Communication connector performance management and control method, center node and storage medium

Abstract

The invention belongs to the field of communication, and particularly relates to a communication connector performance management and control method, a center node and a storage medium, and the method can match local tasks forming new tasks according to the basic loss percentage of each function module of a communication connector of each edge node, so that each edge node can complete the matching task, and the communication connector performance management and control method is realized. According to the communication connector provided by the invention, the deviation of the loss percentage of each functional module of the corresponding communication connector is reduced, so that when the functional module of the communication connector of any edge node is completely lost, other functional modules of the communication connector are nearly completely lost, each communication connector can be fully utilized, and the use cost is greatly reduced.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular to a communication connector performance control method, a central node, and a storage medium. Background Art

[0002] Distributed computing is a technology that distributes computing tasks to multiple computing nodes for joint completion. Usually, the central node splits the tasks and distributes them to the edge nodes for execution. The edge nodes are often equipped with communication connectors to ensure accurate and timely communication with the central node. However, when the edge node is assigned to tasks of different categories, its interaction mode and frequency with the central node are different, so the functional modules of the communication connector on the edge node are also different, which makes the degree of loss of the communication connector different; in the prior art, when the central node assigns tasks, it often assigns the same task to the same edge node for execution. Since the loss progress of each functional module of the communication connector of the edge node is not consistent when the edge node executes the same type of task, after an edge node repeatedly executes the same task, it often causes some functional modules of its communication connector to be worn out and unusable while most modules are intact, causing the user to have to replace the communication connector, making it difficult to fully utilize the communication connector, resulting in an increase in the cost of use. Summary of the invention

[0003] In view of this, an embodiment of the present application provides a communication connector performance control method, a central node and a storage medium, which can solve the above technical problems.

[0004] A first aspect of an embodiment of the present application provides a communication connector performance control method, which is applied to a central node. The communication connector performance control method includes: S1: For each edge node, determine the basic loss percentage of each functional module in the communication connector of the edge node; S2: retrieve all historical tasks completed by the edge node, and classify the historical tasks to obtain several categories of task groups; S3: for each category of task groups, determining the percentage change of loss of each functional module of the communication connector when the edge node performs the historical tasks of the task group; S4: Determine the loss rate of each functional module of the edge node caused by the task of this category according to the loss percentage change; S5: when a new task is received, identifying each local task in the new task; S6: For each local task, match the local task to an edge node according to the determined loss rate and each basic loss percentage, so that after the edge node completes the matched local task, the deviation of the loss percentage of each functional module of the corresponding communication connector is reduced; S7: Send each local task to a matching edge node, so that the edge node completes the local task.

[0005] A second aspect of an embodiment of the present application provides a central node, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the communication connector performance control method.

[0006] A third aspect of an embodiment of the present application provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor executes the steps of the communication connector performance control method.

[0007] Compared with the prior art, the embodiments of the present application have the following beneficial effects: the method provided by the present invention includes, for each edge node, determining the basic loss percentage of each functional module in the communication connector of the edge node; retrieving all historical tasks completed by the edge node, and classifying the historical tasks to obtain task groups of several categories; for each category of task groups, determining the change in loss percentage of each functional module of the communication connector when the edge node executes the historical tasks of the task group; determining the loss rate of each functional module of the edge node caused by the tasks of this category based on the change in loss percentage; when a new task is received, identifying each local task in the new task; for each local task, the local task is divided into the new task and ... The task is matched to an edge node so that after the edge node completes the matched local task, the deviation of the loss percentage of each functional module of the corresponding communication connector is reduced; each local task is sent to the matching edge node so that the edge node completes the local task. In the present application, the local tasks constituting the new task can be matched according to the basic loss percentage of each functional module of the communication connector of each edge node, so that after each edge node completes the matching task, the deviation of the loss percentage of each functional module of the corresponding communication connector is reduced. When a functional module of the communication connector of any edge node is completely lost, the other functional modules of the communication connector are also close to complete loss, so that each communication connector can be fully utilized, which greatly reduces the cost of use. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0009] Figure 1 It is a schematic diagram of the implementation process of the communication connector performance control method provided in the embodiment of the present application; Figure 2 It is a schematic diagram of the implementation environment of the communication connector performance control method provided in the embodiment of the present application; Figure 3 It is a proportional bar diagram of the communication connector performance control method provided in an embodiment of the present application; Figure 4 It is a schematic diagram of the central node provided in an embodiment of the present application. DETAILED DESCRIPTION

[0010] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0011] In order to illustrate the technical solution described in this application, a specific embodiment is provided below for illustration.

[0012] Figure 1 A communication connector performance control method provided in the first embodiment of the present application is shown, which is applied to a central node. The communication connector performance control method includes: S1: For each edge node, determine the basic loss percentage of each functional module in the communication connector of the edge node; S2: retrieve all historical tasks completed by the edge node, and classify the historical tasks to obtain several categories of task groups; S3: for each category of task groups, determining the percentage change of loss of each functional module of the communication connector when the edge node performs the historical tasks of the task group; S4: Determine the loss rate of each functional module of the edge node caused by the task of this category according to the loss percentage change; S5: when a new task is received, identifying each local task in the new task; S6: For each local task, match the local task to an edge node according to the determined loss rate and each basic loss percentage, so that after the edge node completes the matched local task, the deviation of the loss percentage of each functional module of the corresponding communication connector is reduced; S7: Send each local task to a matching edge node, so that the edge node completes the local task.

[0013] In this embodiment, if Figure 2 As shown, the method is executed in the central node, which can be an independent physical server or terminal, or a server cluster composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud servers, cloud databases, cloud storage and CDN; the edge node can be a terminal such as a desktop computer, a laptop computer, a tablet computer, or a server; each edge node is equipped with a communication connector to achieve good communication with the central node; In this embodiment, the functional modules in the communication connector include but are not limited to an optical signal transmission module, an electrical signal transmission module, a power supply module, etc.; the basic loss percentage is the current loss percentage. If the loss percentage is 0, there is no loss; if the loss percentage is 100%, there is complete loss; for each functional module, the central node can monitor its status data in real time, and the data center node of some statuses can directly monitor it. For example, for the signal transmission module, the response rate of its signal transmission can be monitored, and the response efficiency can be compared with a preset response rate-loss percentage comparison table (including the response rate corresponding to each loss percentage of the signal sensor, the higher the loss percentage, the slower the response rate), and then the loss percentage of its signal transmission module can be determined; and the other part of the status data is collected by the status sensor installed at the corresponding functional module in the communication connector and transmitted to the central node. For example, an open circuit voltage sensor is installed at the power supply module to collect the open circuit voltage of the power supply module. The central node can estimate the remaining capacity of the power supply through the open circuit voltage, and then calculate the percentage of the lost capacity. Since the status data can be monitored in real time, the loss percentage can also be determined in real time.

[0014] In this embodiment, the categories of tasks may be parameter calculation, image recognition, interaction with a user terminal, etc.; when nodes execute tasks of different categories and communicate, the mobilization methods of the functional modules in the communication connector are not exactly the same, and the frequency and intensity of the mobilization are not exactly the same. Therefore, when nodes execute different tasks, the functional modules of the communication connector will be damaged to different degrees. The historical tasks of the same category are classified into the same task group, and then the damage of the modules of the communication connector caused by the historical tasks in the task group is determined (that is, how much damage the module has incurred over how long a period of time). The damage rate of the functional modules of the edge node caused by the tasks of this category (including subsequent tasks of this type) can be obtained. Since the damage degree of the communication connector is inconsistent when different edge nodes execute tasks of the same category, it is necessary to execute the above process for each edge node separately to determine the damage rate of each functional module of each edge node caused by each category of tasks. In this embodiment, each new task received by the central node is an overall task composed of several local tasks. After receiving the task, the overall task can be split into local tasks for allocation; if the communication connectors of each edge node are all new communication connectors, that is, the basic loss percentage of each functional module of each communication connector is 0, then the local tasks can be randomly allocated; after each edge node completes the task, the corresponding functional module will generate loss, and the method can be executed when a new task is received next time; In the present application, usually, no matter what type of task the node performs, the various functional modules in the communication connector will be applied, but the degree of application of different modules is different, and the corresponding losses are also different; therefore, when the loss percentage of any communication module of the communication connector reaches 100%, the entire communication connector needs to be replaced, and other functional modules that have not been completely lost are wasted, so that the communication connector is not fully utilized; and through this method, the local tasks that constitute the new task can be matched according to the basic loss percentage of each functional module of the communication connector of each edge node, so that after each edge node completes the matching task, the deviation of the loss percentage of each functional module of the corresponding communication connector is reduced, and then when a functional module of the communication connector of any edge node is completely lost, the other functional modules of the communication connector are also close to complete loss, so that each communication connector can be fully utilized, greatly reducing the cost of use.

[0015] As a preferred embodiment, determining the loss percentage change of each functional module of the communication connector when the edge node executes the historical task of the task group includes: For each historical task in the task group, determine the start time and end time of the historical task execution, as well as the first duration from the start time to the end time; For each functional module of the edge node, retrieve the loss percentage of the functional module at the start time and the loss percentage at the end time; The loss percentage at the start time is subtracted from the loss percentage at the end time to obtain the loss percentage change of the functional module after the edge node completes the historical task.

[0016] The loss rate of each functional module of the edge node determined by the task of this category according to the loss percentage change includes: S41: For each functional module of the edge node, calculate the single loss rate of the functional module for each historical task in the task group of the category; S42: Calculate the average of each single loss rate to obtain the loss rate of the functional module for the task of this category; The single loss rate of the functional module for each historical task in the task group of this category is calculated by the following formula: in, is the single loss rate, is the loss percentage change of the functional module after the edge node completes the historical task. The first duration corresponding to this historical task.

[0017] In this embodiment, the historical tasks are local tasks received by the edge node in the past. When completing the tasks, the edge node will synchronize the task progress to the central node in real time, so that the central node can identify when the edge node starts the task (starting time) and when it completes the task (ending time), thereby determining the first duration. This embodiment obtains the loss rate of each functional module of the edge node caused by the tasks of the same category in history by averaging the loss rate. Since the amount of data samples used for calculation is large, the result has a high degree of accuracy. As a preferred embodiment, matching the local task to an edge node according to the determined loss rate and each basic loss percentage includes: S61: For each edge node, generate a corresponding loss percentage histogram, wherein the loss percentage histogram includes a proportion bar corresponding to each functional module, and the loss percentage corresponding to the height of the proportion bar is the basic loss percentage of the functional module; S62: Select an edge node as a potential matching node; S63: Determine the increase in loss of each functional module corresponding to the communication connector of the potential matching node for the local task; S64: Retrieve the loss percentage bar graph of the potential matching node, and add the loss increase of the corresponding functional module to each proportion bar in the graph; S65: Determine whether the loss percentage corresponding to at least one adjusted proportion column reaches 100%; S66: If not, the deviation of each adjusted scale column is calculated by the following formula: in, is the deviation of each adjusted scale bar, n is the number of scale bars, is the loss percentage corresponding to the i-th scale column, is the mean of the loss percentage corresponding to each proportion column; S67: Match the local task to the corresponding potential matching node with the smallest deviation; S68: If there is at least one adjusted scale column whose corresponding loss percentage reaches 100%, exclude the potential matching node with the loss, and execute steps S62 to S68 until the local task is matched to a potential matching node.

[0018] While identifying each local task, the task amount and category of the local task are identified; determining the loss increase of each functional module corresponding to the communication connector of the potential matching node for the local task includes: S631: Determine a second time duration for completing the local task at the potential matching node according to the task amount and type of the local task; S632: Retrieve all loss rates of the potential matching node corresponding to the category of the local task; S633: Multiply each loss rate by the second duration to obtain the loss increase of the local task for the functional module corresponding to the loss rate.

[0019] In this embodiment, the loss increase is the increased loss percentage; the proportion column is the column corresponding to the basic loss percentage; Figure 3 As shown, the increase in loss of the corresponding functional module is added to the proportional column, that is, the column corresponding to the increase in loss is added to the column corresponding to the basic loss percentage; the new task received by the central node also includes task information, including the task volume and category of each local task of the new task; the central node stores a task volume-completion time comparison table for each edge node for different types of tasks, including the time required for the corresponding edge node to complete the task with different task volumes, and the second time can be determined by comparing the corresponding task volume with the data in the table; the data in the task volume-completion time comparison table can be determined according to the time it takes for the edge node to complete the same historical task (assisted by interpolation calculation).

[0020] As a preferred embodiment, after step S6, the method further includes: S601: For each edge node, split a subtask from the local task matched by the edge node; S602: After the segmentation is completed, determine whether the deviation corresponding to the edge node is reduced, if not, cancel the segmentation; S603: If yes, determine whether there is an adapter node for the subtask in other edge nodes, if not, cancel the division, wherein the adapter node can reduce the corresponding deviation after completing the subtask; S604: If yes, match the subtask to the adapter node; In step S7, if any subtask has been matched to any edge node, the subtask is firstly split out from the corresponding local task, and then the local task and the subtask are sent to the matched edge node.

[0021] The ratio of the task amount of each divided subtask to the corresponding divided local task is a set ratio, and the category of the subtask is the same as the category of the divided local task; Determining whether the deviation of the edge node is reduced includes: The deviation of the corresponding proportion column after the edge node completes the corresponding local task without task segmentation is used as the first deviation; Determine a third time duration for completing the subtask at the edge node according to the task amount and type of the subtask; Retrieve all loss rates of the edge node corresponding to the category of the subtask; Multiply each retrieved loss rate by the third duration to obtain a first sub-loss increase of the subtask for the functional module corresponding to the loss rate; For each adjusted scale column corresponding to the edge node, subtract the corresponding first sub-loss increase from the scale column to obtain an updated scale column; Calculate the deviation of each updated scale bar to obtain a second deviation; It is determined whether the second deviation is less than the first deviation. If so, the deviation of the edge node is reduced; otherwise, the deviation of the edge node is not reduced.

[0022] Determining whether there is an adapter node for the subtask in other edge nodes includes: For each other edge node, the deviation corresponding to the edge node after completing the corresponding local task is used as the third deviation; Determine a fourth duration for completing the subtask at the edge node according to the task amount and type of the subtask; Retrieve all loss rates of the edge node corresponding to the category of the subtask; Multiply each retrieved loss rate by the fourth duration to obtain a second sub-loss increase of the subtask for the functional module corresponding to the loss rate; For each adjusted scale column corresponding to the edge node, add the corresponding first sub-loss increase to the scale column to obtain an updated scale column; Calculate the deviation of each updated scale bar to obtain a fourth deviation; Determine whether the fourth deviation is less than the third deviation, if so, the edge node is a potential adaptation node, otherwise, the edge node is not a potential adaptation node; For each potential adaptation node, subtract the corresponding fourth deviation from the corresponding third deviation to obtain a target difference; The potential adaptation node with the largest target difference is determined as the adaptation node.

[0023] In this embodiment, the set ratio can be 10% or other ratios. For example, if a local task is 'perform image processing on 10 photos', a sub-task of 'perform image processing on 1 photo' can be divided out; if any sub-task has been matched to any edge node, the sub-task and the local task originally matched by the edge node are sent to the edge node; and other edge nodes that have no matching sub-tasks still send the originally matched local tasks to the edge node (if the matched local tasks are divided, the divided local tasks are sent to the edge node); in this embodiment, the method for determining the third duration and the fourth duration is consistent with the second duration; the calculation method of the first deviation, the second deviation, the third deviation, and the fourth deviation is consistent with the method of step S66, which will not be repeated here; through the scheme of this embodiment, the loss deviation of the corresponding communication connector after each edge node completes the task can be further reduced, and the utilization rate of the communication connector can be further improved.

[0024] A central node provided in a second embodiment of the present application includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the communication connector performance control method, specifically including: S1: For each edge node, determine the basic loss percentage of each functional module in the communication connector of the edge node; S2: retrieve all historical tasks completed by the edge node, and classify the historical tasks to obtain several categories of task groups; S3: for each category of task groups, determining the percentage change of loss of each functional module of the communication connector when the edge node performs the historical tasks of the task group; S4: Determine the loss rate of each functional module of the edge node caused by the task of this category according to the loss percentage change; S5: when a new task is received, identifying each local task in the new task; S6: For each local task, match the local task to an edge node according to the determined loss rate and each basic loss percentage, so that after the edge node completes the matched local task, the deviation of the loss percentage of each functional module of the corresponding communication connector is reduced; S7: Send each local task to a matching edge node, so that the edge node completes the local task.

[0025] A storage medium is provided in Embodiment 3 of the present application. A computer program is stored on the storage medium. When the computer program is executed by a processor, the processor executes the steps of the communication connector performance control method, specifically including: S1: For each edge node, determine the basic loss percentage of each functional module in the communication connector of the edge node; S2: retrieve all historical tasks completed by the edge node, and classify the historical tasks to obtain several categories of task groups; S3: for each category of task groups, determining the percentage change of loss of each functional module of the communication connector when the edge node performs the historical tasks of the task group; S4: Determine the loss rate of each functional module of the edge node caused by the task of this category according to the loss percentage change; S5: when a new task is received, identifying each local task in the new task; S6: For each local task, match the local task to an edge node according to the determined loss rate and each basic loss percentage, so that after the edge node completes the matched local task, the deviation of the loss percentage of each functional module of the corresponding communication connector is reduced; S7: Send each local task to a matching edge node, so that the edge node completes the local task.

[0026] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0027] It should be understood that when used in the present application specification, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0028] It should also be understood that the term “and / or” used in the specification of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0029] As used in the present specification, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if [the described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [the described condition or event] is detected" or "in response to detecting [the described condition or event]" depending on the context.

[0030] In addition, in the description of the present specification, the terms "first", "second", "third", etc. are only used to distinguish descriptions, and cannot be understood as indicating or implying relative importance. It should also be understood that although the terms "first", "second", etc. are used to describe various elements in some embodiments of the present application in the text, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, the first table can be named as the second table, and similarly, the second table can be named as the first table, without departing from the scope of the various described embodiments. The first table and the second table are both tables, but they are not the same table.

[0031] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0032] Figure 4 is a schematic diagram of the structure of a central node provided in an embodiment of the present application. Figure 4 As shown, the central node of this embodiment includes: at least one processor ( Figure 4 Only one is shown in the figure), a memory, wherein the memory stores a computer program that can be run on the processor. When the processor executes the computer program, the steps in the above-mentioned various communication connector performance control method embodiments are implemented, such as Figure 1 Steps S1 to S7 are shown.

[0033] The central node may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The central node may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that Figure 4It is only an example of a central node and does not constitute a limitation of the central node. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the central node may also include an input sending device, a network access device, a bus, etc.

[0034] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0035] In some embodiments, the memory may be an internal storage unit of the central node, such as a hard disk or memory of the central node. The memory may also be an external storage device of the central node, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the central node. Furthermore, the memory may include both an internal storage unit of the central node and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory may also be used to temporarily store data that has been sent or is to be sent.

[0036] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0037] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile central node, the mobile central node can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0038] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0039] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0040] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0041] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0042] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A communication connector performance control method, applied to a central node, characterized in that: The communication connector performance control method comprises: S1: For each edge node, determine the basic loss percentage of each functional module in the communication connector of the edge node; S2: retrieve all historical tasks completed by the edge node, and classify the historical tasks to obtain several categories of task groups; S3: for each category of task groups, determining the percentage change of loss of each functional module of the communication connector when the edge node performs the historical tasks of the task group; S4: Determine the loss rate of each functional module of the edge node caused by the task of this category according to the loss percentage change; S5: when a new task is received, identifying each local task in the new task; S6: For each local task, match the local task to an edge node according to the determined loss rate and each basic loss percentage, so that after the edge node completes the matched local task, the deviation of the loss percentage of each functional module of the corresponding communication connector is reduced; S7: Send each local task to a matching edge node, so that the edge node completes the local task.

2. The method according to claim 1, characterized in that Determining the loss percentage change of each functional module of the communication connector when the edge node executes the historical task of the task group includes: For each historical task in the task group, determine the start time and end time of the historical task execution, as well as the first duration from the start time to the end time; For each functional module of the edge node, retrieve the loss percentage of the functional module at the start time and the loss percentage at the end time; The loss percentage at the start time is subtracted from the loss percentage at the end time to obtain the loss percentage change of the functional module after the edge node completes the historical task.

3. The method according to claim 2, characterized in that The loss rate of each functional module of the edge node determined by the task of this category according to the loss percentage change includes: S41: For each functional module of the edge node, calculate the single loss rate of the functional module for each historical task in the task group of the category; S42: Calculate the average of each single loss rate to obtain the loss rate of the functional module for the task of this category; The single loss rate of the functional module for each historical task in the task group of this category is calculated by the following formula: in, is the single loss rate, is the loss percentage change of the functional module after the edge node completes the historical task. The first duration corresponding to this historical task.

4. The method according to claim 3, characterized in that Matching the local task to an edge node according to the determined loss rate and each basic loss percentage includes: S61: For each edge node, generate a corresponding loss percentage histogram, wherein the loss percentage histogram includes a proportion bar corresponding to each functional module, and the loss percentage corresponding to the height of the proportion bar is the basic loss percentage of the functional module; S62: Select an edge node as a potential matching node; S63: Determine the increase in loss of each functional module corresponding to the communication connector of the potential matching node for the local task; S64: Retrieve the loss percentage bar graph of the potential matching node, and add the loss increase of the corresponding functional module to each proportion bar in the graph; S65: Determine whether the loss percentage corresponding to at least one adjusted proportion column reaches 100%; S66: If not, the deviation of each adjusted scale column is calculated by the following formula: in, is the deviation of each adjusted scale bar, n is the number of scale bars, is the loss percentage corresponding to the i-th scale column, is the mean of the loss percentage corresponding to each proportion column; S67: Match the local task to the corresponding potential matching node with the smallest deviation; S68: If there is at least one adjusted scale column whose corresponding loss percentage reaches 100%, exclude the potential matching node with the loss, and execute steps S62 to S68 until the local task is matched to a potential matching node.

5. The method according to claim 4, characterized in that While identifying each local task, the task amount and category of the local task are identified; determining the loss increase of each functional module corresponding to the communication connector of the potential matching node for the local task includes: S631: Determine a second time duration for completing the local task at the potential matching node according to the task amount and type of the local task; S632: Retrieve all loss rates of the potential matching node corresponding to the category of the local task; S633: Multiply each loss rate by the second duration to obtain the increase in loss of the local task for the functional module corresponding to the loss rate.

6. The method according to claim 4, characterized in that After step S6, the method further includes: S601: For each edge node, split a subtask from the local task matched by the edge node; S602: After the segmentation is completed, determine whether the deviation corresponding to the edge node is reduced, if not, cancel the segmentation; S603: If yes, determine whether there is an adapter node for the subtask in other edge nodes, if not, cancel the division, wherein the adapter node can reduce the corresponding deviation after completing the subtask; S604: If yes, match the subtask to the adapter node; In step S7, if any subtask has been matched to any edge node, the subtask is firstly split out from the corresponding local task, and then the local task and the subtask are sent to the matched edge node.

7. The method according to claim 6, characterized in that The ratio of the task amount of each divided subtask to the corresponding divided local task is a set ratio, and the category of the subtask is the same as the category of the divided local task; Determining whether the deviation of the edge node is reduced includes: The deviation of the corresponding proportion column after the edge node completes the corresponding local task without task segmentation is used as the first deviation; Determine a third time duration for completing the subtask at the edge node according to the task amount and type of the subtask; Retrieve all loss rates of the edge node corresponding to the category of the subtask; Multiply each retrieved loss rate by the third duration to obtain a first sub-loss increase of the subtask for the functional module corresponding to the loss rate; For each adjusted scale column corresponding to the edge node, subtract the corresponding first sub-loss increase from the scale column to obtain an updated scale column; Calculate the deviation of each updated scale bar to obtain a second deviation; It is determined whether the second deviation is less than the first deviation. If so, the deviation of the edge node is reduced; otherwise, the deviation of the edge node is not reduced.

8. The method according to claim 7, characterized in that Determining whether there is an adapter node for the subtask in other edge nodes includes: For each other edge node, the deviation corresponding to the edge node after completing the corresponding local task is used as the third deviation; Determine a fourth duration for completing the subtask at the edge node according to the task amount and type of the subtask; Retrieve all loss rates of the edge node corresponding to the category of the subtask; Multiply each retrieved loss rate by the fourth duration to obtain a second sub-loss increase of the subtask for the functional module corresponding to the loss rate; For each adjusted scale column corresponding to the edge node, add the corresponding first sub-loss increase to the scale column to obtain an updated scale column; Calculate the deviation of each updated scale bar to obtain a fourth deviation; Determine whether the fourth deviation is less than the third deviation, if so, the edge node is a potential adaptation node, otherwise, the edge node is not a potential adaptation node; For each potential adaptation node, subtract the corresponding fourth deviation from the corresponding third deviation to obtain a target difference; The potential adaptation node with the largest target difference is determined as the adaptation node.

9. A central node, characterized in that: It comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the communication connector performance control method as described in any one of claims 1 to 8.

10. A storage medium, characterized in that: A computer program is stored on the storage medium. When the computer program is executed by the processor, the processor executes the steps of the communication connector performance control method described in any one of claims 1 to 8.

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