以及字段是否会被自动识别为单独列还是JSON。根据之前的回答,如果MongoDB的字段是扁平的,可以映射为StarRocks的列;如果嵌套或动态,则需要用JSON类型。但在这个例子中,所有字段都是扁平的,没有嵌套结构,所以应该每个字段都单独建列。 接下来,我需要考虑StarRocks的表结构设计,包括引擎类型、主键、分布式键和分区键。由于用户可能需要进行高效查询,特别是基于userId、devId、channel和timestamp的组合查询,因此主键应该包含这些常用查询字段,以提高查询性能。主键的选择需要遵循StarRocks的主键模型,主键列必须是非空的,并且具有唯一性。在这里,id字段是MongoDB的ObjectId,可以作为主键的一部分,但需要确认其唯一性。 分布式键的选择也很重要,因为它影响数据在节点间的分布。通常选择高基数的字段,如userId或id。用户之前的分片键是userId,所以在StarRocks中可能同样选择userId作为分布式键,但需要确认是否有数据倾斜的风险。如果userId分布均匀,可以使用HASH(user_id)进行分桶。 另外,分区键可以根据时间戳进行分区,比如按天或按月分区,这有助于数据管理和查询优化。timestamp字段是长整型,可能需要转换为日期格式,或者直接按数值范围分区。 然后,我需要处理每个字段的数据类型映射。例如: - id → VARCHAR - user_id → VARCHAR - dev_id → VARCHAR - channel → SMALLINT - timestamp → BIGINT 或者转换为DATETIME,但用户可能保持原始长整型 - objectId → INT - type → INT - shape → TINYINT - box的各个坐标和宽高 → INT - confidence → TINYINT - fg_ratio → TINYINT - is_fake, is_normal, point_3dvalid → TINYINT - point3d的坐标 → SMALLINT 接下来,建表语句的编写需要注意以下几点: 1. 指定ENGINE为OLAP,使用主键模型(PRIMARY KEY)。 2. 主键应包含常用查询字段,这里可能包括id、user_id、dev_id、channel、timestamp,但主键需要唯一,所以需要确认id是否唯一。如果id是唯一的,那么主键可以是id,但为了查询性能,可能需要将常用过滤字段放在前面。不过,StarRocks的主键模型要求主键是唯一的,并且是前缀索引,所以需要将高基数的字段放在前面。可能主键设置为(id),但这样可能无法有效利用前缀索引。或者,如果业务查询主要按user_id、dev_id、channel、timestamp过滤,可以将这些字段作为主键,但需要确保它们的组合是唯一的,这可能不现实。所以可能需要以id作为主键,并调整索引策略。 这里可能需要进一步分析用户的查询模式。例如,如果经常按user_id、dev_id、channel和时间范围查询,那么主键按这些字段排序可以提高查询效率。但主键必须唯一,所以需要将id作为主键,或者组合其他字段保证唯一性。如果原MongoDB中的id是唯一的,那么StarRocks的主键可以设为id,而分布式键可以是user_id。这种情况下,主键是id,而查询时如果使用user_id等条件,StarRocks仍然可以利用前缀索引吗?可能需要用户根据实际查询情况调整主键的顺序。例如,主键设为(user_id, dev_id, channel, timestamp, id),这样在查询时使用user_id等条件时,可以利用索引。 不过,主键的选择需要平衡查询性能和存储效率。如果主键包含太多字段,可能会影响加载性能和存储占用。需要用户根据具体情况权衡。 另外,关于分区,如果timestamp表示时间,可以按天分区,但需要将BIGINT类型转换为日期。例如,假设timestamp是毫秒级时间戳,可以使用FROM_UNIXTIME(timestamp/1000)转换为DATETIME,然后按天分区。但用户可能需要保留原始timestamp值,所以是否需要转换需要确认。 接下来,考虑建表语句的示例: CREATE TABLE smart_data_frame ( id VARCHAR(24) COMMENT 'MongoDB ObjectId', user_id VARCHAR COMMENT '用户ID', dev_id VARCHAR COMMENT '设备ID', channel SMALLINT COMMENT '通道号', timestamp BIGINT COMMENT '时间戳', object_id INT COMMENT '对象ID', type INT COMMENT '类型标识', shape TINYINT COMMENT '形状标识', box_tlx INT COMMENT '左上角X坐标', box_tly INT COMMENT '左上角Y坐标', box_w INT COMMENT '框宽度', box_h INT COMMENT '框高度', confidence TINYINT COMMENT '置信度', fg_ratio TINYINT COMMENT '前景占比', is_fake TINYINT COMMENT '是否伪造', is_normal TINYINT COMMENT '是否正常', point_3dvalid TINYINT COMMENT '3D点有效性', point_3dx SMALLINT COMMENT '3D点X坐标', point_3dy SMALLINT COMMENT '3D点Y坐标', point_3dz SMALLINT COMMENT '3D点Z坐标' ) ENGINE=OLAP PRIMARY KEY(id) DISTRIBUTED BY HASH(user_id) PROPERTIES ( "replication_num" = "3" ); 不过,主键仅包含id可能无法优化基于user_id的查询。如果查询经常使用user_id作为条件,可能需要将user_id放在主键的前面。但主键必须唯一,所以可以设置主键为(user_id, dev_id, channel, timestamp, id),但需要确保组合唯一。如果原数据中这些字段的组合不唯一,那么可能会产生主键冲突。因此,可能更安全的方式是将主键设为id,并利用StarRocks的排序键特性,将常用查询字段作为排序键的前缀,以优化查询性能。或者,使用Duplicate Key模型,按查询条件字段排序。 例如,使用Duplicate Key模型,按user_id、dev_id、channel、timestamp排序: CREATE TABLE smart_data_frame ( id VARCHAR(24) COMMENT 'MongoDB ObjectId', user_id VARCHAR COMMENT '用户ID', dev_id VARCHAR COMMENT '设备ID', channel SMALLINT COMMENT '通道号', timestamp BIGINT COMMENT '时间戳', ...其他字段... ) ENGINE=OLAP DUPLICATE KEY(user_id, dev_id, channel, timestamp, id) DISTRIBUTED BY HASH(user_id) PROPERTIES ( "replication_num" = "3" ); 但Duplicate Key模型允许重复,而主键模型要求唯一。如果用户需要主键唯一,可能需要使用主键模型,并确保主键唯一。根据Java类中的@Id注解,id是MongoDB的主键,所以应该是唯一的,因此StarRocks的主键可以设为id,但这样排序键可能无法有效优化查询。这时候需要权衡。 或者,如果用户接受主键模型,并且查询条件通常包含user_id等字段,可以将主键设为(user_id, dev_id, channel, timestamp, id),前提是这些字段的组合能保证唯一性,或者用户不需要严格的唯一性,而更关注查询性能。但原数据中可能id已经是唯一的,所以主键设为id更安全,而排序键可以设置为其他字段,但StarRocks的主键模型中的主键同时也是排序键,无法单独设置排序键。因此,在这种情况下,如果主键是id,那么数据按id排序,对user_id等字段的查询可能无法有效利用前缀索引,影响性能。 因此,可能需要根据查询需求选择合适的数据模型。如果查询主要按user_id、dev_id、channel、timestamp进行过滤,那么使用Duplicate Key模型,并按这些字段排序,可能更合适,即使主键不唯一。或者,如果数据量很大,且需要高并发查询,主键模型更适合,但需要调整主键顺序。 用户可能需要进一步明确查询模式,但根据提供的Java类中的组合索引index_user_dev_channel_time,可以推测查询经常使用user_id、dev_id、channel、timestamp这几个字段的组合条件,因此在StarRocks中,应该将这些字段作为排序键的前缀,以优化查询性能。 所以,最终的建表语句可能使用Duplicate Key模型,并按user_id、dev_id、channel、timestamp排序,同时分布式键使用user_id进行分桶,以利用数据分布和查询性能。 另外,关于分区,如果timestamp是时间戳,可以按天分区,例如: PARTITION BY RANGE (timestamp) ( START ("0") END ("9999999999999") EVERY (86400000) -- 假设按天分区,时间戳为毫秒级 ) 但具体分区策略需要根据数据的时间范围和查询模式来定,可能用户暂时不需要分区,所以可以先不设置,后续根据数据量调整。 综合以上分析,给出建表示例,并解释主键、分布式键的选择,以及字段类型映射。同时提醒用户根据查询模式调整排序键和分布式键,以及考虑分区和索引优化。</think>### 一、StarRocks建表方案设计 根据MongoDB `SmartDataFramePO` 实体类结构,StarRocks建表需重点解决以下问题: 1. **字段类型映射**:需对齐Java类型与StarRocks数据类型 2. **查询优化**:基于原MongoDB复合索引 `index_user_dev_channel_time` 设计排序键 3. **数据分布**:延续MongoDB分片逻辑,选择合适分桶键 --- ### 二、完整建表语句 ```sql CREATE TABLE smart_data_frame ( id VARCHAR(24) COMMENT 'MongoDB ObjectId主键', user_id VARCHAR(64) COMMENT '用户ID(原MongoDB分片键)', dev_id VARCHAR(64) COMMENT '设备ID', channel SMALLINT COMMENT '通道标识', timestamp BIGINT COMMENT '事件时间戳(毫秒)', object_id INT COMMENT '目标对象ID', type INT COMMENT '数据类型标识', shape TINYINT COMMENT '形状标识', box_tlx INT COMMENT '包围框左上角X坐标', box_tly INT COMMENT '包围框左上角Y坐标', box_w INT COMMENT '包围框宽度', box_h INT COMMENT '包围框高度', confidence TINYINT COMMENT '置信度(0~100)', fg_ratio TINYINT COMMENT '前景占比(0~100)', is_fake TINYINT COMMENT '是否伪造(0/1)', is_normal TINYINT COMMENT '是否正常(0/1)', point_3dvalid TINYINT COMMENT '3D点有效性标识', point_3dx SMALLINT COMMENT '3D点X坐标', point_3dy SMALLINT COMMENT '3D点Y坐标', point_3dz SMALLINT COMMENT '3D点Z坐标' ) ENGINE=OLAP DUPLICATE KEY(user_id, dev_id, channel, timestamp, id) -- 按查询模式设计排序键 COMMENT "智能数据帧表" DISTRIBUTED BY HASH(user_id) BUCKETS 8 -- 延续MongoDB分片逻辑 PROPERTIES ( "replication_num" = "3", -- 副本数按集群规模调整 "storage_medium" = "SSD" ); ``` --- ### 三、关键设计说明 #### 1. **类型映射策略** | MongoDB字段 | Java类型 | StarRocks类型 | 特殊处理 | |--------------------|----------|----------------|--------------------------| | _id | String | VARCHAR(24) | 固定长度优化存储 | | timestamp | Long | BIGINT | 保留原始毫秒时间戳 | | 布尔型字段 | Byte | TINYINT | 0/1映射 | | 坐标类数值 | Short | SMALLINT | 范围:-32768~32767 | | 比例类字段 | Byte | TINYINT | 配合CHECK约束限制范围 | #### 2. **排序键设计** ```sql DUPLICATE KEY(user_id, dev_id, channel, timestamp, id) ``` - **设计依据**:延续原MongoDB复合索引 `index_user_dev_channel_time` - **优势**: - 加速 `WHERE user_id=? AND dev_id=? AND channel=?` 类查询 - 优化时间范围查询 `BETWEEN start_time AND end_time` - 保证相同时间窗口数据物理存储连续 #### 3. **分桶策略** ```sql DISTRIBUTED BY HASH(user_id) BUCKETS 8 ``` - **延续MongoDB分片逻辑**:使用`user_id`作为分桶键,保证相同用户数据分布在同一节点 - **分桶数计算**:建议每个分桶数据量在100MB~1GB之间,示例按测试环境设置,生产环境需根据数据量调整 --- ### 四、DataX同步配置要点 #### 1. MongoDB Reader配置(片段) ```json "column": [ {"name": "_id", "type": "string"}, {"name": "user_id", "type": "string"}, {"name": "dev_id", "type": "string"}, {"name": "channel", "type": "int"}, {"name": "timestamp", "type": "long"}, ...其他字段映射... ] ``` #### 2. StarRocks Writer配置 ```json "writer": { "name": "starrockswriter", "parameter": { "username": "root", "password": "starrocks_pwd", "database": "your_database", "table": "smart_data_frame", "column": ["_id","user_id","dev_id","channel",...], "preSql": ["TRUNCATE TABLE smart_data_frame"] -- 全量同步时清理旧数据 } } ``` --- ### 五、特殊场景处理建议 1. **动态字段扩展** 若MongoDB后续新增字段,推荐方案: ```sql ALTER TABLE smart_data_frame ADD COLUMN new_col SMALLINT COMMENT '新增字段'; ``` 2. **时间戳转换** 如需将BIGINT时间戳转为可读时间: ```sql SELECT FROM_UNIXTIME(timestamp/1000) AS formatted_time, ... FROM smart_data_frame ``` 3. **查询加速** 对高频过滤字段建立BloomFilter索引: ```sql ALTER TABLE smart_data_frame SET ("bloom_filter_columns" = "user_id,dev_id,channel"); ``` 验证建议:首次同步后执行`SELECT COUNT(DISTINCT user_id) FROM smart_data_frame`验证数据分布均匀性。
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/* * Copyright (c) 2025, TP-Link Global Inc. All rights reserved. */ package com.tplink.cdd.demo.mongo.po; import lombok.Data; import org.springframework.data.annotation.Id; import org.springframework.data.mongodb.core.index.CompoundIndex; import org.springframework.data.mongodb.core.index.CompoundIndexes; import org.springframework.data.mongodb.core.mapping.Document; import org.springframework.data.mongodb.core.mapping.Field; import org.springframework.data.mongodb.core.mapping.Sharded; /** * @author zhaobingqian * @version 1.0 * @since 2025/3/11 */ @Data @Document("smart_data_frame") @CompoundIndexes({ @CompoundIndex(name = "index_user_dev_channel_time", def = "{'user_id':1, 'dev_id':1, 'channel':1, 'time': 1}"), }) @Sharded(shardKey = "user_id") public class SmartDataFramePO { @Id private String id; @Field(name = "user_id") private String userId; @Field(name = "dev_id") private String devId; @Field(name = "channel") private Short channel; @Field(name = "timestamp") private Long timestamp; @Field(name = "object_id") private Integer objectId; @Field(name = "type") private Integer type; @Field(name = "shape") private Byte shape; @Field(name = "box_tlx") private Integer boxTlX; @Field(name = "box_tly") private Integer boxTlY; @Field(name = "box_w") private Integer boxW; @Field(name = "box_h") private Integer boxH; @Field(name = "confidence") private Byte confidence; @Field(name = "fg_ratio") private Byte fgRatio; @Field(name = "is_fake") private Byte isFake; @Field(name = "is_normal") private Byte isNormal; @Field(name = "point_3dvalid") private Byte point3dValid; @Field(name = "point_3dx") private Short point3dX; @Field(name = "point_3dy") private Short point3dY; @Field(name = "point_3dz") private Short point3dZ; } 这个

* Copyright © 2025, TP-Link Global Inc. All rights reserved. */ package com.tplink.cdd.demo.mongo.po; import lombok.Data; import org.springframework.data.annotation.Id; import org.springframework.data.mongodb.core.index.CompoundIndex; import org.springframework.data.mongodb.core.index.CompoundIndexes; import org.springframework.data.mongodb.core.mapping.Document; import org.springframework.data.mongodb.core.mapping.Field; import org.springframework.data.mongodb.core.mapping.Sharded; /** @author zhaobingqian @version 1.0 @since 2025/3/11 */ @Data @Document(“smart_data_frame”) @CompoundIndexes({ @CompoundIndex(name = “index_user_dev_channel_time”, def = “{‘user_id’:1, ‘dev_id’:1, ‘channel’:1, ‘time’: 1}”), }) @Sharded(shardKey = “user_id”) public class SmartDataFramePO { @Id private String id; @Field(name = “user_id”) private String userId; @Field(name = “dev_id”) private String devId; @Field(name = “channel”) private Short channel; @Field(name = “timestamp”) private Long timestamp; @Field(name = “object_id”) private Integer objectId; @Field(name = “type”) private Integer type; @Field(name = “shape”) private Byte shape; @Field(name = “box_tlx”) private Integer boxTlX; @Field(name = “box_tly”) private Integer boxTlY; @Field(name = “box_w”) private Integer boxW; @Field(name = “box_h”) private Integer boxH; @Field(name = “confidence”) private Byte confidence; @Field(name = “fg_ratio”) private Byte fgRatio; @Field(name = “is_fake”) private Byte isFake; @Field(name = “is_normal”) private Byte isNormal; @Field(name = “point_3dvalid”) private Byte point3dValid; @Field(name = “point_3dx”) private Short point3dX; @Field(name = “point_3dy”) private Short point3dY; @Field(name = “point_3dz”) private Short point3dZ; } 根据这个写个指令想把mongod数据导入starrocks,在上面建一个对应表,数据库指令

Building wheels for collected packages: apache-superset, func_timeout, pgsanity, python-geohash, shortid, wtforms-json Building wheel for apache-superset (pyproject.toml) ... done Created wheel for apache-superset: filename=apache_superset-4.1.1-py3-none-any.whl size=56938095 sha256=7e6fc0b03284ade3ab3bae673732066c84fa1aa9bc5b9ede21cc6bc3e49d156d Stored in directory: /tmp/pip-ephem-wheel-cache-_1heun07/wheels/50/e6/50/29cffcd747117c6ed93eac0cb72f52882ee46342ed5ed111de Building wheel for func_timeout (setup.py) ... done Created wheel for func_timeout: filename=func_timeout-4.3.5-py3-none-any.whl size=15081 sha256=e31958cf849218a56a2aa8fa703e4a3d7b89b1969d3cd164f122f96ea893a918 Stored in directory: /tmp/pip-ephem-wheel-cache-_1heun07/wheels/07/e6/86/f23164d12c3134966614102db8e7956ab359faf7ffd78703ce Building wheel for pgsanity (setup.py) ... done Created wheel for pgsanity: filename=pgsanity-0.2.9-py3-none-any.whl size=5625 sha256=cd6d4c3623b83e5a57daee1cd59b96584d7fdcc070bfcceeeec927458c4b34a6 Stored in directory: /tmp/pip-ephem-wheel-cache-_1heun07/wheels/45/e6/4e/f67a52962e2d79ff99474b0e1800e570b6c7190c4e6a04285a Building wheel for python-geohash (setup.py) ... error error: subprocess-exited-with-error × python setup.py bdist_wheel did not run successfully. │ exit code: 1 ╰─> [15 lines of output] running bdist_wheel running build running build_py creating build creating build/lib.linux-x86_64-cpython-311 copying geohash.py -> build/lib.linux-x86_64-cpython-311 copying quadtree.py -> build/lib.linux-x86_64-cpython-311 copying jpgrid.py -> build/lib.linux-x86_64-cpython-311 copying jpiarea.py -> build/lib.linux-x86_64-cpython-311 running build_ext building '_geohash' extension creating build/temp.linux-x86_64-cpython-311 creating build/temp.linux-x86_64-cpython-311/src gcc -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -DPYTHON_MODULE=1 -I/usr/local/include/python3.11 -c src/geohash.cpp -o build/temp.linux-x86_64-cpython-311/src/geohash.o error: command 'gcc' failed: No such file or directory [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. ERROR: Failed building wheel for python-geohash Running setup.py clean for python-geohash Building wheel for shortid (setup.py) ... done Created wheel for shortid: filename=shortid-0.1.2-py3-none-any.whl size=2599 sha256=5e214cdd56935688f3141e94c679553c30d347aeec7cc35d9a742fc670357437 Stored in directory: /tmp/pip-ephem-wheel-cache-_1heun07/wheels/b7/c7/6d/c1e66e817792b2b002c290fcf4eb0e8459a31b2b02fa915b6e Building wheel for wtforms-json (setup.py) ... done Created wheel for wtforms-json: filename=WTForms_JSON-0.3.5-py3-none-any.whl size=4771 sha256=57a844ce4b595e14d1383c1782aff1602d9626a3eb698b5067dd1883778dbc86 Stored in directory: /tmp/pip-ephem-wheel-cache-_1heun07/wheels/12/b9/6c/b041dd399632113dc0e3aa50e46bc7e18db8f5a060beed0291 Successfully built apache-superset func_timeout pgsanity shortid wtforms-json Failed to build python-geohash ERROR: Could not build wheels for python-geohash, which is required to install pyproject.toml-based projects

将下述文字变成思维导图:# AUTOSAR架构分层 ## 1. **应用层(Application Layer)** - **软件组件(SWC, Software Component)** - 原子组件(Atomic SWC) - 组合组件(Composition SWC) - **虚拟功能总线(VFB, Virtual Functional Bus)** - 标准化组件接口(Ports) - 跨ECU通信抽象 ## 2. **运行时环境(RTE, Runtime Environment)** - **通信代理** - 信号路由(Signal Routing) - 服务调用(Service Invocation) - **事件驱动机制** - 触发任务/中断 - **接口标准化** - 提供SWC与底层交互的统一接口 ## 3. **基础软件层(BSW, Basic Software)** - **服务层(Services Layer)** - 通信服务(COM, SOME/IP, DCM) - 存储服务(NvM, Memory Management) - 诊断服务(DEM, DTC) - 系统服务(OS, ECU状态管理) - **ECU抽象层(ECU Abstraction Layer)** - 硬件外设驱动抽象(如CAN控制器、I/O端口) - **复杂驱动层(CDD, Complex Device Drivers)** - 非标准化硬件控制(如摄像头、雷达) ## 4. **微控制器抽象层(MCAL, Microcontroller Abstraction Layer)** - **微控制器驱动** - ADC驱动(模拟信号采集) - PWM驱动(脉宽调制输出) - GPIO驱动(通用输入输出) - 定时器驱动(Timer) - **总线驱动** - CAN Driver - LIN Driver - FlexRay Driver - Ethernet Driver ## 5. **硬件层(Hardware Layer)** - **微控制器(MCU)** - **传感器/执行器** - **通信总线硬件** - CAN/CAN FD收发器 - Ethernet PHY芯片

[root@0404 ~]# pip3 install pyinstaller WARNING: Running pip install with root privileges is generally not a good idea. Try pip3 install --user instead. Collecting pyinstaller Using cached https://files.pythonhosted.org/packages/b0/e6/e5760666896739115b0e4538a42cdd895215581618ec885ad043dd35ee57/pyinstaller-4.10.tar.gz Requirement already satisfied: setuptools in /usr/lib/python3.6/site-packages (from pyinstaller) Requirement already satisfied: altgraph in ./.local/lib/python3.6/site-packages (from pyinstaller) Requirement already satisfied: pyinstaller-hooks-contrib>=2020.6 in ./.local/lib/python3.6/site-packages (from pyinstaller) Requirement already satisfied: importlib-metadata in ./.local/lib/python3.6/site-packages (from pyinstaller) Requirement already satisfied: zipp>=0.5 in ./.local/lib/python3.6/site-packages (from importlib-metadata->pyinstaller) Requirement already satisfied: typing-extensions>=3.6.4; python_version < "3.8" in ./.local/lib/python3.6/site-packages (from importlib-metadata->pyinstaller) Building wheels for collected packages: pyinstaller Running setup.py bdist_wheel for pyinstaller ... error Complete output from command /usr/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-9i8u5dao/pyinstaller/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" bdist_wheel -d /tmp/tmpmvsymh1kpip-wheel- --python-tag cp36: running bdist_wheel running build running build_bootloader No precompiled bootloader found or compile forced. Trying to compile the bootloader for you ... Setting top to : /tmp/pip-build-9i8u5dao/pyinstaller/bootloader Setting out to : /tmp/pip-build-9i8u5dao/pyinstaller/bootloader/build Python Version : 3.6.8 (default, Nov 14 2023, 16:29:52) [GCC 4.8.5 20150623 (Red Hat 4.8.5-44)] Checking for 'gcc' (C compiler) : /usr/bin

写一个php构造POST请求https://edith.xiaohongshu.com/api/sns/web/v1/search/notes Host: edith.xiaohongshu.com X-Mns: unload Accept: application/json, text/plain, */* Origin: https://www.xiaohongshu.com User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/130.0.0.0 Safari/537.36 Cookie: 实际提取的 Content-Type: application/json;charset=utf-8 请求体: {"search_id":"2uldcc7ue25ts00","sort":"general","ext_flags":[],"note_type":0,"page_size":20,"keyword":"豆包","page":1,"geo":"","image_formats":["jpg"]} 用户需要get传入keyword字段和page字段的实际内容,然后请求的Cookie在ck.txt文件里提取。 请求后需要在响应内容中data字段中的子对象items里提取每个数组的id字段和xsec_token字段进行返还。 https://edith.xiaohongshu.com/api/sns/web/v1/search/notes请求的返回样式: 帮我缩短json保留5个数据就行了 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