A wireless networking method and system based on a computing power cabin

Abstract

This invention relates to the field of wireless networking technology for computing power cabins, specifically, to a wireless networking method and system based on computing power cabins. It includes the following steps: S1, obtaining the number of users served by the computing power cabin and developing corresponding wireless access points based on the number of users; S2, establishing a wireless multi-hop network and configuring an intelligent network controller to include all wireless access points, Mesh, and edge computing nodes in a unified monitoring and management scope; S3, setting fluctuation thresholds and classifying and monitoring the data transmitted between wireless access points and users; by deploying wireless repeaters or enabling Mesh network functionality between computing power cabins, a stable wireless multi-hop network is established to ensure the continuity and high reliability of data transmission. Furthermore, by introducing an intelligent network controller, all wireless access points and the Mesh network are uniformly monitored and managed, achieving optimal allocation and dynamic adjustment of network resources.

Description

Technical Field

[0001] This invention relates to the field of wireless networking technology for computing power cabins, and more specifically, to a wireless networking method and system based on computing power cabins. Background Technology

[0002] A computing power cluster refers to a cluster of container-shaped computing power units that integrate power distribution facilities, computing power servers, environmental monitoring and other equipment, and is designed to provide easily expandable and mobile standardized computing power services.

[0003] Currently, virtualization technology is used to improve server resource utilization. While virtualization can improve resource utilization, performance may be affected when facing large-scale computing tasks. Although distributed computing architecture can improve scalability, it has certain complexities 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 computing power cabin is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a wireless networking method and system based on a computing power cabin to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, one objective of this invention is to provide a wireless networking method based on a computing power cabin, comprising the following steps:

[0006] S1. The number of users who obtain computing power cabin services, and develop corresponding wireless access points based on the number of users;

[0007] S2. Establish a wireless multi-hop network and configure an intelligent network controller to include all wireless access points and Mesh and edge computing nodes in a unified monitoring and management scope.

[0008] S3. Set fluctuation thresholds, classify and monitor the data sources transmitted between the wireless access point and the user, and then combine the historical transmission data of each data source with the real-time transmission data to perform transmission fluctuation analysis. Based on the analysis results, the transmission data of the data source is defined as fluctuating data and static data.

[0009] S4. Install edge computing nodes according to the amount of static data, allocate the static data to the edge computing nodes, then periodically transmit and predict the static data, and dynamically adjust the configuration of the edge computing nodes according to the predicted data.

[0010] S5. Combine the predicted data and real-time transmission data with the fluctuation threshold to perform transmission fluctuation analysis. When the analysis results show that they match the fluctuation data, adjust the static data to the fluctuation data and delete the edge computing node.

[0011] 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 original wireless access point's load status returns to normal.

[0012] As a further improvement to this technical solution, S1 accesses the data control terminal of the computing power cabin, thereby extracting the number of users served by the computing power 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, with each user corresponding to at least one wireless access point.

[0013] As a further improvement to this technical solution, S2 establishes a wireless multi-hop network for wireless access points by deploying wireless repeaters between computing power cabins and enabling Mesh network functionality. At the same time, it configures an intelligent network controller to uniformly monitor and manage all wireless access points and the Mesh network. The allocation and dynamic adjustment of network resources in the computing power cabins are controlled by the intelligent network controller.

[0014] As a further improvement to this technical solution, step S3 is as follows:

[0015] S3.1 Monitor the data transmitted between each wireless access point and the user, then analyze the data source, classify it according to different data sources, and obtain the data source corresponding to the user's transmitted data;

[0016] S3.2 Set the data extraction time period, and 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;

[0017] S3.3. Set a fluctuation threshold based on the amount of data, and then perform transmission fluctuation analysis on the data source based on the historical transmission data and the real-time transmission data in combination 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.

[0018] As a further improvement to this technical solution, step S4 is as follows:

[0019] S4.1 Install edge computing nodes according to the number of data sources for static data defined in S3.3, and establish data transmission between the data sources for static data and the edge computing nodes;

[0020] S4.2. Perform periodic transmission prediction of 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 nodes, increase the configuration of the edge computing nodes 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 nodes, decrease the configuration of the edge computing nodes according to the load demand of the predicted data.

[0021] As a further improvement to this technical solution, step S4 extracts configuration from the wireless access point and installs the edge computing node according to the extracted configuration. Both adding and removing configurations are done by allocating configurations from the wireless access point.

[0022] As a further improvement to this technical solution, step S5 analyzes the results to show that the data conforms to static data and continues monitoring.

[0023] The data source, which will be changed from static data to fluctuating data, will reconnect to the wireless access point.

[0024] As a further improvement to this technical solution, step S6 is as follows:

[0025] S6.1 Monitor the load status of each wireless access point and set normal load thresholds for collaborative calculation and analysis;

[0026] S6.2 When the analysis in S6.1 shows that the load status of the 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. Then transfer the static data to the original wireless access point. Conversely, when the load status of the wireless access point is less than the normal load threshold, maintain normal monitoring.

[0027] The original wireless access point is the wireless access point corresponding to the user to whom the static data belongs.

[0028] The second objective of this invention is to provide a wireless networking system based on a computing power cabin, including any one of the wireless networking methods based on a computing power cabin described above, comprising a network establishment unit, a dynamic configuration unit, and a collaborative computing unit.

[0029] The network establishment unit is used to develop wireless access points and establish wireless multi-hop networks. It is also configured with an intelligent network controller to bring all wireless access points, Mesh, and edge computing nodes under unified monitoring and management.

[0030] The dynamic configuration unit is used to analyze the user's data source, define the transmitted data of the data source as fluctuating data and static data, and perform periodic transmission prediction for static data, and dynamically adjust the configuration of edge computing nodes based on the prediction data.

[0031] The collaborative computing unit is used to combine predicted data and real-time transmission data with fluctuation thresholds to perform transmission fluctuation analysis, while monitoring the load status of each wireless access point and setting normal load thresholds for collaborative computing analysis.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] 1. A wireless networking method and system based on computing power cabins, which establishes a stable wireless multi-hop network by deploying wireless repeaters or enabling Mesh network functionality between computing power cabins to ensure the continuity and high reliability of data transmission. Then, by introducing an intelligent network controller, all wireless access points and the Mesh network are uniformly monitored and managed to achieve optimal allocation and dynamic adjustment of network resources. Finally, edge computing nodes are integrated inside the key computing power cabins to accelerate data processing and response speed and reduce the pressure on the central processing unit.

[0034] 2. A wireless networking method and system based on a computing power cabin, which extracts historical and real-time transmission data from wireless access points and categorizes them according to data sources. Then, based on the historical and real-time transmission data and fluctuation thresholds, it performs transmission fluctuation analysis on the data sources, dividing the transmission data into fluctuating data and static data, providing a basis for subsequent resource management. Next, it installs edge computing nodes according to the number of data sources defining static data, establishes data transmission between the data sources defining static data and the edge computing nodes, performs periodic transmission prediction of static data, and dynamically adjusts the configuration of edge computing nodes based on the prediction data to improve resource utilization efficiency.

[0035] 3. A wireless networking method and system based on a computing power cabin, which monitors the load status of each wireless access point and performs collaborative calculation and analysis by setting a normal load threshold. When the load of a wireless access point is too high, 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, and then the data is transferred back to the original wireless access point, thereby realizing dynamic balancing and optimized allocation of resources. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the computing power cabin structure of the present invention;

[0037] Figure 2 This is an overall flowchart of the present invention;

[0038] Figure 3 This is a flowchart illustrating how the transmitted data from the data source is defined as a static number in this invention.

[0039] Figure 4 This is a flowchart illustrating how the present invention reduces the configuration of edge computing nodes based on the load requirements of predicted data.

[0040] Figure 5 A flowchart illustrating the process of setting a normal load threshold for collaborative calculation and analysis in this invention;

[0041] Figure 6 This is a schematic diagram of the network establishment unit of the present invention.

[0042] The meanings of the labels in the diagram are as follows:

[0043] 10. Network establishment unit; 20. Dynamic configuration unit; 30. Collaborative computing unit. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] like Figure 1-5 As shown, one of the objectives of this invention is to provide a wireless networking method based on a computing power cabin, comprising the following steps:

[0046] S1. The number of users who obtain computing power cabin services, and develop corresponding wireless access points based on the number of users;

[0047] S1 accesses the data control terminal of the computing power cabin, thereby extracting the number of users served by the computing power cabin from the data control terminal, classifying the transmitted data according to the users, and then developing corresponding wireless access points based on the number of users. Each user corresponds to at least one wireless access point. The specific steps are as follows:

[0048] Access the data control terminal of the computing power cabin: Establish a connection with the data control terminal of the computing power cabin through interfaces and protocols to ensure that relevant data can be read;

[0049] Extract the number of users for the computing power cabin service: Use data reading tools or programming languages ​​to extract the number of users from the data control terminal, determine the principle that each user corresponds to at least one wireless access point, 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.

[0050] High-performance wireless access points ensure comprehensive coverage and provide sufficient bandwidth.

[0051] S2. Establish a wireless multi-hop network and configure an intelligent network controller to include all wireless access points and Mesh and edge computing nodes in a unified monitoring and management scope.

[0052] S2 establishes a multi-hop wireless network for wireless access points by deploying wireless repeaters between computing bays and enabling Mesh network functionality. Simultaneously, it configures an intelligent network controller to uniformly monitor and manage all wireless access points and the Mesh network. The allocation and dynamic adjustment of network resources within the computing bays are controlled through the intelligent network controller. The specific steps are as follows:

[0053] Deploy wireless repeaters and enable Mesh network functionality: Select appropriate locations between computing bays to deploy wireless repeaters to ensure signal coverage of all computing bays. At the same time, enable Mesh network functionality in the wireless access points so that the wireless access points can connect to each other to form a multi-hop network.

[0054] Configure the smart network controller: Install the smart network controller and perform initial configuration to bring all wireless access points and Mesh networks under the management of the smart network controller;

[0055] Unified monitoring and management: The status of all wireless access points and the Mesh network is monitored in real time through the intelligent network controller, including signal strength, bandwidth usage, number of connected devices, etc. The intelligent network controller is then used to manage the network, such as adjusting the parameters of wireless access points and optimizing the path selection of the Mesh network.

[0056] Network resource allocation and dynamic adjustment: As the network load changes, the intelligent network controller dynamically adjusts network resources to ensure efficient network operation. When the traffic volume of a certain computing power unit increases, the intelligent network controller can adjust the parameters of nearby wireless access points to increase the bandwidth of that area.

[0057] S3. Set fluctuation thresholds, classify and monitor the data sources transmitted between the wireless access point and the user, and then combine the historical transmission data of each data source with the real-time transmission data to perform transmission fluctuation analysis. Based on the analysis results, the transmission data of the data source is defined as fluctuating data and static data.

[0058] The steps for S3 are as follows:

[0059] S3.1 Monitor the data transmitted between each wireless access point and the user, then analyze the transmitted data data, classify it according to different data sources, and obtain the data source corresponding to the user's transmitted data. The specific steps are as follows:

[0060] Data monitoring: Deploy data monitoring equipment or use network management software in the network to monitor the data transmitted between each wireless access point and users in real time, and record information such as data transmission time, source address, destination address, and data size;

[0061] Data source analysis: Analyze the monitored data, extract the characteristics of the data source, and classify the data source according to the characteristics of the data source.

[0062] S3.2. Set the data extraction time period, and then extract the historical and real-time transmission data of the wireless access point according to the data extraction time period. Then, classify the historical and real-time transmission data according to the data source. The specific steps are as follows:

[0063] Set the data extraction time period: Determine the start and end times of the data to be extracted. This time period can be set according to specific analysis needs. If the data fluctuates greatly, the time interval should be short; conversely, if the data fluctuates little, the time interval should be long.

[0064] Extracting historical and real-time transmission data from wireless access points: Historical transmission data can be extracted from databases, log files, and other locations where historical data is stored. Real-time transmission data can be captured in real time by network monitoring tools or software.

[0065] Categorize based on data source: Analyze the extracted historical and real-time transmission data, determine the data source characteristics of each data source, and categorize the data according to the data source characteristics.

[0066] S3.3. Set a fluctuation threshold based on the amount of data, 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.

[0067] The fluctuation threshold is determined based on the amount of data. The larger the data volume, the smaller the fluctuation threshold, and vice versa. The formula is as follows:

[0068] ;

[0069] Where T is the volatility threshold, V is the volatility, R is the total amount of real-time transmitted data, and H is the total amount of historical transmitted data;

[0070] When V > T, the transmitted data from this data source is defined as fluctuating data;

[0071] When V≤T, the transmitted data of this data source is defined as static data.

[0072] Data sources that do not meet the criteria for extracting time periods are directly defined as fluctuation data.

[0073] S4. Install edge computing nodes according to the amount of static data, allocate the static data to the edge computing nodes, then periodically transmit and predict the static data, and dynamically adjust the configuration of the edge computing nodes according to the predicted data.

[0074] The steps for S4 are as follows:

[0075] S4.1 Install edge computing nodes according to the number of data sources for static data defined in S3.3, and establish data transmission between the data sources for static data and the edge computing nodes;

[0076] S4.2. Periodically predict the transmission of static data and dynamically adjust the configuration of edge computing nodes based on the predicted data. When the load demand of the predicted data exceeds the current configuration of the edge computing nodes, increase the configuration of edge computing nodes 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 nodes, decrease the configuration of edge computing nodes according to the load demand of the predicted data. The specific steps are as follows:

[0077] Determine the number of edge computing nodes: Statistically define the number of data sources for 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;

[0078] Establishing data transmission: Establishing a stable data transmission channel between the data source defined as static data and the edge computing node can be achieved through network configuration, protocol settings, etc.

[0079] Perform periodic transmission forecasting: Collect patterns and characteristics of historical static data transmission, such as changes in data volume over time and transmission frequency, and then use forecasting methods such as time series analysis to predict the amount of static data transmission in the future.

[0080] Dynamic configuration adjustment: Compare the load requirements of the predicted data with the current configuration of the edge computing nodes;

[0081] If the load demand of the predicted data exceeds the current configuration of the edge computing nodes, then increase the configuration of the edge computing nodes, such as increasing computing resources and storage resources.

[0082] If the load demand of the predicted data is less than the current configuration of the edge computing nodes, then reduce the configuration of the edge computing nodes and release excess resources.

[0083] S4 extracts configurations from the wireless access point and installs edge computing nodes based on the extracted configurations. Both adding and removing configurations are done by allocating configurations from the wireless access point.

[0084] Based on the configuration information, install the edge computing node. During the installation process, apply the relevant configuration of the wireless access point to the edge computing node to ensure that the edge computing node can work together with the wireless access point.

[0085] In cases of increased configuration, additional resource allocation can be obtained from the wireless access point and applied to the edge computing node, thereby increasing the computing power, storage capacity, or bandwidth allocation of the edge computing node.

[0086] In cases of reduced configuration, the resource consumption of edge computing nodes can be reduced accordingly based on the configuration of the wireless access points. This can reduce the computing load of edge computing nodes, release some storage resources, or adjust bandwidth allocation.

[0087] S5. Combine the predicted data and real-time transmission data with the fluctuation threshold to perform transmission fluctuation analysis. When the analysis results show that they match the fluctuation data, adjust the static data to the fluctuation data and delete the edge computing node.

[0088] S5 analysis results show consistency with static data, therefore continued monitoring is recommended.

[0089] The data source, which will be changed from static data to fluctuating data, will reconnect to the wireless access point.

[0090] 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 original wireless access point's load status returns to normal.

[0091] The steps for S6 are as follows:

[0092] S6.1 Monitor the load status of each wireless access point and set a normal load threshold for collaborative calculation and analysis; based on the system's performance requirements and experience, set a normal load threshold, which can be adjusted according to different indicators, such as data traffic threshold, number of connected devices threshold, etc.

[0093] S6.2 When the analysis in S6.1 shows that the load status of the 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. Then transfer the static data to the original wireless access point. Conversely, when the load status of the wireless access point is less than the normal load threshold, maintain normal monitoring.

[0094] The original wireless access point is the wireless access point corresponding to the user to whom the static data belongs. When the load status of the original wireless access point returns to normal, the static data that was previously transferred out will be transferred back to the original wireless access point.

[0095] The second objective of this invention is to provide a wireless networking system based on a computing power cabin, including a wireless networking method based on a computing power cabin, comprising a network establishment unit 10, a dynamic configuration unit 20, and a collaborative computing unit 30.

[0096] The network establishment unit 10 is used to develop wireless access points and establish a wireless multi-hop network. At the same time, it is configured with an intelligent network controller to bring all wireless access points and Mesh and edge computing nodes into a unified monitoring and management scope.

[0097] The dynamic configuration unit 20 is used to analyze the user's data source, define the transmitted data of the data source as fluctuating data and static data, and perform periodic transmission prediction for static data, and dynamically adjust the configuration of edge computing nodes based on the prediction data.

[0098] The collaborative computing unit 30 is used to combine predicted data and real-time transmission data with fluctuation thresholds to perform transmission fluctuation analysis, while monitoring the load status of each wireless access point and setting normal load thresholds for collaborative computing analysis.

[0099] The foregoing has shown and described 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 to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A wireless networking method based on a computing power cabin, characterized in that: Comprise the following steps: S1, obtain the number of users of the computing power cabin service, 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 include all wireless access points and Mesh, edge computing nodes in the unified monitoring and management range; S3, set the fluctuation threshold, monitor the data source classification of the data transmitted between the wireless access point and the user, then combine the historical transmission data of each data source with the real-time transmission data to perform transmission fluctuation analysis, and define the transmission data of the data source as fluctuation data and static data according to the analysis result; S4, install edge computing nodes according to the number of static data, and distribute the static data into the edge computing nodes, then periodically predict the transmission of the static data, and dynamically configure and adjust 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 conforms to the fluctuation data, the static data is adjusted to fluctuation data, and the edge computing node is deleted; S6, monitor the load state of each wireless access point, and set a normal load threshold for collaborative calculation and analysis, when the load state of the wireless access point is greater than the normal load threshold, extract the wireless access point with a load state less than the normal load threshold, then transfer the static data until the load state of the original wireless access point returns to normal.

2. The computing power cabin-based wireless networking method according to claim 1, characterized in that: The S1 extracts the number of users of the computing power cabin service in the data control end of the access computing power cabin, classifies the data transmitted by the users, and then develops corresponding wireless access points according to the number of users, with at least one wireless access point corresponding to each user.

3. The method of claim 1, wherein: The S2 deploys wireless repeaters between the computing power cabins and enables the Mesh network function to establish a wireless multi-hop network used by the wireless access points, and configures an intelligent network controller to uniformly monitor and manage all wireless access points and Mesh networks, and the allocation and dynamic adjustment of network resources in the computing power cabin are controlled through the intelligent network controller.

4. The computing power cabin-based wireless networking method of claim 1, wherein: 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 according to different data sources, and obtain the data source corresponding to the user transmission data; S3.2, set a 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; S3.3, set the fluctuation threshold according to the data volume, then combine the historical transmission data and real-time transmission data with the fluctuation threshold to analyze the transmission fluctuation of the data source, 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 fluctuation data, otherwise, 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. The computing power cabin-based wireless networking method of claim 1, wherein: 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 of static data and the edge computing nodes; S4.2, periodically predict the transmission of static data, and dynamically configure and adjust the 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 nodes, increase the configuration of the edge computing nodes according to the load demand of the predicted data; otherwise, when the load demand of the predicted data is less than the current configuration of the edge computing nodes, reduce the configuration of the edge computing nodes according to the load demand of the predicted data.

6. The computing power cabin-based wireless networking method of claim 1, wherein: S4 is extracted from the configuration of the wireless access point, and the edge computing nodes are installed according to the extracted configuration, including increasing the configuration and reducing the configuration, which are both allocated from the configuration of the wireless access point.

7. The computing power cabin-based wireless networking method of claim 1, wherein: S5 is displayed by analyzing the results to conform to static data, and continues to be monitored; The data source adjusted from static data to fluctuating data is reconnected with the wireless access point for data connection.

8. The computing power cabin-based wireless networking method of claim 1, wherein: The steps of S6 are as follows: S6.1, monitor the load state of each wireless access point, and set a normal load threshold for cooperative calculation and analysis; S6.2, when S6.1 analysis shows that the load state of the wireless access point is greater than the normal load threshold, extract the wireless access points with load state less than the normal load threshold, then transfer the static data until the load state of the original wireless access point returns to normal, and then transfer the static data to the original wireless access point; otherwise, 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 static data of the user.

9. A hash power cabin-based wireless networking system comprising the hash power cabin-based wireless networking method of any one of claims 1-8. It includes a network establishment unit (10), a dynamic configuration unit (20), and a cooperative calculation unit (30); The network establishment unit (10) is used to develop wireless access points, establish a wireless multi-hop network, configure an intelligent network controller, and include all wireless access points and Mesh, edge computing nodes in the unified monitoring and management range; The dynamic configuration unit (20) is used to analyze the data sources of users, define the transmission data of the data sources as fluctuating data and static data, periodically predict the transmission of static data, and dynamically configure and adjust the edge computing nodes according to the predicted data; The cooperative calculation unit (30) is used to combine the predicted data and real-time transmission data with a fluctuation threshold for transmission fluctuation analysis, monitor the load state of each wireless access point, and set a normal load threshold for cooperative calculation and analysis.

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