A multi-platform data statistics and management method and system based on edge IoT

Abstract

This invention relates to the field of edge IoT technology, specifically to a multi-platform data statistics and management method and system based on edge IoT, comprising the following steps: In edge IoT application scenarios, based on the distribution range of devices, multiple sub-IoT platforms are deployed in different areas. Multiple sub-IoT platforms report device data and platform data to the upper-level IoT platform through a cascading network channel. The upper-level IoT platform aggregates and organizes the data reported by the sub-IoT platforms. The beneficial effects are: The multi-platform data statistics and management method and system based on edge IoT proposed in this invention, through multi-level deployment of edge IoT, increases the application scenarios of edge IoT, improves the usability of IoT technology, and makes the application scenarios of edge IoT more extensive; it maximizes the real-time data transmission of IoT devices and prioritizes the persistence of important data even in extreme scenarios.

Description

Technical Field

[0001] This invention relates to the field of edge Internet of Things (IoT) technology, specifically to a multi-platform data statistics and management method and system based on edge IoT. Background Technology

[0002] With the continuous development of 5G technology and the increasing maturity of AI technology, the integration of 5G, IoT, and AI is one of the key factors for accelerating technological development and realizing new digital services. Among them, edge IoT, as a link connecting sensing networks and traditional communication networks, can realize protocol conversion between sensing networks and communication networks, as well as between different types of sensing networks, enabling both wide-area interconnection and local-area interconnection.

[0003] Furthermore, edge IoT also possesses IoT device management capabilities. Operators can manage underlying sensing nodes through edge IoT devices, understand relevant information about each node, and achieve remote control. At the same time, as the primary technology for external connectivity in edge IoT, edge IoT faces higher demands for comprehensive functionality. With the increasing number of IoT devices year by year, the pressure to maintain large-scale, long-term connections in edge IoT is gradually increasing.

[0004] Therefore, a multi-platform data statistics and management technology based on edge IoT was designed and invented to solve the data transmission problem in the case of unidirectional communication between multi-level edge IoT; to solve the data governance needs between multi-level edge IoT; to solve the problem of joint operation and maintenance of multi-platforms between multi-level edge IoT; and to solve the problem of how to selectively filter and parse device data between multi-level edge IoT. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-platform data statistics and management method and system based on edge Internet of Things to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-platform data statistics and management method based on edge IoT, the method comprising the following steps:

[0007] In edge IoT application scenarios, based on the distribution range of devices, multiple sub-IoT platforms are deployed in different areas. Multiple sub-IoT platforms report device data and platform data to the upper-level IoT platform through a cascading network channel. The upper-level IoT platform summarizes and organizes the data reported by the sub-IoT platforms.

[0008] In scenarios where multiple platforms work together, permission management is provided for the collaborative management functions and device data collaboration between multiple platforms, with users at different nodes having different data permissions and function permissions.

[0009] In edge IoT multi-platform deployment application scenarios, in order to meet the communication needs between platforms, three data communication modes are established, namely HTTP, MQTT and KAFKA. Different application scenarios can flexibly select the multi-platform communication mode according to their network environment.

[0010] In IoT multi-level platform application scenarios, during the data flow process, the sub-level platforms perform initial screening and filtering of the data flowing upward based on the data type reported by the devices, and report the device's routine attribute data and device function event data separately. Different types of device data are reported according to the data transmission protocol corresponding to the data type.

[0011] In IoT multi-level platform application scenarios, users can use multiple platforms to achieve real-time management and data management of IoT devices within a region.

[0012] Preferred specific operations in edge IoT application scenarios include:

[0013] Based on protocol transmission efficiency and device timeliness requirements, devices within the area are assigned to a separate IoT platform for management, meaning that a large number of IoT devices are managed by multiple edge IoT platforms.

[0014] Multiple sub-IoT platforms are used to achieve real-time management of devices. At the same time, a higher-level IoT platform is established to aggregate data from the sub-IoT platforms. The higher-level IoT platform aggregates and manages the data, which facilitates subsequent data application.

[0015] By building a tree structure across multiple platforms, the child nodes in the tree structure are responsible for real-time management of the devices and collection of device data, while the parent nodes in the tree structure are responsible for receiving data from the child nodes and summarizing and organizing the data.

[0016] Preferably, the specific operations in the scenario of multi-level platform collaborative work include:

[0017] The IoT platform's data permissions and functional permissions are designed separately. For data permissions, tenant organization attributes are defined. Multiple sub-IoT platforms are grouped together based on the differences in the devices they manage, grouping devices with the same product attributes together. Furthermore, devices belonging to the same entity are grouped together based on their actual affiliation. Vertical management of device data is achieved through managing device permissions; that is, by assigning multiple devices to designated tenant organizations, users within those organizations only have data permissions for the assigned devices, thus achieving data permission management. For functional permissions, filter interceptors are added to the platform's functions at the code level. By assigning different functional permissions to users, the filter intercepts and verifies user functional permissions at the code level, achieving platform functional permission management.

[0018] Preferably, the specific operations in the multi-platform deployment application scenario of edge IoT include:

[0019] When the sub-IoT platform synchronizes device data with the upper-level IoT platform in real time, MQTT is selected as the data transmission protocol; when the sub-IoT platform synchronizes device event data with the upper-level IoT platform, HTTP is selected as the data transmission protocol; when the sub-IoT platform synchronizes device operation logs, platform function logs, and other data with the upper-level IoT platform, KAFKA is selected as the data transmission protocol. The appropriate data transmission protocol is flexibly selected for different data transmission scenarios.

[0020] Preferred specific operations in IoT multi-level platform application scenarios include:

[0021] The upper-level IoT platform aggregates and processes the received data, and at the same time, it performs fast searching, aggregation, and sorting of large-scale data based on Elasticsearch to meet users' data retrieval needs.

[0022] Specific operations in IoT multi-level platform application scenarios include:

[0023] The operation and maintenance system registers with the upper-level IoT platform as a device, and reports the operation and maintenance data of the sub-IoT platform and the server as an IoT device. At the same time, the operation and maintenance system supports receiving external instructions and instructions from the upper-level IoT platform, and actively performs operation and maintenance on the lower-level platform and the server it is located on.

[0024] A multi-platform data statistics and management system based on edge IoT, the system consists of a data processing module, an access control module, a communication module, a data transfer module, and a device management module;

[0025] In edge IoT application scenarios, the data processing module divides the distribution range of devices into multiple regions and deploys sub-IoT platforms. Multiple sub-IoT platforms report device data and platform data to the upper-level IoT platform through a cascading network channel. The upper-level IoT platform summarizes and processes the data reported by the sub-IoT platforms.

[0026] The permission management module provides permission management for the collaborative management functions and device data collaboration between multiple platforms in the scenario of multi-level platform collaboration. Users at different nodes have different data permissions and function permissions.

[0027] In the context of multi-platform deployment of edge IoT applications, the communication module establishes three data communication modes to meet the communication needs between platforms: HTTP, MQTT, and KAFKA. Different application scenarios can flexibly select the inter-platform communication mode according to their network environment.

[0028] In the data flow module, in the application scenario of IoT multi-level platform, during the data flow process of the multi-level platform, the sub-level platform performs initial screening and filtering of the data flowing upward according to the data type reported by the device, and reports the device's regular attribute data and device function event data separately. Different types of device data are reported according to the data transmission protocol corresponding to the data type.

[0029] In IoT multi-level platform application scenarios, the device management module allows users to manage IoT devices within a region in real time and manage data through multiple platforms.

[0030] Preferably, the data processing module, based on protocol transmission efficiency and device timeliness requirements, assigns devices within the area to a separate IoT platform for management, that is, entrusts the management of a large number of IoT devices to multiple edge IoT platforms;

[0031] Multiple sub-IoT platforms are used to achieve real-time management of devices. At the same time, a higher-level IoT platform is established to aggregate data from the sub-IoT platforms. The higher-level IoT platform aggregates and manages the data, which facilitates subsequent data application.

[0032] By building a tree structure across multiple platforms, the child nodes in the tree structure are responsible for real-time management of the devices and collection of device data, while the parent nodes in the tree structure are responsible for receiving data from the child nodes and summarizing and organizing the data.

[0033] Preferably, the permission management module designs data permissions and functional permissions of the IoT platform separately. For data permissions, tenant organization attributes are defined, and multiple sub-IoT platforms are grouped together based on the differences in the devices they manage. Devices with the same product attribute are grouped together, and devices with the same actual affiliation are grouped together. Vertical management of device data is achieved by managing device permissions. That is, by assigning multiple devices to a designated tenant organization, users under the tenant organization only have data permissions for the assigned devices, thus realizing data permission management. For functional permissions, a filter interceptor is added to the platform functions at the code level. By assigning different functional permissions to users, the filter intercepts and verifies the user's functional permissions at the code level, thus realizing platform functional permission management.

[0034] Preferably, the communication module selects MQTT as the data transmission protocol when the sub-IoT platform synchronizes device data with the upper-level IoT platform in real time; selects HTTP as the data transmission protocol when the sub-IoT platform synchronizes device event data with the upper-level IoT platform; and selects KAFKA as the data transmission protocol when the sub-IoT platform synchronizes device operation logs, platform function logs, and other data with the upper-level IoT platform, flexibly selecting the appropriate data transmission protocol for different data transmission scenarios.

[0035] Preferably, in the data transfer module, the upper-level IoT platform aggregates and processes the received data, and simultaneously performs rapid searching, aggregation, and sorting of large-scale data based on Elasticsearch to meet users' data retrieval needs;

[0036] The device management module allows the operation and maintenance system to register as a device in the upper-level IoT platform. It reports the operation and maintenance data of the sub-IoT platform and the server as an IoT device. At the same time, the operation and maintenance system supports receiving external instructions and instructions from the upper-level IoT platform, and actively performs operation and maintenance on the lower-level platform and the server it is located on.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] The multi-platform data statistics and management method and system based on edge IoT proposed in this invention expands the application scenarios of edge IoT and improves the availability of IoT technology through multi-level deployment of edge IoT, making the application scenarios of edge IoT more extensive; it maximizes the real-time data transmission of IoT devices and prioritizes the persistence of important data even in extreme scenarios; it solves the data silo problem of small IoT platforms, enabling all IoT platforms to aggregate data and extract data value based on data channels; and it improves the robustness of IoT platforms and provides automated operation and maintenance capabilities based on the operation and maintenance system. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the main structure of the multi-platform data statistics and management system for edge IoT of the present invention;

[0040] Figure 2 This is a schematic diagram illustrating the application scenario of the present invention in a multi-platform deployment of edge IoT;

[0041] Figure 3 This is a schematic diagram illustrating the application scenario of the multi-level IoT platform of the present invention;

[0042] Figure 4 This is a schematic diagram illustrating the data summary of the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the present invention clear and complete, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only some, not all, embodiments of the present invention, and are merely illustrative of the embodiments of the present invention. They are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0044] Please see Figures 1 to 4This invention provides a technical solution: a multi-platform data statistics and management method based on edge IoT, the method comprising the following steps:

[0045] In edge IoT applications, many scenarios present common challenges due to the wide distribution and dispersed locations of IoT devices. Effectively managing large numbers of IoT devices is a key issue in many edge IoT scenarios. To address this, we divide a large number of IoT devices within a specific area into separate IoT platforms based on the characteristics of their transmission protocols and the requirements for protocol transmission efficiency and device timeliness. Each IoT platform can manage and receive IoT devices within its current area in real time, providing functions such as data reporting and command issuance. This achieves high-efficiency, low-latency, and highly accurate operation and maintenance management of IoT devices. In our hierarchical platform design, the smallest sub-level platform directly interacts with the devices. Real-time device management and device data management are crucial requirements in IoT applications. We achieve real-time device management by dividing the platform into multiple sub-IoT platforms. Simultaneously, a higher-level IoT platform is established to aggregate data from the sub-IoT platforms, facilitating subsequent data application. By building a tree structure across multiple platforms, the child nodes in the tree structure are responsible for real-time management of the devices and collection of device data, while the parent nodes in the tree structure are responsible for receiving data from the child nodes and summarizing and organizing the data.

[0046] In multi-level platform collaboration scenarios, permission management is required for collaborative management functions and device data collaboration across multiple platforms. Users at different nodes need different data and function permissions. To address these requirements, we designed data and function permissions for the IoT platform separately. Firstly, for data permissions, we defined tenant organization attributes. Multiple sub-IoT platforms group devices with the same product attribute together based on the differences in the devices they manage. Simultaneously, based on the actual affiliation of the devices, devices with the same affiliation are grouped together. Vertical management of device data is achieved through managing device permissions; that is, by assigning multiple devices to a designated tenant organization, users within that organization only have data permissions for the assigned devices, thus implementing data permission management. Secondly, for function permissions, we added filter interceptors to the platform functions at the code level. By assigning different function permissions to users, the filter intercepts and verifies user function permissions at the code level, achieving platform function permission management.

[0047] In edge IoT multi-platform deployment application scenarios, in order to meet the communication needs between platforms, we have established three data communication modes, namely HTTP, MQTT and KAFKA. Different application scenarios can flexibly choose the multi-platform communication method according to their network environment.

[0048] In multi-platform data synchronization scenarios, MQTT focuses on data, while HTTP focuses on documents. HTTP is a request-response protocol for client-server computation, while Hypertext Transfer Protocol (HTTP) is a simple request-response protocol that typically runs on TCP. This protocol specifies what messages a client might send to the server and what responses it might receive. Request and response message headers are given in ASCII format, while the message content has a MIME-like format. MQTT (Message Queuing Telemetry Transport) is a publish / subscribe messaging protocol based on the ISO standard (ISO / IEC PRF 20922). Built on top of TCP / IP, the publish / subscribe messaging model provides users with independent devices, thus increasing the stability of the entire system. Even if one client fails, the entire system can still function normally. Similar to MQTT, Kafka is a fast, scalable, high-throughput, fault-tolerant distributed publish-subscribe messaging system written in Scala and Java, capable of delivering messages from one endpoint to another. Kafka categorizes messages by Topic when saving them, with the message sender being the producer and the message receiver being the consumer.

[0049] MQTT and Kafka are two different protocols. Although both are related to the publish / subscribe paradigm, their application scenarios and goals differ slightly. Kafka is used to handle big data, focusing on data storage and retrieval. Its message passing mechanism emphasizes data stream processing, targeting real-time streaming data processing scenarios. MQTT, on the other hand, is used to handle the Internet of Things (IoT), focusing on client-server communication. Its message passing mechanism emphasizes message delivery and processing, targeting scenarios that receive, process, and respond to messages from a large number of IoT devices.

[0050] Considering the advantages and differences among the above protocols, we generally choose MQTT as the data transmission protocol when the sub-IoT platform synchronizes device data with the upper-level IoT platform in real time; we generally choose HTTP as the data transmission protocol when the sub-IoT platform synchronizes device event data with the upper-level IoT platform; and we choose Kafka as the data transmission protocol when the sub-IoT platform synchronizes device operation logs, platform function logs, and other data with the upper-level IoT platform. The appropriate data transmission protocol should be flexibly selected for different data transmission scenarios.

[0051] In IoT multi-level platform application scenarios, during the data flow process, the sub-level platforms can perform initial screening and filtering of the data flowing upwards based on the data type reported by the devices, and report the device's routine attribute data and device function event data separately. Different types of device data are reported according to the data transmission protocol corresponding to the data type.

[0052] The upper-level IoT platform will aggregate and process the received data, and use Elasticsearch (ES) to quickly search, aggregate, and sort large-scale data to meet users' data retrieval needs.

[0053] In multi-level IoT platform application scenarios, users manage IoT devices and data within a region in real time through multiple platforms. Given this context, the simultaneous operation of multiple platforms significantly increases the complexity of platform maintenance. To address this, we configure a separate, lightweight maintenance system for each platform. This system operates as an independent process on the same server as the platform, sending commands to the IoT platform services and monitoring their operational status. Simultaneously, the maintenance system registers with the higher-level IoT platform as a device, reporting maintenance data such as the operational status of sub-IoT platforms and servers. Furthermore, the maintenance system supports receiving external commands from the higher-level IoT platform and proactively maintaining lower-level platforms and their respective servers. Example

[0054] Based on Embodiment 1, a multi-platform data statistics and management system based on edge IoT is proposed. The system consists of a data processing module, a permission management module, a communication module, a data flow module, and a device management module.

[0055] In edge IoT application scenarios, the data processing module divides the area into multiple regions and deploys sub-IoT platforms based on the distribution range of devices. Multiple sub-IoT platforms report device data and platform data to the upper-level IoT platform through cascading network channels. The upper-level IoT platform summarizes and processes the data reported by the sub-IoT platforms. According to the protocol transmission efficiency and device timeliness requirements, the devices in the area are assigned to a separate IoT platform for management, that is, a large number of IoT devices are managed by multiple edge IoT platforms.

[0056] Multiple sub-IoT platforms are used to achieve real-time management of devices. At the same time, a higher-level IoT platform is established to aggregate data from the sub-IoT platforms. The higher-level IoT platform aggregates and manages the data, which facilitates subsequent data application.

[0057] By building a tree structure across multiple platforms, the child nodes in the tree structure are responsible for real-time management of the devices and collection of device data, while the parent nodes in the tree structure are responsible for receiving data from the child nodes and summarizing and organizing the data.

[0058] The access control module provides access control for collaborative management functions and device data between multiple platforms in multi-level platform collaboration scenarios. Users at different nodes have different data and function permissions. The module separates data and function permissions for the IoT platform. For data permissions, tenant organization attributes are defined. Multiple sub-IoT platforms group devices with the same product attribute together based on the differences in the devices they manage, and also group devices with the same ownership group together. Vertical management of device data is achieved by managing device permissions; that is, by assigning multiple devices to a designated tenant organization, users within that organization only have data permissions for the assigned devices, thus achieving data access control. For function permissions, a filter interceptor is added to the platform functions at the code level. By assigning different function permissions to users, the filter intercepts and verifies user function permissions at the code level, achieving platform function permission management.

[0059] In edge IoT multi-platform deployment scenarios, the communication module establishes three data communication modes—HTTP, MQTT, and KAFKA—to meet inter-platform communication needs. Different application scenarios flexibly select the appropriate inter-platform communication method based on their network environment. When a sub-IoT platform synchronizes device data with a higher-level IoT platform in real time, MQTT is selected as the data transmission protocol. When a sub-IoT platform synchronizes device event data with a higher-level IoT platform, HTTP is selected as the data transmission protocol. When a sub-IoT platform synchronizes device operation logs, platform function logs, and other data with a higher-level IoT platform, KAFKA is selected as the data transmission protocol. The appropriate data transmission protocol is flexibly selected for different data transmission scenarios.

[0060] In the data flow module, within the multi-level IoT platform application scenario, during the data flow process, sub-level platforms perform initial screening and filtering of the data flowing upwards based on the data type reported by the devices, separating the reporting of routine device attribute data from device functional event data, and reporting different types of device data according to the data transmission protocols corresponding to the data types; the upper-level IoT platform aggregates and processes the received data, and simultaneously performs rapid searching, aggregation, and sorting of large-scale data based on Elasticsearch to meet user data retrieval needs.

[0061] In multi-level IoT platform application scenarios, the device management module enables users to manage IoT devices within a region in real time and manage data through multiple platforms. The operation and maintenance system registers as a device in the upper-level IoT platform and reports the operation and maintenance data of the sub-IoT platform and the server as an IoT device. At the same time, the operation and maintenance system supports receiving external instructions and instructions from the upper-level IoT platform to proactively perform operation and maintenance on the lower-level platform and the server it is located on.

[0062] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-platform data statistics and management method based on edge IoT, characterized in that: The method includes the following steps: In edge IoT application scenarios, based on the distribution range of devices, multiple sub-IoT platforms are deployed in different areas. Multiple sub-IoT platforms report device data and platform data to the upper-level IoT platform through a cascading network channel. The upper-level IoT platform summarizes and organizes the data based on the data reported by the sub-IoT platforms. In scenarios where multiple platforms work together, permission management is provided for the collaborative management functions and device data collaboration between multiple platforms, with users at different nodes having different data permissions and function permissions. In the multi-platform deployment application scenario of edge IoT, in order to meet the communication needs between platforms, three data communication modes are established, namely HTTP, MQTT and KAFKA. Different application scenarios select the multi-platform communication mode according to their network environment. In the application scenario of multi-level IoT platforms, during the data flow process, the sub-IoT platforms perform initial screening and filtering of the data flowing upward based on the data type reported by the devices, and report the device's routine attribute data and device function event data separately. Different types of device data are reported according to the data transmission protocol corresponding to the data type. In IoT multi-level platform application scenarios, users can achieve real-time management and data management of IoT devices in the area through multiple platforms; Specific operations in edge IoT application scenarios include: Based on protocol transmission efficiency and device timeliness requirements, devices within the area are assigned to a separate IoT platform for management, meaning that a large number of IoT devices are managed by multiple edge IoT platforms. Multiple sub-IoT platforms are used to achieve real-time management of devices. At the same time, a higher-level IoT platform is established to aggregate data from the sub-IoT platforms. The higher-level IoT platform aggregates and manages the data, which facilitates subsequent data application. By building a tree structure across multiple platforms, the child nodes in the tree structure are responsible for real-time management of the devices and collection of device data, while the parent nodes in the tree structure are responsible for receiving data from the child nodes and summarizing and organizing the data. The specific operations in the scenario of multi-level platform collaboration include: The IoT platform's data permissions and functional permissions are designed separately. For data permissions, tenant organization attributes are defined. Multiple sub-IoT platforms are grouped together based on the differences in the devices they manage, grouping devices with the same product attributes together. Furthermore, devices belonging to the same entity are grouped together based on their actual affiliation. Vertical management of device data is achieved through device permission management; that is, by assigning multiple devices to designated tenant organizations, users within those organizations only have data permissions for the assigned devices, thus achieving data permission management. For functional permissions, filter interceptors are added to the platform's functions at the code level. By assigning different functional permissions to users, the filter intercepts and verifies user functional permissions at the code level, achieving platform functional permission management.

2. The multi-platform data statistics and management method based on edge IoT according to claim 1, characterized in that: Specific operations in multi-platform deployment scenarios for edge IoT applications include: When the sub-IoT platform synchronizes device data with the upper-level IoT platform in real time, MQTT is selected as the data transmission protocol; when the sub-IoT platform synchronizes device event data with the upper-level IoT platform, HTTP is selected as the data transmission protocol; when the sub-IoT platform synchronizes device operation logs and platform function logs with the upper-level IoT platform, KAFKA is selected as the data transmission protocol.

3. The multi-platform data statistics and management method based on edge IoT according to claim 1, characterized in that: Specific operations in IoT multi-level platform application scenarios include: The upper-level IoT platform aggregates and processes the received data, and at the same time, it searches, aggregates, and sorts large-scale data based on Elasticsearch to meet users' data retrieval needs. Specific operations in IoT multi-level platform application scenarios include: The operation and maintenance system registers with the upper-level IoT platform as a device, and reports the operation and maintenance data of the sub-IoT platform and the server as an IoT device. At the same time, the operation and maintenance system supports receiving external instructions and instructions from the upper-level IoT platform, and actively performs operation and maintenance on the sub-IoT platform and the server it is located on.

4. A multi-platform data statistics and management system based on edge IoT, characterized in that: The system consists of a data processing module, a permission management module, a communication module, a data transfer module, and a device management module. In edge IoT application scenarios, the data processing module divides the distribution range of devices into multiple regions and deploys sub-IoT platforms. Multiple sub-IoT platforms report device data and platform data to the upper-level IoT platform through a cascading network channel. The upper-level IoT platform summarizes and processes the data reported by the sub-IoT platforms. The permission management module provides permission management for the collaborative management functions and device data collaboration between multiple platforms in the scenario of multi-level platform collaboration. Users at different nodes have different data permissions and function permissions. In the context of multi-platform deployment of edge IoT applications, the communication module establishes three data communication modes to meet the communication needs between platforms: HTTP, MQTT, and KAFKA. Different application scenarios select the appropriate inter-platform communication mode based on their network environment. In the application scenario of IoT multi-level platform, the data flow module allows the sub-level platform to perform initial screening and filtering of the data flowing upward based on the data type reported by the device. It separates the device's regular attribute data from the device's functional event data and reports different types of device data according to the data transmission protocol corresponding to the data type. In IoT multi-level platform application scenarios, the device management module enables users to manage IoT devices in the area in real time and manage data through multiple platforms. The data processing module, based on protocol transmission efficiency and device timeliness requirements, assigns devices within the area to a separate IoT platform for management, that is, entrusts the management of a large number of IoT devices to multiple edge IoT platforms; Multiple sub-IoT platforms are used to achieve real-time management of devices. At the same time, a higher-level IoT platform is established to aggregate data from the sub-IoT platforms. The higher-level IoT platform aggregates and manages the data, which facilitates subsequent data application. By building a tree structure among multiple platforms, the sub-IoT platforms in the tree structure are responsible for real-time management of devices and collection of device data, while the upper-level IoT platforms in the tree structure are responsible for receiving data from the sub-IoT platforms and summarizing and organizing the data. The permission management module separates data permissions and functional permissions for the IoT platform. For data permissions, tenant organization attributes are defined, and multiple sub-IoT platforms group devices with the same product attribute together based on the differences in the devices they manage. Furthermore, devices belonging to the same entity are grouped together based on their actual affiliation. Vertical management of device data is achieved by managing device permissions; that is, by assigning multiple devices to designated tenant organizations, users within those organizations only have data permissions for the assigned devices, thus realizing data permission management. For functional permissions, filter interceptors are added to the platform functions at the code level. By assigning different functional permissions to users, the filter intercepts and verifies user functional permissions at the code level, thus realizing platform functional permission management.

5. A multi-platform data statistics and management system based on edge IoT according to claim 4, characterized in that: The communication module uses MQTT as the data transmission protocol when the sub-IoT platform synchronizes device data with the upper-level IoT platform in real time; uses HTTP as the data transmission protocol when the sub-IoT platform synchronizes device event data with the upper-level IoT platform; and uses KAFKA as the data transmission protocol when the sub-IoT platform synchronizes device operation logs and platform function logs with the upper-level IoT platform.

6. The multi-platform data statistics and management system based on edge IoT according to claim 5, characterized in that: The data transfer module allows the upper-level IoT platform to aggregate and process the received data, and simultaneously searches, aggregates, and sorts large-scale data based on Elasticsearch (ES) to meet users' data retrieval needs. The device management module allows the operation and maintenance system to register as a device in the upper-level IoT platform. It reports the operation and maintenance data of the sub-IoT platform and the server as an IoT device. At the same time, the operation and maintenance system supports receiving external instructions and instructions from the upper-level IoT platform to actively maintain the sub-IoT platform and its server.

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