A collaborative operation method and system for edge computing nodes

Abstract

The present application relates to the field of edge computing technology, and discloses a collaborative operation method and system for edge computing nodes, which includes matching a first node group, calculating a comprehensive cognitive index, screening a second node group, updating the second node group, and calculating a comprehensive verification index. The system corresponds to the method. The present application deeply analyzes the task characteristics before task assignment, matches nodes with functions and computing power, avoids blind assignment, comprehensively evaluates nodes through self-cognition and mutual cognition, screens out a second node group that better meets the task computing power requirements, improves resource utilization, dynamically updates the node group during task execution, promptly eliminates inefficient nodes and supplements suitable nodes, ensures that computing tasks are carried out smoothly and efficiently, generates maintenance instructions based on the comprehensive verification index, changes passive maintenance to active maintenance, reduces resource waste and safety hazards, and improves the overall efficiency and reliability of collaborative operations of edge computing nodes.

Description

Technical Field

[0001] The present application relates to the field of edge computing technology, and specifically to a collaborative operation method and system for edge computing nodes. Background Art

[0002] With the rapid development of technologies like the Internet of Things and 5G, edge computing has emerged and is being widely applied in numerous fields. Edge computing places computing and data storage close to the data source or user end to reduce data transmission latency and improve response speed. However, existing technologies still face numerous challenges in the collaborative operation of edge computing nodes, as follows:

[0003] Traditional edge computing node collaboration often lacks a comprehensive and accurate understanding and evaluation mechanism for nodes. When allocating tasks, it is difficult to properly match nodes based on their actual capabilities and status, which can lead to some nodes being overloaded and others being idle, reducing overall computing efficiency.

[0004] During task execution, there is a lack of effective dynamic adjustment strategies, which prevents the timely elimination of inefficient nodes and the addition of suitable nodes. This can lead to computing tasks being stalled, delayed, or even failing.

[0005] The maintenance of edge computing nodes mostly relies on regular manual inspections or fixed maintenance cycles. There is a lack of an intelligent maintenance instruction generation mechanism based on the actual operation status of the nodes and the performance of collaborative operations. This makes it difficult to achieve accurate maintenance, easily resulting in resource waste and potential safety hazards, and unable to fully utilize the advantages of collaborative operations of edge computing nodes.

[0006] In summary, a new technical solution for collaborative operation of edge computing nodes is urgently needed to solve the above problems. Summary of the Invention

[0007] The purpose of this application is to provide a collaborative operation method and system for edge computing nodes to solve the technical problems raised in the above background technology.

[0008] To achieve the above objectives, this application discloses the following technical solutions:

[0009] In a first aspect, the present application discloses a collaborative operation method of edge computing nodes, the method comprising:

[0010] S1: parse the computing task and match it to obtain a corresponding first node group; wherein the first node group is a collection of edge computing nodes that functionally correspond to the computing task;

[0011] S2: Comprehensively recognize each edge computing node in the first node group to obtain a corresponding comprehensive recognition index; wherein the comprehensive recognition index is calculated by a self-recognition score and a mutual recognition score, the self-recognition score is the result of the edge computing node's self-recognition, and the mutual recognition score is the result of the edge computing nodes' mutual recognition;

[0012] S3: Using the comprehensive cognitive index of the first node group and combining it with a preset node allocation rule, a second node group is obtained; wherein the node allocation rule is used to select a set of edge computing nodes in the first node group that meet the computing power of the computing task, and the second node group is a set of edge computing nodes corresponding to the computing task in terms of computing power, and the number of nodes in the second node group is less than or equal to the number of nodes in the first node group;

[0013] S4: Execute the computing task using the second node group, continuously update the second node group using a preset node self-elimination rule and a node screening rule, and execute the computing task using the updated second node group after the update until completion; wherein the node self-elimination rule is used to filter out edge computing nodes in the second node group that do not meet the computing efficiency requirements when executing the computing task, and the node screening rule is used to filter out edge computing nodes in the first node group that meet the computing power requirements of the computing task;

[0014] S5: Comprehensively verify each edge computing node in the second node group to obtain a corresponding comprehensive verification index and generate a corresponding node maintenance instruction; wherein, the comprehensive verification index is calculated by the self-verification score and the mutual verification score, the self-verification score is the result of self-verification of the edge computing node, the mutual verification score is the result of mutual verification of the edge computing node, and the node maintenance instruction is used to maintain the edge computing node.

[0015] Preferably, the analytical calculation task is specifically:

[0016] Obtaining the computing task;

[0017] The computing task is parsed to obtain a corresponding computing amount, computing type, and computing priority; wherein the computing amount, computing type, and computing priority are used to match the first node group.

[0018] Preferably, the calculation process of the comprehensive cognitive index is:

[0019] Obtaining the first node group;

[0020] Performing comprehensive cognition on the first node group, wherein the comprehensive cognition includes self-cognition and mutual cognition;

[0021] The self-cognition is the cognition performed by the edge computing node based on its own real-time operation status, and the self-cognition score is calculated;

[0022] The mutual recognition is the recognition performed by the edge computing node based on the real-time operation status of the edge computing nodes of the same type as itself, and the mutual recognition score is calculated;

[0023] The comprehensive cognitive index is calculated using the self-cognition score and the mutual cognition score.

[0024] Preferably, the self-cognition is specifically: calculating the self-cognition score based on the real-time operation status of the edge computing node and combining the historical computing efficiency;

[0025] The mutual recognition is specifically: based on the real-time operation status of edge computing nodes of the same type, and combined with the historical computing efficiency of edge computing nodes of the same type, a mutual recognition score is calculated.

[0026] Preferably, the screening process of the second node group includes:

[0027] Analyze the computing task to obtain a corresponding computing power allocation plan;

[0028] The node allocation rule is called, and in combination with the computing power allocation plan, edge computing nodes that meet the computing power of the computing task are screened in the first node group to obtain the second node group.

[0029] Preferably, the updating process of the second node group includes:

[0030] Using the node self-elimination rule to filter edge computing nodes in the second node group that do not meet the computing efficiency when executing the computing task, and eliminating the calculated minimum value;

[0031] The node self-elimination rules are as follows:

[0032]

[0033] Among them, EN i is the edge computing node numbered i that is screened out and does not meet the computing efficiency in the second node group when executing the computing task, min is the minimum value operator, The integral of the efficiency P of the edge computing node numbered i in the preset elimination period T, P i_τ is the computational efficiency threshold of the preset edge computing node numbered i, α i is the efficiency compensation value of the edge computing node numbered i, which is used to represent the compensation for the computing efficiency of the edge computing node during the elimination cycle.

[0034] Preferably, the updating process of the second node group further includes:

[0035] When an edge computing node in the second node group that does not meet the computing efficiency when executing the computing task is screened out, the sub-computing task of the edge computing node is obtained, and the node screening rule is called to screen the edge computing nodes in the first node group that meet the computing power of the computing task, and the computing tasks of the eliminated edge computing nodes are assigned to the screened edge computing nodes.

[0036] Preferably, the calculation process of the comprehensive verification index is:

[0037] Obtaining each edge computing node in the second node group that performs the computing task;

[0038] Performing comprehensive verification on each edge computing node in the second node group that performs the computing task, the comprehensive verification including self-verification and mutual verification;

[0039] The self-verification is a verification performed by the edge computing node based on its own operation records, and the self-verification score is calculated;

[0040] The mutual verification is performed by the edge computing node based on the operation records of the edge computing nodes of the same type as itself, and a mutual verification score is calculated;

[0041] The comprehensive verification index is calculated using the self-verification score and the mutual verification score.

[0042] Preferably, the self-verification is specifically: calculating the self-verification score based on the operation record of the edge computing node and combining the self-cognition evaluation;

[0043] The mutual verification is specifically as follows: based on the operation records of the edge computing nodes of the same type, the mutual verification score is calculated in combination with the historical computing efficiency of the edge computing nodes of the same type.

[0044] In a second aspect, the present application discloses a collaborative operation system of edge computing nodes, which is applicable to the collaborative operation method of edge computing nodes as described above, and includes:

[0045] A first node group matching module, configured to: parse the computing task and match it to obtain a corresponding first node group; wherein the first node group is a collection of edge computing nodes that functionally correspond to the computing task;

[0046] A comprehensive cognitive index calculation module, wherein the comprehensive cognitive index calculation module is configured to: comprehensively recognize each edge computing node in the first node group to obtain a corresponding comprehensive cognitive index; wherein the comprehensive cognitive index is calculated by a self-cognition score and a mutual cognition score, wherein the self-cognition score is the result of the edge computing node performing self-cognition, and the mutual cognition score is the result of the edge computing nodes performing mutual cognition;

[0047] a second node group screening module, the second node group screening module being configured to: use the comprehensive cognitive index of the first node group in combination with a preset node allocation rule to screen and obtain a second node group; wherein the node allocation rule is used to screen a set of edge computing nodes in the first node group that meet the computing power of the computing task, the second node group being a set of edge computing nodes corresponding to the computing task in terms of computing power, and the number of nodes in the second node group being less than or equal to the number of nodes in the first node group;

[0048] A second node group updating module, wherein the second node group updating module is configured to: use the second node group to perform the computing task, continuously update the second node group using a preset node self-elimination rule and a node screening rule, and use the updated second node group to perform the computing task until completion after the update; wherein the node self-elimination rule is used to screen out edge computing nodes in the second node group that do not meet the computing efficiency when performing the computing task, and the node screening rule is used to screen out edge computing nodes in the first node group that meet the computing power of the computing task;

[0049] A comprehensive verification index calculation module, wherein the comprehensive verification index calculation module is configured to: comprehensively verify each edge computing node in the second node group to obtain a corresponding comprehensive verification index and generate a corresponding node maintenance instruction; wherein, the comprehensive verification index is calculated by a self-verification score and a mutual verification score, the self-verification score is the result of self-verification of the edge computing node, the mutual verification score is the result of mutual verification of the edge computing node, and the node maintenance instruction is used to maintain the edge computing node.

[0050] Beneficial effects: The collaborative operation method and system of the edge computing nodes of the present application utilize a series of processes to analyze computing tasks and match the first node group, comprehensively calculate the comprehensive cognitive index of each node, screen the second node group based on the comprehensive cognitive index combined with the node allocation rules, execute tasks and continuously update the second node group, and comprehensively verify and generate maintenance instructions to achieve efficient and accurate collaborative operation of edge computing nodes. Before task assignment, in-depth analysis of task characteristics is carried out, and nodes are matched in combination with functions and computing power to avoid blind assignment. Through self-cognition and mutual cognition, the nodes are comprehensively evaluated to select the second node group that better meets the computing power requirements of the task, thereby improving resource utilization. During task execution, the node group is dynamically updated, inefficient nodes are eliminated in a timely manner, and suitable nodes are added to ensure that computing tasks are carried out smoothly and efficiently. Finally, maintenance instructions are generated based on the comprehensive verification index, changing from passive to active maintenance, reducing resource waste and safety hazards, and improving the overall efficiency and reliability of collaborative operations of edge computing nodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 A flowchart of a collaborative operation method for edge computing nodes provided in an embodiment of the present application;

[0053] Figure 2 A structural block diagram of the collaborative operation system of edge computing nodes provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following is a clear and complete description of the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0055] In this document, the term "comprising" is intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0056] The first aspect of this embodiment discloses Figure 1 A collaborative operation method of edge computing nodes is shown, the method comprising:

[0057] S1: Analyze the computing task and match it to obtain a corresponding first node group; the first node group is a collection of edge computing nodes that functionally correspond to the computing task;

[0058] S2: Comprehensively recognize each edge computing node in the first node group to obtain a corresponding comprehensive recognition index; wherein the comprehensive recognition index is calculated by the self-recognition score and the mutual recognition score, the self-recognition score is the result of the edge computing node's self-recognition, and the mutual recognition score is the result of the edge computing nodes' mutual recognition;

[0059] S3: Using the comprehensive cognitive index of the first node group and a preset node allocation rule, a second node group is obtained. The node allocation rule is used to select a set of edge computing nodes in the first node group that meet the computing power of the computing task. The second node group is a set of edge computing nodes that meet the computing power of the computing task, and the number of nodes in the second node group is less than or equal to the number of nodes in the first node group.

[0060] S4: Execute the computing task using the second node group, continuously update the second node group using the preset node self-elimination rule and node screening rule, and execute the computing task using the updated second node group until the task is completed; wherein the node self-elimination rule is used to filter out edge computing nodes in the second node group that do not meet the computing efficiency requirements when executing the computing task, and the node screening rule is used to filter out edge computing nodes in the first node group that meet the computing power requirements of the computing task;

[0061] S5: Comprehensively verify each edge computing node in the second node group to obtain a corresponding comprehensive verification index and generate a corresponding node maintenance instruction; wherein, the comprehensive verification index is calculated by the self-verification score and the mutual verification score, the self-verification score is the result of the self-verification of the edge computing node, and the mutual verification score is the result of the mutual verification of the edge computing node. The node maintenance instruction is used to maintain the edge computing node.

[0062] It should be noted that the node maintenance instruction of this embodiment is an instruction for maintaining edge computing nodes that is well known to existing technical personnel in this field, and its triggering mechanism can be, but is not limited to, comparing the obtained comprehensive verification index with the preset comprehensive verification index to trigger the node maintenance instruction.

[0063] Through the above, this embodiment utilizes a series of processes to analyze computing tasks and match the first node group, comprehensively calculate the comprehensive cognitive index of each node, screen the second node group based on the comprehensive cognitive index combined with the node allocation rules, execute tasks and continuously update the second node group, and comprehensively verify and generate maintenance instructions to achieve efficient and accurate edge computing node collaboration. Before task assignment, in-depth analysis of task characteristics, combining functions and computing power to match nodes, to avoid blind assignment. Through self-cognition and mutual cognition, the nodes are comprehensively evaluated to select the second node group that better meets the task computing power requirements and improve resource utilization. During task execution, the node group is dynamically updated, inefficient nodes are eliminated in a timely manner, and suitable nodes are added to ensure that computing tasks are carried out smoothly and efficiently. Finally, maintenance instructions are generated based on the comprehensive verification index, changing from passive to active maintenance, reducing resource waste and safety hazards, and improving the overall performance and reliability of edge computing node collaboration.

[0064] Specifically, the analytical calculation tasks are as follows:

[0065] Get computing tasks;

[0066] The computing task is parsed to obtain a corresponding computing amount, computing type, and computing priority; wherein the computing amount, computing type, and computing priority are used to match the first node group.

[0067] It should be noted that the analytical calculation tasks of this embodiment are based on the experience known to technical personnel in this field, and the division of calculation amount, calculation type and calculation priority all correspond to the matching of the first node group, thereby achieving a larger range of node screening.

[0068] Through the above, this embodiment provides an accurate basis for subsequent node matching and task allocation, thereby accurately locating the first node group corresponding to the task function among many edge computing nodes, making the initial matching of tasks and nodes more targeted, and avoiding node selection errors or resource waste due to unclear task information. In a simple example, when processing image recognition tasks, nodes with image processing capabilities can be quickly screened out based on the amount of computation and type, laying the foundation for subsequent efficient collaborative operations, and effectively improving the accuracy and efficiency of edge computing node collaborative operations at the initial stage of the task.

[0069] Specifically, the calculation process of the comprehensive cognitive index is as follows:

[0070] Get the first node group;

[0071] Conduct comprehensive cognition on the first node group, which includes self-cognition and mutual cognition;

[0072] Self-cognition is the cognition of the edge computing node based on its own real-time operation status, and the self-cognition score is calculated;

[0073] Mutual recognition is the recognition of edge computing nodes based on the real-time operation status of edge computing nodes of the same type as themselves, and the calculation of mutual recognition scores;

[0074] The comprehensive cognitive index was calculated using the self-cognition score and the mutual cognition score.

[0075] It should be noted that the comprehensive cognition of this embodiment optimizes the cognition of edge computing nodes, optimizing self-cognition through mutual cognition data, thereby providing more accurate data for screening the second node group. Furthermore, the comprehensive cognition index calculation method of this embodiment can be, but is not limited to, calculating by assigning corresponding values ​​to self-cognition scores and mutual cognition scores.

[0076] Through the above, this embodiment realizes a comprehensive and objective evaluation of edge computing nodes. Self-cognition is based on its own real-time operating conditions and historical computing efficiency, and mutual cognition is based on the real-time and historical conditions of nodes of the same type. The comprehensive cognition index derived from the combination of the two can reflect the node status in multiple dimensions, thereby overcoming the limitations of traditional single-dimensional evaluation and more accurately judging the adaptability of nodes in collaborative operations. In a simple example, when allocating big data processing tasks, nodes with stable and efficient computing can be screened out based on the comprehensive cognition index, reducing computing delays and errors caused by node performance fluctuations or poor adaptation, thereby improving the reliability and scientificity of node selection in collaborative operations of edge computing nodes.

[0077] Specifically, self-cognition is: based on the real-time operation of the edge computing node and combined with the historical computing efficiency, the self-cognition score is calculated;

[0078] Mutual recognition is specifically: based on the real-time operation status of edge computing nodes of the same type and combined with the historical computing efficiency of edge computing nodes of the same type, the mutual recognition score is calculated.

[0079] As a preferred implementation of this embodiment, this embodiment uses a self-cognition score formula to calculate the self-cognition score, wherein the self-cognition score formula is specifically:

[0080]

[0081] Among them, ScS i is the self-awareness score of the edge computing node numbered i, ScS p_i is the self-cognitive evaluation of the edge computing node numbered i based on the real-time operation status. It should be noted that the calculation of the self-cognitive evaluation can be any existing self-cognitive evaluation method of the edge computing node. For example, the self-cognitive evaluation is performed based on the computing efficiency (completion, accuracy, etc.) of the edge computing node. is the average value of the historical computing efficiency of the edge computing node numbered i, thereby realizing the correction of self-cognition of the real-time operation status in combination with historical data.

[0082] As a preferred implementation of this embodiment, this embodiment uses a mutual recognition score formula to calculate the mutual recognition score, wherein the mutual recognition score formula is specifically:

[0083]

[0084] Among them, McS i is the mutual recognition score, It is the average value of the historical computing efficiency of the edge computing nodes of the same type as the edge computing node numbered i, thereby achieving mutual understanding of the real-time operation status combined with the historical data of the collaborative operation.

[0085] Specifically, the screening process of the second node group includes:

[0086] Analyze the computing tasks to obtain the corresponding computing power allocation plan;

[0087] The node allocation rule is called, and in combination with the computing power allocation plan, the edge computing nodes that meet the computing power of the computing task are screened in the first node group to obtain the second node group.

[0088] It should be noted that the node allocation rules of this embodiment are obtained based on the computing power allocation experience known to those skilled in the art.

[0089] Through the above, this embodiment uses the analysis of computing tasks to obtain a computing power allocation plan and combines the node allocation rules to screen the second node group, thereby achieving precise adaptation of task computing power and node resources. Based on the computing power requirements analyzed by the computing task, the node allocation rules are called to screen in the first node group to ensure that the selected second node group can meet the task computing power requirements and achieve optimization in terms of the number of nodes. In a simple example, when processing complex mathematical model calculation tasks, the required number and type of nodes can be accurately determined according to the computing power allocation plan to avoid excess or insufficient node resources, so that the collaborative operation of edge computing nodes is more reasonable and efficient in computing power allocation, ensuring the smooth progress of tasks.

[0090] Specifically, the update process of the second node group includes:

[0091] Use the node self-elimination rule to filter out edge computing nodes in the second node group that do not meet the computing efficiency when executing the computing task, and eliminate the calculated minimum value;

[0092] The specific rules for node self-elimination are as follows:

[0093]

[0094] Among them, EN i is the edge computing node numbered i that does not meet the computing efficiency in the second node group when executing the computing task. min is the minimum value operator. The integral of the efficiency P of the edge computing node numbered i in the preset elimination period T, P i_τ is the computational efficiency threshold of the preset edge computing node numbered i, α i is the efficiency compensation value of the edge computing node numbered i, and the efficiency compensation value is used to characterize the compensation for the computing efficiency of the edge computing node during the elimination cycle. In a specific example, this embodiment uses the historical records of the compensation for the computing efficiency of the edge computing node during the elimination cycle to fit the efficiency compensation value.

[0095] Through the above, this embodiment uses the node self-elimination rule to realize the dynamic optimization of edge computing nodes during task execution. By setting a screening formula based on computing efficiency integral, threshold and compensation value, inefficient nodes in the second node group can be identified and eliminated in a timely manner. When processing continuous data flow computing tasks, computing freezes caused by node performance degradation or failure can be effectively eliminated to ensure the smoothness and stability of task execution. At the same time, the mechanism of eliminating inefficient nodes and supplementing suitable nodes enables the collaborative operation of edge computing nodes to adjust resource allocation in real time according to task progress, thereby improving overall computing efficiency and adaptability.

[0096] Specifically, the update process of the second node group further includes:

[0097] When an edge computing node that does not meet the computing efficiency in the second node group when executing a computing task is screened out, the sub-computing task of the edge computing node is obtained, and the node screening rule is called to screen the edge computing nodes with computing power that meet the computing task in the first node group, and the computing tasks of the eliminated edge computing nodes are assigned to the screened edge computing nodes.

[0098] It is understandable that although edge computing nodes each perform corresponding sub-computing tasks, the computing task is only completed after each sub-computing task is completed. Therefore, this embodiment designs node self-elimination rules and configures corresponding node screening rules to perform node self-updates, thereby optimizing the collaborative operation of edge computing nodes. It should be noted that the node screening rules of this embodiment are based on the computing power screening rules obtained based on experience known to those skilled in the art.

[0099] Through the above, this embodiment utilizes the mechanism of transferring sub-computing tasks when eliminating inefficient nodes to achieve seamless task execution and efficient resource utilization. When it is found that there are nodes in the second node group that do not meet the computing efficiency, their sub-tasks are quickly obtained and suitable nodes are selected in the first node group to take over. For example, in a multi-stage data processing task, if a node has a problem in the middle stage, its unfinished tasks can be assigned in time to avoid task interruption and idle resources, ensure the continuity and efficiency of the collaborative operation of edge computing nodes, and reduce the impact of node failure or inefficiency on the entire task process.

[0100] Specifically, the calculation process of the comprehensive verification index is as follows:

[0101] Obtain each edge computing node that performs computing tasks in the second node group;

[0102] Performing comprehensive verification on each edge computing node that performs computing tasks in the second node group, the comprehensive verification including self-verification and mutual verification;

[0103] Self-verification is the verification performed by the edge computing node based on its own operation records, and the self-verification score is calculated;

[0104] Mutual authentication is performed by an edge computing node based on the operation records of edge computing nodes of the same type as itself, and a mutual authentication score is calculated;

[0105] The comprehensive validation index is calculated using the self-validation score and the mutual validation score.

[0106] It should be noted that the comprehensive verification of this embodiment is an optimization of the verification of edge computing nodes. The purpose of verification is to provide a data foundation for the maintenance of edge computing nodes. By using mutual verification data, self-verification is optimized, thereby providing more accurate data for the screening of edge computing node maintenance. Furthermore, the comprehensive verification index calculation method of this embodiment can be, but is not limited to, calculating by assigning corresponding values ​​to self-verification scores and mutual verification scores.

[0107] Specifically, self-verification involves calculating a self-verification score based on the operating records of the edge computing nodes and combining it with self-cognitive evaluation;

[0108] Mutual verification is specifically as follows: based on the operation records of edge computing nodes of the same type, and combined with the historical computing efficiency of edge computing nodes of the same type, the mutual verification score is calculated.

[0109] As a preferred implementation of this embodiment, this embodiment uses a self-verification score formula to calculate the self-verification score, wherein the self-verification score formula is specifically:

[0110]

[0111] Among them, SvS v_i is the self-verification score of the edge computing node numbered i, ScS real_i It is a self-verification evaluation of the execution sub-computing task of the edge computing node numbered i, and the method of self-verification evaluation corresponds to the aforementioned self-cognition evaluation, thereby realizing the self-verification correction of the operation status record combined with the self-cognition evaluation.

[0112] As a preferred implementation of this embodiment, this embodiment uses a mutual authentication score formula to calculate the mutual authentication score, wherein the mutual authentication score formula is specifically:

[0113]

[0114] Among them, MvS i To achieve mutual verification of the scores, the real-time operation status can be verified by combining the historical data of the collaborative operation, thereby achieving mutual verification of the scores by combining the historical computing efficiency of the same type of edge computing nodes.

[0115] Through the above, this embodiment uses self-verification and mutual verification combined with historical computing efficiency to calculate a comprehensive verification index, thereby achieving precise guidance for the generation of edge computing node maintenance instructions. The self-verification score reflects the quality of its own operation, the mutual verification score reflects the comparison of similar situations, and the comprehensive verification index combines the two to accurately locate node problems. In a specific example, in the edge computing of the Industrial Internet of Things, based on this index, personalized maintenance instructions can be generated for different node problems, such as focusing on checking the network module for nodes with unstable data transmission, and optimizing algorithms for nodes with large calculation errors, thereby improving the accuracy and effectiveness of node maintenance in the collaborative operation of edge computing nodes and extending the service life and reliability of nodes.

[0116] The second aspect of this embodiment discloses Figure 2 A collaborative operation system of edge computing nodes is shown, and the system is applicable to the collaborative operation method of edge computing nodes as described above. The system includes:

[0117] A first node group matching module is configured to: parse the computing task and match it to obtain a corresponding first node group; wherein the first node group is a collection of edge computing nodes that functionally correspond to the computing task;

[0118] A comprehensive cognitive index calculation module is configured to: comprehensively recognize each edge computing node in the first node group to obtain a corresponding comprehensive cognitive index; wherein the comprehensive cognitive index is calculated by a self-cognition score and a mutual cognition score, wherein the self-cognition score is the result of the edge computing node's self-cognition, and the mutual cognition score is the result of the edge computing nodes' mutual cognition;

[0119] A second node group screening module is configured to: use the comprehensive cognitive index of the first node group in combination with a preset node allocation rule to screen and obtain a second node group; wherein the node allocation rule is used to screen a set of edge computing nodes in the first node group that meet the computing power of the computing task; the second node group is a set of edge computing nodes corresponding to the computing task in terms of computing power, and the number of nodes in the second node group is less than or equal to the number of nodes in the first node group;

[0120] A second node group update module, wherein the second node group update module is configured to: utilize the second node group to perform the computing task, continuously utilize preset node self-elimination rules and node screening rules to update the second node group, and utilize the updated second node group to perform the computing task until completion after the update; wherein the node self-elimination rules are used to screen out edge computing nodes in the second node group that do not meet the computing efficiency requirements when performing the computing task, and the node screening rules are used to screen out edge computing nodes in the first node group that meet the computing power requirements for the computing task;

[0121] A comprehensive verification index calculation module, the comprehensive verification index calculation module is configured to: comprehensively verify each edge computing node in the second node group to obtain a corresponding comprehensive verification index and generate a corresponding node maintenance instruction; wherein the comprehensive verification index is calculated by the self-verification score and the mutual verification score, the self-verification score is the result of self-verification of the edge computing node, and the mutual verification score is the result of mutual verification of the edge computing node. The node maintenance instruction is used to maintain the edge computing node.

[0122] It should be noted that the collaborative operation system of the edge computing nodes of this embodiment corresponds to the collaborative operation method of the edge computing nodes mentioned above. Therefore, the contents not specifically described in the collaborative operation system of the edge computing nodes of this embodiment may include but are not limited to functional definitions, working principles and technical effects, etc., and may refer to the records in the collaborative operation method of the edge computing nodes mentioned above. This text will not elaborate on them here.

[0123] In summary, the collaborative operation method and system of the edge computing nodes of this embodiment utilizes a series of processes to analyze computing tasks and match the first node group, comprehensively calculate the comprehensive cognitive index of each node, screen the second node group based on the comprehensive cognitive index combined with the node allocation rules, execute tasks and continuously update the second node group, and comprehensively verify and generate maintenance instructions to achieve efficient and accurate collaborative operation of edge computing nodes. Before task assignment, in-depth analysis of task characteristics is carried out, and nodes are matched in combination with functions and computing power to avoid blind assignment. Through self-cognition and mutual cognition, the nodes are comprehensively evaluated to select the second node group that better meets the computing power requirements of the task, thereby improving resource utilization. During task execution, the node group is dynamically updated, inefficient nodes are eliminated in a timely manner, and suitable nodes are added to ensure that computing tasks are carried out smoothly and efficiently. Finally, maintenance instructions are generated based on the comprehensive verification index, changing from passive to active maintenance, reducing resource waste and safety hazards, and improving the overall efficiency and reliability of collaborative operations of edge computing nodes.

[0124] In the embodiments provided herein, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to implement the functions described herein, or a combination thereof. For software implementation, part or all of the processes of the embodiments can be completed by instructing the relevant hardware through a computer program. When implemented, the above program can be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein the communication media include any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that a computer can access. The computer-readable storage medium can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0125] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent replacements for some of the technical features therein. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A collaborative operation method of edge computing nodes, characterized in that: The method includes: S1: parse the computing task and match it to obtain a corresponding first node group; wherein the first node group is a collection of edge computing nodes that functionally correspond to the computing task; S2: Comprehensively recognize each edge computing node in the first node group to obtain a corresponding comprehensive recognition index; wherein the comprehensive recognition index is calculated by a self-recognition score and a mutual recognition score, the self-recognition score is the result of the edge computing node's self-recognition, and the mutual recognition score is the result of the edge computing nodes' mutual recognition; S3: Using the comprehensive cognitive index of the first node group and combining it with a preset node allocation rule, a second node group is obtained; wherein the node allocation rule is used to select a set of edge computing nodes in the first node group that meet the computing power of the computing task, and the second node group is a set of edge computing nodes corresponding to the computing task in terms of computing power, and the number of nodes in the second node group is less than or equal to the number of nodes in the first node group; S4: Execute the computing task using the second node group, continuously update the second node group using a preset node self-elimination rule and a node screening rule, and execute the computing task using the updated second node group after the update until completion; wherein the node self-elimination rule is used to filter out edge computing nodes in the second node group that do not meet the computing efficiency requirements when executing the computing task, and the node screening rule is used to filter out edge computing nodes in the first node group that meet the computing power requirements of the computing task; S5: Comprehensively verify each edge computing node in the second node group to obtain a corresponding comprehensive verification index and generate a corresponding node maintenance instruction; wherein, the comprehensive verification index is calculated by the self-verification score and the mutual verification score, the self-verification score is the result of self-verification of the edge computing node, the mutual verification score is the result of mutual verification of the edge computing node, and the node maintenance instruction is used to maintain the edge computing node.

2. The collaborative operation method of edge computing nodes according to claim 1, characterized in that: The analytical calculation tasks are specifically: Obtaining the computing task; The computing task is parsed to obtain a corresponding computing amount, computing type, and computing priority; wherein the computing amount, computing type, and computing priority are used to match the first node group.

3. The collaborative operation method of edge computing nodes according to claim 1, characterized in that: The calculation process of the comprehensive cognitive index is as follows: Obtaining the first node group; Performing comprehensive cognition on the first node group, wherein the comprehensive cognition includes self-cognition and mutual cognition; The self-cognition is the cognition performed by the edge computing node based on its own real-time operation status, and the self-cognition score is calculated; The mutual recognition is the recognition performed by the edge computing node based on the real-time operation status of the edge computing nodes of the same type as itself, and the mutual recognition score is calculated; The comprehensive cognitive index is calculated using the self-cognition score and the mutual cognition score.

4. The collaborative operation method of edge computing nodes according to claim 3, characterized in that: The self-cognition is specifically: based on the real-time operation of the edge computing node and combined with the historical computing efficiency, the self-cognition score is calculated; The mutual recognition is specifically: based on the real-time operation status of edge computing nodes of the same type, and combined with the historical computing efficiency of edge computing nodes of the same type, a mutual recognition score is calculated.

5. The collaborative operation method of edge computing nodes according to claim 1, characterized in that: The screening process of the second node group includes: Analyze the computing task to obtain a corresponding computing power allocation plan; The node allocation rule is called, and in combination with the computing power allocation plan, edge computing nodes that meet the computing power of the computing task are screened in the first node group to obtain the second node group.

6. The collaborative operation method of edge computing nodes according to claim 1, characterized in that: The updating process of the second node group includes: Using the node self-elimination rule to filter edge computing nodes in the second node group that do not meet the computing efficiency when executing the computing task, and eliminating the calculated minimum value; The node self-elimination rules are as follows: Among them, EN i is the edge computing node numbered i that is screened out and does not meet the computing efficiency in the second node group when executing the computing task, min is the minimum value operator, The integral of the efficiency P of the edge computing node numbered i in the preset elimination period T, P i_τ is the computational efficiency threshold of the preset edge computing node numbered i, α i is the efficiency compensation value of the edge computing node numbered i, which is used to represent the compensation for the computing efficiency of the edge computing node during the elimination cycle.

7. The collaborative operation method of edge computing nodes according to claim 1, characterized in that: The updating process of the second node group further includes: When an edge computing node in the second node group that does not meet the computing efficiency when executing the computing task is screened out, the sub-computing task of the edge computing node is obtained, and the node screening rule is called to screen the edge computing nodes in the first node group that meet the computing power of the computing task, and the computing tasks of the eliminated edge computing nodes are assigned to the screened edge computing nodes.

8. The collaborative operation method of edge computing nodes according to claim 1, characterized in that: The calculation process of the comprehensive verification index is as follows: Obtaining each edge computing node in the second node group that performs the computing task; Performing comprehensive verification on each edge computing node in the second node group that performs the computing task, the comprehensive verification including self-verification and mutual verification; The self-verification is a verification performed by the edge computing node based on its own operation records, and the self-verification score is calculated; The mutual verification is performed by the edge computing node based on the operation records of the edge computing nodes of the same type as itself, and a mutual verification score is calculated; The comprehensive verification index is calculated using the self-verification score and the mutual verification score.

9. The collaborative operation method of edge computing nodes according to claim 8, characterized in that: The self-verification is specifically: based on the operation record of the edge computing node, and combined with the self-cognition evaluation, the self-verification score is calculated; The mutual verification is specifically as follows: based on the operation records of the edge computing nodes of the same type, the mutual verification score is calculated in combination with the historical computing efficiency of the edge computing nodes of the same type.

10. A collaborative operation system of edge computing nodes, the system being applicable to the collaborative operation method of edge computing nodes as described in any one of claims 1 to 9, characterized in that: The system includes: A first node group matching module, configured to: parse the computing task and match it to obtain a corresponding first node group; wherein the first node group is a collection of edge computing nodes that functionally correspond to the computing task; A comprehensive cognitive index calculation module, wherein the comprehensive cognitive index calculation module is configured to: comprehensively recognize each edge computing node in the first node group to obtain a corresponding comprehensive cognitive index; wherein the comprehensive cognitive index is calculated by a self-cognition score and a mutual cognition score, wherein the self-cognition score is the result of the edge computing node performing self-cognition, and the mutual cognition score is the result of the edge computing nodes performing mutual cognition; a second node group screening module, the second node group screening module being configured to: use the comprehensive cognitive index of the first node group in combination with a preset node allocation rule to screen and obtain a second node group; wherein the node allocation rule is used to screen a set of edge computing nodes in the first node group that meet the computing power of the computing task, the second node group being a set of edge computing nodes corresponding to the computing task in terms of computing power, and the number of nodes in the second node group being less than or equal to the number of nodes in the first node group; A second node group updating module, wherein the second node group updating module is configured to: use the second node group to perform the computing task, continuously update the second node group using a preset node self-elimination rule and a node screening rule, and use the updated second node group to perform the computing task until completion after the update; wherein the node self-elimination rule is used to screen out edge computing nodes in the second node group that do not meet the computing efficiency when performing the computing task, and the node screening rule is used to screen out edge computing nodes in the first node group that meet the computing power of the computing task; A comprehensive verification index calculation module, wherein the comprehensive verification index calculation module is configured to: comprehensively verify each edge computing node in the second node group to obtain a corresponding comprehensive verification index and generate a corresponding node maintenance instruction; wherein, the comprehensive verification index is calculated by a self-verification score and a mutual verification score, the self-verification score is the result of self-verification of the edge computing node, the mutual verification score is the result of mutual verification of the edge computing node, and the node maintenance instruction is used to maintain the edge computing node.

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