Photovoltaic power station data operation and maintenance management system and management method thereof

Abstract

The invention relates to the technical field of digital information processing, and provides a photovoltaic power station data operation and maintenance management system, and a data exchange network service management system comprises a server, a cloud management module, an edge calculation module, a data exchange module, a performance evaluation module, an intelligent routing module and a self-adaptive data synchronization module. The performance evaluation module is used for monitoring key performance indexes of the network state of the whole system, evaluating the current running states of the cloud, the edge nodes and the network state through the key performance indexes to form an evaluation result, and feeding back the result to the cloud management module, the edge calculation module, the self-adaptive data synchronization module and the intelligent routing module; and the data exchange module is responsible for data synchronization and exchange between the cloud and the edge nodes, so that the data consistency of the edge nodes in the system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of digital information processing, and in particular to a photovoltaic power station data operation and maintenance management system and a management method thereof. Background Art

[0002] With the continuous popularization and improvement of photovoltaic power stations, the power generation operation and post-maintenance statistical management system of photovoltaic power stations must also be upgraded synchronously to ensure the real-time and accuracy of data transmission.

[0003] With the rise of edge computing, more and more computing tasks and data processing need to be completed on edge devices close to the data source. For example, the real-time transmission of operation and maintenance data of each photovoltaic power station can be achieved through collaborative computing with the network service manager built at the site to improve response speed and reduce data transmission delay.

[0004] Although the existing site network service management technology can record the network service status of a user on a certain terminal (such as a mobile phone), and provide users with continuous network services by updating the network service status when the user goes online again on another terminal (such as a computer), it still cannot utilize cloud-edge collaboration to participate in data services, resulting in low cloud-edge collaboration efficiency, difficult data synchronization and poor real-time performance.

[0005] At the same time, there are still the following problems in the prior art: 1. Currently, most systems either rely on full cloud computing or delegate some computing to the edge, but lack effective collaborative management between the two. This results in the inability to fully utilize cloud and edge computing resources, and widespread problems such as high data transmission latency and untimely synchronization.

[0006] 2. Traditional data transmission and synchronization mechanisms often adopt fixed strategies and lack flexibility. They cannot effectively respond to fluctuations in the network environment or changes in system load, resulting in low data transmission efficiency and even data loss or long delays. In addition, traditional routing algorithms are also difficult to dynamically adjust the transmission path, resulting in a waste of network resources and reduced transmission efficiency.

[0007] 3. Traditional security measures are usually based on static encryption mechanisms, which are difficult to provide effective protection in complex dynamic network environments. In addition, the distributed storage and processing of data also puts higher requirements on existing security management, and existing technologies cannot meet these requirements.

[0008] The present invention has been made to solve the common problems in this field, such as low efficiency, difficult data synchronization, poor real-time performance, low collaboration efficiency between cloud computing and edge computing, and difficulty in real-time data processing and transmission. A data management system is proposed for cloud-edge collaborative management of server sites based on the network transmission efficiency of photovoltaic power station power generation and operation and maintenance data. Summary of the invention

[0009] The purpose of the present invention is to propose a photovoltaic power station data operation and maintenance management system and a management method thereof in view of the current deficiencies.

[0010] In order to overcome the shortcomings of the prior art, the present invention adopts the following technical solutions: A photovoltaic power station data operation and maintenance management system, the data exchange network service management system includes a server, the data exchange network service management system also includes a cloud management module, an edge computing module, a data exchange module, a performance evaluation module, an intelligent routing module, and an adaptive data synchronization module, the server is respectively connected to the cloud management module, the edge computing module, the data exchange module, the performance evaluation module, the intelligent routing module, and the adaptive data synchronization module; The performance evaluation module is used to monitor the key performance indicators of the network status of the entire system, and evaluates the current operating status of the cloud, edge nodes, and network status through the key performance indicators to form an evaluation result, and feeds back the result to the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module to trigger corresponding management actions. The cloud management module is responsible for network service management and global resource scheduling. According to the feedback from the performance evaluation module, it adjusts the resource allocation strategy and coordinates the task execution of each edge node. The intelligent routing module selects the best data transmission path according to the real-time network status, edge node load and data transmission requirements. The adaptive data synchronization module dynamically adjusts the frequency, data volume and synchronization strategy of data synchronization according to the changes in the network status and system resources provided by the performance evaluation module. The data exchange module is responsible for data synchronization and exchange between the cloud and edge nodes to ensure the data consistency of each edge node in the system. The edge computing module is deployed on the edge node close to the data source, responsible for real-time processing and preliminary analysis of local data, and executes localized data synchronization tasks according to the strategy provided by the adaptive data synchronization module, and reports the processing results to the cloud.

[0011] Optionally, the data exchange module includes a data transmission unit, a cache and queue management unit, and a data synchronization control unit. The data transmission unit manages the data transmission process between the cloud and the edge nodes. The cache and queue management unit is used to temporarily store the data to be transmitted when the network condition is poor or the system resources are tight, and manage the priority of data transmission to ensure that key data is transmitted first. The data synchronization control unit is used to coordinate the data synchronization process between the cloud and each edge node to ensure the data consistency of each edge node; The data synchronization unit adjusts the synchronization frequency and data volume according to the parameters of the adaptive data synchronization module.

[0012] Optionally, the performance evaluation module includes a transmission performance monitoring unit and an evaluation unit, the transmission performance monitoring unit monitors the key performance indicators of the entire system network status, and the evaluation unit evaluates the current operating status of the cloud, edge node, and network status according to the key performance indicators to form an evaluation result; The transmission performance monitoring unit includes a bandwidth monitoring component, a delay monitoring component, a packet loss rate monitoring component, a node resource utilization monitoring component, and a network health monitoring component. The bandwidth monitoring component monitors the actual network bandwidth usage between the cloud and the edge node, detects the available bandwidth and bandwidth utilization, the delay monitoring component measures the transmission delay of data from the cloud to the edge node or from the edge node to the cloud, the packet loss rate monitoring component detects the proportion of data packets lost during data transmission, the node resource utilization monitoring component monitors the resource usage of the cloud and the edge node, and the network health monitoring component evaluates the overall health status of the network connection.

[0013] Optionally, the evaluation unit obtains the key performance indicators collected by the transmission performance monitoring unit, and calculates the service status index S(t) at time t: ; Where B(t) is the network bandwidth utilization at time t, B max is the maximum network bandwidth of the system, L(t) is the network delay at time t, P(t) is the packet loss rate at time t, and U CPU (t) is the CPU utilization at time t, U men (t) is the memory utilization at time t, H(t) is the network health status index at time t; If the service status index S(t) at time t is less than or equal to the trigger threshold S set by the system threshold , the results are fed back to the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module to trigger corresponding management actions.

[0014] Optionally, the adaptive data synchronization module includes a synchronization parameter management unit, a policy decision unit, and a synchronization control unit. The synchronization parameter management unit stores and manages the synchronization frequency, data volume, and synchronization policy used for data synchronization. The policy decision unit determines the policy adjustment of data synchronization in real time based on S(t) provided by the performance evaluation module and the current transmission status. The synchronization control unit executes the instructions of the policy decision unit to actually control the frequency, data volume, and transmission path of data synchronization.

[0015] Optionally, the intelligent routing module includes a network status acquisition unit, a node load monitoring unit, a demand analysis unit, a path optimization unit, and a routing policy management unit. The network status acquisition unit acquires the latest network status data from the performance evaluation module, the node load monitoring unit monitors the current load of each edge node, the demand analysis unit analyzes the current data transmission demand, the path optimization unit calculates the optimal data transmission path based on the network status data, edge node load and data transmission demand acquired from the performance evaluation module, and the routing policy management unit stores and manages routing strategies and selection rules under different situations.

[0016] In addition, the present invention also provides a data exchange network service management method based on cloud-edge collaboration, and the data exchange network service management method comprises the following steps: S1. Start the cloud management module, edge computing module, adaptive data synchronization module, performance evaluation module and intelligent routing module, load the initial configuration of each module, and ensure the normal operation of the communication channel and basic functions; S2, the performance evaluation module monitors the key performance indicators of the entire system network status in real time, and calculates the service status index S(t) at time t based on the key performance indicators, and uses the service status index S(t) as the comprehensive evaluation result of the current operation status of the system, and feeds back the service status index S(t) to the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module; S3, the cloud management module adjusts the global resource allocation strategy based on the feedback from the performance evaluation module to optimize the resource utilization of the cloud and edge nodes; S4, the intelligent routing module calculates the score of each available path according to the real-time network status, edge node load information and data transmission requirements provided by the performance evaluation module, selects the path with the highest score as the best path for current data transmission, and applies it to the actual data transmission process; S5, the adaptive data synchronization module dynamically adjusts the frequency, data volume and synchronization strategy of data synchronization according to the network status and system resource changes provided by the performance evaluation module; S6. The edge computing module performs localized data synchronization tasks according to the strategy of the adaptive data synchronization module, and maintains data consistency with the cloud and other edge nodes through the data exchange module; S7. During the data synchronization and transmission process, the performance evaluation module continuously monitors key performance indicators, evaluates the service status in real time, and feeds back to the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module, and repeats the operations of steps S3 to S6.

[0017] Optionally, the data exchange network service management method further includes: When the performance evaluation module detects that the service status index S(t) is lower than the emergency threshold, it immediately sends an alarm to the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module, and enables the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module to take emergency response measures according to the alarm information.

[0018] Optionally, the data exchange network service management method also includes: in step S6, after the data processing is completed, the edge computing module reports the processing results to the cloud and synchronizes the latest data to the cloud and other edge nodes.

[0019] Optionally, the data exchange network service management method further includes: in step S5, the synchronization strategy of the adaptive data synchronization module includes: an efficient synchronization strategy, a balanced synchronization strategy and a resource-saving synchronization strategy.

[0020] The beneficial effects achieved by the present invention are: 1. Through the cooperation of the performance evaluation module and the data exchange module, the reliability and efficiency of data synchronization are guaranteed under various network conditions, data loss and transmission errors are reduced, the technical defects of traditional data synchronization difficulties are overcome, and the entire system is ensured to have the advantages of strong data monitoring capabilities, good real-time performance, high cloud-edge collaboration capabilities, and high data processing and transmission efficiency.

[0021] 2. Through the cooperation between the intelligent routing module and the adaptive data synchronization module, dynamic path optimization and strategy adjustment, the collaboration efficiency between cloud computing and edge computing is improved, ensuring that the entire system has the advantages of high transmission path stability, strong dynamic adjustment capability of data transmission, good data integrity and high stability of the data transmission process; 3. Through the close collaboration between the cloud management module and the edge computing module, the resource usage of each node in the system can be monitored in real time, and the task load of the edge node can be dynamically adjusted according to the feedback information provided by the performance evaluation module to ensure that high-priority tasks are processed in a timely manner; 4. Through the coordinated cooperation of the adaptive data synchronization module and the data exchange module, the entire system can dynamically adjust the frequency, data volume and synchronization strategy of data synchronization according to the real-time network status and changes in system resources, thereby optimizing the synchronization performance, ensuring the data consistency between the cloud and edge nodes, and significantly reducing the transmission bottleneck in the synchronization process; 5. Through the cooperation of the performance evaluation module and the adaptive data synchronization module, the utilization efficiency of network resources can be optimized to the maximum extent without sacrificing the reliability of data synchronization, overcoming the synchronization failure or delay problem caused by the mismatch between data synchronization and network conditions in traditional systems, and ensuring that the entire system has the advantages of high data synchronization flexibility, significantly enhanced data synchronization efficiency, good data transmission and synchronization real-time, and strong service management reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention will be further understood from the following description in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0023] Figure 1 It is an overall block diagram of the present invention.

[0024] Figure 2 It is a block diagram of the data exchange module of the present invention.

[0025] Figure 3 Schematic diagram of the evaluation process of the performance evaluation module of the present invention.

[0026] Figure 4 It is a block diagram of the adaptive data synchronization module of the present invention.

[0027] Figure 5 A block diagram of determining an optimal path for the path optimization unit of the present invention. DETAILED DESCRIPTION

[0028] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual sizes. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0029] according to Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 As shown, this embodiment provides a photovoltaic power station data operation and maintenance management system, the data exchange network service management system includes a server, and also includes a cloud management module, an edge computing module, a data exchange module, a performance evaluation module, an intelligent routing module, and an adaptive data synchronization module. The server is connected to the cloud management module, the edge computing module, the data exchange module, the performance evaluation module, the intelligent routing module, and the adaptive data synchronization module, respectively, and the intermediate data and process data of the cloud management module, the edge computing module, the data exchange module, the performance evaluation module, the intelligent routing module, and the adaptive data synchronization module are stored in the database of the server for query or call; The performance evaluation module is used to monitor the key performance indicators of the network status of the entire system, and evaluates the current operating status of the cloud, edge nodes, and network status through the key performance indicators to form an evaluation result, and feeds back the result to the cloud management module, edge computing module, adaptive data synchronization module, and intelligent routing module to trigger corresponding management actions. The cloud management module is responsible for network service management and global resource scheduling. According to the feedback from the performance evaluation module, it adjusts the resource allocation strategy and coordinates the task execution of each edge node. The intelligent routing module selects the best data transmission path according to the real-time network status, edge node load, and data transmission requirements. The adaptive data synchronization module dynamically adjusts the frequency, data volume, and synchronization strategy of data synchronization according to the changes in the network status and system resources provided by the performance evaluation module. The data exchange module is responsible for data synchronization and exchange between the cloud and edge nodes to ensure the data consistency of each edge node in the system. The edge computing module is deployed on the edge node close to the data source, responsible for real-time processing and preliminary analysis of local data, and executes localized data synchronization tasks according to the strategy provided by the adaptive data synchronization module, and reports the processing results to the cloud. The system also includes a central processor, which is respectively connected to the cloud management module, the edge computing module, the data exchange module, the performance evaluation module, the intelligent routing module and the adaptive data synchronization module, and centrally controls the cloud management module, the edge computing module, the data exchange module, the performance evaluation module, the intelligent routing module and the adaptive data synchronization module based on the central processor, and stores the control data of the central processor in the database to improve the management efficiency and accuracy of the entire system; Optionally, the performance evaluation module includes a transmission performance monitoring unit and an evaluation unit, the transmission performance monitoring unit monitors the key performance indicators of the entire system network status, and the evaluation unit evaluates the current operating status of the cloud, edge node, and network status according to the key performance indicators to form an evaluation result; The transmission performance monitoring unit includes a bandwidth monitoring component, a delay monitoring component, a packet loss rate monitoring component, a node resource utilization monitoring component, and a network health monitoring component. The bandwidth monitoring component monitors the actual network bandwidth usage between the cloud and the edge node, detects the available bandwidth and the bandwidth utilization, the delay monitoring component measures the transmission delay of data from the cloud to the edge node or from the edge node to the cloud, the packet loss rate monitoring component detects the proportion of data packets lost during data transmission, the node resource utilization monitoring component monitors the resource usage of the cloud and the edge node, and the network health monitoring component evaluates the overall health status of the network connection; The bandwidth monitoring component measures the actual available bandwidth and utilization rate through real-time traffic monitoring tools or active detection packets; the delay monitoring component monitors network delay in real time by sending time-stamped data packets and calculating round-trip time; Bandwidth and latency monitoring tools include but are not limited to the following: Pingdom, Speedtest API, Iperf, Wireshark; Among them, Pingdom is an online service that is mainly used to monitor network availability and response time; it helps monitor network latency and evaluates the stability and response time of network connections through regular Ping tests; Speedtest provides an API interface integrated into the system for real-time monitoring of the upstream and downstream speeds of network bandwidth; it helps identify network bottlenecks by testing the bandwidth from the server to the client; Iperf is a command-line tool specifically used to test network bandwidth and performance, which measures network bandwidth by establishing TCP / UDP connections and evaluates network latency and jitter; Wireshark is a network protocol analyzer that monitors network traffic in real time, analyzes network latency and packet loss, and deeply analyzes the detailed information of each data packet to help identify network problems; Of course, those skilled in the art can monitor the bandwidth using means similar to the above tools, so they will not be described in detail in this embodiment; The packet loss rate monitoring component calculates the packet loss rate by comparing the number of sent packets and received packets; The packet loss rate monitoring component includes but is not limited to the following: ThousandEyes, MTR, and Smokeping; Among them, ThousandEyes is a network monitoring platform that can monitor network performance, including packet loss rate, network latency and bandwidth, through global test nodes; it is suitable for monitoring wide area networks and Internet connections; among them, MTR is a network diagnostic tool that combines traceroute and ping functions; it can measure the packet loss rate and latency of each hop while displaying the network path, helping to identify the root cause of network problems; Smokeping is a network latency monitoring tool that helps administrators track changes in network performance by repeatedly pinging the target host and drawing historical trend charts of latency and packet loss rate; The node resource utilization monitoring component monitors memory usage in real time through a system monitoring tool or an API; The node resource utilization monitoring component includes an online tool and a local tool; The online tools include Datadog and New Relic; Among them, Datadog is a powerful cloud monitoring and analysis tool that can monitor system resource utilization (CPU, memory, disk I / O, etc.) in real time, integrate data from multiple cloud platforms, physical servers, and virtual machines, and provide a unified monitoring view; New Relic provides full-stack monitoring services to help users monitor server resource utilization in real time, including CPU, memory, disk usage, and application performance, and also provides detailed performance analysis and reporting functions; Local tools include Prometheus+Grafana and Nagios; Among them, Prometheus is an open source monitoring and alarm system, which is often used in conjunction with Grafana (a powerful data visualization tool); Prometheus can collect resource utilization data from different hosts and services, and display it in real time through the visual charts provided by Grafana; Nagios is a widely used open source monitoring system that can monitor the resource utilization of the server in real time, monitor CPU usage, memory utilization, disk space, etc., and issue alarms when abnormalities occur; The network health monitoring component calculates the jitter value by analyzing the delay difference of multiple data packets within a period of time; The network health monitoring components include but are not limited to the following: Catchpoint, Zabbix, Cacti; Among them, Catchpoint provides network monitoring services, including link health, network latency, packet loss rate, etc. It can comprehensively monitor the availability and performance of Internet services through globally distributed monitoring points; Zabbix is ​​an open source monitoring tool that supports real-time monitoring of network health, link stability, network device performance, etc. It can directly monitor the status of network devices and servers through SNMP, IPMI and other protocols; Cacti is a data monitoring and graphical tool based on RRDTool. It can monitor the health status of network devices through SNMP protocol, including link utilization, error rate and stability. Optionally, the evaluation unit obtains the key performance indicators collected by the transmission performance monitoring unit, and calculates the service status index S(t) at time t: ; Where B(t) is the network bandwidth utilization at time t, B max is the maximum network bandwidth of the system, L(t) is the network delay at time t, which is calculated by measuring the round-trip time (RTT) of the data packet from the source to the destination, and this value is directly measured, P(t) is the packet loss rate at time t, and U CPU (t) is the CPU utilization at time t, which can be directly measured by monitoring tools (e.g., free (Linux), vmstat (Linux), top and htop (Linux), and Windows Task Manager (Windows Task Manager)). men (t) is the memory utilization at time t, which can be directly measured by monitoring tools (e.g., free (Linux), vmstat (Linux), top and htop (Linux), and Windows Task Manager (Windows Task Manager)), H(t) is the network health status index at time t; Among them, the network bandwidth utilization B(t) at time t is calculated according to the following formula: ; In the formula, B max is the maximum network bandwidth of the system, in bps (bits per second), C(t) is the actual data transmission rate at time t, in bps (bits per second), and its value is calculated by the following formula: ; Where ΔD(t) is the total amount of data transmitted through the network interface within the time interval Δt, in bits. Δt is the time interval, usually in seconds. ΔD(t) is obtained by monitoring the amount of data sent and received by the network interface. Its value is collected by most network monitoring tools (such as Wireshark and Performance Monitor). Δt is the monitoring time window, and its value is set by the system according to actual needs, usually ranging from a few seconds to tens of seconds. Among them, the value of B(t) is between 0 and 1, 1 means that the current bandwidth is fully occupied; In addition, the network health status index H(t) at time t is calculated according to the following formula: ; Where J(t) is the network jitter value, and its value satisfies: , where N is the number of packets. For each pair of adjacent packets, the delay difference is calculated: , T recv (i) is the reception time of the i-th data packet; E(t) is the error rate. First, we monitor the statistical information of the network equipment or network protocol (such as TCP / IP) within a certain period of time and record the total number of packets N. total and the number of error packets N error , its value is calculated according to the following formula: ; Where N error (t) The number of recorded error packets can be directly extracted from the statistical information of network devices or network protocols (such as TCP / IP), N total (t) is the total number of packets, which is directly extracted from the statistical information of network devices or network protocols (such as TCP / IP); P(t) is the packet loss rate. First, we use network monitoring tools (such as Ping, Iperf, etc.) to monitor the sending and receiving of data packets within a period of time and record the number of lost data packets N. lost And the total number of packets sent N sent , and calculated according to the following formula: ; Where N lost (t) is the number of lost data packets, which is obtained through network monitoring tools (such as Ping, Iperf, etc.), N sent (t) is the total number of packets sent, which is obtained through network monitoring tools (such as Ping, Iperf, etc.); Sim is the link stability value. First, we monitor and record the number of network connection interruptions N within a period of time. dis , Connection duration T up and the connection interruption time T down , and evaluate the link stability index Sim(t) based on the number of interruptions and the interruption time: ; Where N dis (t) is the number of network connection interruptions, which is detected and collected by network monitoring tools or network management systems, such as Ping tools, network management systems (NMS), system logs (Syslog), and network performance monitoring tools. up (t) is the connection duration, which is detected and collected by network monitoring tools or network management systems, such as Ping tools, network management systems (NMS), system logs (Syslog), and network performance monitoring tools. down (t) is the connection interruption time, which is detected and collected by network monitoring tools or network management systems, such as Ping tools, network management systems (NMS), system logs (Syslog), and network performance monitoring tools; If the service status index S(t) at time t is less than or equal to the trigger threshold S set by the system threshold , then the results are fed back to the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module to trigger corresponding management actions; If the service status index S(t) at time t is greater than the trigger threshold S set by the system threshold , the system is in a healthy state, no management action is required, and the system continues to operate according to the current policy; Optionally, the adaptive data synchronization module includes a synchronization parameter management unit, a policy decision unit, and a synchronization control unit. The synchronization parameter management unit stores and manages the synchronization frequency, data volume, and synchronization policy for data synchronization. The policy decision unit determines the policy adjustment of data synchronization in real time according to S(t) provided by the performance evaluation module and the current transmission status. The synchronization control unit executes the instructions of the policy decision unit to actually control the frequency, data volume, and transmission path of data synchronization. The synchronization parameter management unit includes a cache memory, a non-volatile memory, a register file, a real-time clock, and a parameter controller. The cache memory is used to store temporary data of synchronization parameters, such as the most recently calculated synchronization frequency, data volume, etc., and can quickly access these data and provide them to the policy decision unit for use; the non-volatile memory is used to persistently store synchronization parameters and policy configurations, and the data of synchronization parameters and policy configurations will not be lost when the system is restarted or powered off, ensuring that the parameter configuration of the system is still available when it is started next time; the register file is used to store key parameters in the current calculation process, such as the current synchronization frequency, the current data volume, etc.; the real-time clock is used to provide an accurate timestamp for the management of synchronization parameters, ensuring that the management and update of all parameters can be carried out in an accurate time sequence; the parameter controller is used to coordinate the data flow between the cache memory, the non-volatile memory and the register file, ensuring that the synchronization parameters can be quickly and correctly accessed and updated when needed; The policy decision unit obtains the evaluation result of the evaluation module and calculates the synchronization frequency f(t): ; In the formula, f max is the maximum synchronization frequency allowed by the system (times / second), S(t) is the service status index at the current moment, S optimal The service status index under ideal conditions (usually set to 1 or the index value corresponding to the optimal operating state of the system), and its value is set by the system; Among them, when S(t) is close to S optimal When , the synchronization frequency f(t) approaches the maximum value fmax; When S(t) is lower than S optimal When , the synchronization frequency f(t) decreases accordingly, reducing the system and network load; The policy decision unit obtains the evaluation result of the evaluation module and calculates the synchronization data volume D(t): ; Where D max is the maximum amount of synchronized data allowed by the system (unit: MB or GB), S(t) is the service status index at the current moment, S optimal It is the service status index under ideal conditions, and its value is set by the system; Among them, when the current service status index S(t) is higher than the trigger threshold S set by the system threshold When , the data volume D(t) increases, making full use of system resources and network bandwidth; When the current service status index S(t) is lower than or equal to the trigger threshold S set by the system thresholdWhen , the data volume D(t) decreases to prevent network congestion or system overload; The strategy decision unit obtains the evaluation result of the evaluation module and calculates the synchronization strategy Strategy(t): ; In the formula, High Efficiency Strategy is an efficient strategy, Balanced Strategy is a balanced strategy, Low Resource Strategy is a low resource strategy, S high It is an upper threshold of the service status index S(t), indicating the lower limit of the service status index when the system is in good condition. When this value is exceeded, the system is considered to be in good condition and a strategy with higher resource consumption but better efficiency is selected. S low It is a lower limit threshold of the service status index S(t), which indicates the upper limit of the service status index when the system is in a bad state. When it is lower than this value, the system is considered to be in a bad state and needs to choose a strategy with lower resource consumption but stronger stability. In this embodiment, an upper threshold S of the service status index S(t) is high , and a lower threshold S of the service status index S(t) low The system or operator sets or takes values ​​according to actual conditions. This is a technical means well known to those skilled in the art. Those skilled in the art can refer to relevant technical manuals to learn about this technology, so it will not be described in detail in this embodiment. Among them, the specific operations of High Efficiency Strategy are: 1) Choose a transmission protocol with high reliability and high speed (such as TCP); 2) Use less data compression or no compression transmission to reduce processing time; 3) Select the shortest path or high-bandwidth path for data transmission; The specific operations of Balanced Strategy are as follows: 1) Use a more balanced transport protocol (such as compressed TCP); 2) A moderate compression algorithm to reduce the amount of data transmitted without significantly increasing processing time; 3) Choose a compromise transmission path that takes into account both transmission speed and reliability; The specific operations of Low Resource Strategy are as follows: 1) Choose a lightweight but potentially unreliable transport protocol (such as UDP); 2) Use a data compression algorithm with a higher compression ratio to reduce the amount of transmitted data; 3) Select redundant paths or paths with better stability for transmission, giving priority to ensuring transmission reliability; The synchronization control unit includes a data transmission controller, a path manager, a buffer manager, and a network interface controller. The data transmission controller is used to manage and control the actual data transmission process, including data packaging, sending, receiving and transmission rate control. The path manager receives the best path information provided by the intelligent routing module and configures the network interface to use the path for data transmission. The buffer manager is used to manage the buffer of the transmitted data to ensure that the data is not lost or disordered during the transmission process. The network interface controller provides an interface connected to the physical network and is responsible for the actual transmission of data in the network. The synchronization control unit executes the instructions of the policy decision unit according to the following steps to actually control the frequency, data volume and transmission path of data synchronization: S41, receiving instructions and initialization: The synchronization control unit receives the synchronization frequency f(t), the synchronization data volume D(t) and the synchronization strategy Strategy(t) calculated by the strategy decision unit; Initialize the required resources and hardware modules, such as network interfaces, buffers, path managers, etc., to prepare for the upcoming data synchronization; S42. Prepare data and package it: Extract the data to be synchronized from the storage or cache, and select the data block according to the specified data volume D(t); According to the synchronization strategy Strategy(t), the data is preprocessed, which includes data compression (if specified by the strategy) or encryption (if required by the strategy); Packaging the processed data into a format suitable for network transmission, usually including encapsulation into data packets (such as TCP or UDP packets); S43, Path selection and configuration: Call the path manager, receive the best path information provided by the intelligent routing module, and configure the network interface to use the path for data transmission; If the network conditions change, if the path performance degrades or is interrupted, the path manager can respond quickly and notify the intelligent routing module to re-evaluate the path to ensure transmission stability; S44, Data transmission and sending: Start data transmission and send the packaged data to the target edge node or cloud through the network interface according to the predetermined synchronization frequency f(t); During data transmission, real-time monitoring of transmission status (such as transmission rate, error rate, and packet loss rate) is performed to ensure smooth transmission. S45, receiving data and unpacking: At the destination node, the synchronous control unit at the receiving end receives the data packet and stores it in a buffer; Unpack the received data packets, extract the original data, and decrypt and decompress them as needed (if the data was previously encrypted and compressed); Verify the integrity and correctness of the data. If data is found to be damaged or lost, initiate a retransmission mechanism. S46. Error detection and handling: During the transmission process, if an error is detected (such as packet loss, transmission interruption), it is immediately reported to the policy decision unit or the upper module to start the error handling process; According to the instructions of the policy decision unit, perform data retransmission, path switching or synchronization frequency adjustment to ensure the completion of the synchronization task; S47, Confirmation and Completion: When all data packets are successfully transmitted and confirmation (ACK) is received, the synchronization task is completed; Feedback the completed transmission status to the policy decision unit and record the synchronization log for subsequent analysis and optimization; S48. Clean up and prepare for the next synchronization: Release the resources occupied during this synchronization process, such as buffer cleaning, network interface reset, etc.; Prepare the initialization work for the next synchronization cycle and reconfigure and prepare resources according to new instructions and policies; Through the cooperation between the synchronization control unit and the policy decision unit, the formulated synchronization strategy is transformed into actual operation, ensuring that data can be transmitted to the target node through the optimal path according to the specified frequency, data volume and strategy. At the same time, the entire transmission process is monitored and adjusted in real time to cope with possible network fluctuations or system resource changes, ensuring the efficiency, reliability and security of data synchronization; In this embodiment, through the cooperation of the performance evaluation module and the data exchange module, the reliability and efficiency of data synchronization are guaranteed under various network conditions, data loss and transmission errors are reduced, the technical defects of traditional data synchronization difficulties are overcome, and the entire system is guaranteed to have the advantages of strong data monitoring capability, good real-time performance, high cloud-edge collaboration capability, and high data processing and transmission efficiency; Through the cooperation of the performance evaluation module and the adaptive data synchronization module, the utilization efficiency of network resources can be optimized to the maximum extent without sacrificing the reliability of data synchronization, overcoming the synchronization failure or delay problem caused by the mismatch between data synchronization and network conditions in the traditional system, ensuring that the entire system has the advantages of high data synchronization flexibility, significantly enhanced data synchronization efficiency, good data transmission and synchronization real-time performance, and strong service management reliability; Optionally, the intelligent routing module includes a network status acquisition unit, a node load monitoring unit, a demand analysis unit, a path optimization unit, and a routing policy management unit, wherein the network status acquisition unit acquires the latest network status data from the performance evaluation module, the node load monitoring unit monitors the current load of each edge node, the demand analysis unit analyzes the current data transmission demand, the path optimization unit calculates the best data transmission path based on the network status data, node load and data transmission demand acquired from the performance evaluation module, and the routing policy management unit stores and manages routing policies and selection rules under different circumstances; The network status acquisition unit includes an interface controller, a data buffer memory, and a communication protocol processor. The interface controller is responsible for managing the communication interface with the performance evaluation module, and receives the network status data from the performance evaluation module through the interface. The data buffer memory temporarily stores the network status data received from the performance evaluation module so that the subsequent processing unit can perform analysis and calculation. The communication protocol processor processes the communication protocol to ensure that the communication with the performance evaluation module complies with the specified protocol (such as TCP / IP, HTTP, REST API, etc.); Specifically, the interface controller establishes a communication connection with the performance evaluation module, and uses a specific protocol (such as REST API call, message queue, or Socket communication) to obtain the latest network status data from the performance evaluation module; the acquired data is parsed by the communication protocol processor and stored in the data buffer memory, waiting for further processing; the network status acquisition unit passes the buffered data to the path optimization unit and the reason strategy management unit for their calculation and analysis; The node load monitoring unit includes a hardware sensor interface, a load monitor, and a data processor. The hardware sensor interface is connected to a sensor or monitoring device on the node to collect the load conditions of each edge node (such as CPU utilization, memory usage, etc.) in real time. The load monitor monitors the resource utilization of each edge node (such as CPU, memory, I / O usage) in real time and summarizes and processes the data. The data processor processes the data collected from the sensor interface and the load monitor, extracts key load indicators and formats them into usable data. Specifically, the hardware sensor interface is connected to the sensors on the edge nodes to continuously collect the usage of resources such as CPU, memory, I / O, etc. Data processing: the load monitor analyzes the real-time data and calculates various load indicators, such as average CPU utilization, memory utilization, etc. The processed data is summarized and stored by the data processing unit. Data transmission: the processed load data is transmitted through the system internal bus or network to the path optimization unit or other modules that need this information; The demand analysis unit includes a demand analysis processor, a task scheduler, and a policy buffer. The demand analysis processor is used to analyze and process data transmission requirements, including the amount of data transmitted, real-time requirements, and priority. The task scheduler schedules and prioritizes transmission tasks according to the results of the demand analysis processor to ensure that high-priority tasks are processed in a timely manner. The policy buffer stores and manages the current demand analysis results and policies for subsequent modules to call when performing path optimization or task execution. wherein the amount of data to be transmitted is obtained from the task request, wherein the amount of data to be transmitted is determined by the amount of data D(t) calculated by the policy decision unit; Real-time requirements refer to the tolerance of a task to delays and how quickly the transmission needs to be completed. If the application or user does not explicitly specify real-time requirements, the system sets a default delay tolerance or completion time limit based on the task type and priority. For example: video conferencing: the real-time requirement is low delay, usually no more than 100ms; file transfer: the real-time requirement is medium delay, which may allow a delay of 1-5 minutes. Priority refers to the relative importance of a task in the system, which determines the order in which the task is processed when resources are competing; Meanwhile, priority determination methods include: nature of task, user or application designation, and automatic system allocation; Among them, 1) when allocating tasks according to their nature: Critical tasks: such as emergency data transmission, alarm data, etc., have the highest priority and require priority processing; Ordinary tasks: such as regular file transfers, with medium priority; Background tasks: such as scheduled backups, which have a lower priority and are executed later; 2) User or application specified: User settings: When a task is initiated, the user specifies the priority of the task, and the system processes it according to the user settings; Application settings: Some applications set task priorities based on business logic; 3) Automatic allocation by the system: Dynamic adjustment: The system dynamically adjusts the priority of tasks based on the current network status, node load, task urgency, etc.; Based on historical data: The system automatically assigns appropriate priorities to new tasks based on past task processing times and urgency; In this embodiment, the priority setting is arranged in the following order: High priority: emergency event processing, real-time control data transmission; Medium priority: regular business data transmission, user requests; Low priority: backup and background synchronization tasks; The path optimization unit is based on the network status data, node load and data transmission requirements obtained from the performance evaluation module, that is, there are n paths, each path P i A set of key performance indicators x i =(B i ,L i ,P loss,i ,C i ,M i ) indicates that each path P is calculated i The comprehensive score Score (P i ): ; In the formula, B i For path P i The network bandwidth (unit: bps) of the path P is directly measured by network monitoring tools (such as iftop, nload, Wireshark) i The available bandwidth on i For path P i The network delay (unit: ms) is measured directly on the path P using tools such as ping and traceroute. i The network delay, P loss,i For path P i The packet loss rate (percentage) of the path P is directly measured by network monitoring tools (such as Wireshark or iperf). i Packet loss on C i For path P i The average CPU utilization (percentage) of the nodes passed by the path P i Use top, htop, sar and other system tools to directly measure the CPU utilization of each node on each node. i For path P i The average memory utilization (percentage) of the nodes passed through. Its value is directly measured by tools such as free, vmstat, and top. target is the maximum bandwidth, whose value is set by the system, B max，Ltarget is the minimum delay (close to 0), and its value is set by the system. loss,target is the minimum packet loss rate (close to 0), and its value is set by the system. target is the minimum CPU utilization (close to 0), and its value is set by the system. target It is the minimum memory utilization (close to 0), and its value is set by the system; The path optimization unit is configured to optimize each path P. iThe comprehensive score Score (P i ) Select the smallest path P best As the best path: ; In the formula, n is the total number of paths, Score(P1) is the comprehensive score of the first path, Score(P2) is the comprehensive score of the second path, and Score(P n ) is the comprehensive score of the nth path; The routing policy management unit includes a policy database, a policy selection engine, a policy updater, and a policy execution engine. The policy database stores all predefined routing policies and rules, including policies for different network conditions and priority rules related to transmission requirements. The policy selection engine selects the most appropriate routing policy from the policy database according to the current network status, node load, and data transmission requirements. The policy updater regularly updates and optimizes the routing policy to adapt to new network conditions and transmission requirements; this includes improvements to existing policies and the introduction of new policies. The policy execution engine actually executes the selected routing policy and controls the selection and management of data transmission paths. The routing policy management unit is also provided with a fault-tolerant mechanism. During the policy execution process, if it is found that the executed policy does not meet the expected effect or a network failure occurs, the routing policy management unit can promptly fall back to the previous stable policy or switch to the backup policy; Specific fault-tolerance mechanism measures include: saving the currently executed strategy, and when a problem occurs with the new strategy, being able to quickly roll back to the previous strategy; If the current strategy fails, immediately switch to the predefined backup strategy to ensure system continuity; In addition, the routing policy management unit is specifically implemented including the following steps: S51, strategy initialization and storage: At startup, load all predefined routing policies into the policy database; Define policy matching rules under various network states and demand conditions; S52, Strategy matching and selection: During operation, the policy selection engine selects the most appropriate policy from the policy database based on real-time network status, node load, and transmission requirements; The routing policy management unit collaborates with the path optimization unit to ensure that the selected policy can be actually applied; S53, Strategy Execution and Monitoring: The policy execution engine actually configures the routing path and transmission parameters according to the selected policy; Monitor the execution effect of the strategy in real time. If it is found that the strategy does not achieve the expected effect, the fault tolerance mechanism may be triggered for adjustment; S54, Strategy update and optimization: Regularly analyze operational data and update and optimize existing strategies based on the analysis results; System administrators can make manual policy adjustments based on business needs; S55, Fault Tolerance and Fallback: During the policy execution process, if an abnormal situation is detected, the system will automatically fall back to the last stable policy or switch to the backup policy to ensure the continuity and reliability of data transmission; In this embodiment, through the cooperation between the intelligent routing module and the adaptive data synchronization module, dynamic path optimization and strategy adjustment, the collaboration efficiency between cloud computing and edge computing is greatly improved, ensuring that the entire system has the advantages of high transmission path stability, strong data transmission dynamic adjustment capability, good data integrity and high data transmission process stability; The cloud management module includes a service registration and discovery unit, a load balancing unit, a resource monitoring and management unit, a policy management unit, and an edge node management unit. The service registration and discovery unit is responsible for managing the registration, update, and discovery of all network services in the system to ensure that the services can be correctly called and accessed. The load balancing unit is used to ensure that the requests for each service can be evenly distributed to multiple servers or nodes to avoid single-point overload. The resource monitoring and management unit monitors all resources in the system (such as CPU, memory, bandwidth, etc.) in real time and dynamically allocates resources according to demand. The policy management unit is used to manage global resource scheduling policies and adjust and optimize these policies based on feedback from the performance evaluation module. The edge node management unit is used to communicate and coordinate with each edge node to ensure that tasks are executed on the node as planned; The cloud management module is executed according to the following steps: S61, Initialization and Configuration: Start the system, and the cloud management module initializes all units (service registration and discovery unit, load balancing unit, resource monitoring and management unit, policy management unit, edge node management unit); Load configuration: load initial service registration information, resource management policies, load balancing rules, and edge node information from configuration files or databases; Connecting to the performance evaluation module, establishing communication with the performance evaluation module, and preparing to receive real-time feedback data; S62, Service Registration and Discovery: Service registration,The service registration and discovery unit receives registration requests from various network services, including information such as service name, address, port, version, etc.; Store service information and store registered service information in the service directory for subsequent query and discovery; Service discovery: When other modules or nodes in the system need to access a service, they query the service directory and return the location information of the service. Dynamic update: when the status of a service (online, offline, migration) changes, the service catalog is updated in a timely manner; S63. Resource monitoring and management: Resource monitoring,The resource monitoring and management unit regularly collects the resource usage of each server and node in the system, including CPU utilization, memory usage, network bandwidth, etc.; Resource status reporting, which summarizes monitoring data and reports the current resource status to the policy management unit through the communication interface; Resource allocation: dynamically allocate resources based on current task requirements and system load; for example, allocate more CPU and memory resources to high-priority tasks; Adjustment and optimization: according to the instructions of the policy management unit, adjust the resource allocation strategy and optimize the utilization of system resources; S64, Load Balancing: Request reception: the load balancing unit receives service requests from clients; Policy application, based on the preset load balancing strategy (such as polling, minimum number of connections, resource awareness, etc.), determines which server or node to distribute the request to; Request distribution: forwarding requests to the selected service instance to ensure that the requests can be processed in a timely manner; Dynamic adjustment: monitor the load of each service instance in real time and adjust the load balancing strategy when necessary to avoid overloading a certain instance. S65, Strategy Management: Policy loading: The policy management unit loads the global resource scheduling policy, including task scheduling priority, resource allocation rules, fault handling solutions, etc. Receiving feedback, the policy management unit receives real-time feedback data from the performance evaluation module, including the service status index S(t) and other key performance indicators; Policy adjustment: dynamically adjust resource allocation and task scheduling policies based on feedback from the performance evaluation module. For example, when the CPU utilization of a node is too high, the policy management unit instructs the resource monitoring and management unit to reallocate resources or adjust task priorities. Strategy optimization: Based on historical data and operating performance, regular optimization of existing strategies is carried out to ensure that the system is always in the best operating state; S66, Edge Node Management: Node communication: the edge node management unit maintains communication with each edge node through the network to monitor its status and task execution; Task distribution: according to the global resource scheduling strategy, task instructions are sent to appropriate edge nodes; Execution monitoring: real-time monitoring of the task execution status of edge nodes to ensure that tasks are completed as planned; record the progress of task execution and report it to the cloud management module; Fault handling: If an edge node fails or has insufficient resources, the edge node management unit will transfer the task to other available nodes to ensure successful completion of the task; S67, System operation and maintenance: Continuous monitoring: All units are running continuously to monitor service status, resource usage, and task execution status to ensure smooth operation of the system; Log recording and analysis: record system operation logs, including service call status, resource allocation decisions, task execution status, etc.; regularly analyze log data to identify potential system bottlenecks or optimization opportunities; Fault alarm and recovery: if an abnormal situation (such as service interruption or resource exhaustion) is detected, the system will issue an alarm and automatically take recovery measures; S68, System optimization and upgrade: Strategy upgrade: regularly optimize and upgrade resource scheduling and task management strategies based on operational data and feedback; Service extension: When a new service is launched or an existing service needs to be expanded, it is registered and configured through the service registration and discovery unit to ensure that the new service can be integrated and used; Resource expansion: Expand the resources of the cloud and edge nodes according to business needs (such as adding new servers or upgrading hardware) to ensure that the system can support more tasks and higher loads; In this embodiment, through the collaborative work of the service registration and discovery unit, the load balancing unit, the resource monitoring and management unit, the policy management unit and the edge node management unit, the cloud management module can realize the management of network services and the scheduling of global resources; through real-time monitoring and dynamic adjustment, it ensures that the system operates efficiently under various conditions, and continuously optimizes the strategy based on feedback to improve the overall performance and stability of the system; The edge computing module includes a data collection and preprocessing unit, a data analysis unit, and a result reporting unit. The data collection and preprocessing unit is responsible for collecting data from local data sources (such as sensors, devices or local databases) and preprocessing the data. The data analysis unit performs preliminary analysis of local data and generates key analysis results or summary data. The result reporting unit is responsible for reporting the data processing and analysis results on the edge node to the cloud management module or the central server. The edge computing module implements real-time processing and analysis of local data according to the following steps, executes data synchronization tasks according to the strategy provided by the adaptive data synchronization module, and finally reports the processing results to the cloud management module: S71, system initialization: Module startup: The edge computing module is started on the edge node and all units are initialized, including the data collection and preprocessing unit, the data analysis unit, and the result reporting unit; Resource configuration: The module configures the interface and data storage resources to connect to the local data source, and prepares for data collection and processing; S72. Data collection and preprocessing: Real-time data collection: The data collection and preprocessing unit collects data in real time from local data sources (such as sensors, devices, or local databases); the collected data includes various forms of raw data, such as sensor readings, device status, or locally generated log data; Data preprocessing: preprocess the collected data, including data cleaning (removing noise and invalid data), format conversion (such as converting data into a standardized format) and data aggregation (such as calculating averages or summarizing data); the preprocessed data is stored in the local cache for use by the data analysis unit; Among them, data cleaning, format conversion, and data aggregation are technical means well known to those skilled in the art, and thus will not be described in detail in this embodiment; S73. Data analysis: Analysis model loading: The data analysis unit loads preset analysis models or algorithms; these models include simple statistical analysis, pattern recognition algorithms, or rule-based processing; Real-time analysis: Use the loaded analytical model to analyze the pre-processed data in real time to generate key analytical results or summary data; the analysis includes anomaly detection, trend analysis or data classification; Analysis results storage: Analysis results are stored in a local database or cache and are ready for synchronization or reporting; S74. Localized data synchronization: Receiving synchronization strategy: The edge computing module receives the synchronization strategy from the adaptive data synchronization module, wherein the synchronization strategy includes information such as synchronization frequency, data volume, and synchronization path; Data selection: According to the synchronization strategy, select the data to be synchronized (which may be part of the preprocessed data or analysis results) and prepare for data synchronization; Perform data synchronization: Perform data synchronization tasks to synchronize selected data to the cloud management module or other edge nodes; synchronization is achieved through multiple communication protocols (such as MQTT, HTTP, CoAP) to ensure that data is transmitted as required; S75. Result report: Generate report documents: The result reporting unit integrates the analysis results and related information during the synchronization process to generate a standardized report document; the document includes data summary, key analysis results, synchronization status and other relevant metadata (such as timestamp, node ID); Send report: Send report documents to cloud management module or central server through secure communication channels (such as encrypted REST API or message queue); Among them, the report includes single data transmission results or periodic summary reports; Confirmation and archiving: After receiving the confirmation information from the cloud management module, the reporting process and results will be archived locally for subsequent audit and analysis; S76, dynamic adjustment and feedback processing: Receive feedback: The edge computing module receives feedback from the cloud management module or the adaptive data synchronization module, including new synchronization strategy adjustments, resource allocation suggestions, or task priority changes; Adjust processing flow: Dynamically adjust data collection, analysis and synchronization strategies based on the received feedback; for example, adjust data collection frequency, modify analysis model or change synchronization target node; Continuous optimization: The edge computing module continuously optimizes the local processing process to improve processing efficiency and the timeliness of data synchronization, ensuring seamless collaboration between edge nodes and cloud systems; Through the close collaboration between the cloud management module and the edge computing module, the resource usage of each node in the system can be monitored in real time, and the task load of the edge node can be dynamically adjusted according to the feedback information provided by the performance evaluation module to ensure that high-priority tasks are processed in a timely manner; Optionally, the data exchange module includes a data transmission unit, a cache and queue management unit, and a data synchronization control unit. The data transmission unit manages the data transmission process between the cloud and the edge nodes. The cache and queue management unit is used to temporarily store the data to be transmitted when the network condition is poor or the system resources are tight, and manage the priority of data transmission to ensure that key data is transmitted first. The data synchronization control unit is used to coordinate the data synchronization process between the cloud and each edge node to ensure the data consistency of each edge node; Wherein, the data synchronization unit adjusts the synchronization frequency and data volume according to the parameters of the adaptive data synchronization module; The data transmission unit manages the data transmission process between the cloud and the edge node according to the following steps: S11. Configure the transmission path, set the network interface parameters, ensure that the data transmission unit can establish a connection with the cloud and edge nodes, and select a suitable transmission protocol (such as TCP or UDP) and configure the protocol parameters according to the current network status and task requirements; S12, encapsulate the data to be transmitted into a suitable network transmission format, add necessary information such as the source address, destination address and check code, start the data transmission process through the selected transmission path and protocol, and send the encapsulated data packet to the target edge node; S13, Real-time monitoring and adjustment: During the transmission process, key performance indicators such as network bandwidth, delay, and packet loss rate are monitored in real time; Dynamically adjust the transmission rate according to monitoring data to ensure the stability and efficiency of the transmission process; If the network conditions change, adjust the transmission strategy or request the intelligent routing module to change the transmission path; S14, data reception and decapsulation: At the receiving end, the data packet is received and its integrity is verified, and the checksum is checked to ensure that the data has not been tampered with or damaged; Decapsulate the received valid data packets, extract the original data and pass it to the subsequent processing module or the application of the edge node; S15, Error handling and retransmission: If an error or packet loss is found during data reception, an error report or retransmission request is immediately sent to the sender; After receiving the retransmission request, the sender resends the lost or damaged data packet to ensure that the data eventually reaches the destination node intact. S16, transmission task completed: After confirming that all data packets have been transmitted without error, a confirmation message is sent to the sender to end the data transmission task; Record the log data of this transmission, including transmission time, data volume, error rate and other information, to provide a basis for subsequent analysis and optimization; The cache and queue management unit includes a cache, a cache controller, a priority queue manager, a compressor, and a decompressor. The cache is used to temporarily store data to be transmitted, especially when the network condition is poor or the system resources are tight, the cache plays the role of temporary data storage to avoid data loss or system overload, the cache controller is responsible for managing the read and write operations of the cache data, the priority queue manager is responsible for prioritizing the data in the cache and scheduling the data for transmission; the compressor is used to compress the data to be stored to reduce the data volume, and the decompressor is used to restore the original state of the data before data transmission; when the amount of cached data is large or the network bandwidth is limited, the compression module compresses the data to be stored to reduce the data volume; the decompression module restores the original state of the data before data transmission; The priority queue manager allocates data to different queues according to the importance and urgency of the data, ensuring that critical data can be processed first; The specific working process of the cache and queue management unit includes the following steps: S21, data reception and preliminary buffering: The data transmission unit stores the received data to be transmitted in the cache to cope with sudden network or system resource problems; the cache controller manages the storage location of the data and ensures that the data remains consistent in the cache; S22, priority determination and queue allocation: The priority queue manager classifies the data in the cache according to the pre-set priority rules and allocates it to the corresponding priority queue; if the network or system resources return to normal, the priority queue manager will give priority to the data in the high priority queue for transmission; S23, data compression and decompression: If the cache space is limited or the network bandwidth is tight, the compressor compresses the data to be transmitted to reduce the amount of data; when the data is ready to be transmitted, the decompressor decompresses the high-priority data to ensure that it is transmitted in its original format; S24, Dynamic Scheduling and Transmission: The priority queue manager dynamically schedules data and determines the order of data transmission based on the current network status and resource conditions; the cache controller coordinates the interaction between cache data and the data transmission unit to ensure that data is read out of the cache and transmitted in priority order; S25, cache update and release: Once the data is successfully transmitted, the cache controller releases the transmitted data from the cache to make room for storing new data to be transmitted; the storage management unit updates the cache status to ensure the rational use of cache space and data consistency; The data synchronization control unit performs the data synchronization process according to the following steps: S31. Initialize the synchronization environment: Read synchronization policy parameters from system configuration, such as synchronization frequency, data volume, and priority settings; Initialize the communication channel with each edge node to ensure smooth data transmission between the cloud and edge nodes; S32, data consistency check: The data synchronization control unit sends a consistency check request to the cloud and each edge node, requiring the node to return the verification information of the current data (such as version number, checksum or timestamp); Compare the verification information returned by each node, identify nodes with inconsistent data, and mark the data sets that need to be synchronized; S33, synchronous task scheduling: Determine the priority and order of data synchronization based on the synchronization strategy; high-priority data (such as critical business data with high real-time requirements) will be synchronized first; start the synchronization task to transfer the data to be synchronized from the cloud to the target edge node, or synchronize between edge nodes; S34, conflict detection and resolution: During the synchronization process, data modification requests are monitored in real time. If it is detected that multiple nodes are modifying the same data at the same time, the conflict detection mechanism is immediately started; the preset conflict resolution strategy is applied (such as the version with a newer timestamp overwriting the old version), the conflict is resolved, and the final data is synchronized to all nodes; S35, Data synchronization execution and monitoring: According to the scheduling order, data synchronization tasks are executed step by step to synchronize data from the cloud to each edge node or between edge nodes; synchronization progress and network status are monitored in real time to ensure that synchronization tasks are completed as expected; in case of network fluctuations or node failures, synchronization strategies are adjusted immediately or synchronization is retried; S36. Synchronous result verification: After completing data synchronization, the data synchronization control unit performs data consistency check again to verify whether the data of all nodes are consistent; if the verification passes, the synchronization log is recorded and the synchronization task is confirmed to be completed; if the verification fails, the required synchronization task is rescheduled; S37, Synchronous logging and optimization: Record detailed information of each synchronization task in the log, including synchronization time, data volume, conflict resolution process, etc.; regularly analyze the synchronization log, optimize the synchronization strategy, and improve the efficiency and reliability of the next synchronization process; In addition, the present invention also provides a data exchange network service management method based on cloud-edge collaboration, and the data exchange network service management method comprises the following steps: S1. Start the cloud management module, edge computing module, adaptive data synchronization module, performance evaluation module and intelligent routing module, load the initial configuration of each module, and ensure the normal operation of the communication channel and basic functions; S2, the performance evaluation module monitors the key performance indicators of the entire system network status in real time, and calculates the service status index S(t) at time t based on the key performance indicators, and uses the service status index S(t) as the comprehensive evaluation result of the current operation status of the system, and feeds back the service status index S(t) to the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module; S3, the cloud management module adjusts the global resource allocation strategy based on the feedback from the performance evaluation module to optimize the resource utilization of the cloud and edge nodes; S4, the intelligent routing module calculates the score of each available path according to the real-time network status, edge node load information and data transmission requirements provided by the performance evaluation module, selects the path with the highest score as the best path for current data transmission, and applies it to the actual data transmission process; S5, the adaptive data synchronization module dynamically adjusts the frequency, data volume and synchronization strategy of data synchronization according to the network status and system resource changes provided by the performance evaluation module; S6. The edge computing module performs localized data synchronization tasks according to the strategy of the adaptive data synchronization module, and maintains data consistency with the cloud and other edge nodes through the data exchange module; S7. During the data synchronization and transmission process, the performance evaluation module continuously monitors the key performance indicators, evaluates the service status in real time, and feeds back to the cloud management module, the edge computing module, the adaptive data synchronization module and the intelligent routing module, and repeats the operations of step S3 to step S6; Optionally, the data exchange network service management method further includes: When the performance evaluation module detects that the service status index S(t) is lower than the emergency threshold, it immediately sends an alarm to the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module, and enables the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module to take emergency response measures according to the alarm information; Optionally, the data exchange network service management method further includes: in step S6, after the data processing is completed, the edge computing module reports the processing result to the cloud, and synchronizes the latest data to the cloud and other edge nodes; Optionally, the data exchange network service management method further includes: in step S5, the synchronization strategy of the adaptive data synchronization module includes: an efficient synchronization strategy, a balanced synchronization strategy and a resource-saving synchronization strategy; Through the coordinated cooperation of the adaptive data synchronization module and the data exchange module, the entire system can dynamically adjust the frequency, data volume and synchronization strategy of data synchronization according to the real-time network status and changes in system resources, thereby optimizing synchronization performance, ensuring data consistency between the cloud and edge nodes, and significantly reducing the transmission bottleneck during the synchronization process.

[0030] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein are updated with the development of technology.

Claims

1. A photovoltaic power station data operation and maintenance management system, comprising a server, characterized in that: The data exchange network service management system also includes a cloud management module, an edge computing module, a data exchange module, a performance evaluation module, an intelligent routing module, and an adaptive data synchronization module, and the server is connected to the cloud management module, the edge computing module, the data exchange module, the performance evaluation module, the intelligent routing module, and the adaptive data synchronization module respectively; The performance evaluation module is used to monitor the key performance indicators of the network status of the entire system, and evaluates the current operating status of the cloud, edge nodes, and network status through the key performance indicators to form an evaluation result, and feeds back the result to the cloud management module, edge computing module, adaptive data synchronization module, and intelligent routing module to trigger corresponding management actions. The cloud management module is responsible for network service management and global resource scheduling. According to the feedback from the performance evaluation module, it adjusts the resource allocation strategy and coordinates the task execution of each edge node. The intelligent routing module selects the best data transmission path according to the real-time network status, edge node load, and data transmission requirements. The adaptive data synchronization module dynamically adjusts the frequency, data volume, and synchronization strategy of data synchronization according to the changes in the network status and system resources provided by the performance evaluation module. The data exchange module is responsible for data synchronization and exchange between the cloud and edge nodes to ensure the data consistency of each edge node in the system. The edge computing module is deployed on the edge node close to the data source, responsible for real-time processing and preliminary analysis of local data, and executes localized data synchronization tasks according to the strategy provided by the adaptive data synchronization module, and reports the processing results to the cloud. The data exchange module includes a data transmission unit, a cache and queue management unit, and a data synchronization control unit. The data transmission unit manages the data transmission process between the cloud and the edge nodes. The cache and queue management unit is used to temporarily store the data to be transmitted when the network condition is poor or the system resources are tight, and manage the priority of data transmission to ensure that key data is transmitted first. The data synchronization control unit is used to coordinate the data synchronization process between the cloud and each edge node to ensure the data consistency of each edge node; The data synchronization unit adjusts the synchronization frequency and data volume according to the parameters of the adaptive data synchronization module.

2. A photovoltaic power station data operation and maintenance management system according to claim 1, characterized in that: The performance evaluation module includes a transmission performance monitoring unit and an evaluation unit, wherein the transmission performance monitoring unit monitors the key performance indicators of the network status of the entire system, and the evaluation unit evaluates the current operating status of the cloud, edge nodes, and network status according to the key performance indicators to form an evaluation result; The transmission performance monitoring unit includes a bandwidth monitoring component, a delay monitoring component, a packet loss rate monitoring component, a node resource utilization monitoring component, and a network health monitoring component. The bandwidth monitoring component monitors the actual network bandwidth usage between the cloud and the edge node, detects the available bandwidth and bandwidth utilization, the delay monitoring component measures the transmission delay of data from the cloud to the edge node or from the edge node to the cloud, the packet loss rate monitoring component detects the proportion of data packets lost during data transmission, the node resource utilization monitoring component monitors the resource usage of the cloud and the edge node, and the network health monitoring component evaluates the overall health status of the network connection.

3. A photovoltaic power station data operation and maintenance management system according to claim 2, characterized in that: The evaluation unit obtains the key performance indicators collected by the transmission performance monitoring unit, and calculates the service status index S(t) at time t: ； Where B(t) is the network bandwidth utilization at time t, B max is the maximum network bandwidth of the system, L(t) is the network delay at time t, P(t) is the packet loss rate at time t, and U CPU (t) is the CPU utilization at time t, U men (t) is the memory utilization at time t, H(t) is the network health status index at time t; If the service status index S(t) at time t is less than or equal to the trigger threshold S set by the system threshold , the results are fed back to the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module to trigger corresponding management actions.

4. A photovoltaic power station data operation and maintenance management system according to claim 3, characterized in that: The adaptive data synchronization module includes a synchronization parameter management unit, a policy decision unit, and a synchronization control unit. The synchronization parameter management unit stores and manages the synchronization frequency, data volume, and synchronization policy used for data synchronization. The policy decision unit determines the policy adjustment of data synchronization in real time based on S(t) provided by the performance evaluation module and the current transmission status. The synchronization control unit executes the instructions of the policy decision unit to actually control the frequency, data volume, and transmission path of data synchronization.

5. A photovoltaic power station data operation and maintenance management system according to claim 4, characterized in that: The intelligent routing module includes a network status acquisition unit, a node load monitoring unit, a demand analysis unit, a path optimization unit, and a routing policy management unit. The network status acquisition unit acquires the latest network status data from the performance evaluation module, the node load monitoring unit monitors the current load of each edge node, the demand analysis unit analyzes the current data transmission demand, the path optimization unit calculates the best data transmission path based on the network status data, edge node load and data transmission demand acquired from the performance evaluation module, and the routing policy management unit stores and manages routing strategies and selection rules under different situations.

6. A management method using a photovoltaic power station data operation and maintenance management system as claimed in claim 5, characterized in that: The data exchange network service management method comprises the following steps: S1. Start the cloud management module, edge computing module, adaptive data synchronization module, performance evaluation module and intelligent routing module, load the initial configuration of each module, and ensure the normal operation of the communication channel and basic functions; S2, the performance evaluation module monitors the key performance indicators of the overall system network status in real time, and calculates the service status index S(t) at time t based on the key performance indicators, and uses the service status index S(t) as the comprehensive evaluation result of the current operating status of the system, and feeds back the service status index S(t) to the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module; S3, the cloud management module adjusts the global resource allocation strategy based on the feedback from the performance evaluation module to optimize the resource utilization of the cloud and edge nodes; S4, the intelligent routing module calculates the score of each available path according to the real-time network status, edge node load information and data transmission requirements provided by the performance evaluation module, selects the path with the highest score as the best path for current data transmission, and applies it to the actual data transmission process; S5, the adaptive data synchronization module dynamically adjusts the frequency, data volume and synchronization strategy of data synchronization according to the network status and system resource changes provided by the performance evaluation module; S6. The edge computing module performs localized data synchronization tasks according to the strategy of the adaptive data synchronization module, and maintains data consistency with the cloud and other edge nodes through the data exchange module; S7. During the data synchronization and transmission process, the performance evaluation module continuously monitors key performance indicators, evaluates the service status in real time, and feeds back to the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module, and repeats the operations of steps S3 to S6.

7. A management method for a photovoltaic power station data operation and maintenance management system according to claim 6, characterized in that: The data exchange network service management method also includes: When the performance evaluation module detects that the service status index S(t) is lower than the emergency threshold, it immediately sends an alarm to the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module, and enables the cloud management module, edge computing module, adaptive data synchronization module and intelligent routing module to take emergency response measures according to the alarm information.

8. A management method for a photovoltaic power station data operation and maintenance management system according to claim 7, characterized in that: The data exchange network service management method also includes: in step S6, after the data processing is completed, the edge computing module reports the processing result to the cloud and synchronizes the latest data to the cloud and other edge nodes.

9. A management method for a photovoltaic power station data operation and maintenance management system according to claim 8, characterized in that: The data exchange network service management method further includes: in step S5, the synchronization strategy of the adaptive data synchronization module includes: an efficient synchronization strategy, a balanced synchronization strategy and a resource-saving synchronization strategy.

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