A cloud-edge-end data collaborative management system and method based on the Internet of Things

Abstract

The present invention discloses a cloud-edge-end data collaborative management system and method based on the Internet of Things, and belongs to the field of cloud-edge data collaborative technology. The system includes: a cloud-side IoT management platform, a cloud-edge collaborative module, an edge-side general management platform, and an application-end platform; the cloud-side IoT management platform is connected to the cloud-edge collaborative module; the cloud-edge collaborative module is connected to the edge-side general management platform; the cloud-side IoT management platform and the edge-side general management platform are respectively connected to the application-end platform. The present invention uses a cloud-edge-end integrated collaborative computing architecture to build capability components and tool chains based on the kernel of a domestic cloud operating system based on private cloud resources, so as to realize the reasonable allocation of storage, computing and other capabilities and resources in the cloud, edge, and end systems. Under the entire cloud-edge collaborative construction system, each edge node is monitored, warned, and analyzed in real time, effectively supporting the safe and stable operation of the Internet of Things platform.

Description

Technical Field

[0001] The present invention relates to the field of cloud-edge data collaboration technology, and specifically to a cloud-edge data collaboration management system and method based on the Internet of Things. Background Art

[0002] At the current level of technology, it is estimated that there are more than 10 billion terminals and sensors in the world, of which my country accounts for about 7% of the global number. The scale of its nodes is much larger than that of the Internet, and the number of IoT access nodes is showing an explosive growth trend. The development of the Internet of Things has brought new challenges to the real-time performance and network bandwidth of network transmission, and also put forward new requirements for data storage, analysis and processing. The centralized cloud computing model can no longer meet the needs of efficient transmission and processing of massive data under the Internet of Everything. With the development of 5G, edge computing has emerged. Cloud-edge collaboration extends cloud native capabilities to the edge, adopts the edge node mode, and sinks data processing, business applications, AI models, etc. to the edge for execution, solving problems such as real-time response, data privacy, and maintenance convenience when the Internet of Things is implemented, thereby meeting the key needs of industry digitalization in agile connection, real-time business, data optimization, application intelligence, security and privacy protection.

[0003] However, the growth of data shows an upward trend, and there is an explosion at a certain point in the trend. This requires that the number of edge nodes has the ability to cover the burst data. In some time, the edge nodes are idle. During the period of data surge, how to call the idle edge nodes and which edge nodes to call first are still the key issues under the collaboration of IoT cloud and edge. Summary of the invention

[0004] The purpose of the present invention is to provide a cloud-edge-end data collaborative management system and method based on the Internet of Things to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a cloud-edge-end data collaborative management system based on the Internet of Things, the system comprising: a cloud-side IoT management platform, a cloud-edge collaborative module, an edge-side general management platform, and an application-end platform;

[0006] The cloud-side IoT management platform is used to build integrated device connection, message routing and data processing and analysis. One end supports connecting IoT devices and collecting device data to the cloud-side IoT management platform; it provides standard API interface services, and sends instructions to the device side by calling the platform API interface to achieve remote control; the cloud-edge collaboration module is used to achieve file collaboration, data collaboration, algorithm collaboration and application management collaboration between the cloud-side IoT management platform and the edge-side general management platform; the edge-side general management platform is used to provide edge computing capabilities extended by the cloud-side IoT management platform. Based on edge intelligent gateways or devices, on the one hand, it connects various IoT sensor terminals to achieve access and conversion of various IoT protocols, and realizes data collection and aggregation; on the other hand, it collaborates with the cloud-side IoT management platform to provide data services, intelligent algorithm analysis, and expand application capabilities; the application-side platform is built based on the end-side open source operating system, facing smart sensors, smart actuators and aggregation collectors, and builds basic middleware, industry frameworks and scenario applications on the operating system kernel to adapt to the IoT platform system;

[0007] The cloud-side IoT management platform is connected to the cloud-edge collaboration module; the cloud-edge collaboration module is connected to the edge-side general management platform; the cloud-side IoT management platform and the edge-side general management platform are respectively connected to the application-side platform.

[0008] According to the above technical solution, the cloud-side IoT management platform also includes a data access unit, a data storage unit and a data cache unit;

[0009] The data access unit connects to edge industrial intelligent devices, IoT intelligent gateways, video intelligent gateways, intelligent operation and maintenance devices, and IoT component devices to achieve automatic data collection and access. The accessed data includes time series data, relational data, and audio and video data.

[0010] The data access unit meets the following requirements:

[0011] Supports storing different types of data in different databases and provides integrated dependency packages;

[0012] Unstructured data supports multiple network protocols, including mainstream protocols such as HTTP, HTTPS, and FTP;

[0013] Capable of collecting data in multiple formats, including HTML, XML, JSON, audio and video, etc.

[0014] Support login authentication methods such as HTTP and OAUTH;

[0015] It can be manually triggered or automatically scheduled. Automatic scheduling should provide multiple operation strategies including fixed time intervals, specified time points, and specified time ranges;

[0016] It has complete log auditing capabilities and can record all operation logs;

[0017] With sequential breakpoint resume function;

[0018] It has graphic management functions and supports authentication configuration, file target location configuration, and verification processing configuration.

[0019] The data storage unit provides storage support for multiple types of data provided by different data types and application scenarios based on the data component storage function, and builds a visual interface for query and maintenance; specifically includes:

[0020] Provide storage capabilities for various types of data, such as IoT time series data, hotspot data, audio and video data, etc. Support the transmission of different types of data to the big data platform through integrated docking;

[0021] It includes the storage platform usage statistics function. Through the visual interface, you can query the total amount of storage resources, used space, remaining space and other information, and can use email, SMS, handheld applications or other methods to push and send storage resource usage statistics to management personnel at regular intervals.

[0022] Maintain data information through a visual interface;

[0023] It has data export and backup functions, supports manual export and data backup, and can regularly back up key platform data;

[0024] Equipped with a comprehensive data security strategy, data security is ensured through user authentication, data encryption storage and other methods;

[0025] The redundancy method should be used to improve the high availability of data. At the same time, the platform should support dynamic expansion and be able to adjust the storage space according to the usage of the platform;

[0026] Important data can be stored permanently.

[0027] The data cache unit is used for temporary storage of device measurement data on the application side, and supports the function of synchronizing data to the cloud-side IoT management platform, realizing the ability to collect data monitoring, manage source data quality, supplement data, and resume transmission after power outages.

[0028] According to the above technical solution, the cloud-edge collaboration module includes a file collaboration unit, a data collaboration unit, an algorithm collaboration unit and an application management collaboration unit.

[0029] The file coordination unit refers to the resource coordination capability of the edge node to provide file storage and transmission, schedule upload and manage edge node file resources, such as log files; receive and execute files and storage sent from the cloud, and manage the life cycle of edge-side files in the cloud, including file upload, download, storage, backup, deletion and update;

[0030] The data collaboration unit refers to preprocessing and simple analysis of the original data according to the model or business rules, and uploading it to the cloud; the cloud stores, analyzes and mines the value of the preprocessed and simply analyzed data. The edge component provides a standard data interaction SDK, supports upstream and downstream data channels, and realizes the joint maintenance of device data, edge node configuration, and application configuration data;

[0031] The algorithm coordination unit refers to calling the edge computing power when the data flow exceeds the threshold, deploying the model on the edge side through the algorithm coordination mechanism, and performing localized fast-response edge processing;

[0032] The application management collaboration unit refers to the unified management of business applications in the cloud, deploying applications on nodes according to the requirements of cloud-edge business scenarios, upgrading business applications through cloud-edge application collaboration, supporting containerized deployment, supporting container management and application collaborative management; the container management refers to supporting container file downloading, viewing container file download progress, installing, modifying, upgrading containers, starting, stopping containers, uninstalling containers and viewing container running status; the application collaborative management includes supporting application file downloading, viewing application file download progress, installing and upgrading applications, starting and stopping applications, uninstalling applications, viewing application running status, and application metering and performance monitoring.

[0033] According to the above technical solution, the edge-side universal management platform includes an edge container engine processing unit, a message service unit, and a trusted component unit;

[0034] The edge container engine processing unit provides Docker container support, provides independent container image services and isolates image metadata, achieves full concurrency, and supports priority configuration and switching of resource occupancy modes between nodes; the message service unit is used to build the message service middleware RabbitMQ, provides runtime support for programming languages, and provides an autonomous and controllable database; the trusted component unit is used to ensure computer hardware security and user identification and authentication, and set access control of the operating system.

[0035] According to the above technical solution, the construction of the application-side platform operating system includes:

[0036] Capability components; the capability components include: IoT protocol library, object model, algorithm warehouse and equipment operation and maintenance monitoring;

[0037] Basic middleware;

[0038] and system hardware.

[0039] A cloud-edge-end data collaborative management method based on the Internet of Things, the method comprising the following steps:

[0040] S1. The IoT device receives the remote control command and generates device data to the cloud-side IoT management platform. The cloud-side IoT management platform has a built-in data threshold. When the data capacity exceeds the data threshold, the cloud-edge collaboration is called;

[0041] S2. Call all idle edge nodes, and form a collaborative management model based on the call analysis data of each idle edge node;

[0042] S3. Based on the collaborative management model, output the priority of calling idle edge nodes, form a call instruction list, and feed it back to the cloud-edge collaboration;

[0043] S4. Connect various IoT sensor terminals on the general management platform to access and convert various IoT protocols and collect and aggregate data. At the same time, provide data services and intelligent algorithm analysis to expand application capabilities.

[0044] S5. Build an open source operating system on the application-side platform, build basic middleware, industry frameworks and scenario applications on top of the operating system kernel, adapt to the Internet of Things platform, and form command control of the Internet of Things platform.

[0045] According to the above technical solution, forming a collaborative management model includes:

[0046] Obtain call analysis data of idle edge nodes, where the call analysis data refers to the time node of each time the edge node is called and the parameter nodes in the calling process; the time node refers to the difference between the time points at which two adjacent calls start; the parameter nodes include the number of nodes where data flows during the calling process, the average delay speed of data processing for each call, the data storage capacity for each call, and the average duration of data feedback for each call.

[0047] According to the above technical solution, it also includes:

[0048] Take the call analysis data from the idle edge node historical data to form the first matrix:

[0049]

[0050] Among them, a n1 、a n2 、a n3 、a n4 、a n5Represents five indicators of any sample edge node, which refers to the difference between the start time of two adjacent calls; the number of nodes where data flows during the call, the average delay speed of data processing for each call, the data storage capacity for each call, and the average data feedback time for each call; n represents the number of samples;

[0051] Normalize the first matrix and calculate the mean and standard deviation by column:

[0052]

[0053] Among them, a j represents the mean; S j represents standard deviation; M ij represents standardized data; j represents the index value, ranging from 1 to 5; i represents the sample value, ranging from 1 to n;

[0054] The first matrix is ​​transformed based on the standardized data to form the second matrix:

[0055]

[0056] Calculate the covariance matrix of the second matrix. The covariance matrix is ​​written as;

[0057]

[0058] Among them, R n1 , R n2 , R n3 , R n4 , R n5 Represents M n1 、M n2 、M n3 、M n4 、M n5 The corresponding covariance;

[0059] Using the characteristic equation |R-εE|, where ε is the eigenvalue, E is the identity matrix, and R is the covariance matrix; all eigenvalues ​​ε that meet the conditions are formed;

[0060] Substitute each eigenvalue ε into the characteristic equation |R-εE|H=0 to obtain the corresponding eigenvector H under each eigenvalue;

[0061] Sort the eigenvalues ​​from large to small, select the five largest ones, and then use their corresponding eigenvectors as row vectors to form an eigenvector matrix; multiply the eigenvector matrix with the first matrix to form a converted data matrix T = [A1 A2 A3 A4 A5];

[0062] Then the node influence capability equation in the call analysis data of the edge node is: Y = A1*k1+A2*k2+A3*k3+A4*k4+A5*k5;

[0063] Among them, Y represents the call impact index of the edge node; k1, k2, k3, k4, and k5 represent the difference between the start time points of each adjacent call of any edge node; the number of nodes where data flows during the call process, the average delay speed of data processing for each call, the data storage capacity of each call, and the average data feedback time for each call; the smaller the difference between the start time points of each adjacent call of any edge node, the more trustworthy it is when it is frequently called; the fewer the number of nodes where data flows during the call process, the higher the overall utilization rate; the lower the average delay speed of data processing for each call, the lower the node delay; the smaller the data storage capacity for each call, the higher the node processing capability; and the shorter the average data feedback time for each call, the stronger the node feedback capability;

[0064] Among all idle edge nodes, a sequence is formed in ascending order of the call impact index, and a call instruction list is formed according to the sequence and fed back to the cloud-edge collaboration.

[0065] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, through an integrated cloud-edge-end collaborative computing architecture, builds a capability component and tool chain based on the kernel of the domestic cloud operating system based on private cloud resources, realizes the reasonable allocation of storage, computing and other capabilities and resources in the cloud, edge and end subsystems, realizes the interconnection and efficient collaboration between different subsystems through the network, and realizes the connection of objects, people, systems and information in many application scenarios in the field of the Internet of Things, provides full-service, low-latency, large-capacity, intelligent, and multi-level services, meets the needs of comprehensive perception, reliable transmission, collaborative interaction, intelligent data processing, data privacy protection, etc., and under the entire cloud-edge collaborative construction system, real-time monitoring, early warning and analysis of each edge node are performed to meet the data traffic carrying requirements of the management information area, effectively supporting the safe and stable operation of the Internet of Things platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0067] In the attached picture:

[0068] Figure 1 It is a framework diagram of a cloud-edge-end data collaborative management system based on the Internet of Things in the present invention. DETAILED DESCRIPTION

[0069] 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.

[0070] See also Figure 1 , in the first embodiment of the present invention, a cloud-edge-end data collaborative management system based on the Internet of Things is provided, the system comprising: a cloud-side IoT management platform, a cloud-edge collaborative module, an edge-side general management platform, and an application-end platform;

[0071] The cloud-side IoT management platform is used to build integrated device connection, message routing and data processing and analysis. One end supports connecting IoT devices and collecting device data to the cloud-side IoT management platform; it provides standard API interface services, and sends instructions to the device side by calling the platform API interface to achieve remote control; the cloud-edge collaboration module is used to achieve file collaboration, data collaboration, algorithm collaboration and application management collaboration between the cloud-side IoT management platform and the edge-side general management platform; the edge-side general management platform is used to provide edge computing capabilities extended by the cloud-side IoT management platform. Based on edge intelligent gateways or devices, on the one hand, it connects various IoT sensor terminals to achieve access and conversion of various IoT protocols, and realizes data collection and aggregation; on the other hand, it collaborates with the cloud-side IoT management platform to provide data services, intelligent algorithm analysis, and expand application capabilities; the application-side platform is built based on the end-side open source operating system, facing smart sensors, smart actuators and aggregation collectors, and builds basic middleware, industry frameworks and scenario applications on the operating system kernel to adapt to the IoT platform system;

[0072] The cloud-side IoT management platform is connected to the cloud-edge collaboration module; the cloud-edge collaboration module is connected to the edge-side general management platform; the cloud-side IoT management platform and the edge-side general management platform are respectively connected to the application-side platform.

[0073] The cloud-side IoT management platform also includes a data access unit, a data storage unit and a data cache unit;

[0074] The data access unit connects to edge industrial intelligent devices, IoT intelligent gateways, video intelligent gateways, intelligent operation and maintenance devices, and IoT component devices to achieve automatic data collection and access. The accessed data includes time series data, relational data, and audio and video data.

[0075] The data storage unit provides storage support for multiple types of data provided by the data component storage function for different data types and application scenarios, and builds a visual interface for query and maintenance;

[0076] The data cache unit is used for temporary storage of device measurement data on the application side, and supports the function of synchronizing data to the cloud-side IoT management platform, realizing the ability to collect data monitoring, manage source data quality, supplement data, and resume transmission after power outages.

[0077] The cloud-edge collaboration module includes a file collaboration unit, a data collaboration unit, an algorithm collaboration unit, and an application management collaboration unit.

[0078] The file coordination unit refers to the resource coordination capability of the edge node to provide file storage and transmission, schedule upload and manage edge node file resources; receive and execute files and storage sent from the cloud, and manage the life cycle of edge-side files in the cloud, including file upload, download, storage, backup, deletion and update;

[0079] The data collaboration unit refers to preprocessing and simple analysis of the original data according to the model or business rules, and uploading it to the cloud; the cloud stores, analyzes and mines the value of the preprocessed and simply analyzed data. The edge component provides a standard data interaction SDK, supports upstream and downstream data channels, and realizes the joint maintenance of device data, edge node configuration, and application configuration data;

[0080] The algorithm coordination unit refers to calling the edge computing power when the data flow exceeds the threshold, deploying the model on the edge side through the algorithm coordination mechanism, and performing localized fast-response edge processing;

[0081] The application management collaboration unit refers to the unified management of business applications in the cloud, deploying applications on nodes according to the requirements of cloud-edge business scenarios, upgrading business applications through cloud-edge application collaboration, supporting containerized deployment, supporting container management and application collaborative management; the container management refers to supporting container file downloading, viewing container file download progress, installing, modifying, upgrading containers, starting, stopping containers, uninstalling containers and viewing container running status; the application collaborative management includes supporting application file downloading, viewing application file download progress, installing and upgrading applications, starting and stopping applications, uninstalling applications, viewing application running status, and application metering and performance monitoring.

[0082] The edge-side universal management platform includes an edge container engine processing unit, a message service unit, and a trusted component unit;

[0083] The edge container engine processing unit provides Docker container support, provides independent container image services and isolates image metadata, achieves full concurrency, and supports priority configuration and switching of resource occupancy modes between nodes; the message service unit is used to build the message service middleware RabbitMQ, provides runtime support for programming languages, and provides an autonomous and controllable database; the trusted component unit is used to ensure computer hardware security and user identification and authentication, and set access control of the operating system.

[0084] The application-side platform operating system construction includes:

[0085] Capability components; the capability components include: IoT protocol library, object model, algorithm warehouse and equipment operation and maintenance monitoring;

[0086] Basic middleware;

[0087] and system hardware.

[0088] In this embodiment, a cloud-edge-device data collaborative management method based on the Internet of Things is also included, and the method includes:

[0089] S1. The IoT device receives the remote control command and generates device data to the cloud-side IoT management platform. The cloud-side IoT management platform has a built-in data threshold. When the data capacity exceeds the data threshold, the cloud-edge collaboration is called;

[0090] S2. Call all idle edge nodes, and form a collaborative management model based on the call analysis data of each idle edge node;

[0091] S3. Based on the collaborative management model, output the priority of calling idle edge nodes, form a call instruction list, and feed it back to the cloud-edge collaboration;

[0092] S4. Connect various IoT sensor terminals on the general management platform to access and convert various IoT protocols and collect and aggregate data. At the same time, provide data services and intelligent algorithm analysis to expand application capabilities.

[0093] S5. Build an open source operating system on the application-side platform, build basic middleware, industry frameworks and scenario applications on top of the operating system kernel, adapt to the Internet of Things platform, and form command control of the Internet of Things platform.

[0094] The forming of the collaborative management model comprises:

[0095] Obtain call analysis data of idle edge nodes, where the call analysis data refers to the time node of each time the edge node is called and the parameter nodes in the calling process; the time node refers to the difference between the time points at which two adjacent calls start; the parameter nodes include the number of nodes where data flows during the calling process, the average delay speed of data processing for each call, the data storage capacity for each call, and the average duration of data feedback for each call.

[0096] Also includes:

[0097] Take the call analysis data from the idle edge node historical data to form the first matrix:

[0098]

[0099] Among them, a n1 、a n2 、a n3 、a n4 、a n5 Represents five indicators of any sample edge node, which refers to the difference between the start time of two adjacent calls; the number of nodes where data flows during the call, the average delay speed of data processing for each call, the data storage capacity for each call, and the average data feedback time for each call; n represents the number of samples;

[0100] Normalize the first matrix and calculate the mean and standard deviation by column:

[0101]

[0102]

[0103] Among them, a j represents the mean; S j represents standard deviation; M ij represents standardized data; j represents the index value, ranging from 1 to 5; i represents the sample value, ranging from 1 to n;

[0104] The first matrix is ​​transformed based on the standardized data to form the second matrix:

[0105]

[0106] Calculate the covariance matrix of the second matrix. The covariance matrix is ​​written as;

[0107]

[0108] Among them, R n1 , R n2 , R n3 , R n4 , R n5 Represents Mn1 、M n2 、M n3 、M n4 、M n5 The corresponding covariance;

[0109] Using the characteristic equation |R-εE|, where ε is the eigenvalue, E is the identity matrix, and R is the covariance matrix; all eigenvalues ​​ε that meet the conditions are formed;

[0110] Substitute each eigenvalue ε into the characteristic equation |R-εE|H=0 to obtain the corresponding eigenvector H under each eigenvalue;

[0111] Sort the eigenvalues ​​from large to small, select the five largest ones, and then use their corresponding eigenvectors as row vectors to form an eigenvector matrix; multiply the eigenvector matrix with the first matrix to form a converted data matrix T = [A1 A2 A3 A4 A5];

[0112] Then the node influence capability equation in the call analysis data of the edge node is: Y = A1*k1+A2*k2+A3*k3+A4*k4+A5*k5;

[0113] Among them, Y represents the call impact index of the edge node; k1, k2, k3, k4, and k5 represent the difference between the start time points of each adjacent call of any edge node; the number of nodes where data flows during the call process, the average delay speed of data processing for each call, the data storage capacity for each call, and the average data feedback time for each call;

[0114] Among all idle edge nodes, a sequence is formed in ascending order of the call impact index, and a call instruction list is formed according to the sequence and fed back to the cloud-edge collaboration.

[0115] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0116] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A cloud-edge-end data collaborative management system based on the Internet of Things, characterized by: The system includes: cloud-side IoT management platform, cloud-edge collaboration module, edge-side general management platform and application-side platform; The cloud-side IoT management platform is used to build integrated device connection, message routing and data processing and analysis. One end supports connecting IoT devices and collecting device data to the cloud-side IoT management platform; it provides standard API interface services, and sends instructions to the device side by calling the platform API interface to achieve remote control; the cloud-edge collaboration module is used to achieve file collaboration, data collaboration, algorithm collaboration and application management collaboration between the cloud-side IoT management platform and the edge-side general management platform; the edge-side general management platform is used to provide edge computing capabilities extended by the cloud-side IoT management platform. Based on edge intelligent gateways or devices, on the one hand, it connects various IoT sensor terminals to achieve access and conversion of various IoT protocols, and realizes data collection and aggregation; on the other hand, it collaborates with the cloud-side IoT management platform to provide data services, intelligent algorithm analysis, and expand application capabilities; the application-side platform is built based on the end-side open source operating system, facing smart sensors, smart actuators and aggregation collectors, and builds basic middleware, industry frameworks and scenario applications on the operating system kernel to adapt to the IoT platform system; The cloud-side IoT management platform is connected to the cloud-edge collaboration module; the cloud-edge collaboration module is connected to the edge-side general management platform; the cloud-side IoT management platform and the edge-side general management platform are respectively connected to the application-side platform; The cloud-side IoT management platform also includes a data access unit, a data storage unit and a data cache unit; The data access unit connects to edge industrial intelligent devices, IoT intelligent gateways, video intelligent gateways, intelligent operation and maintenance devices, and IoT component devices to achieve automatic data collection and access. The accessed data includes time series data, relational data, and audio and video data. The data storage unit provides storage support for multiple types of data provided by the data component storage function for different data types and application scenarios, and builds a visual interface for query and maintenance; The data cache unit is used for temporary storage of device measurement data on the application side, and supports the function of synchronizing data to the cloud-side IoT management platform, realizing the ability to collect data monitoring, manage source data quality, supplement data, and resume transmission after power outages.

2. According to the cloud-edge-end data collaborative management system based on the Internet of Things according to claim 1, it is characterized by: The cloud-edge collaboration module includes a file collaboration unit, a data collaboration unit, an algorithm collaboration unit, and an application management collaboration unit. The file coordination unit refers to the resource coordination capability of the edge node to provide file storage and transmission, schedule upload and manage edge node file resources; receive and execute files and storage sent from the cloud, and manage the life cycle of edge-side files in the cloud, including file upload, download, storage, backup, deletion and update; The data collaboration unit refers to pre-processing and simple analysis of the original data according to the model or business rules, and uploading it to the cloud; The cloud stores, analyzes, and mines the value of pre-processed and simply analyzed data. The edge component provides a standard data interaction SDK, supports upstream and downstream data channels, and realizes the joint maintenance of device data, edge node configuration, and application configuration data. The algorithm coordination unit refers to calling the edge computing power when the data flow exceeds the threshold, deploying the model on the edge side through the algorithm coordination mechanism, and performing localized fast-response edge processing; The application management collaboration unit refers to the unified management of business applications in the cloud, deploying applications on nodes according to the requirements of cloud-edge business scenarios, upgrading business applications through cloud-edge application collaboration, supporting containerized deployment, supporting container management and application collaborative management; the container management refers to supporting container file downloading, viewing container file download progress, installing, modifying, upgrading containers, starting, stopping containers, uninstalling containers and viewing container running status; the application collaborative management includes supporting application file downloading, viewing application file download progress, installing and upgrading applications, starting and stopping applications, uninstalling applications, viewing application running status, and application metering and performance monitoring.

3. According to the cloud-edge-end data collaborative management system based on the Internet of Things according to claim 1, it is characterized by: The edge-side universal management platform includes an edge container engine processing unit, a message service unit, and a trusted component unit; The edge container engine processing unit provides Docker container support, provides independent container image services and isolates image metadata, achieves full concurrency, and supports priority configuration and switching of resource occupancy modes between nodes; the message service unit is used to build the message service middleware RabbitMQ, provides runtime support for programming languages, and provides an autonomous and controllable database; the trusted component unit is used to ensure computer hardware security and user identification and authentication, and set access control of the operating system.

4. According to the cloud-edge-end data collaborative management system based on the Internet of Things according to claim 1, it is characterized by: The application-side platform operating system construction includes: Capability components; the capability components include: IoT protocol library, object model, algorithm warehouse and equipment operation and maintenance monitoring; Basic middleware; and system hardware.

5. A cloud-edge-device data collaborative management method based on the Internet of Things, using a cloud-edge-device data collaborative management system based on the Internet of Things as claimed in claim 1, characterized in that: The method comprises the following steps: S1. The IoT device receives the remote control command and generates device data to the cloud-side IoT management platform. The cloud-side IoT management platform has a built-in data threshold. When the data capacity exceeds the data threshold, the cloud-edge collaboration is called; S2. Call all idle edge nodes, and form a collaborative management model based on the call analysis data of each idle edge node; S3. Based on the collaborative management model, output the priority of calling idle edge nodes, form a call instruction list, and feed it back to the cloud-edge collaboration; S4. Connect various IoT sensor terminals on the general management platform to access and convert various IoT protocols and collect and aggregate data. At the same time, provide data services and intelligent algorithm analysis to expand application capabilities. S5. Build an open source operating system on the application-side platform, build basic middleware, industry frameworks and scenario applications on top of the operating system kernel, adapt to the Internet of Things platform, and form command control of the Internet of Things platform.

6. The method for collaborative management of cloud-edge-end data based on the Internet of Things according to claim 5 is characterized in that: The forming of the collaborative management model comprises: Obtain call analysis data of idle edge nodes, where the call analysis data refers to the time node of each time the edge node is called and the parameter nodes in the calling process; the time node refers to the difference between the time points at which two adjacent calls start; the parameter nodes include the number of nodes where data flows during the calling process, the average delay speed of data processing for each call, the data storage capacity for each call, and the average duration of data feedback for each call.

7. The method for collaborative management of cloud-edge-device data based on the Internet of Things according to claim 6 is characterized in that: Also includes: Take the call analysis data from the idle edge node historical data to form the first matrix: Among them, a n1 、a n2 、a n3 、a n4 、a n5 Represents five indicators of any sample edge node, which refers to the difference between the start time of two adjacent calls; the number of nodes where data flows during the call, the average delay speed of data processing for each call, the data storage capacity for each call, and the average data feedback time for each call; n represents the number of samples; Normalize the first matrix and calculate the mean and standard deviation by column: Among them, a j represents the mean; S j represents standard deviation; M ij represents standardized data; j represents the index value, ranging from 1 to 5; i represents the sample value, ranging from 1 to n; The first matrix is ​​transformed based on the standardized data to form the second matrix: Calculate the covariance matrix of the second matrix. The covariance matrix is ​​written as; Among them, R n1 , R n2 , R n3 , R n4 , R n5 Represents M n1 、M n2 、M n3 、M n4 、M n5 The corresponding covariance; Using the characteristic equation |R-εE|, where ε is the eigenvalue, E is the identity matrix, and R is the covariance matrix; all eigenvalues ​​ε that meet the conditions are formed; Substitute each eigenvalue ε into the characteristic equation |R-εE|H=0 to obtain the corresponding eigenvector H under each eigenvalue; Sort the eigenvalues ​​from large to small, select the five largest ones, and then use their corresponding eigenvectors as row vectors to form an eigenvector matrix; multiply the eigenvector matrix with the first matrix to form a converted data matrix T = [A1A2 A3 A4 A5]; Then the node influence capability equation in the call analysis data of the edge node is: Y = A1*k1+A2*k2+A3*k3+A4*k4+A5*k5; Among them, Y represents the call impact index of the edge node; k1, k2, k3, k4, and k5 represent the difference between the start time points of each adjacent call of any edge node; the number of nodes where data flows during the call process, the average delay speed of data processing for each call, the data storage capacity for each call, and the average data feedback time for each call; Among all idle edge nodes, a sequence is formed in ascending order of the call impact index, and a call instruction list is formed according to the sequence and fed back to the cloud-edge collaboration.

Images