Intelligent cloud platform for Internet of Things data service

Abstract

The invention provides an intelligent cloud platform for Internet of Things data service, the architecture of the platform adopts a cloud service architecture, and the cloud service architecture is composed of four basic service units, including a data acquisition unit, a data processing unit, a data alarm unit and a background management unit; the data acquisition units, the data processing units and the background management unit are constructed in a distributed construction mode, the data processing units call computing power through a distributed architecture, and when a single data acquisition unit uploads acquisition data, the computing power of a plurality of data processing units is called in a centralized manner through the distributed architecture; when a plurality of data acquisition units upload and acquire data, the data processing unit correspondingly processes the upload data according to a corresponding proportion in the distributed architecture, the data acquisition units interact with equipment with data to be acquired in real time, a plurality of extensible interfaces are arranged on the data acquisition units, and the data acquisition units support a plurality of data sources.

Description

Technical Field

[0001] The present invention belongs to the field of cloud platforms, and in particular relates to an intelligent cloud platform for IoT data services. Background Art

[0002] At present, in the current wave of digital transformation, intelligent cloud platforms are more prominent. This platform aims to integrate cutting-edge technologies such as the Internet of Things, big data, cloud computing and artificial intelligence to provide efficient and intelligent data processing and services for all walks of life. This technology helps enterprises to deeply mine and apply IoT data and promote the intelligent upgrade of their businesses by building a comprehensive platform that integrates data collection, storage, analysis and optimization.

[0003] Although the IoT data service intelligent cloud platform has shown great potential in improving enterprise data processing capabilities and business efficiency, existing technologies still have certain shortcomings. First, security issues cannot be ignored. With the widespread access of IoT devices, the risk of data leakage and hacker attacks has increased significantly. Although cloud platforms usually take multiple security measures, they are facing increasing complexity.

[0004] Although the IoT data service intelligent cloud platform has shown great potential in improving enterprise data processing capabilities and business efficiency, existing technologies still have certain shortcomings. First, security issues cannot be ignored. With the widespread access to IoT devices, the risk of data leakage and hacker attacks has increased significantly. Although cloud platforms usually take multiple security measures, in the face of increasingly complex network attacks, how to ensure the absolute security of data during transmission and storage is still a major challenge currently faced.

[0005] The real-time and accuracy of data processing needs to be improved. IoT data is massive, heterogeneous, and high-speed, which places extremely high demands on the processing capabilities of cloud platforms. Existing technologies may experience delays or inaccurate processing when processing large-scale data, affecting the timeliness and effectiveness of business decisions.

[0006] The compatibility and scalability of the platform are also a major pain point. IoT devices and data formats vary greatly among different industries and enterprises. How to achieve cross-platform and cross-system data interconnection and interoperability, as well as flexibly expand platform functions according to business needs, are issues that need to be addressed urgently.

[0007] Cost is also a key factor that restricts the widespread application of IoT data service intelligent cloud platforms. Although cloud platforms can significantly reduce the IT construction and maintenance costs of enterprises, the initial deployment and customized development costs are still high, which may be difficult for small and medium-sized enterprises to bear.

[0008] Data privacy and compliance issues cannot be ignored. As countries continue to improve their data protection laws and regulations, enterprises must strictly abide by relevant laws and regulations when using cloud platforms to process IoT data, ensure the legal and compliant use of data, and avoid legal risks. An intelligent cloud platform for IoT data services is urgently needed. Summary of the invention

[0009] The present invention proposes an intelligent cloud platform for IoT data services, which solves the technical problems of efficient data collection, processing, alarm and background management in IoT data services, and realizes efficient call of computing power and real-time processing and monitoring of data through a distributed architecture.

[0010] 6. The technical solution of the present invention is implemented as follows: the architecture of the platform adopts a cloud service architecture, which is composed of four basic service units, including a data acquisition unit, a data processing unit, a data alarm unit and a background management unit; the data acquisition unit, the data processing unit and the background management unit are constructed in a distributed manner, wherein the data processing unit calls the computing power through the distributed architecture, and when a single data acquisition unit uploads the collected data, the computing power of several data processing units is centrally called through the distributed architecture; when calling, the uploaded data is divided into multiple subtasks according to type, scale or priority through the task slicing manager, and the subtasks are allocated to different data processing nodes by using a distributed message queue; an intelligent scheduler is introduced to dynamically adjust the allocation ratio and priority of subtasks based on the real-time monitored node computing power status and historical task processing mode to ensure that high-load tasks are processed first, and an elastic expansion mechanism is adopted to automatically call idle nodes from the distributed architecture when the computing power is insufficient, and release resources after the task is completed; a fault-tolerant mechanism is used to ensure seamless migration of tasks when a node fails;

[0011] When processing the uploaded collected data by multiple data collection units, the data processing unit processes the uploaded data in a corresponding proportion in the distributed architecture. The data collection unit interacts with the device to be collected in real time. The data collection unit is provided with several extensible interfaces. The data collection unit supports multiple data sources, and each data source is configured with a corresponding number of data interfaces. Each data interface of the data collection unit corresponds to a transmission protocol. When connecting to a device, data can be obtained by specifying the corresponding protocol and corresponding parameters. When connecting to several devices, the associated data is synchronously collected by configuring the device object, specifying the protocol and address-related information.

[0012] The data alarm unit compares the device data processed by the data processing unit with a preset alarm threshold to determine whether the device data exceeds the threshold. If the threshold is exceeded, an alarm is triggered for the device;

[0013] The background management unit performs user management, role management, menu management, department management, position management, dictionary management and log management in the background interface.

[0014] In terms of data collection, the existing technology often adopts a centralized data collection method, which is prone to bottlenecks when processing large amounts of data, resulting in low data collection efficiency. The intelligent cloud platform uses distributed data collection units, each of which has independent data collection capabilities, and can expand interfaces to support multiple data sources. This design not only improves the efficiency of data collection, but also enhances the scalability and flexibility of the system. In terms of data collection, the existing technology often adopts a centralized data collection method, which is prone to bottlenecks when processing large amounts of data, resulting in low data collection efficiency. The intelligent cloud platform uses distributed data collection units, each of which has independent data collection capabilities, and can expand interfaces to support multiple data sources. This design not only improves the efficiency of data collection, but also enhances the scalability and flexibility of the system.

[0015] In terms of data alarms, existing technologies often use simple threshold judgment methods, which lack flexibility and accuracy. The intelligent cloud platform uses the data alarm unit to compare the processed equipment with the pre-set alarm threshold, which can more accurately determine whether the equipment data exceeds the threshold and trigger the alarm in time. This design not only improves the accuracy of the alarm, but also helps to timely discover and handle potential equipment failures. In terms of background management, existing technologies usually provide limited background management functions and cannot meet complex management needs. The intelligent cloud platform provides a comprehensive background management unit, including user management, role management, menu management, department management, position management, dictionary management and log management, which can meet the management needs of different levels and roles and improve the maintainability and security of the system.

[0016] As a preferred implementation, the user management of the background management unit is to add, delete, modify and check users in the platform and manage permissions. User management is associated with users, roles and menus to define the corresponding operations of users in the system. Permission management includes the permissions and responsibility scope of users, as well as the user's access rights to resources.

[0017] Role management is to add, delete, modify and check roles in the background interface. Role management is a supplement to user management. By binding users with roles, role management is performed on users.

[0018] Menu management is to add, delete, modify and check the menus in the background interface. Menu management is to add, delete, modify and check the management information and supplement the user management authority. The menu used is the user interface;

[0019] Department management is the management of personnel, including the allocation of personnel authority, creation of departments and establishment of department personnel.

[0020] Position management is used for the management of personnel by departments and is used in conjunction with department management. Department management is used to create departments, and position management is used to manage department personnel.

[0021] Dictionary management is used to add, delete, modify and query key values ​​of dictionary types involved in the menu. Dictionary types include custom fields for unit and department names and device dictionary information.

[0022] Log management is used to manage user operations and record the user's operation process.

[0023] As a preferred implementation, the data acquisition unit collects data sent by various types of IoT devices and synchronizes it to the data storage module for backup. In the data storage stage, the data storage module diverts data according to the data type and stores the data in the corresponding database through different storage strategies. For time series data, the data storage module uses a time series database for storage, and for added, deleted, modified and queried data, the data storage module stores it in a Mysql database.

[0024] As a preferred embodiment, the data processing unit is used to process, clean and analyze data, and supports displaying the processing results in a corresponding form; the data processing unit provides a data visualization tool, draws standard type charts by writing corresponding functions, and displays the processed data through standard type icons.

[0025] As a preferred embodiment, the data processing unit includes data cleaning services, data storage services, data extraction services, data synchronization services, data collection services, data visualization services and system configuration services;

[0026] The data cleaning service completes the data cleaning operation by writing rules; provides a rule configuration interface, and configures the cleaning rules through the interface;

[0027] Data storage services provide common data writing, reading, updating, and deleting methods; provide different data sources for selection and support different table structures; provide SQL query methods to complete database operations by calling related stored procedures or SQL;

[0028] The data extraction service provides support for data writing by extracting data into the cache;

[0029] The data synchronization service provides a data synchronization interface and encapsulates the data synchronization algorithm in the interface to perform data synchronization when receiving uploaded data;

[0030] The data collection service processes the uploaded data at a preset time through a general scheduled task execution mechanism;

[0031] The data visualization service provides a visualization interface, and different standard types of charts can be generated by writing corresponding functions;

[0032] The system configuration service provides configuration functions for log level, date and time, cache, data type and data source information.

[0033] After adopting the above technical solution, the beneficial effect of the present invention is: by adopting a data acquisition unit with a distributed architecture, the platform realizes efficient data acquisition and processing. This design not only improves the real-time and accuracy of data acquisition, but also enhances the scalability and flexibility of the system. With the continuous increase of IoT devices and the rapid growth of data volume, this efficient data acquisition and processing capability is particularly important; the platform realizes intelligent alarm management through the data alarm unit. By comparing the processed equipment with the pre-set alarm threshold, the platform can promptly detect and handle potential equipment failures, thereby improving the reliability and stability of the equipment. This intelligent alarm management method not only improves the accuracy and timeliness of alarms, but also helps to reduce operation and maintenance costs and improve work efficiency.

[0034] The platform also provides comprehensive background management functions, including user management, role management, menu management, department management, position management, dictionary management and log management. These functions can meet the management needs of different levels and roles, and improve the maintainability and security of the system. Through the background management interface, administrators can easily view and manage system resources, monitor device status, process alarm information, etc., so as to achieve comprehensive control of IoT data services. It supports multiple transmission protocols and data interfaces, and can seamlessly connect and interact with different types of IoT devices. This design not only improves the compatibility and flexibility of the system, but also helps to reduce the difficulty and cost of system integration and deployment. With the continuous development and popularization of IoT technology, this compatibility and flexibility will become an important competitive advantage of the IoT data service platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0036] Figure 1 It is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Example:

[0039] like Figure 1 As shown, an intelligent cloud platform for IoT data services, the core working principle of which is based on the cloud service architecture, realizes comprehensive management and efficient service of IoT data through the collaborative work of four basic service units: data collection unit, data processing unit, data alarm unit and background management unit.

[0040] The data acquisition unit is responsible for interacting with the device to be collected in real time. It supports multiple data sources through its extensible interface and configures a corresponding number of data interfaces for each data source. Each data interface corresponds to a specific transmission protocol. When connecting a device, you only need to specify the corresponding protocol and parameters to obtain data. When connecting multiple devices, the data acquisition unit can configure the device object, specify the protocol and address related information, and realize the synchronous collection of related data.

[0041] The collected data is then transmitted to the data processing unit, which uses the distributed architecture to call computing power to process the data. Whether it is data uploaded by a single data collection unit or data uploaded by multiple data collection units at the same time, the data processing unit can dynamically adjust computing power resources within the distributed architecture according to the size and complexity of the data to ensure the efficiency and real-time performance of data processing.

[0042] The processed data will be transmitted to the data alarm unit, which will compare the device with the pre-set alarm threshold to determine whether the device data exceeds the threshold. Once the data exceeds the set threshold, the data alarm unit will immediately trigger an alarm and notify relevant personnel to handle it.

[0043] The backend management unit provides comprehensive backend management functions, including user management, role management, menu management, department management, position management, dictionary management and log management, etc., enabling system administrators to easily manage user permissions, monitor system status, process log information, etc.

[0044] Specific work scenarios and processes: Assume that a smart factory uses the smart cloud platform for IoT data services. Multiple sensors and devices are installed in the factory to monitor various parameters in the production process in real time, such as temperature, humidity, pressure, etc.

[0045] The data acquisition unit connects to sensors and equipment in the factory through its scalable interface, and configures the corresponding data interface and transmission protocol for each data source. When the sensors and equipment start working, the data acquisition unit can collect various parameter data in real time and transmit it to the data processing unit. After receiving the data, the data processing unit calls computing power through a distributed architecture to process and analyze the data. According to the complexity and scale of the data, the data processing unit can dynamically adjust the computing power resources to ensure the efficiency and real-time performance of data processing. The processed data is transmitted to the data alarm unit. The data alarm unit compares the processed data with the pre-set alarm threshold.

[0046] For example, when the temperature data monitored by a sensor exceeds the set safety threshold, the data alarm unit will immediately trigger an alarm to notify the factory managers and production personnel. Managers can view the alarm information through the background management unit, understand the specific situation, and take appropriate measures to deal with it. The background management unit also provides comprehensive background management functions, allowing factory managers to easily manage user permissions, monitor system status, process log information, etc. These functions provide strong support for the factory's safe production and efficient operation.

[0047] The user management of the background management unit is to add, delete, modify and check users in the platform and manage their rights. User management is associated with users, roles and menus to define the corresponding operations of users in the system. Rights management includes the rights and responsibilities of users and the access rights of users to resources.

[0048] Role management is to add, delete, modify and check roles in the background interface. Role management is a supplement to user management. By binding users with roles, role management is performed on users.

[0049] Menu management is to add, delete, modify and check the menus in the background interface. Menu management is to add, delete, modify and check the management information and supplement the user management authority. The menu used is the user interface;

[0050] Department management is the management of personnel, including the allocation of personnel authority, creation of departments and establishment of department personnel.

[0051] Position management is used for the management of personnel by departments and is used in conjunction with department management. Department management is used to create departments, and position management is used to manage department personnel.

[0052] Dictionary management is used to add, delete, modify and query key values ​​of dictionary types involved in the menu. Dictionary types include custom fields for unit and department names and device dictionary information.

[0053] Log management is used to manage user operations and record the user's operation process.

[0054] The background management unit is more comprehensive and detailed in function design. Compared with the existing technology, its differences are mainly reflected in the following aspects:

[0055] In terms of user management, this implementation not only implements basic operations of adding, deleting, modifying and checking users, but also realizes refined management of user rights through the association of users, roles and menus. This design enables the system to assign corresponding operation rights to different users according to their roles and responsibilities, thereby improving the security and controllability of the system. However, the existing technology often only provides simple user management functions and lacks in-depth management of user rights.

[0056] In terms of role management, this implementation achieves flexible management of user roles by binding roles to users. This design enables the system to quickly create and modify roles and assign roles to users according to different business needs, thereby improving the flexibility and scalability of the system. However, the role management in the prior art is often relatively simple and lacks effective integration with user management.

[0057] In terms of menu management, department management, position management, dictionary management and log management, this implementation also provides more comprehensive and detailed functions. For example, menu management not only realizes the addition, deletion, modification and query of menus, but also combines it with user management permissions, improving the usability and security of the system; department management and position management realize the fine allocation of personnel permissions and effective management of department personnel; dictionary management provides the function of customizing key values, so that the system can better adapt to different business needs; log management records the user's operation process, providing strong support for system auditing and troubleshooting.

[0058] The data acquisition unit collects data sent by various IoT devices and synchronizes it to the data storage module for backup. During the data storage stage, the data storage module diverts data according to the data type and stores the data in the corresponding database through different storage strategies. For time series data, the data storage module uses a time series database for storage, and for added, deleted, modified and queried data, the data storage module stores it in a MySQL database.

[0059] In the data storage stage, the data storage module of this embodiment can be diverted according to the data type, and different storage strategies are used to store the data in the corresponding database. This design not only improves the efficiency and reliability of data storage, but also enables the system to better adapt to different types of data storage requirements. However, the prior art often only adopts a single storage strategy, lacking in-depth consideration of data types and storage requirements. For time series data, this embodiment adopts a time series database for storage. A time series database is a database specifically used for storing and analyzing time series data, with efficient data writing, reading and query performance. This design enables the system to better process and analyze time series data, and improves the real-time and accuracy of data. However, the prior art often uses traditional relational databases to store time series data, which has performance bottlenecks and query efficiency problems. For data that is added, deleted, modified and checked, this embodiment uses a Mysql database for storage. Mysql is a widely used open source relational database management system with advantages such as high performance, reliability and ease of use. This design enables the system to better process and manage data that is added, deleted, modified and checked, and improves the availability and scalability of data.

[0060] The data processing unit is used to process, clean and analyze the data, and supports displaying the processing results in a corresponding form; the data processing unit provides a data visualization tool, draws a standard type chart by writing a corresponding function, and displays the processed data through a standard type icon. The data processing unit of this embodiment not only supports the processing and analysis of data, but also provides a data visualization tool that can display the processing results in the form of a chart. This design enables users to understand the processing results and analysis of the data more intuitively, and improves the readability and ease of use of the data. However, the prior art often only provides simple data processing functions and lacks support for data visualization. The data cleaning service completes the data cleaning operation by writing rules, and provides a rule configuration interface, so that users can flexibly configure the cleaning rules according to their needs. This design improves the efficiency and accuracy of data cleaning and reduces the difficulty and cost of data cleaning. However, the data cleaning of the prior art often relies on manual operation or simple script processing, lacking flexibility and scalability.

[0061] The data storage service supports different data sources and table structures, and provides SQL query methods; the data extraction service provides support for data writing by extracting data into the cache; the data synchronization service realizes real-time data synchronization by providing data synchronization interfaces and algorithms; the data collection service realizes the scheduled processing of uploaded data through the scheduled task execution mechanism; the data visualization service provides the function of visual interface and function generation chart; the system configuration service provides the configuration function of log level, date and time, cache, data type and data source information. These functions enable the system to better adapt to different business needs and data processing needs, and improve the flexibility and scalability of the system.

[0062] The data processing unit includes data cleaning services, data storage services, data extraction services, data synchronization services, data collection services, data visualization services, and system configuration services;

[0063] The data cleaning service completes the data cleaning operation by writing rules; provides a rule configuration interface, and configures the cleaning rules through the interface;

[0064] Data storage services provide common data writing, reading, updating, and deleting methods; provide different data sources for selection and support different table structures; provide SQL query methods to complete database operations by calling related stored procedures or SQL;

[0065] The data extraction service provides support for data writing by extracting data into the cache;

[0066] The data synchronization service provides a data synchronization interface and encapsulates the data synchronization algorithm in the interface to perform data synchronization when receiving uploaded data;

[0067] The data collection service processes the uploaded data at a preset time through a general scheduled task execution mechanism;

[0068] The data visualization service provides a visualization interface, and different standard types of charts can be generated by writing corresponding functions;

[0069] The system configuration service provides configuration functions for log level, date and time, cache, data type and data source information.

[0070] From the perspective of architecture, the data processing unit of this embodiment adopts a modular design concept, encapsulating different data processing functions into independent modules, such as data cleaning services, data storage services, data extraction services, etc. This design not only improves the maintainability and scalability of the system, but also enables each module to cooperate with each other to jointly complete complex data processing tasks. However, the data processing unit of the prior art often adopts an integrated design, with each function tightly coupled, lacking flexibility and scalability.

[0071] In terms of flexibility, the data processing unit of this embodiment provides a wealth of configuration options and interfaces, allowing users to flexibly configure and use each module according to their needs. For example, users can set data cleaning rules through the configuration interface, select different data sources and table structures for data storage, and generate different types of charts by writing functions. This design enables the system to better adapt to different business scenarios and data processing requirements, and improves the flexibility and adaptability of the system.

[0072] The data processing unit of this embodiment also focuses on the collaborative work with other system components. For example, the data collected by the data acquisition unit can be transmitted to the data processing unit in real time for processing and analysis; the data alarm unit can trigger corresponding alarms based on the processed data; and the background management unit can configure and manage the data processing unit. This collaborative work mechanism enables the entire system to form an organic whole, improving the overall performance and reliability of the system.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent cloud platform for IoT data services, characterized in that: The architecture of the platform adopts a cloud service architecture, which is composed of four basic service units, including a data collection unit, a data processing unit, a data alarm unit and a background management unit; the data collection unit, the data processing unit and the background management unit are constructed in a distributed manner, wherein the data processing unit calls computing power through a distributed architecture, and when a single data collection unit uploads collected data, the computing power of several data processing units is centrally called through the distributed architecture; when calling, the uploaded data is divided into multiple subtasks according to type, scale or priority through the task slicing manager, and the subtasks are allocated to different data processing nodes using a distributed message queue; an intelligent scheduler is introduced to dynamically adjust the allocation ratio and priority of subtasks based on the real-time monitored node computing power status and historical task processing mode to ensure that high-load tasks are processed first, and an elastic expansion mechanism is adopted to automatically call idle nodes from the distributed architecture when computing power is insufficient, and release resources after the task is completed; a fault-tolerant mechanism is used to ensure seamless migration of tasks when a node fails; When processing the uploaded collected data by multiple data collection units, the data processing unit processes the uploaded data in a corresponding proportion in the distributed architecture. The data collection unit interacts with the device to be collected in real time. The data collection unit is provided with several extensible interfaces. The data collection unit supports multiple data sources, and each data source is configured with a corresponding number of data interfaces. Each data interface of the data collection unit corresponds to a transmission protocol. When connecting to a device, data can be obtained by specifying the corresponding protocol and corresponding parameters. When connecting to several devices, the associated data is synchronously collected by configuring the device object, specifying the protocol and address-related information. The data alarm unit compares the device data processed by the data processing unit with a preset alarm threshold to determine whether the device data exceeds the threshold. If the threshold is exceeded, an alarm is triggered for the device. The background management unit performs user management, role management, menu management, department management, position management, dictionary management and log management in the background interface.

2. The intelligent cloud platform for IoT data services according to claim 1, characterized in that: The user management of the background management unit is to add, delete, modify and check users in the platform and manage their rights. User management is associated with users, roles and menus to define the corresponding operations of users in the system. Rights management includes the rights and responsibilities of users and the access rights of users to resources. Role management is to add, delete, modify and check roles in the background interface. Role management is a supplement to user management. By binding users with roles, role management is performed on users. Menu management is to add, delete, modify and check the menus in the background interface. Menu management is to add, delete, modify and check the management information and supplement the user management authority. The menu used is the user interface; Department management is the management of personnel, including the allocation of personnel authority, creation of departments and establishment of department personnel. Position management is used for the management of personnel by departments and is used in conjunction with department management. Department management is used to create departments, and position management is used to manage department personnel. Dictionary management is used to add, delete, modify and query key values ​​of dictionary types involved in the menu. Dictionary types include custom fields for unit and department names and device dictionary information. Log management is used to manage user operations and record the user's operation process.

3. The intelligent cloud platform for IoT data services according to claim 1, characterized in that: The data acquisition unit collects data sent by various IoT devices and synchronizes it to the data storage module for backup. During the data storage stage, the data storage module diverts data according to the data type and stores the data in the corresponding database through different storage strategies. For time series data, the data storage module uses a time series database for storage, and for added, deleted, modified and queried data, the data storage module stores it in a MySQL database.

4. The intelligent cloud platform for IoT data services according to claim 1, characterized in that: The data processing unit is used to process, clean and analyze the data, and supports displaying the processing results in a corresponding form; the data processing unit provides a data visualization tool, draws standard type charts by writing corresponding functions, and displays the processed data through standard type icons.

5. The intelligent cloud platform for IoT data services according to claim 4, characterized in that: The data processing unit includes data cleaning services, data storage services, data extraction services, data synchronization services, data collection services, data visualization services, and system configuration services; The data cleaning service completes the data cleaning operation by writing rules; provides a rule configuration interface, and configures the cleaning rules through the interface; Data storage services provide common data writing, reading, updating, and deleting methods; provide different data sources for selection, and support different table structures; Provide SQL query methods to complete database operations by calling related stored procedures or SQL; The data extraction service provides support for data writing by extracting data into the cache; The data synchronization service provides a data synchronization interface and encapsulates the data synchronization algorithm in the interface to perform data synchronization when receiving uploaded data; The data collection service processes the uploaded data at a preset time through a general scheduled task execution mechanism; The data visualization service provides a visualization interface, and different standard types of charts can be generated by writing corresponding functions; The system configuration service provides configuration functions for log level, date and time, cache, data type and data source information.

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