A platform information low-latency concurrent communication transmission method and system

Abstract

The application relates to the technical field of data transmission, in particular to a platform information low-latency concurrent communication transmission method and system, which comprises the following steps: acquiring state information data and transmission resource data of each edge device at each collection time, and position information of an edge computing node; acquiring 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; acquiring a response correlation difference value between each edge device and other edge devices; acquiring a demand correlation response value of each edge device; acquiring resource division data set based on the demand correlation response value, a resource request amount and a priority; and realizing low-latency concurrent communication based on the resource division data set and in combination with a clustering algorithm. The application reduces the influence of edge device state information difference on resource configuration.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data transmission, in particular to a platform information low-latency concurrent communication transmission method and system. BACKGROUND

[0002] Low-latency concurrent communication of industrial Internet of Things platform refers to the ability to quickly and efficiently realize data transmission between multiple devices in the industrial Internet of Things platform network environment, and to minimize the delay time of data transmission. This communication method is crucial for industrial applications with high real-time requirements, such as industrial automation, intelligent transportation, and intelligent energy. Low latency means that data can be transmitted from the sending end to the receiving end 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 to improve the overall efficiency and throughput of the system.

[0003] Traditional industrial Internet of Things platform concurrent communication methods mainly cover wireless network transmission and wired network transmission. In terms of implementing low-latency concurrent communication of industrial Internet of Things platform, technologies such as Time-Sensitive Networking (TSN), Software-Defined Networking (SDN), and edge computing are often used. However, edge computing requires accurate resource allocation and deployment to ensure that critical tasks have sufficient computing and storage resources. At the same time, the heterogeneity of edge devices increases the difficulty of resource management. With the development of industrial Internet of Things platform networks, the number of edge devices and nodes is large, and the processing tasks generated are numerous. In the process of industrial Internet of Things platform, the relationship between nodes that need to be connected and transmitted is complex, and the data storage and processing capabilities of different edge devices are different. This situation makes the accuracy of resource allocation in industrial Internet of Things platform concurrent communication low, making it difficult to meet the needs of different tasks. Inaccurate resource allocation may cause some tasks to be unable to obtain the required resources in a timely manner, affecting the time of industrial Internet of Things platform concurrent communication and reducing the overall performance and efficiency of the system. SUMMARY

[0004] To solve the above technical problems, the purpose of the present application is to provide a platform information low-latency concurrent communication transmission method and system, and the technical solution adopted is as follows:

[0005] In a first aspect, the present application provides a platform information low-latency concurrent communication transmission method, which comprises the following steps:

[0006] Obtain the state information data and transmission resource data of each edge device at each collection time, and the location information of the edge computing node; the state information data includes CPU usage, memory capacity, GPU usage, storage capacity, uplink and downlink traffic data, data transmission frequency, and location information data, and the transmission resource data includes resource request quantity and priority;

[0007] Based on the difference between the state information data of the same category, the state information difference sequence of each edge device is obtained; based on the average distance between the state information difference sequence and other state information difference sequences, the communication state difference value of the state information difference sequence of each edge device is obtained;

[0008] Based on the change trend of the communication state difference value, the distance between the edge device and the edge computing node, the response correlation difference value between each edge device and other edge devices is obtained; based on the average of all communication state difference values of each edge device and the response correlation difference value, the demand correlation response value of each edge device is obtained;

[0009] Based on the demand correlation response value, the resource request quantity and the priority, the resource division data set is obtained; based on the resource division data set, the low-latency concurrent communication is realized by combining the clustering algorithm.

[0010] Further, the state information difference sequence is obtained by:

[0011] For the state information data of each edge device at each collection time, the sequence composed of all state information data except position information data is taken as the instantaneous state data sequence of each edge device at each collection time;

[0012] For each edge device, the difference between the elements of the same bit sequence in the instantaneous state data sequences of any two collection times is arranged according to the corresponding bit sequence to form a sequence, which is taken as the state information difference sequence of each edge device.

[0013] Further, the communication state difference value is obtained by:

[0014] For each state information difference sequence of each edge device, the average distance between the state information difference sequence and the state information difference sequence of all other edge devices is calculated as the communication state difference value of the state information difference sequence of each edge device.

[0015] Further, the response correlation difference value is obtained by:

[0016] Based on the change trend of the communication state difference value, the trend statistic of each edge device is obtained;

[0017] Based on the distance between the edge device and the edge computing node, the communication transmission distribution sequence of each edge device is obtained;

[0018] Based on the trend statistic and the communication transmission distribution sequence, the response correlation difference value between each edge device and other edge devices is obtained, and the formula is: ; In the formula, represents the th and the a response correlation difference value between the edge devices in response to the response correlation feature; and respectively represent a trend statistic of the state information change of the first and the second edge device; respectively represent a trend statistic of the state information change of the first and the second edge device; respectively represent a communication transmission distribution sequence of the first and the second edge device; respectively represent a communication transmission distribution sequence of the first represent the Manhattan distance between and .

[0019] Further, the method for obtaining the trend statistic is:

[0020] all the communication state difference values of each edge device as the input of the Mann-Kendall trend check algorithm, and output the trend statistic of each edge device.

[0021] Further, the method for obtaining the communication transmission distribution sequence is:

[0022] number all the edge computing nodes;

[0023] For each edge device, calculate the distance between the edge device and the location of each edge computing node as a second distance, sort the numbers of the edge computing nodes in the order of the second distances of the edge computing nodes from small to large, and obtain the communication transmission distribution sequence of each edge device.

[0024] Further, the calculation formula of the demand correlation response value is: ; in the formula, represents the demand correlation response value of the first edge device; represents the response correlation difference value between the first and the second edge device; represents the response correlation difference value between the first represents the average of all the communication state difference values of the first edge device; represents the number of edge devices.

[0025] Further, the resource division data set comprises:

[0026] an array composed of the demand correlation response value, the resource request amount and the priority of each edge device as the resource division array of each edge device;

[0027] a set composed of the resource division arrays of all the edge devices mapped into a three-dimensional space coordinate system as the resource division data set.

[0028] Further, the resource partitioning dataset is used in combination with a clustering algorithm to achieve low-latency concurrent communication, including:

[0029] The resource partitioning dataset is processed using a clustering algorithm to obtain groups of edge devices;

[0030] For each group of edge devices, the mean of the Euclidean distances between each edge computing node and all edge devices in the group is calculated as a first mean value, and a sequence formed by arranging the numbers of all edge computing nodes in ascending order of the first mean value is taken as a candidate node sequence for each group of edge devices.

[0031] For each candidate node sequence of each group of edge devices, the group of edge devices is taken as the preferred edge device group corresponding to the first number in the candidate node sequence, and resource allocation is performed preferentially for the preferred edge device group of each edge computing node, and the preferred resource allocation is performed according to the priority of the devices in the preferred edge device group.

[0032] In a second aspect, the embodiments of the present 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, and the processor implements the steps of the method described in any one of the above aspects when executing the computer program.

[0033] The present application has at least the following beneficial effects:

[0034] The present application proposes a platform information low-latency concurrent communication transmission method and system, which considers the process of concurrent communication of edge devices in industrial production, and due to the large number of edge devices and nodes, the large number of processing tasks, the complex node relationship that needs 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 internet platform is low.

[0035] The response correlation difference value of the communication response correlation characteristics between different edge devices is obtained, which has the beneficial effect of accurately analyzing the correlation response characteristics of the concurrent communication data transmission of different edge devices by combining the change difference of each edge device at different times in the concurrent communication transmission process and the distribution characteristics between the edge devices and the edge computing nodes.

[0036] The state information of concurrent communication of different edge devices in industrial production and the correlation characteristics of distribution are analyzed, the demand correlation response value of each edge device participating in concurrent communication data resource allocation in industrial production is calculated by synthesizing the correlation characteristic differences of all edge devices, the demand correlation response value, resource request quantity and priority are synthesized for division, and resource configuration is completed according to the division result. Its beneficial effects are that the demand correlation response between different edge devices and the distribution difference relative to the edge computing node are fully considered, the resources of the edge computing node of the industrial internet platform are optimally configured, the influence of the state information difference of the edge device on the resource configuration is reduced, and low-latency concurrent communication of the industrial internet platform is realized. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0038] Figure 1 A step flow chart of a platform information low-latency concurrent communication transmission method provided by an embodiment of the present application is shown in the figure.

[0039] Figure 2 A distribution connection schematic diagram of edge devices and edge computing nodes provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0040] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the specific embodiments, structure, features and effects of the platform information low-latency concurrent communication transmission method and system according to the present application are described in detail as follows. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0041] 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 the present application belongs.

[0042] The specific scheme of the platform information low-latency concurrent communication transmission method and system provided by the present application is described in detail below with reference to the drawings.

[0043] Please refer to Figure 1Fig. 1 shows a flow chart of a method for low-latency concurrent communication transmission of platform information according to an embodiment of the present application, which comprises the following steps:

[0044] In step S1, the state information data of the edge device of the industrial internet-of-things platform in the industrial production process is acquired, and the acquired data is preprocessed.

[0045] In the industrial production process, the transmission of data information between multiple devices in the Internet-of-Things environment can realize the automation of industrial production and the real-time control and monitoring in the production process. The low-latency concurrent communication of the industrial internet-of-things platform can be realized by using edge computing. However, the distribution difference between different edge computing nodes and edge devices and the state information feature difference of the edge devices are large in the actual industrial production process, which leads to the deviation of the concurrent communication resource allocation of the industrial internet-of-things platform and affects the concurrent communication efficiency of the industrial production device.

[0046] Therefore, in the present application, the state information data and transmission resource data of the edge device and the edge computing node in the industrial production process are acquired. The state information data includes the CPU usage rate, memory capacity, GPU usage rate, storage capacity, uplink and downlink traffic data, data transmission frequency, and location information data of the device. The transmission resource data includes resource request quantity and priority. Specifically, the above information data can be acquired by the edge device operating system, the router, and the positioning device of the edge device. Due to the complexity of the industrial production environment, there may be interference in the state information data collected by the edge device. The data is preprocessed by data cleaning. The preprocessing process in the present embodiment includes outlier detection, missing value filling, and noise reduction processing. The data cleaning process is a known technology to those skilled in the art, and will not be described here.

[0047] In an implementation manner of the present 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.

[0048] An exemplary, Figure 2 Fig. 2 shows a distribution connection diagram of the edge device and the edge computing node in the industrial production process according to an embodiment of the present application. Wherein, 1-3 respectively represent the data center, the edge device and the wireless transmission link, and 5-7 respectively represent the server, the wireless access node and the router.

[0049] In step S2, the state information feature of the device running is acquired according to the state information data of each device in the industrial production process, the resource allocation feature of the edge device is analyzed, and the demand correlation response value of each edge device for concurrent communication is calculated based on the analysis result.

[0050] Generally, in the industrial production process, the concurrent communication transmission resources of different devices are uniformly allocated to realize data transmission sharing and data analysis. However, in the time industrial production process, the number of edge devices and the processing tasks of edge devices at different times are different, and the priorities of the processing tasks of edge devices in different time periods are also different. Therefore, in order to improve the concurrent communication transmission efficiency of multiple devices in the industrial production process, it is necessary to further improve the resource allocation accuracy of edge devices. However, the existing edge computing method does not consider the heterogeneous characteristics of actual industrial production devices and the associated response state between devices and edge computing nodes, resulting in low concurrent communication transmission efficiency of the industrial internet platform.

[0051] To solve the above problems, the distribution characteristics and state information response state of edge devices and edge computing nodes in the industrial production process are analyzed in the present application, and then the resource allocation of concurrent communication in the industrial production process is analyzed based on the distribution characteristics and response relationship of edge devices and edge computing nodes. The specific resource allocation process is as follows:

[0052] Because the data volume and the complexity of processing tasks of concurrent communication between different devices in different time periods are different, and the storage and processing capabilities of edge computing nodes also have great differences, the existing resource allocation method does not fully consider the associated response of state information data of different devices and the distribution heterogeneity characteristics of edge computing nodes. Therefore, only through the resource allocation based on data volume and processing priority cannot avoid the influence of the state information difference of edge devices on resource allocation.

[0053] Therefore, in the present application, the state information data of each edge device in the industrial production process is collected, and the correlation of concurrent communication is analyzed according to the state response characteristics of different edge devices.

[0054] Specifically, for the state information data of each edge device at each collection time, all the state information data except the position information data are combined to form a sequence as the instantaneous state data sequence of the concurrent communication associated response at each collection time; the difference value of the same order value elements between the instantaneous state data sequences corresponding to any two collection times of each edge device in the industrial production process is obtained, and the sequence composed of all the difference values is taken as the state information difference sequence of data transmission at different times of each edge device.

[0055] Further, in order to accurately reflect the state information change characteristics of the concurrent communication data transmission process of different edge devices in the industrial production process, for each state information difference sequence of each edge device, the DTW distance between the state information difference sequence and each other state information difference sequence is calculated, the mean of all the DTW distances is calculated, and the mean is taken as the communication state difference value of each state information difference sequence of each edge device. The larger the communication state difference value is, the greater the difference in state change of the concurrent communication data transmission process of the edge device at different times is. Wherein, the DTW distance is a known technology, and this embodiment will not be repeated.

[0056] Further, all the communication state difference values corresponding to each edge device are taken as input, and the Mann-Kendall trend check algorithm is used to obtain the trend statistic of each edge device The larger the trend statistic is, the greater the state change difference trend of the edge device is, and the more frequent the state change of the edge device in processing tasks is. The Mann-Kendall trend check algorithm is a known technology, and this embodiment will not be repeated.

[0057] Considering the state change characteristics of each edge device in processing tasks, combining the change difference of each edge device at different times in the concurrent communication transmission process and the distribution characteristics between the edge device and the edge computing node, the correlation response characteristics of the concurrent communication data transmission of different edge devices are analyzed. Specifically, all the edge computing nodes are numbered, for example, if there are edge computing nodes, the number is 1 ; the Euclidean distance between each edge device and the location of each edge computing node is calculated, and the sequence composed of the numbers of all edge nodes in ascending order of the Euclidean distance is taken as the communication transmission distribution sequence of each edge device .

[0058] According to the state change characteristics of the processing tasks of each edge device and the distribution characteristics relative to the edge computing node, the response correlation difference value of the communication response correlation characteristics between different edge devices is calculated .

[0059] Wherein, the response correlation difference value between different edge devices is positively correlated with the state information change difference between different edge devices and the distribution difference between different edge devices.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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, a demand correlation response value of the first edge device; a response correlation difference value between the first edge device and the second edge device; a mean value of all communication state difference values of the first edge device;

[0064] It should be noted that the greater the demand correlation response value of the calculated concurrent communication resource allocation, the greater the associated response difference of the current edge device relative to the state change of the processing task of other devices, and the greater the influence on the resource configuration division; on the contrary, the smaller the demand correlation response value, the smaller the influence on the resource configuration division.

[0065] Further, the array composed of the demand correlation response value, the resource request amount and the priority corresponding to each edge device is taken as the resource division array of each edge device for concurrent communication data transmission The resource division array corresponding to each edge device is taken as the coordinate mapped into a three-dimensional space coordinate system, wherein is the axis coordinate representing the demand correlation response value; is the axis coordinate representing the resource request amount; is the axis coordinate representing the priority. The set composed of the mapping of all edge devices in the three-dimensional rectangular coordinate system is taken as the resource division dataset.

[0066] Step S3, according to the demand correlation response value, the resource configuration division of the edge device of the concurrent communication process of the industrial Internet of Things platform is carried out, according to the resource configuration division result of the edge device in the concurrent communication process of the industrial Internet of Things platform, the resource of the communication transmission process of the industrial Internet of Things platform is configured, and the low-latency concurrent communication of the industrial Internet of Things platform is realized through the configuration result.

[0067] Further, in order to accurately perform resource configuration division of concurrent communication in industrial production process, the resource division dataset is taken as input, the K-means algorithm is used to obtain the division result of the resource division dataset, the clustering distance is Euclidean distance, and each cluster is output, wherein the division number of the cluster is In the embodiment, the value of r is 5, the K-means algorithm is a technology known to those skilled in the art, and the specific calculation process will not be described.

[0068] ​​​​​​​​According to the partition result of the resource partition dataset, a partition result of all edge devices in the industrial production process is obtained, wherein each group of edge devices in the partition result is edge devices with close demand correlation response values, demand request amounts and priorities in concurrent communication data transmission.

[0069] For each group of edge devices, the average of the Euclidean distances between each edge computing node and all edge devices in the group is calculated as a first average value, and a sequence formed by arranging the numbers of all edge computing nodes in ascending order of the first average values is taken as a candidate node sequence of each group of edge devices.

[0070] Further, for each edge computing node, resource partition is performed. Specifically, for the candidate node sequence of each group of edge devices, each group of edge devices is taken as a preferred edge device group of the edge computing node corresponding to the first number in the candidate node sequence; resource configuration is preferentially performed on the preferred edge device group of each edge computing node, and preferential resource configuration is performed on the preferred edge devices in the preferred edge device group according to the priorities of the preferred edge devices, so as to complete resource configuration for concurrent communication of the industrial Internet of Things platform and realize low-latency concurrent communication.

[0071] Specifically, only as an example, for example, after partition, five groups of edge devices are , and there are three edge computing nodes, the numbers of the edge computing nodes are 1, 2 and 3; the candidate nodes corresponding to each group of edge devices are , , , , respectively; for the edge computing node with the number 2, preferential resource configuration is performed as and , wherein if the priority , resource configuration is first performed on , and the demand request amounts of all edge devices in are obtained in descending order of the demand correlation response values, so as to complete resource configuration for concurrent communication of the industrial Internet of Things platform and realize low-latency concurrent communication.

[0072] Based on the same inventive concept as the above method, the embodiments of the present application also provide a platform information low-latency concurrent communication transmission system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the platform information low-latency concurrent communication transmission methods in the above platform information low-latency concurrent communication transmission method when executing the computer program.

[0073] It should be noted that the above-mentioned order of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of the present application. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0074] Each of the embodiments in the present application is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0075] The above is only the preferred embodiment of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present 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, low-latency concurrent communication is achieved by combining clustering algorithms. 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. 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 Manhattan distance between them; 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; Indicates the number of edge devices; 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.

2. 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.

3. The platform information low-latency concurrent communication transmission method as described in claim 1, 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.

4. The platform information low-latency concurrent communication transmission method as described in claim 1, 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.

5. 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.

6. 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 1-5.

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