Wireless networking method and system based on computing power cabin

Abstract

The invention relates to the technical field of computing power cabin wireless networking, in particular to a wireless networking method and system based on a computing power cabin. Comprising the following steps: S1, acquiring the number of users served by a computing power cabin, and developing corresponding wireless access points according to the number of the users; s2, a wireless multi-hop network is established, an intelligent network controller is configured at the same time, and all wireless access points, Mesh and edge computing nodes are included in a unified monitoring and management range; s3, setting a fluctuation threshold value, and performing data source classification monitoring on data transmitted between the wireless access point and the user; wireless repeaters are deployed between computing power cabins or a Mesh network function is started, a stable wireless multi-hop network is established, continuity and high reliability of data transmission are ensured, an intelligent network controller is introduced, all wireless access points and the Mesh network are monitored and managed in a unified mode, and optimal distribution and dynamic adjustment of network resources are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless networking of computing power cabins, and in particular to a wireless networking method and system based on computing power cabins. Background Art

[0002] A computing cabin cluster refers to a cluster composed of multiple container-shaped computing units that integrate power distribution facilities, computing servers, environmental monitoring equipment and other equipment. It aims to provide easily scalable and portable standardized computing services.

[0003] At present, virtualization technology is used to improve server resource utilization. Although virtualization technology can improve resource utilization, performance may be affected when facing large-scale computing tasks. Although distributed computing architecture can improve scalability, it has certain complexity in network connection and data synchronization, which increases the management difficulty and maintenance cost of the system. Therefore, a wireless networking method and system based on a computing cabin are proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a wireless networking method and system based on a computing power cabin to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, one of the objectives of the present invention is to provide a wireless networking method based on a computing cabin, comprising the following steps: S1. Obtain the number of users served by the computing cabin and develop corresponding wireless access points according to the number of users; S2. Establish a wireless multi-hop network and configure an intelligent network controller to bring all wireless access points and Mesh and edge computing nodes into unified monitoring and management scope; S3, setting a fluctuation threshold, classifying and monitoring the data source of the data transmitted between the wireless access point and the user, and then combining the historical transmission data of each data source with the real-time transmission data to perform transmission fluctuation analysis, and defining the transmission data of the data source as fluctuation data and static data according to the analysis results; S4. Install edge computing nodes according to the amount of static data, distribute the static data to the edge computing nodes, and then make regular transmission predictions for the static data, and dynamically adjust the configuration of the edge computing nodes according to the predicted data; S5. Combine the predicted data and the real-time transmission data with the fluctuation threshold to perform transmission fluctuation analysis. When the analysis result shows that it meets the fluctuation data, adjust the static data to the fluctuation data and delete the edge computing node. S6. Monitor the load status of each wireless access point and set a normal load threshold for collaborative calculation and analysis. When the load status of a wireless access point is greater than the normal load threshold, extract the wireless access points whose load status is less than the normal load threshold, and then transfer the static data until the load status of the original wireless access point returns to normal.

[0006] As a further improvement of the present technical solution, S1 accesses the data control terminal of the computing cabin to extract the number of users served by the computing cabin in the data control terminal, classifies the transmitted data according to the users, and then develops corresponding wireless access points according to the number of users, and each user corresponds to at least one wireless access point.

[0007] As a further improvement of the present technical solution, the S2 establishes a wireless multi-hop network used by wireless access points by deploying wireless repeaters between the computing power cabins and enabling the Mesh network function. At the same time, an intelligent network controller is configured to uniformly monitor and manage all wireless access points and Mesh networks. The allocation and dynamic adjustment of network resources in the computing power cabin are controlled by the intelligent network controller.

[0008] As a further improvement of the technical solution, the steps of S3 are as follows: S3.1, monitor the data transmitted between each wireless access point and the user, then analyze the data source of the transmitted data, classify it according to different data sources, and obtain the data source corresponding to the user's transmission data; S3.2, setting a data extraction time period, then extracting historical transmission data and real-time transmission data of the wireless access point according to the data extraction time period, and classifying the historical transmission data and real-time transmission data according to the data source; S3.3. Set a fluctuation threshold according to the data volume, and then perform transmission fluctuation analysis on the data source based on the historical transmission data and real-time transmission data combined with the fluctuation threshold. When the fluctuation rate between the historical transmission data and the real-time transmission data is greater than the fluctuation threshold, the transmission data of the data source is defined as fluctuating data. Conversely, when the fluctuation rate between the historical transmission data and the real-time transmission data is less than the fluctuation threshold, the transmission data of the data source is defined as static data.

[0009] As a further improvement of the technical solution, the step of S4 is as follows: S4.1. Install edge computing nodes according to the number of data sources of static data defined in S3.3, and establish data transmission between the data sources defining the static data and the edge computing nodes; S4.2. Perform regular transmission predictions on static data, and dynamically adjust the configuration of edge computing nodes based on the predicted data. When the load demand of the predicted data is greater than the current configuration of the edge computing node, increase the configuration of the edge computing node based on the load demand of the predicted data. Conversely, when the load demand of the predicted data is less than the current configuration of the edge computing node, reduce the configuration of the edge computing node based on the load demand of the predicted data.

[0010] As a further improvement of the present technical solution, S4 extracts configuration from the wireless access point and installs the edge computing node according to the extracted configuration, including adding configuration and reducing configuration, both of which are configuration allocation from the wireless access point.

[0011] As a further improvement of the technical solution, the S5 analyzes the result and shows that it meets the static data, and continues to monitor; The data source that is adjusted from static data to fluctuating data is reconnected to the wireless access point.

[0012] As a further improvement of the technical solution, the step of S6 is as follows: S6.1. Monitor the load status of each wireless access point and set a normal load threshold for collaborative computing and analysis; S6.2. When the analysis in S6.1 shows that the load state of the wireless access point is greater than the normal load threshold, the wireless access point whose load state is less than the normal load threshold is extracted, and then the static data is transferred until the load state of the original wireless access point returns to normal, and then the static data is transferred to the original wireless access point. Conversely, when the load state of the wireless access point is less than the normal load threshold, normal monitoring is maintained; The original wireless access point is the wireless access point corresponding to the user to which the static data belongs.

[0013] The second object of the present invention is to provide a wireless networking system based on a computing cabin, including any one of the wireless networking methods based on a computing cabin described above, including a network establishment unit, a dynamic configuration unit and a collaborative computing unit; The network establishment unit is used to develop wireless access points and establish a wireless multi-hop network, and configure an intelligent network controller to bring all wireless access points and Mesh and edge computing nodes into a unified monitoring and management scope; The dynamic configuration unit is used to analyze the user's data source, define the transmission data of the data source as fluctuating data and static data, and perform regular transmission prediction on the static data, and dynamically adjust the configuration of the edge computing node according to the predicted data; The collaborative computing unit is used to combine the predicted data and the real-time transmission data with the fluctuation threshold to perform transmission fluctuation analysis, while monitoring the load status of each wireless access point and setting a normal load threshold for collaborative computing analysis.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. A wireless networking method and system based on computing power cabins, which establishes a stable wireless multi-hop network by deploying wireless repeaters between computing power cabins or enabling Mesh network functions to ensure the continuity and high reliability of data transmission. Then, by introducing an intelligent network controller, all wireless access points and Mesh networks are uniformly monitored and managed to achieve optimal allocation and dynamic adjustment of network resources. After that, edge computing nodes are integrated inside key computing power cabins to accelerate data processing and response speed and reduce the pressure on the central processing unit.

[0015] 2. A wireless networking method and system based on a computing power cabin, which extracts historical transmission data and real-time transmission data of wireless access points and classifies them according to data sources, then analyzes the transmission fluctuation of the data source based on the historical transmission data and real-time transmission data combined with the fluctuation threshold, and divides the transmission data of the data source into fluctuating data and static data, providing a basis for subsequent resource management, and then installs edge computing nodes according to the number of data sources defining static data, and establishes data transmission between the data sources defining static data and the edge computing nodes, performs regular transmission predictions on static data, and dynamically adjusts the configuration of edge computing nodes according to the predicted data to improve resource utilization efficiency.

[0016] 3. A wireless networking method and system based on a computing cabin monitors the load status of each wireless access point and sets a normal load threshold for collaborative computing and analysis. When the load of a wireless access point is too high, the static data is transferred to a wireless access point with a lower load until the load of the original wireless access point returns to normal. The data is then transferred back to the original wireless access point to achieve dynamic balance and optimal allocation of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the structure of the computing module of the present invention; Figure 2 It is the overall flow chart of the present invention; Figure 3 A flowchart of the present invention for defining the transmission data of a data source as a static number; Figure 4 A flowchart of reducing the configuration of edge computing nodes according to the load demand of predicted data in the present invention; Figure 5 A flowchart of setting a normal load threshold for collaborative computing analysis in the present invention; Figure 6It is a structural principle diagram of the network establishment unit of the present invention.

[0018] The meaning of each number in the figure is: 10. Network establishment unit; 20. Dynamic configuration unit; 30. Collaborative computing unit. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] like Figure 1-5 As shown, one of the purposes of the present invention is to provide a wireless networking method based on a computing cabin, comprising the following steps: S1. Obtain the number of users served by the computing cabin and develop corresponding wireless access points according to the number of users; S1 accesses the data control terminal of the computing cabin, thereby extracting the number of users served by the computing cabin from the data control terminal, and classifies the transmitted data according to the users, and then develops corresponding wireless access points according to the number of users. Each user corresponds to at least one wireless access point. The specific steps are as follows: Access the data control terminal of the computing cabin: establish a connection with the data control terminal of the computing cabin through the interface and protocol to ensure that relevant data can be read; Extract the number of users served by the computing cabin: Use data reading tools or programming languages ​​to extract user quantity information from the data control terminal, determine the principle that each user corresponds to at least one wireless access point, and calculate the required number of wireless access points. If the number of users is n, then the number of wireless access points is at least n.

[0021] High-performance wireless access points ensure full coverage and provide sufficient bandwidth.

[0022] S2. Establish a wireless multi-hop network and configure an intelligent network controller to bring all wireless access points and Mesh and edge computing nodes into unified monitoring and management scope; S2 deploys wireless repeaters between computing cabins and enables the Mesh network function to establish a wireless multi-hop network used by wireless access points. At the same time, it configures an intelligent network controller to uniformly monitor and manage all wireless access points and Mesh networks. The allocation and dynamic adjustment of network resources in the computing cabin are controlled by the intelligent network controller. The specific steps are as follows: Deploy wireless repeaters and enable Mesh network functions: Deploy wireless repeaters at appropriate locations between computing power cabins to ensure that the signal can cover all computing power cabins. At the same time, enable the Mesh network function in the wireless access points so that each wireless access point can be connected to each other to form a multi-hop network. Configure the smart network controller: Install the smart network controller and perform initial settings to bring all wireless access points and Mesh networks under the management scope of the smart network controller; Unified monitoring and management: The intelligent network controller monitors the status of all wireless access points and Mesh networks in real time, including signal strength, bandwidth usage, number of connected devices, etc., and then uses the intelligent network controller to manage the network, such as adjusting the parameters of wireless access points and optimizing the path selection of Mesh networks. Network resource allocation and dynamic adjustment: As the network load changes, the intelligent network controller dynamically adjusts network resources to ensure efficient operation of the network. When the business volume of a computing power cabin increases, the intelligent network controller can adjust the parameters of nearby wireless access points to increase the bandwidth of the area.

[0023] S3, setting a fluctuation threshold, classifying and monitoring the data source of the data transmitted between the wireless access point and the user, and then combining the historical transmission data of each data source with the real-time transmission data to perform transmission fluctuation analysis, and defining the transmission data of the data source as fluctuation data and static data according to the analysis results; The steps for S3 are as follows: S3.1. Monitor the data transmitted between each wireless access point and the user, and then analyze the data source of the transmitted data, classify it according to different data sources, and obtain the data source corresponding to the user's transmission data. The specific steps are as follows: Data monitoring: Deploy data monitoring equipment in the network or use network management software to monitor the data transmitted between each wireless access point and the user in real time, and record the data transmission time, source address, destination address, data size and other information; Data source analysis: Analyze the monitored data, extract the characteristics of the data source, and classify them according to the characteristics of the data source.

[0024] S3.2, set the data extraction time period, then extract the historical transmission data and real-time transmission data of the wireless access point according to the data extraction time period, and classify the historical transmission data and real-time transmission data according to the data source. The specific steps are as follows: Set the data extraction time period: determine the start and end time of the data to be extracted. This time period can be set according to specific analysis requirements. If the data fluctuates greatly, the time interval will be short. Conversely, if the data fluctuates slightly, the time interval will be long. Extract historical transmission data and real-time transmission data of wireless access points: For historical transmission data, it can be extracted from databases and log files that store historical data. For real-time transmission data, the data being transmitted by wireless access points can be captured in real time through network monitoring tools or software. Classify by data source: Analyze the extracted historical transmission data and real-time transmission data, determine the data source characteristics of each data, and classify the data according to the data source characteristics.

[0025] S3.3. Set a fluctuation threshold according to the data volume, and then perform transmission fluctuation analysis on the data source based on the historical transmission data and the real-time transmission data combined with the fluctuation threshold. When the fluctuation rate between the historical transmission data and the real-time transmission data is greater than the fluctuation threshold, the transmission data of the data source is defined as fluctuating data. On the contrary, when the fluctuation rate between the historical transmission data and the real-time transmission data is less than the fluctuation threshold, the transmission data of the data source is defined as static data. The fluctuation threshold is determined according to the amount of data. The larger the amount of data, the smaller the fluctuation threshold, and the smaller the amount of data, the larger the fluctuation threshold. The formula is as follows: ; Among them, T is the fluctuation threshold, V is the volatility, R is the total amount of real-time transmission data, and H is the total amount of historical transmission data; When V>T, the transmission data of the data source is defined as fluctuating data; When V≤T, the transmission data of the data source is defined as static data.

[0026] Data sources that do not meet the extraction time period are directly defined as fluctuating data.

[0027] S4. Install edge computing nodes according to the amount of static data, distribute the static data to the edge computing nodes, and then make regular transmission predictions for the static data, and dynamically adjust the configuration of the edge computing nodes according to the predicted data; The steps of S4 are as follows: S4.1. Install edge computing nodes according to the number of data sources of static data defined in S3.3, and establish data transmission between the data sources defining the static data and the edge computing nodes; S4.2, perform regular transmission prediction for static data, and dynamically adjust the configuration of edge computing nodes according to the predicted data. When the load demand of the predicted data is greater than the current configuration of the edge computing node, the configuration of the edge computing node is increased according to the load demand of the predicted data. Conversely, when the load demand of the predicted data is less than the current configuration of the edge computing node, the configuration of the edge computing node is reduced according to the load demand of the predicted data. The specific steps are as follows: Determine the number of edge computing nodes: Count the number of data sources defined as static data, determine the number of data sources that each edge computing node can serve, and thus calculate the number of edge computing nodes that need to be installed; Establish data transmission: Establish a stable data transmission channel between the data source defined as static data and the edge computing node, which can be achieved through network configuration, protocol settings, etc. Perform regular transmission forecasts: collect the rules and characteristics of historical static data transmission, such as the change in data volume over time, transmission frequency, etc., and then use forecasting methods such as time series analysis to forecast the static data transmission volume in the future; Dynamic configuration adjustment: Compare the load requirements of the predicted data with the current configuration of the edge computing nodes; If the load demand of the predicted data is greater than the current configuration of the edge computing node, increase the configuration of the edge computing node, such as increasing computing resources and storage resources; If the load demand of the predicted data is less than the current configuration of the edge computing node, the configuration of the edge computing node is reduced to release excess resources.

[0028] S4 extracts configuration from wireless access points and installs edge computing nodes according to the extracted configuration, including adding and reducing configurations, which are all configured from wireless access points.

[0029] Install edge computing nodes according to the configuration information. During the installation process, apply the relevant configurations of the wireless access points to the edge computing nodes to ensure that the edge computing nodes can work together with the wireless access points. For the case of adding configuration, additional resource allocation is obtained from the wireless access point and applied to the edge computing node, which can increase the computing power, storage capacity or bandwidth allocation of the edge computing node; In the case of reducing the configuration, the resource occupation of the edge computing node is reduced accordingly according to the configuration adjustment of the wireless access point, which can reduce the computing load of the edge computing node, release some storage resources or adjust the bandwidth allocation.

[0030] S5. Combine the predicted data and the real-time transmission data with the fluctuation threshold to perform transmission fluctuation analysis. When the analysis result shows that it meets the fluctuation data, adjust the static data to the fluctuation data and delete the edge computing node. S5 analyzes the results and shows that it meets the static data, so continue monitoring; The data source that is adjusted from static data to fluctuating data is reconnected to the wireless access point.

[0031] S6. Monitor the load status of each wireless access point and set a normal load threshold for collaborative calculation and analysis. When the load status of a wireless access point is greater than the normal load threshold, extract the wireless access points whose load status is less than the normal load threshold, and then transfer the static data until the load status of the original wireless access point returns to normal.

[0032] The steps of S6 are as follows: S6.1. Monitor the load status of each wireless access point and set a normal load threshold for collaborative computing and analysis. According to the system performance requirements and experience, set a normal load threshold, which can be adjusted according to different indicators, such as data flow threshold, connected device quantity threshold, etc.

[0033] S6.2. When the analysis in S6.1 shows that the load state of the wireless access point is greater than the normal load threshold, the wireless access point whose load state is less than the normal load threshold is extracted, and then the static data is transferred until the load state of the original wireless access point returns to normal, and then the static data is transferred to the original wireless access point. Conversely, when the load state of the wireless access point is less than the normal load threshold, normal monitoring is maintained; The original wireless access point is the wireless access point corresponding to the user to which the static data belongs. When the load state of the original wireless access point returns to normal, the static data previously transferred out is transferred back to the original wireless access point.

[0034] The second object of the present invention is to provide a wireless networking system based on a computing cabin, including any one of the above-mentioned wireless networking methods based on a computing cabin, including a network establishment unit 10, a dynamic configuration unit 20 and a collaborative computing unit 30; The network establishment unit 10 is used to develop wireless access points and establish a wireless multi-hop network, and configure an intelligent network controller to bring all wireless access points and Mesh and edge computing nodes into a unified monitoring and management scope; The dynamic configuration unit 20 is used to analyze the user's data source, define the transmission data of the data source as fluctuating data and static data, and perform regular transmission prediction on the static data, and dynamically adjust the configuration of the edge computing node according to the predicted data; The collaborative calculation unit 30 is used to combine the predicted data and the real-time transmission data with the fluctuation threshold to perform transmission fluctuation analysis, monitor the load status of each wireless access point, and set a normal load threshold for collaborative calculation analysis.

[0035] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A wireless networking method based on a computing cabin, characterized in that: The steps include: S1. Obtain the number of users served by the computing cabin and develop corresponding wireless access points according to the number of users; S2. Establish a wireless multi-hop network and configure an intelligent network controller to bring all wireless access points and Mesh and edge computing nodes into unified monitoring and management scope; S3, setting a fluctuation threshold, classifying and monitoring the data source of the data transmitted between the wireless access point and the user, and then combining the historical transmission data of each data source with the real-time transmission data to perform transmission fluctuation analysis, and defining the transmission data of the data source as fluctuation data and static data according to the analysis results; S4. Install edge computing nodes according to the amount of static data, distribute the static data to the edge computing nodes, and then make regular transmission predictions for the static data, and dynamically adjust the configuration of the edge computing nodes according to the predicted data; S5. Combine the predicted data and the real-time transmission data with the fluctuation threshold to perform transmission fluctuation analysis. When the analysis result shows that it meets the fluctuation data, adjust the static data to the fluctuation data and delete the edge computing node. S6. Monitor the load status of each wireless access point and set a normal load threshold for collaborative calculation and analysis. When the load status of a wireless access point is greater than the normal load threshold, extract the wireless access points whose load status is less than the normal load threshold, and then transfer the static data until the load status of the original wireless access point returns to normal.

2. According to claim 1, a wireless networking method based on a computing cabin is characterized in that: The S1 accesses the data control terminal of the computing cabin, thereby extracting the number of users served by the computing cabin in the data control terminal, classifying the transmitted data according to the users, and then developing corresponding wireless access points according to the number of users, and each user corresponds to at least one wireless access point.

3. According to claim 1, a wireless networking method based on a computing cabin is characterized in that: The S2 establishes a wireless multi-hop network used by wireless access points by deploying wireless repeaters between the computing power cabins and enabling the Mesh network function. At the same time, an intelligent network controller is configured to uniformly monitor and manage all wireless access points and Mesh networks. The allocation and dynamic adjustment of network resources in the computing power cabin are controlled by the intelligent network controller.

4. According to claim 1, a wireless networking method based on a computing cabin is characterized in that: The steps of S3 are as follows: S3.1, monitor the data transmitted between each wireless access point and the user, then analyze the data source of the transmitted data, classify it according to different data sources, and obtain the data source corresponding to the user's transmission data; S3.2, setting a data extraction time period, then extracting historical transmission data and real-time transmission data of the wireless access point according to the data extraction time period, and classifying the historical transmission data and real-time transmission data according to the data source; S3.

3. Set a fluctuation threshold according to the data volume, and then perform transmission fluctuation analysis on the data source based on the historical transmission data and real-time transmission data combined with the fluctuation threshold. When the fluctuation rate between the historical transmission data and the real-time transmission data is greater than the fluctuation threshold, the transmission data of the data source is defined as fluctuating data. Conversely, when the fluctuation rate between the historical transmission data and the real-time transmission data is less than the fluctuation threshold, the transmission data of the data source is defined as static data.

5. According to claim 1, a wireless networking method based on a computing cabin is characterized in that: The steps of S4 are as follows: S4.

1. Install edge computing nodes according to the number of data sources of static data defined in S3.3, and establish data transmission between the data sources defining the static data and the edge computing nodes; S4.

2. Perform regular transmission predictions on static data, and dynamically adjust the configuration of edge computing nodes based on the predicted data. When the load demand of the predicted data is greater than the current configuration of the edge computing node, increase the configuration of the edge computing node based on the load demand of the predicted data. Conversely, when the load demand of the predicted data is less than the current configuration of the edge computing node, reduce the configuration of the edge computing node based on the load demand of the predicted data.

6. According to claim 1, a wireless networking method based on a computing cabin is characterized in that: The S4 extracts configuration from the wireless access point and installs the edge computing node according to the extracted configuration, including adding configuration and reducing configuration, both of which are configuration allocation from the wireless access point.

7. The wireless networking method based on the computing cabin according to claim 1 is characterized in that: The S5 analyzes the result and shows that it meets the static data, and continues to monitor; The data source that is adjusted from static data to fluctuating data is reconnected to the wireless access point.

8. The wireless networking method based on computing cabin according to claim 1 is characterized in that: The steps of S6 are as follows: S6.

1. Monitor the load status of each wireless access point and set a normal load threshold for collaborative computing and analysis; S6.

2. When the analysis in S6.1 shows that the load state of the wireless access point is greater than the normal load threshold, the wireless access point whose load state is less than the normal load threshold is extracted, and then the static data is transferred until the load state of the original wireless access point returns to normal, and then the static data is transferred to the original wireless access point. Conversely, when the load state of the wireless access point is less than the normal load threshold, normal monitoring is maintained; The original wireless access point is the wireless access point corresponding to the user to which the static data belongs.

9. A method for realizing a wireless networking system based on a computing power cabin, comprising a wireless networking method based on a computing power cabin as claimed in any one of claims 1 to 8, characterized in that: It includes a network establishment unit (10), a dynamic configuration unit (20) and a collaborative computing unit (30); The network establishment unit (10) is used to develop wireless access points and establish a wireless multi-hop network, and configure an intelligent network controller to bring all wireless access points and Mesh and edge computing nodes into a unified monitoring and management scope; The dynamic configuration unit (20) is used to analyze the user's data source, define the transmission data of the data source as fluctuating data and static data, and perform regular transmission prediction on the static data, and dynamically adjust the configuration of the edge computing node according to the predicted data; The collaborative calculation unit (30) is used to combine the predicted data and the real-time transmission data with the fluctuation threshold to perform transmission fluctuation analysis, while monitoring the load state of each wireless access point and setting a normal load threshold to perform collaborative calculation analysis.

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