Platform information low-delay concurrent communication transmission method and system

Abstract

The invention relates to the technical field of data transmission, in particular to a platform information low-delay concurrent communication transmission method and system, and the method comprises the steps: obtaining the state information data and transmission resource data of each edge device at each collection moment, and the position information of an edge calculation node; obtaining each state information difference sequence of each edge device; acquiring a communication state difference value of each state information difference sequence of each edge device; obtaining a response association difference value between each edge device and other edge devices; obtaining a demand association response value of each edge device; obtaining a resource division data set based on the demand association response value, the resource request quantity and the priority; and realizing low-delay concurrent communication based on the resource division data set by combining a clustering algorithm. According to the invention, the influence of the state information difference of the edge equipment on resource configuration is reduced.

Description

Technical Field

[0001] This application relates to the field of data transmission technology, specifically to a method and system for low-latency concurrent communication transmission of platform information. Background Technology

[0002] Low-latency concurrent communication in industrial IoT platforms refers to the ability to quickly and efficiently transmit data between multiple devices within an industrial IoT platform network environment, minimizing data transmission latency. This communication method is crucial for industrial applications with high real-time requirements, such as industrial automation, intelligent transportation, and smart energy. Low latency means that data can be transmitted from the sender to the receiver in a very short time to meet the needs of real-time control, monitoring, and decision-making. Concurrent communication emphasizes that multiple devices can transmit data simultaneously, improving the overall efficiency and throughput of the system.

[0003] Traditional concurrent communication methods for industrial IoT platforms primarily encompass wireless network transmission and wired network transmission. To achieve low-latency concurrent communication on industrial IoT platforms, technologies such as Time-Sensitive Networking (TSN), Software-Defined Networking (SDN), and edge computing are commonly employed. However, edge computing requires accurate resource allocation and deployment to ensure critical tasks receive sufficient computing and storage resources. Simultaneously, the heterogeneity of edge devices increases the difficulty of resource management. With the development of industrial IoT platforms, the number of edge devices and nodes is enormous, generating numerous processing tasks. In industrial IoT platform processes, the relationships between connected and transmitting nodes are complex, and different edge devices have varying data storage and processing capabilities. This situation results in low precision in resource allocation during concurrent communication on industrial IoT platforms, making it difficult to meet the needs of different tasks. Inaccurate resource allocation may cause some tasks to fail to obtain the necessary resources in a timely manner, thus affecting the timeliness of concurrent communication on the industrial IoT platform and reducing the overall performance and efficiency of the system. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a low-latency concurrent communication transmission method and system for platform information, the specific technical solution of which is as follows: In a first aspect, embodiments of this application provide a method for low-latency concurrent communication transmission of platform information, the method comprising the following steps: The system acquires status information data and transmission resource data of each edge device at each acquisition time, as well as location information of edge computing nodes. The status information data includes CPU utilization, memory capacity, GPU utilization, storage capacity, uplink and downlink traffic data, data transmission frequency, and location information data. The transmission resource data includes resource request volume and priority. Based on the differences between state information data of the same category, obtain the state information difference sequence of each edge device; based on the average distance between each state information difference sequence and other state information difference sequences, obtain the communication state difference value of each state information difference sequence of each edge device. Based on the changing trend of communication status difference values ​​and the distance between edge devices and edge computing nodes, obtain the response correlation difference value between each edge device and other edge devices; based on the average of all communication status difference values ​​of each edge device and the response correlation difference value, obtain the demand correlation response value of each edge device. A resource partitioning dataset is obtained based on the demand-related response value, resource request volume, and priority; based on the resource partitioning dataset, a clustering algorithm is used to achieve low-latency concurrent communication.

[0005] Furthermore, the method for obtaining the state information difference sequence is as follows: For the status information data of each edge device at each acquisition time, the sequence of all status information data except for the location information data is taken as the instantaneous status data sequence of each edge device at each acquisition time. For each edge device, the difference between elements with the same position in the instantaneous state data sequence at any two acquisition times is arranged according to the corresponding position to form a sequence, which is used as the state information difference sequence of each edge device.

[0006] Furthermore, the method for obtaining the communication status difference value is as follows: For each state information difference sequence of each edge device, calculate the average distance between the state information difference sequence and the state information difference sequences of all other edge devices, and use it as the communication state difference value of each edge device's state information difference sequence.

[0007] Furthermore, the method for obtaining the response correlation difference value is as follows: Obtain trend statistics for each edge device based on the changing trend of communication status difference values; Based on the distance between edge devices and edge computing nodes, obtain the communication transmission distribution sequence of each edge device; Based on trend statistics and communication transmission distribution sequences, the response correlation difference value between each edge device and other edge devices is obtained, using the following formula: In the formula, Indicates the first The and the first The response correlation difference value of communication response correlation characteristics between edge devices; and They represent the first The and the first A statistical measure of the trend of changes in the status information of an edge device; and They represent the first The and the first Communication transmission distribution sequence of each edge device express and The Manhattan distance between them.

[0008] Furthermore, the method for obtaining the trend statistics is as follows: The Mann-Kendall trend verification algorithm takes all communication status differences of each edge device as input and outputs trend statistics for each edge device.

[0009] Furthermore, the method for obtaining the communication transmission distribution sequence is as follows: Number all edge computing nodes; For each edge device, the distance between the edge device and the location of each edge computing node is calculated as the second distance. The numbers of each edge computing node are sorted in ascending order according to the second distance between the edge computing nodes to obtain the communication transmission distribution sequence of each edge device.

[0010] Furthermore, the formula for calculating the demand-related response value is as follows: In the formula, Indicates the first The demand-related response value of each edge device; Indicates the first The and the first The response correlation difference value between the edge devices; Indicates the first The mean of all communication status differences for each edge device; This indicates the number of edge devices.

[0011] Furthermore, the resource partitioning dataset includes: The array consisting of the demand-related response value, resource request amount, and priority of each edge device is used as the resource allocation array for each edge device. The resource partitioning dataset is a collection formed by mapping the resource partitioning arrays of all edge devices to a three-dimensional spatial coordinate system.

[0012] Furthermore, the method of achieving low-latency concurrent communication based on resource-partitioned datasets and clustering algorithms includes: Clustering algorithms are used to partition the resource dataset and obtain the edge devices in each group; For each group of edge devices, the mean of the Euclidean distance between each edge computing node and all edge devices in the group is calculated as the first mean. The sequence formed by arranging the numbers of all edge computing nodes in ascending order of the first mean is used as the candidate node sequence for each group of edge devices. For each group of edge devices, the edge devices in each group are selected as the preferred edge device group for the edge computing node corresponding to the first number in the candidate node sequence. For each edge computing node, the preferred edge device group is prioritized for resource allocation, and the resources are allocated according to the priority of the devices in the preferred device group.

[0013] Secondly, embodiments of this application also provide a platform information low-latency concurrent communication transmission system, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0014] This application has at least the following beneficial effects: This application proposes a low-latency concurrent communication transmission method and system for platform information. It considers that during the concurrent communication of edge devices in industrial production, due to the large number of edge devices and nodes, the numerous processing tasks, the complex relationships between nodes that need to be connected and transmitted, and the different data storage and processing capabilities of different edge devices, the accuracy of resource allocation in concurrent communication of industrial IoT platforms is relatively low. The advantage of obtaining the response correlation difference value of the communication response correlation characteristics between different edge devices is that it combines the changes of each edge device at different times during the concurrent communication transmission process and the distribution characteristics between edge devices and edge computing nodes to accurately analyze the correlation response characteristics of concurrent communication data transmission of different edge devices. This paper analyzes the status information and distribution correlation characteristics of concurrent communication of different edge devices in industrial production. By comprehensively considering the differences in correlation characteristics of all edge devices, the paper calculates the demand correlation response value of each edge device in the allocation of concurrent communication data resources in industrial production. The paper divides the edge devices based on the demand correlation response value, resource request volume, and priority. Resource allocation is completed based on the division results. The beneficial effect is that it fully considers the demand correlation response between different edge devices and the distribution differences relative to edge computing nodes, optimizes the resource allocation of edge computing nodes of industrial IoT platform, reduces the impact of edge device status information differences on resource allocation, and realizes low-latency concurrent communication of industrial IoT platform. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of a low-latency concurrent communication transmission method for platform information provided in one embodiment of this application; Figure 2 This is a schematic diagram illustrating the distributed connection of edge devices and edge computing nodes according to one embodiment of this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive objective, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a platform information low-latency concurrent communication transmission method and system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details a specific scheme for a platform information low-latency concurrent communication transmission method and system provided in this application.

[0020] Please see Figure 1 The diagram illustrates a flowchart of a low-latency concurrent communication transmission method for platform information according to an embodiment of this application. The method includes the following steps: Step S1: Obtain status information data of edge devices in the industrial IoT platform during the industrial production process, and preprocess the obtained data.

[0021] In industrial production, data transmission between multiple devices in an IoT environment can enable industrial automation and real-time control and monitoring. Edge computing can achieve low-latency concurrent communication on industrial IoT platforms. However, in actual industrial production, the distribution differences between different edge computing nodes and edge devices, as well as the differences in the status information characteristics of edge devices, lead to deviations in the allocation of concurrent communication resources on industrial IoT platforms, affecting the efficiency of concurrent communication of industrial production equipment.

[0022] Therefore, this application acquires status information data and transmission resource data of edge devices and edge computing nodes during industrial production. The status information data includes the device's CPU utilization, memory capacity, GPU utilization, storage capacity, uplink and downlink traffic data, data transmission frequency, and location information data. The transmission resource data includes resource request volume and priority. Specifically, the above information data can be acquired through the edge device's operating system, router, and positioning device. Due to the complexity of the industrial production environment, the status information data collected by the edge devices may contain interference. Preprocessing is performed through data cleaning. In this embodiment, the preprocessing process includes outlier detection, missing value imputation, and noise reduction. The data cleaning process is a technique well-known to those skilled in the art and will not be described in detail here.

[0023] In one implementation of this application, the edge computing node can be a data center, and the edge device can include a server, a wireless access node, and a router, etc.

[0024] For example, Figure 2 This diagram illustrates the distributed connection of edge devices and edge computing nodes in an industrial production process, as provided in an embodiment of this application. In this diagram, 1-3 represent the data center, edge device, and wireless transmission link, respectively, and 5-7 represent the server, wireless access node, and router, respectively.

[0025] Step S2: Obtain the status information characteristics of each device in the industrial production process, analyze the resource allocation characteristics of the edge devices, and calculate the demand-related response value for concurrent communication of each edge device based on the analysis results.

[0026] Typically, in industrial production, resources are evenly distributed during concurrent communication transmission between different devices to achieve data sharing and analysis. However, the number of edge devices and their processing tasks vary at different times during industrial production, and the priority of edge device processing tasks also differs at different times. Therefore, to improve the efficiency of concurrent communication transmission between multiple devices in industrial production, it is necessary to further improve the accuracy of edge device resource allocation. However, existing edge computing methods do not consider the heterogeneous characteristics of actual industrial production equipment and the associated response status between devices and edge computing nodes when allocating resources, resulting in low transmission efficiency of concurrent communication on industrial IoT platforms.

[0027] To address the aforementioned issues, this application analyzes the resource allocation for concurrent communication in industrial production processes based on the distribution characteristics and status information response states of edge devices and edge computing nodes. Furthermore, it implements resource allocation for edge devices in industrial production processes based on the distribution characteristics and response relationships of edge devices and edge computing nodes. The specific resource allocation process is as follows: Because the amount of data transmitted concurrently between different devices in industrial production processes varies at different times and the complexity of processing tasks also differs, and the storage and processing capabilities of edge computing nodes also vary significantly, existing resource allocation methods do not fully consider the correlation response of status information data of different devices and the heterogeneous distribution characteristics of edge computing nodes. Resource allocation based solely on data volume and processing priority cannot avoid the impact of differences in edge device status information on resource allocation.

[0028] Therefore, this application collects status information data of each edge device in industrial production and analyzes the correlation of concurrent communication based on the status response characteristics of different edge devices.

[0029] Specifically, for the status information data of each edge device at each acquisition time, the sequence of all status information data except for location information data is used as the instantaneous status data sequence of the concurrent communication associated response at each acquisition time; the difference of the same order value elements between any two acquisition times of each edge device in the industrial production process is obtained, and the sequence of all the differences is used as the status information difference sequence of each edge device at different times.

[0030] Furthermore, to accurately reflect the state information change characteristics of different edge devices during concurrent communication data transmission in industrial production, for each edge device's state information difference sequence, the DTW distance between the state information difference sequence and each other state information difference sequence is calculated. The average of all the DTW distances is then calculated, and this average is used as the communication state difference value for each edge device's state information difference sequence. The larger the communication state difference value, the greater the difference in state changes during concurrent communication data transmission at different times for the edge device. The DTW distance is a well-known technique and will not be elaborated upon in this embodiment.

[0031] Furthermore, using all communication status differences for each edge device as input, the Mann-Kendall trend verification algorithm is employed to obtain trend statistics for each edge device. The larger the trend statistic, the greater the difference in the state changes of the edge devices, and the more frequent the state changes of the edge device processing tasks. The Mann-Kendall trend verification algorithm is a well-known technology and will not be described in detail in this embodiment.

[0032] Considering the state change characteristics of each edge device's processing task, and combining the differences in changes of each edge device at different times during concurrent communication transmission, as well as the distribution characteristics between edge devices and edge computing nodes, the correlation response characteristics of concurrent communication data transmission of different edge devices are analyzed. Specifically, all edge computing nodes are numbered; for example, if there are... If there are 1 edge computing nodes, then they are numbered 1~ Calculate the Euclidean distance between each edge device and each edge computing node, and use the sequence formed by sorting all edge node numbers in ascending order of the Euclidean distance as the communication transmission distribution sequence for each edge device. .

[0033] Calculate the response correlation difference value of the communication response correlation characteristics between different edge devices based on the state change characteristics of the processing tasks of each edge device and the distribution characteristics relative to the edge computing nodes. .

[0034] Among them, the response correlation difference values ​​between different edge devices are positively correlated with the differences in state information changes between different edge devices and the differences in distribution between different edge devices.

[0035] The greater the difference in state information changes between different edge devices, the more inconsistent the fluctuation trends of their operating states during concurrent communication. This indicates significant differences in their dependence on edge computing resources and their response behavior. In this case, differentiated resource allocation strategies are needed to avoid resource conflicts and scheduling delays, thus the greater the difference in response correlation between different edge devices. Conversely, the greater the distribution difference between different edge devices, the more significant the differences in their distance from the edge computing node in physical space or network topology. This can lead to significant differences in data transmission path length, signal attenuation, network congestion, etc., thereby affecting the real-time performance of data uploading and command issuance. In this case, the greater the difference in response correlation between different edge devices.

[0036] In this embodiment, the specific calculation formula is as follows: In the formula, Indicates the first The and the first The response correlation difference value between the edge devices; and They represent the first The and the first Trend statistics for individual edge devices; and They represent the first The and the first Communication transmission distribution sequence of each edge device express and The Euclidean distance between them is used to characterize the difference in communication transmission distribution between the two edge devices. Specifically, the Euclidean distance between them is used to characterize the difference in communication transmission distribution between the two edge devices. Recorded as the number The and the first The differences in state information changes between edge devices will Recorded as the number The and the first Distribution differences between edge devices.

[0037] It should be noted that the larger the calculated communication response correlation difference value, the greater the difference between the two edge devices relative to the distribution difference of the computing node and the difference in state information changes, and the greater the correlation response difference in concurrent communication data transmission.

[0038] Based on the above calculations, the status information and distribution correlation characteristics of concurrent communication of different edge devices in industrial production are analyzed. Furthermore, by comprehensively considering the differences in correlation characteristics of all edge devices, the demand correlation response value for each edge device participating in the allocation of concurrent communication data resources in industrial production is calculated. The specific calculation formula is as follows: In the formula, Indicates the first The demand-related response value of each edge device; Indicates the first The and the first The response correlation difference value between the edge devices; Indicates the first The mean of all communication status differences for each edge device; This indicates the number of edge devices.

[0039] It should be noted that the larger the calculated concurrent communication resource allocation demand correlation response value, the greater the difference in the correlation response of the current edge device with the status change of the task being processed by other devices, and the greater the impact on resource allocation; conversely, the smaller the demand correlation response value, the smaller the impact on resource allocation.

[0040] Furthermore, an array consisting of the demand-related response value, resource request quantity, and priority for each edge device is used as the resource allocation array for concurrent communication and data transmission of each edge device. The resource partitioning array corresponding to each edge device is used as the coordinates mapped to a three-dimensional spatial coordinate system, where for The axis coordinates represent the demand-related response values; for The axis coordinates represent the amount of resource requested; for The axis coordinates represent priorities. The resource partitioning dataset is a set composed of the mappings of all edge devices in a three-dimensional Cartesian coordinate system.

[0041] Step S3: Allocate resources for edge devices in the concurrent communication process of the industrial IoT platform according to the demand-related response value. Based on the resource allocation results of the edge devices in the concurrent communication process of the industrial IoT platform, configure the resources for the communication transmission process of the industrial IoT platform. Through the configuration results, realize low-latency concurrent communication of the industrial IoT platform.

[0042] Furthermore, to accurately allocate resources for concurrent communication in industrial production, the resource allocation dataset is used as input, and the K-means algorithm is employed to obtain the allocation results. The clustering distance is Euclidean distance, and each cluster is output, where the number of clusters is... In this embodiment, the value of r is 5. The K-means algorithm is a well-known technique to those skilled in the art, and the specific calculation process will not be described in detail.

[0043] Based on the partitioning results of the resource partitioning dataset, the partitioning results of all edge devices in the industrial production process are obtained. In the partitioning results, each group of edge devices is considered as edge devices with similar concurrent communication data transmission requirements, response values, request volumes, and priorities.

[0044] For each group of edge devices, the mean of the Euclidean distance between each edge computing node and all edge devices in the group is calculated as the first mean. The sequence formed by arranging the numbers of all edge computing nodes in ascending order of the first mean is used as the candidate node sequence for each group of edge devices.

[0045] Furthermore, resources are allocated for each edge computing node. Specifically, for each group of candidate node sequences, each group of edge devices is selected as the preferred edge device group for the edge computing node corresponding to the first number in the candidate node sequence. Resources are allocated preferentially to the preferred edge device groups of each edge computing node, according to the priority of the preferred devices in the preferred device group, to complete the resource allocation for concurrent communication of the industrial IoT platform and achieve low-latency concurrent communication.

[0046] Specifically, as an example only, for instance, after dividing the edge devices into 5 groups, they are respectively... There are 3 edge computing nodes, numbered 1, 2, and 3; the candidate nodes for each group of edge devices are as follows: , , , , For edge computing node number 2, resource allocation will be prioritized as follows: and If the priority Then first of all Configure resources, All edge devices in the system sequentially obtain the corresponding demand request volume according to the demand-related response value in descending order, complete the resource allocation for concurrent communication of the industrial IoT platform, and realize low-latency concurrent communication.

[0047] Based on the same inventive concept as the above method, this application embodiment also provides a platform information low-latency concurrent communication transmission system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the platform information low-latency concurrent communication transmission methods described above.

[0048] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0049] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0050] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for low-latency concurrent communication transmission of platform information, characterized in that, The method includes the following steps: The system acquires status information data and transmission resource data of each edge device at each acquisition time, as well as location information of edge computing nodes. The status information data includes CPU utilization, memory capacity, GPU utilization, storage capacity, uplink and downlink traffic data, data transmission frequency, and location information data. The transmission resource data includes resource request volume and priority. Based on the differences between state information data of the same category, obtain the state information difference sequence of each edge device; based on the average distance between each state information difference sequence and other state information difference sequences, obtain the communication state difference value of each state information difference sequence of each edge device. Based on the changing trend of communication status difference values ​​and the distance between edge devices and edge computing nodes, obtain the response correlation difference value between each edge device and other edge devices; based on the average of all communication status difference values ​​of each edge device and the response correlation difference value, obtain the demand correlation response value of each edge device. A resource partitioning dataset is obtained based on the demand-related response value, resource request volume, and priority; based on the resource partitioning dataset, a clustering algorithm is used to achieve low-latency concurrent communication.

2. The platform information low-latency concurrent communication transmission method as described in claim 1, characterized in that, The method for obtaining the state information difference sequence is as follows: For the status information data of each edge device at each acquisition time, the sequence of all status information data except for the location information data is taken as the instantaneous status data sequence of each edge device at each acquisition time. For each edge device, the difference between elements with the same position in the instantaneous state data sequence at any two acquisition times is arranged according to the corresponding position to form a sequence, which is used as the state information difference sequence of each edge device.

3. The platform information low-latency concurrent communication transmission method as described in claim 1, characterized in that, The method for obtaining the communication status difference value is as follows: For each state information difference sequence of each edge device, calculate the average distance between the state information difference sequence and the state information difference sequences of all other edge devices, and use it as the communication state difference value of each edge device's state information difference sequence.

4. The platform information low-latency concurrent communication transmission method as described in claim 1, characterized in that, The method for obtaining the response correlation difference value is as follows: Obtain trend statistics for each edge device based on the changing trend of communication status difference values; Based on the distance between edge devices and edge computing nodes, obtain the communication transmission distribution sequence of each edge device; Based on trend statistics and communication transmission distribution sequences, the response correlation difference value between each edge device and other edge devices is obtained, using the following formula: In the formula, Indicates the first The and the first The response correlation difference value of communication response correlation characteristics between edge devices; and They represent the first The and the first A statistical measure of the trend of changes in the status information of an edge device; and They represent the first The and the first Communication transmission distribution sequence of each edge device express and The Manhattan distance between them.

5. The platform information low-latency concurrent communication transmission method as described in claim 4, characterized in that, The method for obtaining the trend statistics is as follows: The Mann-Kendall trend verification algorithm takes all communication status differences of each edge device as input and outputs trend statistics for each edge device.

6. The platform information low-latency concurrent communication transmission method as described in claim 4, characterized in that, The method for obtaining the communication transmission distribution sequence is as follows: Number all edge computing nodes; For each edge device, the distance between the edge device and the location of each edge computing node is calculated as the second distance. The numbers of each edge computing node are sorted in ascending order according to the second distance between the edge computing nodes to obtain the communication transmission distribution sequence of each edge device.

7. The platform information low-latency concurrent communication transmission method as described in claim 1, characterized in that, The formula for calculating the demand-related response value is as follows: In the formula, Indicates the first The demand-related response value of each edge device; Indicates the first The and the first The response correlation difference value between the edge devices; Indicates the first The mean of all communication status differences for each edge device; This indicates the number of edge devices.

8. The platform information low-latency concurrent communication transmission method as described in claim 1, characterized in that, The resource partitioning dataset includes: The array consisting of the demand-related response value, resource request amount, and priority of each edge device is used as the resource allocation array for each edge device. The resource partitioning dataset is a collection formed by mapping the resource partitioning arrays of all edge devices to a three-dimensional spatial coordinate system.

9. The platform information low-latency concurrent communication transmission method as described in claim 1, characterized in that, The method of achieving low-latency concurrent communication based on resource-partitioned datasets and clustering algorithms includes: Clustering algorithms are used to partition the resource dataset and obtain the edge devices in each group; For each group of edge devices, the mean of the Euclidean distance between each edge computing node and all edge devices in the group is calculated as the first mean. The sequence formed by arranging the numbers of all edge computing nodes in ascending order of the first mean is used as the candidate node sequence for each group of edge devices. For each group of edge devices, the edge devices in each group are selected as the preferred edge device group for the edge computing node corresponding to the first number in the candidate node sequence. For each edge computing node, the preferred edge device group is prioritized for resource allocation, and the resources are allocated according to the priority of the devices in the preferred device group.

10. A platform information low-latency concurrent communication transmission system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the platform information low-latency concurrent communication transmission method as described in any one of claims 1-9.

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