Multi-device collaborative management system based on cloud edge collaboration

Abstract

The invention discloses a multi-device cooperative management system based on cloud edge cooperation, and relates to the technical field of Internet of Things communication, and the system comprises a device management module which is used for connecting edge devices, carrying out the centralized management of the edge devices, and carrying out the optimal scheduling based on the functions and distribution positions of different edge devices; and the data acquisition module is connected with the equipment management module. According to the multi-device collaborative management system based on cloud edge collaboration, a comprehensive and accurate data basis is provided for multi-device collaborative management, and information exchange and collaborative work between devices are promoted; the optimization decision-making unit can formulate an equipment optimization management strategy according to the operation scheme generated by the big data analysis unit and the prediction result obtained by the prediction analysis unit, and continuously performs optimization adjustment according to the real-time operation condition of each piece of edge equipment; therefore, the system can adapt to continuously changing equipment operation environments and task requirements, and more efficient multi-equipment collaborative management is realized.

Description

Technical Field

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

[0002] Under the current technical environment, the collaborative management of multiple devices faces a series of challenges. On the one hand, the communication stability between devices is insufficient and is easily affected by factors such as network fluctuations and interference, resulting in delays, interruptions or even failures in collaborative operations. Although the existing multi-device collaborative management systems can realize the collaborative work of devices to a certain extent, they still lack intelligent decision-making capabilities when faced with complex tasks and dynamically changing environments. The system cannot automatically adjust the collaborative strategy according to real-time conditions and still requires manual intervention. In addition, the system does not have an accurate and timely understanding of the functional characteristics of the equipment, making it difficult to accurately match task requirements during equipment scheduling. In terms of network resource utilization, the optimization of data transmission paths is not efficient enough to ensure full utilization of bandwidth. Moreover, the lack of accurate prediction of the operating status of equipment and an effective optimization decision-making mechanism makes it difficult to respond to possible equipment problems in advance and improve collaborative work efficiency. In summary, there is an urgent need for a multi-device collaborative management system based on cloud-edge collaboration to solve these problems. Summary of the invention

[0003] In order to solve the above technical problems, the present invention is implemented through the following technical solutions: a multi-device collaborative management system based on cloud-edge collaboration, including a device management module, which is used to connect edge devices and centrally manage edge devices, and optimize the scheduling based on the functions of different edge devices and their distribution locations; The data acquisition module is connected to the device management module and is used to collect the operating data of each edge device in real time, including operating status data, device operation data and environmental monitoring data, and classify and store the collected data; The cloud analysis module is connected to the data acquisition module to receive the classified and stored data, analyze the data of each edge device based on the big data analysis algorithm, and formulate device optimization management strategies; The collaborative control module is connected to the device management module and the cloud analysis module, and is used to collaboratively control each edge device according to the optimization management strategy generated by the cloud analysis module to ensure that the devices achieve optimal resource allocation and collaborative work during the collaborative operation process; The data feedback module is connected to the collaborative control module and is used to feed back the real-time operating status of each edge device to the cloud analysis module.

[0004] Preferably, the device management module includes: The device registration unit is used to register the identities and manage the permissions of newly added edge devices, ensuring that new edge devices can join the collaborative management after obtaining the corresponding permissions in the cloud-edge collaborative system; The device scheduling unit is used to dynamically schedule edge devices based on their physical location, function, and load to ensure balanced workloads on each edge device. The device monitoring unit is used to monitor the operating status of each edge device in real time, identify abnormal conditions in the operation of the edge device and generate device alarm signals.

[0005] Preferably, the process of the device registration unit performing identity registration and authority management on the newly added edge device is as follows: When a new edge device is connected to the system, the device registration unit first discovers the new edge device through the MQTT communication protocol and Bluetooth low energy scanning technology; after discovering the new edge device, the device registration unit verifies the identity information of the new edge device. The identity information verification includes checking the unique identifier and digital certificate of the new edge device. When the identity information provided by the new edge device matches the legal device information pre-set by the collaborative management system, the identity authentication is passed; otherwise, the registration will be rejected; during the identity information verification of the new edge device, the collaborative management system assigns a unique serial number and encryption key to each legal device. When registering, the new device must provide the correct serial number and authentication information encrypted by the key to ensure the authenticity of its identity; when the identity information of the new edge device is verified, the device registration unit assigns corresponding permissions to it according to the type, function and security level of the new edge device; permissions include access to special data and the ability to perform special operations; finally, the device registration unit stores the registration information of the new edge device in the collaborative management system database, and updates the corresponding device list and permission management data; in this way, other modules in the system can effectively collaboratively manage the newly registered edge devices based on this information.

[0006] Preferably, the process of the device scheduling unit dynamically scheduling the edge device is as follows: The device scheduling unit first obtains the physical location information of the edge device through network communication based on the GPS positioning data reported by the edge device itself; then, through the device information reported by the edge device, it understands the functional characteristics of the edge device and determines the function of the edge device; then, it continuously monitors the load of the edge device, which includes CPU usage, memory occupancy, and network bandwidth usage; during this period, the sensor device can periodically send information packets containing its own location coordinates and current working status to the device scheduling unit; when a new work task appears, the device scheduling unit analyzes the work task; determines the functional type of the edge device required for the work task, the requirements for response time, and the amount of data processing, and obtains the task analysis results; for example, a video surveillance task may require a device with a high-definition camera function and has high real-time requirements, and the device scheduling unit will screen suitable equipment based on these requirements; according to the task Based on the service analysis results and the collected device information, the device scheduling unit screens out qualified edge devices; then gives priority to edge devices that are physically close to the task execution area to reduce data transmission delays; at the same time, the edge devices are screened based on whether their functions match the task requirements and whether the load conditions are within an acceptable range; after screening out the corresponding edge devices, the device scheduling unit makes a scheduling decision; determines that the corresponding edge devices participate in the execution of the work tasks and allocates the corresponding task shares; when multiple devices are required to complete the task collaboratively, the device scheduling unit is used to coordinate the communication and data interaction between edge devices; the device starts to execute the work tasks based on the scheduling decision, and the device scheduling unit continuously monitors the status of the edge devices during the execution of the work tasks; when it is found that the edge device fails, the load is too high or other abnormal conditions occur, adjustments are made, and the edge device screening and scheduling decisions are re-made to ensure the smooth progress of the task.

[0007] Preferably, the process of the equipment monitoring unit identifying abnormal conditions in equipment operation and generating equipment alarm signals is as follows: The device monitoring unit continuously collects the operating status data of the edge device through the communication connection established with each edge device; the operating status data includes the CPU usage rate, memory occupancy rate, network bandwidth usage and sensor readings of the edge device; at the same time, the working mode and connection status of the edge device are also collected; for example, for an industrial sensor device, the monitoring unit will obtain its measurement data and status information of whether the device is normally connected to the network in real time; then analyze based on the collected edge device operating status data; through the set thresholds and rules, determine whether the edge device is in normal operating state; at the same time, use data analysis algorithms such as trend analysis and pattern recognition to identify potential abnormal situations; when the analysis results show that the operating status of the device does not meet the normal standards, the device monitoring unit determines it as an abnormal situation; abnormal situations include hardware failures, software errors, communication problems, etc.; when an abnormality is identified, the device monitoring unit immediately generates an alarm signal, which may include the identification information of the device, the type of abnormality, the severity, etc.; at the same time, different alarm methods are adopted according to the severity of the abnormality, and different alarm methods include sound alarms, pop-up window prompts, sending emails and SMS notifications; The device management module also includes a device resource allocation unit, which is used to realize dynamic allocation of computing resources, storage resources and network resources among devices, so that the devices can flexibly respond to changes in resource requirements, avoid stopping the device from working due to insufficient resources or reducing system efficiency due to resource waste; The device management module also includes a device resource allocation unit for implementing dynamic allocation of computing resources, storage resources and network resources among devices.

[0008] Preferably, in the multi-device collaborative management system based on cloud-edge collaboration, the optimization process for optimizing the data transmission path for network resources to ensure efficient use of bandwidth is as follows: First, use network detection tools to understand whether the connection between different edge devices is wired or wireless, as well as the maximum transmission speed and average delay time of each link, determine the connection relationship between each device, the bandwidth capacity, delay and reliability of the network link, and obtain the network topology analysis results; Continuously monitor the data traffic in the network, including the amount of data sent and received by each edge device and the time distribution of the traffic. At the same time, use the traffic prediction algorithm to predict the trend of network traffic changes in the future based on historical traffic data and current task requirements, and obtain the results of traffic monitoring prediction. Then, based on the results of network topology analysis and traffic monitoring prediction, the shortest path algorithm is used to determine the optimal path for data transmission. The shortest path algorithm takes into account factors such as the path's bandwidth, delay, reliability, and current traffic load. During data transmission, the network status and changes in task requirements are continuously monitored; if it is found that the current data transmission path is no longer optimal, or if network failure or traffic congestion occurs, dynamic adjustments are made in a timely manner; In order to further improve bandwidth utilization, a load balancing strategy is adopted; data traffic is evenly distributed to different network paths to avoid some paths being overloaded while other paths are idle.

[0009] Preferably, the data acquisition module includes: Data collection terminal, which is deployed on each edge device to collect the operating data of the edge device in real time; The data preprocessing unit is used to preprocess the collected operation data. The preprocessing includes denoising, data format conversion and abnormal data removal to obtain the original data and ensure that the data transmitted to the cloud analysis module is accurate and reliable; The data storage unit is used to classify and store the raw data obtained after preprocessing. The classified stored data includes the operating status data, historical operation data and environmental monitoring data of the edge device. It is also used for two-way transmission of data between the edge device and the cloud, so that data can be seamlessly shared between the cloud and edge devices.

[0010] Preferably, the cloud analysis module includes: A big data analysis unit, used to analyze the working status of each edge device based on the original data of the edge device and generate an operation plan; The prediction analysis unit is used to predict the future operation trend of the equipment based on the historical operation data and the current working status data, obtain the prediction results, and identify the possible abnormalities and failures of the equipment in advance; The optimization decision unit is used to formulate equipment optimization management strategies based on the operation plan generated by the big data analysis unit and the prediction results obtained by the prediction analysis unit, so as to optimize the collaborative working efficiency between the equipment; The feedback processing unit is used to receive feedback reports from each edge device in real time, adjust and optimize the new management strategy generated by the decision-making unit, and ensure that the management strategy can be dynamically adjusted at any time according to the actual operation of the device; The process of the big data analysis unit generating the equipment operation optimization plan is as follows: Based on the original data, extract the features reflecting the working status of the equipment from the classified and stored data; for performance indicator data, extract the mean, variance, maximum and minimum values ​​within the time period; for sensor data, extract the change trend and periodicity of the data; for operation log data, extract the running time of the equipment and the number of task executions; Then, the feature importance evaluation method of random forest is used to perform feature selection on the extracted features and remove redundant and irrelevant features to improve the efficiency and accuracy of the analysis; Then, the KMeans clustering algorithm is used to divide the devices into different clusters according to their feature vectors. Each cluster represents a specific type of working status, and edge devices with similar working status are grouped. Through cluster analysis, the distribution of working status of devices can be better understood, providing a basis for subsequent optimization. Then, a nonlinear regression model is used to predict future resource requirements based on the historical resource utilization data of the equipment, and a clustering algorithm is used to group equipment with similar working conditions for targeted optimization. For sensor data and performance indicators with time series characteristics, time series analysis methods are used for prediction and anomaly detection. Based on the results of data analysis, the working status of the equipment is evaluated and evaluation indicators are set; the current status of the equipment is determined by comparing the actual characteristics of the equipment with the standard values ​​of the evaluation indicators, and the current status of the equipment is evaluated and divided into equipment with problems and equipment without problems; For devices that are evaluated to have problems, problem diagnosis is performed to obtain problem diagnosis results, and the root cause of the problem is determined by combining the device's characteristic data, operation logs, and the results of the data analysis model; when the performance of the device is poor, the resource utilization rate, network communication status, and software operation logs of the device are analyzed to determine the inducing factors of the poor performance, wherein the inducing factors include hardware failure, software error, and insufficient resources; According to the working status evaluation and problem diagnosis results of the edge device, formulate corresponding management strategies; management strategies include device configuration adjustment, task scheduling optimization, resource allocation adjustment, and software upgrade; convert management strategies into operation plans for corresponding edge devices, and evaluate the operation plans; during the evaluation period, evaluate the feasibility and cost-effectiveness of the plan and the improvement effect on device performance; for a software upgrade plan, it is necessary to consider the risks of the upgrade, the impact on the normal operation of the device, and the expected performance improvement after the upgrade; generate an operation plan for a certain device, including adjusting the power management settings of the device to reduce energy consumption, optimizing the cooling system of the device to improve stability, and evaluating the cost savings and performance improvement effects after the implementation of the operation plan; implement the operation plan on the corresponding edge device, and continuously monitor the operating status of the edge device to verify and compare the effectiveness of the operation plan; when it is found that the operation plan is not effective, adjust and improve the operation plan in a timely manner; after implementing the software upgrade plan, observe whether the performance indicators of the device are improved. If the expected effect is not achieved, further analyze the reasons and make adjustments; for example, after implementing a network resource allocation optimization plan, evaluate the implementation effect of the plan by monitoring indicators such as the network delay and upload and download speed of the device; The process of the prediction analysis unit predicting the future operation trend of the equipment based on the historical operation data and the current working status data is as follows: First, collect the historical operation data and current working status data of the equipment; the historical operation data includes the performance indicators, sensor data and operation logs of the equipment in the past time period; the current working status data is obtained through the data acquisition terminal, including the current values ​​of various performance indicators and the connection status of the equipment; sort out the collected data, remove abnormal values ​​and noise, and ensure the accuracy and reliability of the data; for example, remove abnormal values ​​in the temperature sensor data that obviously deviate from the normal range; then extract key features based on the historical operation data and the current working status data, and the key features include statistical features, trend features, and periodic features of the data; according to the functions and prediction requirements of the edge device, train it based on the historical operation data based on the time series analysis model, adjust the parameters of the model, so that the time series analysis model can accurately fit the historical data; input the extracted key features into the trained time series analysis model to predict the future operation trend of the equipment and obtain the prediction results; the prediction content includes the performance indicator change trend of the equipment, the possible failure risk, and the change of resource demand; Evaluate the forecast results by comparing them with the actual data and calculating the forecast error; judge their accuracy and reliability; when the forecast results are not ideal, analyze the reasons and adjust the parameters of the time series analysis model, add more features and choose a different forecast model; In the multi-device collaborative management system based on cloud-edge collaboration, the simplified process of the optimization decision unit further optimizing the optimization management strategy into a new management strategy is as follows: Obtain the operation plan generated by the big data analysis unit and the prediction results obtained by the prediction analysis unit, and obtain the real-time operation data of each edge device; evaluate the existing optimization management strategy, and analyze the effectiveness and adaptability of the optimization management strategy under the current edge device operation; consider whether the strategy can meet the actual needs of the equipment and whether it can achieve the goal of improving collaborative work efficiency; compare the differences between the prediction results and the current actual operation status, as well as the differences between the existing optimization management strategy and the actual needs; determine the direction that needs to be adjusted and optimized; formulate an adjustment plan based on the results of the difference analysis; determine the direction and specific measures of the adjustment; including adjustment of equipment task allocation, optimization of resource allocation and improvement of collaborative workflow; generate a new management strategy based on the adjustment plan; the new management strategy should fully consider the results of big data analysis and predictive analysis, as well as the real-time operation of the equipment; ensure that the new management strategy can better adapt to the dynamic changes of the equipment and improve the efficiency of collaborative work; after the implementation of the new management strategy, continuously monitor the operation of the equipment to verify the effectiveness of the new management strategy; if problems are found in the new management strategy or it fails to achieve the expected results, provide timely feedback and adjustments.

[0011] Preferably, the collaborative control module includes: The device control unit is used to issue instructions to each edge device according to the optimization management strategy generated by the cloud analysis module to ensure that each edge device can perform work tasks according to the optimal strategy; The load balancing unit is used to distribute the load between edge devices to ensure that the workload of the devices is balanced during the collaborative work process, avoiding overloading or idleness of some devices; Fault-tolerant control unit, used to automatically start redundant edge devices or adjust the work tasks of other edge devices when edge devices fail or performance degrades, to ensure the stability and reliability of the overall operation of the system; The real-time adjustment unit is used to dynamically adjust the work tasks of the edge devices according to the real-time operation status of the edge devices to ensure the flexibility and efficiency of the system operation.

[0012] Preferably, the data feedback module includes: A feedback analysis unit, which is used to analyze the operation data, identify potential problems in the edge device execution management strategy, and generate feedback reports; The feedback transmission unit is used to transmit the feedback report generated by the feedback analysis unit to the cloud analysis module, so that the cloud analysis module can perform further optimization analysis and strategy adjustment based on the feedback report.

[0013] The present invention provides a multi-device collaborative management system based on cloud-edge collaboration, which has the following beneficial effects: 1. The multi-device collaborative management system based on cloud-edge collaboration centrally manages and optimizes the scheduling of edge devices through the device management module, and dynamically schedules the devices according to the physical location, function and load conditions of the devices, so that the devices can perform tasks at the most appropriate time and place, greatly improving the collaborative work efficiency between multiple devices. The collaborative control module can collaboratively control each edge device according to the optimization management strategy generated by the cloud analysis module to ensure coordinated work between devices. In other words, the system has intelligent decision-making capabilities when facing complex tasks and dynamically changing environments. It can automatically adjust the collaborative strategy according to real-time conditions without human intervention, thereby realizing collaborative management and control between multiple devices, and the intelligent decision-making capability is guaranteed.

[0014] 2. In this multi-device collaborative management system based on cloud-edge collaboration, the device resource allocation unit can realize dynamic allocation of computing resources, storage resources and network resources among devices; reasonably allocate resources according to the actual needs and load conditions of the equipment to avoid resource waste and bottleneck problems; for network resources, optimize the data transmission path to ensure efficient use of bandwidth; adopt technologies such as the shortest path algorithm to select the optimal data transmission path based on factors such as network topology and traffic monitoring prediction results to improve network transmission efficiency and reduce delays and congestion; the device monitoring unit can monitor the operating status of each edge device in real time, identify abnormal conditions in device operation and generate device alarm signals; promptly detect equipment failure, excessive load or other abnormal conditions so as to take corresponding measures to deal with them and ensure the stable operation of the system; the fault-tolerant control unit in the collaborative control module can start the working tasks of redundant edge devices when an edge device fails to ensure the continuous operation of the system; the predictive analysis unit predicts the future operating trend of the equipment based on historical operating data and current working status, providing forward-looking decision support for equipment management.

[0015] 3. In this multi-device collaborative management system based on cloud-edge collaboration, the data acquisition module can collect the operating data of each edge device in real time, and classify and store the collected data; at the same time, the data storage unit also supports two-way transmission of data between edge devices and the cloud, so that data can be seamlessly shared between the cloud and edge devices; this provides a comprehensive and accurate data basis for multi-device collaborative management, and promotes information exchange and collaborative work between devices; the optimization decision-making unit can formulate equipment optimization management strategies based on the operating plans generated by the big data analysis unit and the prediction results obtained by the prediction analysis unit, and continuously optimize and adjust according to the real-time operating conditions of each edge device; this dynamic optimization decision-making mechanism enables the system to adapt to the ever-changing equipment operating environment and task requirements, and achieve more efficient multi-device collaborative management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of a multi-device collaborative management system based on cloud-edge collaboration in the present invention. DETAILED DESCRIPTION

[0017] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0018] like Figure 1 As shown, the present invention provides a technical solution: a multi-device collaborative management system based on cloud-edge collaboration, including a device management module, which is used to connect edge devices and centrally manage edge devices, and optimize the scheduling based on the functions of different edge devices and their distribution locations; The data acquisition module is connected to the device management module and is used to collect the operating data of each edge device in real time, including operating status data, device operation data and environmental monitoring data, and classify and store the collected data; The cloud analysis module is connected to the data acquisition module to receive the classified and stored data, analyze the data of each edge device based on the big data analysis algorithm, and formulate device optimization management strategies; The collaborative control module is connected to the device management module and the cloud analysis module, and is used to collaboratively control each edge device according to the optimization management strategy generated by the cloud analysis module to ensure that the devices achieve optimal resource allocation and collaborative work during the collaborative operation process; The data feedback module is connected to the collaborative control module and is used to feed back the real-time operating status of each edge device to the cloud analysis module.

[0019] The device management module includes: The device registration unit is used to register the identities and manage the permissions of newly added edge devices, ensuring that new edge devices can join the collaborative management after obtaining the corresponding permissions in the cloud-edge collaborative system; The device scheduling unit is used to dynamically schedule edge devices based on their physical location, function, and load to ensure balanced workloads on each edge device. The device monitoring unit is used to monitor the operating status of each edge device in real time, identify abnormal conditions in the operation of the edge device and generate device alarm signals.

[0020] The process of the device registration unit performing identity registration and authority management on the newly added edge device is as follows: When a new edge device is connected to the system, the device registration unit first discovers the new edge device through the MQTT communication protocol and Bluetooth low energy scanning technology. After discovering the new edge device, the device registration unit verifies the identity information of the new edge device. The identity information verification includes checking the unique identifier and digital certificate of the new edge device. When the identity information provided by the new edge device matches the legal device information pre-set by the collaborative management system, the identity authentication is passed; otherwise, the registration will be rejected; when the identity information of the new edge device is verified, the device registration unit assigns corresponding permissions to it according to the type, function and security level of the new edge device; permissions include access to special data and the ability to perform special operations; individual edge devices will be given higher permissions, able to access sensitive data and perform important control operations, while ordinary edge devices will only have limited permissions to ensure the security and stability of the system; finally, the device registration unit stores the registration information of the new edge device in the collaborative management system database, and updates the corresponding device list and permission management data; in this way, other modules in the system can effectively collaboratively manage the newly registered edge devices based on this information; when other devices need to communicate or collaborate with the newly registered device, they can query its permissions and connection information from the database to ensure the security and smoothness of communication.

[0021] It should be further explained that during the specific implementation process, during the identity verification of newly added edge devices, the collaborative management system assigns a unique serial number and encryption key to each legitimate device. When registering, the new device must provide the correct serial number and authentication information encrypted by the key to ensure the authenticity of its identity.

[0022] The process of the device scheduling unit dynamically scheduling the edge device is as follows: The device scheduling unit first obtains the physical location information of the edge device through network communication based on the GPS positioning data reported by the edge device itself; then, through the device information reported by the edge device, it understands the functional characteristics of the edge device and determines the function of the edge device. For example, the device information reported by the edge device mentions whether the edge device is a sensor, actuator or computing device; then, the load of the edge device is continuously monitored, and the load includes CPU usage, memory occupancy and network bandwidth usage; during this period, the sensor device can regularly send information packets containing its own location coordinates and current working status to the device scheduling unit; when a new work task appears, the device scheduling unit analyzes the work task; determines the functional type of the edge device required for the work task, the response time requirements and the data processing volume, and obtains the task analysis results; for example, a video surveillance task may require a device with a high-definition camera function and has high real-time requirements. The device scheduling unit will screen suitable devices based on these requirements.

[0023] Based on the task analysis results and the collected device information, the device scheduling unit selects qualified edge devices; then gives priority to edge devices that are physically close to the task execution area to reduce data transmission delays; at the same time, the edge devices are screened based on whether their functions match the task requirements and whether the load conditions are within an acceptable range; for a task to monitor the environment in a specific area, the device scheduling unit will select edge devices with lighter loads and environmental monitoring functions near the area to allocate corresponding work tasks; after selecting the corresponding edge devices, the device scheduling unit makes a scheduling decision; determines the corresponding edge devices to participate in the execution of the work tasks and allocates the corresponding task shares; when multiple devices need to collaborate When completing a task, the device scheduling unit is used to coordinate the communication and data interaction between edge devices. For a complex data processing task, multiple computing devices may be scheduled to perform calculations together and determine the distribution of data and the way to summarize the results. The device starts to execute the work task based on the scheduling decision, and the device scheduling unit continuously monitors the status of edge devices during the execution of the work task. When an edge device is found to have a fault, excessive load or other abnormal conditions, adjustments are made and edge device screening and scheduling decisions are re-made to ensure the smooth progress of the task. In other words, when a device suddenly fails during the execution of a task, the device scheduling unit will quickly select other suitable edge devices as replacements and reallocate the work task.

[0024] It should be further explained that, in the specific implementation process, edge devices are centrally managed and optimized through the device management module, and dynamically scheduled according to the physical location, function and load of the equipment, so that the equipment can perform tasks at the most appropriate time and place, greatly improving the efficiency of collaborative work among multiple devices; for example, in a smart factory, equipment with different functions can respond and collaborate quickly according to the needs of production tasks, reducing production time and costs.

[0025] The process of the equipment monitoring unit identifying abnormal conditions in equipment operation and generating equipment alarm signals is as follows: The device monitoring unit continuously collects the operating status data of the edge device through the communication connection established with each edge device; the operating status data includes the CPU usage rate, memory occupancy rate, network bandwidth usage and sensor readings of the edge device; at the same time, the working mode and connection status of the edge device are also collected; for example, for an industrial sensor device, the monitoring unit will obtain its measurement data and status information of whether the device is normally connected to the network in real time; then analyze based on the collected edge device operating status data; through the set thresholds and rules, determine whether the edge device is in normal operation; for example, if the CPU usage rate exceeds a certain percentage, or the sensor reading exceeds the preset reasonable range, it may be regarded as an abnormal situation; at the same time, use data analysis algorithms, such as trend analysis, pattern recognition, etc., to identify potential abnormal situations; for example, by observing the changing trend of sensor data, if there is a sudden and large fluctuation, it may indicate that there is a problem with the device; when the analysis results show that the operating status of the device does not meet the normal standard, the device monitoring unit determines it as an abnormal situation; abnormal situations include hardware failure, software error, communication problems, etc.; for example, if the device suddenly loses connection, or continuously outputs wrong sensor data, it is identified as an abnormal situation; when an abnormality is identified, the device monitoring unit immediately generates an alarm signal.

[0026] The alarm signal may include the identification information of the device, the type of abnormality, the severity, etc. At the same time, different alarm methods are adopted according to the severity of the abnormality, and the different alarm methods include sound alarm, pop-up window prompt, email and SMS notification. For example, for serious equipment failure, a strong sound alarm will be issued and an SMS notification will be sent to the administrator at the same time, so that timely measures can be taken to deal with it. The device management module also includes a device resource allocation unit, which is used to realize dynamic allocation of computing resources, storage resources and network resources among devices, so that the devices can flexibly respond to changes in resource requirements, avoid stopping the device from working due to insufficient resources or reducing system efficiency due to resource waste; The device management module also includes a device resource allocation unit for implementing dynamic allocation of computing resources, storage resources and network resources among devices.

[0027] In the multi-device collaborative management system based on cloud-edge collaboration, the optimization process for optimizing the data transmission path for network resources to ensure efficient use of bandwidth is as follows: First, use network detection tools to understand whether the connection between different edge devices is wired or wireless, as well as the maximum transmission speed and average delay time of each link, determine the connection relationship between each device, the bandwidth capacity, delay and reliability of the network link, and obtain the network topology analysis results; Continuously monitor the data traffic in the network, including the amount of data sent and received by each edge device and the time distribution of the traffic; at the same time, use the traffic prediction algorithm to predict the trend of network traffic changes in the future based on historical traffic data and current task requirements, and obtain the results of traffic monitoring prediction; for example, by analyzing the traffic pattern of a device in a specific time period in the past, predict the amount of traffic that the device may generate in a similar time period; Then, based on the results of network topology analysis and traffic monitoring prediction, the shortest path algorithm is used to determine the optimal path for data transmission. The shortest path algorithm considers factors including the bandwidth, delay, reliability and current traffic load of the path. For example, when using the Dijkstra algorithm, the bandwidth of the path is used as the weight, and the path with the largest bandwidth and the smallest delay is selected as the data transmission path. During data transmission, the network status and changes in task requirements are continuously monitored; if it is found that the current data transmission path is no longer optimal, or if there is a network failure or traffic congestion, dynamic adjustments are made in a timely manner; for example, if the bandwidth of a link suddenly drops or a failure occurs, a new optimal path is recalculated and selected for data transmission; In order to further improve bandwidth utilization, a load balancing strategy is adopted; data traffic is evenly distributed to different network paths to avoid overloading some paths while leaving other paths idle; that is, when multiple devices send a large amount of data at the same time, data can be distributed to different paths for transmission based on the bandwidth and load of the paths to ensure load balancing of the entire network; The device resource allocation unit can realize the dynamic allocation of computing resources, storage resources and network resources between devices; according to the actual needs and load conditions of the devices, resources can be reasonably allocated to avoid resource waste and bottleneck problems.

[0028] The data acquisition module comprises: Data collection terminal, which is deployed on each edge device to collect the operating data of the edge device in real time; The data preprocessing unit is used to preprocess the collected operation data. The preprocessing includes denoising, data format conversion and abnormal data removal to obtain the original data and ensure that the data transmitted to the cloud analysis module is accurate and reliable; The data storage unit is used to classify and store the raw data obtained after preprocessing. The classified stored data includes the operating status data, historical operation data and environmental monitoring data of the edge device. It is also used for two-way transmission of data between the edge device and the cloud, so that data can be seamlessly shared between the cloud and edge devices.

[0029] It should be further explained that, in the specific implementation process, the data acquisition module can collect the operating data of each edge device in real time, and classify and store the collected data; at the same time, the data storage unit also supports two-way transmission of data between edge devices and the cloud, so that data can be seamlessly shared between the cloud and edge devices; this provides a comprehensive and accurate data foundation for the collaborative management of multiple devices, and promotes information exchange and collaborative work between devices; for example, in a smart home system, different smart devices can achieve more intelligent scene linkage and services by sharing data.

[0030] The cloud analysis module includes: A big data analysis unit, used to analyze the working status of each edge device based on the original data of the edge device and generate an operation plan; The prediction analysis unit is used to predict the future operation trend of the equipment based on the historical operation data and the current working status data, obtain the prediction results, and identify the possible abnormalities and failures of the equipment in advance; The optimization decision unit is used to formulate equipment optimization management strategies based on the operation plan generated by the big data analysis unit and the prediction results obtained by the prediction analysis unit, so as to optimize the collaborative working efficiency between the equipment; The feedback processing unit is used to receive feedback reports from each edge device in real time, adjust and optimize the new management strategy generated by the decision-making unit, and ensure that the management strategy can be dynamically adjusted at any time according to the actual operation of the device; The process of the big data analysis unit generating the equipment operation optimization plan is as follows: Based on the original data, extract features that reflect the working status of the equipment from the classified and stored data; for performance indicator data, extract the mean, variance, maximum and minimum values ​​within the time period; for sensor data, extract the change trend and periodicity of the data; for operation log data, extract the running time of the equipment and the number of task executions; for example, extract features such as the average usage rate and standard deviation of the usage rate in the past hour from the CPU usage rate data, and extract features such as the rising trend and fluctuation range of the temperature from the temperature sensor data; Then, the feature importance evaluation method of random forest is used to select the extracted features and remove redundant and irrelevant features to improve the efficiency and accuracy of the analysis; that is, by calculating the correlation between each feature and the working status of the equipment, the features with higher correlation can be selected for subsequent analysis; Then, the KMeans clustering algorithm is used to divide the devices into different clusters according to their feature vectors. Each cluster represents a specific type of working status, and edge devices with similar working status are grouped. Cluster analysis can better understand the distribution of the working status of the devices and provide a basis for subsequent optimization. For example, a group of industrial devices is clustered according to their temperature, CPU usage and other characteristics, and divided into three clusters of high load, medium load and low load. Then, a nonlinear regression model is used to predict future resource requirements based on the historical resource utilization data of the equipment, and a clustering algorithm is used to group equipment with similar working status for targeted optimization. For example, a nonlinear regression model is established to predict the relationship between the equipment's response time and CPU utilization and network latency; the relationship between the equipment's performance indicators (such as response time, throughput, etc.) and influencing factors (such as equipment load, network status, etc.); the performance of the equipment under different conditions is predicted to provide a reference for resource allocation and task scheduling; among them, for sensor data and performance indicators with time series characteristics, a time series analysis method is used for prediction and anomaly detection; similarly, the autoregressive integral moving average model can be used to predict the temperature data of the equipment in order to detect potential overheating problems in advance; the isolation forest algorithm in the anomaly detection algorithm is used to detect abnormal changes in equipment performance indicators and detect equipment failures in a timely manner; by performing time series analysis on the equipment's network traffic data, the network traffic trend in the future period is predicted to provide a basis for network resource allocation; Based on the results of data analysis, the working status of the equipment is evaluated and evaluation indicators are set, such as equipment load level (high, medium, low), performance (excellent, good, average, poor), and health status (normal, warning, faulty). By comparing the actual characteristics of the equipment with the standard values ​​of the evaluation indicators, the current status of the equipment is determined, and the current status of the equipment is evaluated and divided into equipment with problems and equipment without problems. For example, based on indicators such as the CPU usage rate and memory occupancy rate of the equipment, the equipment load level is divided into three levels: high, medium, and low. If the CPU usage rate of a device exceeds 80% and the memory usage rate exceeds 70%, the device is considered to be in a high load state. For devices that are evaluated to have problems, problem diagnosis is performed to obtain problem diagnosis results, and the root cause of the problem is determined by combining the device's characteristic data, operation logs, and the results of the data analysis model; when the performance of the device is poor, the resource utilization, network communication status, and software operation logs of the device are analyzed to determine the inducing factors of the poor performance, wherein the inducing factors include hardware failure, software errors, and insufficient resources; for example, if the response time of a device increases significantly, by checking indicators such as network latency, CPU usage, and disk read and write speeds, it is determined whether the problem is caused by a network problem or excessive device load; Develop corresponding management strategies based on the working status assessment and problem diagnosis results of edge devices; management strategies include device configuration adjustment, task scheduling optimization, resource allocation adjustment, and software upgrade; for example, for high-load devices, consider adjusting task priorities and allocating more computing resources or storage resources; for devices with network problems, optimize network configuration and adjust data transmission paths, etc.; for example, if the temperature of a device is too high, possible management strategies include adding cooling devices, adjusting the operating frequency of the device to reduce heat, etc.; convert the management strategy into an operating plan for the corresponding edge device and evaluate the operating plan; during the evaluation period, combine the feasibility and cost-effectiveness of the plan with the effect of improving device performance; for example, for a software upgrade plan, it is necessary to consider the risks of the upgrade, the impact on the normal operation of the device, and the impact on the performance of the device. The impact of normal operation and the expected performance improvement after the upgrade; for example, generate an operation plan for a certain device, including adjusting the power management settings of the device to reduce energy consumption, optimizing the cooling system of the device to improve stability, and evaluate the cost savings and performance improvement effects after the implementation of the operation plan; implement the operation plan on the corresponding edge device, and continuously monitor the operating status of the edge device to verify and compare the effectiveness of the operation plan; when it is found that the operation plan is not effective, adjust and improve the operation plan in a timely manner; for example, after implementing the software upgrade plan, observe whether the performance indicators of the device are improved. If the expected effect is not achieved, further analyze the reasons and make adjustments; for example, after implementing a network resource allocation optimization plan, evaluate the implementation effect of the plan by monitoring the network delay, upload and download speed and other indicators of the device; The process of the prediction analysis unit predicting the future operation trend of the equipment based on the historical operation data and the current working status data is as follows: First, collect the historical operation data and current working status data of the equipment; the historical operation data includes the performance indicators of the equipment in the past time period (such as CPU usage, memory occupancy, network bandwidth usage, etc.), sensor data (such as temperature, humidity, pressure, etc.) and the operation log of the equipment; the current working status data is obtained through the data acquisition terminal, including the current values ​​of various performance indicators and the connection status of the equipment; sort out the collected data, remove abnormal values ​​and noise, and ensure the accuracy and reliability of the data; for example, remove abnormal values ​​in the temperature sensor data that obviously deviate from the normal range; then extract key features based on the historical operation data and the current working status data, the key features include statistical features of the data (such as mean, variance, maximum value, minimum value, etc.), trend features (such as rising or falling trends of the data), periodic features (if the data has periodic waves For example, extract the average usage rate and usage rate change trend in the past period of time from the CPU usage rate data; according to the function and prediction requirements of the edge device, train it with historical operation data based on the time series analysis model, adjust the parameters of the model, so that the time series analysis model can accurately fit the historical data; for example, use the time series analysis model to train the temperature data of the device, and by continuously adjusting the parameters of the model, the model can better capture the time series characteristics of the temperature data; input the extracted key features into the trained time series analysis model to predict the future operation trend of the device and obtain the prediction results; the prediction content includes the performance indicator change trend of the device, the possible failure risk, and the change of resource demand; for example, predict whether the CPU usage of the device will continue to rise in the future, or whether the device is at risk of failure due to overheating; Evaluate the prediction results by comparing them with the actual data and calculating the prediction error; judge their accuracy and reliability; when the prediction results are not ideal, analyze the reasons and adjust the parameters of the time series analysis model, add more features and choose different prediction models; for example, if it is found that the predicted CPU usage rate deviates greatly from the actual value, further analyze the data features, adjust the model parameters or try other more suitable models; In the multi-device collaborative management system based on cloud-edge collaboration, the simplified process of the optimization decision unit further optimizing the optimization management strategy into a new management strategy is as follows: Obtain the operation plan generated by the big data analysis unit and the prediction results obtained by the prediction analysis unit, and obtain the real-time operation data of each edge device; evaluate the existing optimization management strategy, and analyze the effectiveness and adaptability of the optimization management strategy under the current edge device operation conditions; consider whether the strategy can meet the actual needs of the equipment and whether it can achieve the goal of improving collaborative work efficiency; for example, check whether the provisions on equipment resource allocation in the optimization management strategy match the current equipment load; compare the difference between the prediction results and the current actual operation conditions, and the difference between the existing optimization management strategy and the actual needs; determine the direction that needs to be adjusted and optimized; for example, if the prediction results show that the equipment load will increase in the future, and the existing strategy does not have corresponding resource allocation measures, then this is a difference point that needs to be optimized; formulate an adjustment plan based on the results of the difference analysis; determine the direction and specific measures of adjustment; including adjustment of equipment task allocation, Optimization of resource allocation and improvement of collaborative workflow; for example, if it is found that some equipment may have resource shortages in the future, plan to transfer some tasks from these equipment to equipment with abundant resources; generate new management strategies based on adjustment plans; new management strategies should fully consider the results of big data analysis and predictive analysis, as well as the real-time operation of the equipment; ensure that the new management strategies can better adapt to the dynamic changes of equipment and improve collaborative work efficiency; for example, formulate new resource allocation plans, and dynamically adjust the allocation of computing resources, storage resources and network resources according to the load forecast and real-time status of the equipment; after the new management strategy is implemented, continuously monitor the operation of the equipment to verify the effectiveness of the new management strategy; if problems are found in the new management strategy or it fails to achieve the expected results, provide feedback and adjustments in a timely manner; for example, by comparing the equipment collaborative work efficiency indicators before and after the implementation of the new management strategy, evaluate the effectiveness of the new management strategy, and further optimize it according to actual conditions.

[0031] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. A multi-device collaborative management system based on cloud-edge collaboration, characterized in that: include: The device management module is used to connect edge devices and centrally manage them, optimizing the scheduling based on the functions of different edge devices and their distribution locations; The data acquisition module is connected to the device management module and is used to collect the operating data of each edge device in real time, including operating status data, device operation data and environmental monitoring data, and classify and store the collected data; The cloud analysis module is connected to the data acquisition module to receive the classified and stored data, analyze the data of each edge device based on the big data analysis algorithm, and formulate device optimization management strategies; A collaborative control module, connected to the device management module and the cloud analysis module, is used to collaboratively control each edge device according to the optimization management strategy generated by the cloud analysis module; The data feedback module is connected to the collaborative control module and is used to feed back the real-time operating status of each edge device to the cloud analysis module.

2. According to claim 1, a multi-device collaborative management system based on cloud-edge collaboration is characterized in that: The device management module includes: Device registration unit, used to register the identity and manage the permissions of newly added edge devices; Device scheduling unit, used to dynamically schedule edge devices based on their physical location, function, and load conditions; The device monitoring unit is used to monitor the operating status of each edge device in real time, identify abnormal conditions in the operation of the edge device and generate device alarm signals.

3. According to claim 2, a multi-device collaborative management system based on cloud-edge collaboration is characterized in that: The process of the device registration unit performing identity registration and authority management on the newly added edge device is as follows: When a new edge device is connected to the system, the device registration unit first discovers the new edge device through the MQTT communication protocol and Bluetooth low energy scanning technology; after discovering the new edge device, the device registration unit verifies the identity information of the new edge device. The identity information verification includes checking the unique identifier and digital certificate of the new edge device. When the identity information provided by the new edge device matches the legitimate device information pre-set by the collaborative management system, the identity authentication is passed; otherwise, the registration will be rejected; when the identity information of the new edge device is verified, the device registration unit assigns corresponding permissions to it according to the type, function and security level of the new edge device; permissions include access to special data and the ability to perform special operations; finally, the device registration unit stores the registration information of the new edge device in the collaborative management system database, and updates the corresponding device list and permission management data.

4. According to claim 3, a multi-device collaborative management system based on cloud-edge collaboration is characterized in that: The process of the device scheduling unit dynamically scheduling the edge device is as follows: The device scheduling unit first obtains the physical location information of the edge device through network communication based on the GPS positioning data reported by the edge device itself; then, through the device information reported by the edge device, it understands the functional characteristics of the edge device, determines the function of the edge device, and then continuously monitors the load of the edge device, which includes CPU usage, memory occupancy, and network bandwidth usage; when a new work task appears, the device scheduling unit analyzes the work task; determines the functional type of the edge device required for the work task, the response time requirement, and the data processing volume, and obtains the task analysis result; According to the task analysis results and the collected device information, the device scheduling unit selects qualified edge devices; then gives priority to edge devices that are physically close to the task execution area; at the same time, the edge devices are screened based on whether their functions match the task requirements and whether the load conditions are within the acceptable range; after selecting the corresponding edge devices, the device scheduling unit makes a scheduling decision; determines that the corresponding edge devices participate in the execution of the work task and allocates the corresponding task share; when multiple devices need to collaborate to complete the task, the device scheduling unit is used to coordinate the communication and data interaction between edge devices; the device starts to execute the work task based on the scheduling decision, and the device scheduling unit continuously monitors the status of the edge devices during the execution of the work task; When an edge device is found to be faulty or overloaded, adjustments are made and edge device screening and scheduling decisions are re-made.

5. According to claim 4, a multi-device collaborative management system based on cloud-edge collaboration is characterized in that: The process of the equipment monitoring unit identifying abnormal conditions in equipment operation and generating equipment alarm signals is as follows: The device monitoring unit continuously collects the operating status data of the edge devices through the communication connection established with each edge device; the operating status data includes the CPU usage rate, memory occupancy rate, network bandwidth usage and sensor readings of the edge devices; at the same time, the working mode and connection status of the edge devices are also collected; then, analysis is performed based on the collected edge device operating status data; through the set thresholds and rules, it is determined whether the edge device is in a normal operating state; when the analysis results show that the operating status of the device does not meet the normal standards, the device monitoring unit determines it as an abnormal situation; when an abnormality is identified, the device monitoring unit immediately generates an alarm signal; The device management module also includes a device resource allocation unit, which is used to realize dynamic allocation of computing resources, storage resources and network resources among devices; The device management module also includes a device resource allocation unit for implementing dynamic allocation of computing resources, storage resources and network resources among devices.

6. A multi-device collaborative management system based on cloud-edge collaboration according to claim 5, characterized in that: In the multi-device collaborative management system based on cloud-edge collaboration, the optimization process for optimizing the data transmission path for network resources to ensure efficient use of bandwidth is as follows: First, use network detection tools to understand whether the connection between different edge devices is wired or wireless, as well as the maximum transmission speed and average delay time of each link, determine the connection relationship between each device, the bandwidth capacity, delay and reliability of the network link, and obtain the network topology analysis results; Continuously monitor the data traffic in the network, including the amount of data sent and received by each edge device and the time distribution of the traffic. At the same time, use the traffic prediction algorithm to predict the trend of network traffic changes in the future based on historical traffic data and current task requirements, and obtain the results of traffic monitoring prediction. Then, based on the results of network topology analysis and traffic monitoring prediction, the shortest path algorithm is used to determine the optimal path for data transmission. The shortest path algorithm takes into account factors such as the path's bandwidth, delay, reliability, and current traffic load. During the data transmission process, continuously monitor changes in network conditions and task requirements; if it is found that the current data transmission path is no longer optimal, or if network failures or traffic congestion occur, make dynamic adjustments in a timely manner.

7. The multi-device collaborative management system based on cloud-edge collaboration according to claim 6, characterized in that: The data acquisition module comprises: Data collection terminal, which is deployed on each edge device to collect the operating data of the edge device in real time; A data preprocessing unit is used to preprocess the collected operation data, including denoising, data format conversion and abnormal data elimination to obtain original data; The data storage unit is used to classify and store the raw data obtained after preprocessing. The classified stored data includes the operating status data, historical operation data and environmental monitoring data of the edge device. It is also used for two-way transmission of data between the edge device and the cloud, so that data can be seamlessly shared between the cloud and edge devices.

8. The multi-device collaborative management system based on cloud-edge collaboration according to claim 7 is characterized by: The cloud analysis module includes: A big data analysis unit, used to analyze the working status of each edge device based on the original data of the edge device and generate an operation plan; A prediction analysis unit is used to predict the future operation trend of the equipment based on historical operation data and current working status data to obtain prediction results; An optimization decision-making unit is used to formulate an equipment optimization management strategy based on the operation plan generated by the big data analysis unit and the prediction results obtained by the prediction analysis unit; A feedback processing unit is used to receive feedback reports from each edge device in real time and adjust the new management strategy generated by the optimization decision unit; The process of the big data analysis unit generating the equipment operation optimization plan is as follows: Based on the original data, extract the features reflecting the working status of the equipment from the classified and stored data; for the performance indicator data, extract the mean, variance, maximum and minimum values ​​within the time period; for the sensor data, extract the change trend and periodicity of the data; for the operation log data, extract the running time of the equipment and the number of task executions; then use the random forest feature importance evaluation method to select the extracted features and remove redundant and irrelevant features. Then use the KMeans clustering algorithm to divide the equipment into different clusters according to the feature vector of the equipment. Each cluster represents a specific working status type, and the edge devices with similar working status are grouped; Then, a nonlinear regression model is used to predict future resource requirements based on the historical resource utilization data of the equipment, and a clustering algorithm is used to group equipment with similar working status. For sensor data and performance indicators with time series characteristics, time series analysis methods are used for prediction and anomaly detection. Based on the results of data analysis, the working status of the equipment is evaluated and evaluation indicators are set; the current status of the equipment is determined by comparing the actual characteristics of the equipment with the standard values ​​of the evaluation indicators, and the current status of the equipment is evaluated and divided into equipment with problems and equipment without problems; For devices that are evaluated to have problems, problem diagnosis is performed to obtain problem diagnosis results, and the root cause of the problem is determined by combining the device's characteristic data, operation logs, and the results of the data analysis model; when the performance of the device is poor, the resource utilization rate, network communication status, and software operation logs of the device are analyzed to determine the inducing factors of the poor performance, wherein the inducing factors include hardware failure, software error, and insufficient resources; Formulate management strategies based on the working status evaluation and problem diagnosis results of edge devices; management strategies include device configuration adjustment, task scheduling optimization, resource allocation adjustment, and software upgrade; convert management strategies into operation plans for edge devices and evaluate the operation plans; during the evaluation period, evaluate the feasibility and cost-effectiveness of the plans as well as the effect of improving device performance, implement the operation plans on edge devices, continuously monitor the operating status of edge devices, and verify and compare the effectiveness of the operation plans; when the operation plan is found to be ineffective, adjust and improve the operation plan; The process of the prediction analysis unit predicting the future operation trend of the equipment based on the historical operation data and the current working status data is as follows: First, collect the historical operation data and current working status data of the equipment; the historical operation data includes the performance indicators, sensor data and operation logs of the equipment in the past time period; the current working status data is obtained through the data acquisition terminal, including the current performance indicator values ​​and the connection status of the equipment; sort the collected data to remove outliers and noise; then extract key features based on the historical operation data and the current working status data, and the key features include statistical features, trend features, and periodic features of the data; according to the functions and prediction requirements of the edge device, train it based on the historical operation data based on the time series analysis model, adjust the parameters of the model, and make the time series analysis model fit the historical data; input the extracted key features into the trained time series analysis model to predict the future operation trend of the equipment and obtain the prediction results; the prediction content includes the performance indicator change trend of the equipment, the risk of failure, and the change of resource demand; Evaluate the forecast results by comparing them with the actual data and calculating the forecast error; when the forecast results are not ideal, analyze the reasons and adjust the parameters of the time series analysis model; In the multi-device collaborative management system based on cloud-edge collaboration, the simplified process of the optimization decision unit further optimizing the optimization management strategy into a new management strategy is as follows: Obtain the operation plan generated by the big data analysis unit and the prediction results obtained by the prediction analysis unit, and obtain the real-time operation data of each edge device; evaluate the existing optimization management strategy, and analyze the effectiveness and adaptability of the optimization management strategy under the current edge device operation conditions; compare the difference between the prediction results and the current actual operation conditions, as well as the difference between the existing optimization management strategy and the actual needs; determine the direction that needs to be adjusted and optimized; formulate an adjustment plan based on the results of the difference analysis; determine the direction and specific measures of the adjustment; including the adjustment of equipment task allocation, optimization of resource allocation and improvement of collaborative workflow; generate a new management strategy based on the adjustment plan; after the implementation of the new management strategy, continuously monitor the operation of the equipment to verify the effectiveness of the new management strategy.

9. The multi-device collaborative management system based on cloud-edge collaboration according to claim 8, characterized in that: The collaborative control module includes: The device control unit is used to issue instructions to each edge device according to the optimization management strategy generated by the cloud analysis module; A load balancing unit, used to distribute the load among edge devices; A fault-tolerant control unit, used to start the working tasks of the redundant edge devices when an edge device fails; The real-time adjustment unit is used to dynamically adjust the work tasks of the edge device according to the real-time operation status of the edge device.

10. The multi-device collaborative management system based on cloud-edge collaboration according to claim 9, characterized in that: The data feedback module comprises: A feedback analysis unit, which is used to analyze the operation data, identify potential problems in the edge device execution management strategy, and generate feedback reports; The feedback transmission unit is used to transmit the feedback report generated by the feedback analysis unit to the cloud analysis module, so that the cloud analysis module can perform further optimization analysis and strategy adjustment based on the feedback report.

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