A method for controlling the performance of a communication connector, a central node, and a storage medium

Abstract

This application belongs to the field of communications, and particularly relates to a method for controlling the performance of a communication connector, a central node, and a storage medium. The method can match local tasks that make up a new task according to the basic loss percentages of each functional module of the communication connectors 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. Furthermore, 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. Therefore, each communication connector can be fully utilized, greatly reducing the usage cost.

Description

Technical Field

[0001] This application belongs to the field of communications, and particularly relates to a method for controlling the performance of a communication connector, a central node, and a storage medium. Background Art

[0002] Distributed computing is a technology that distributes computing tasks to multiple computing nodes to complete together. Usually, a central node splits tasks and assigns them to each edge node for execution. Communication connectors are often installed on edge nodes to ensure accurate and timely communication with the central node;

[0003] However, when edge nodes are assigned different types of tasks, their interaction methods, frequencies, etc. with the central node are all different. Therefore, the functional modules of the communication connectors used on the edge nodes are also different, resulting in different degrees of loss of the communication connectors; in the prior art, when the central node assigns tasks, it often assigns the same type of task to the same edge node for execution. Since the loss progress of each functional module of the communication connector on the edge node is not consistent when executing the same type of task, after an edge node repeatedly executes the same task many times, it often causes individual functional modules of its communication connector to be damaged beyond use while most modules are intact, resulting in the user having to replace the communication connector, making it difficult to fully utilize the communication connector and leading to an increase in usage costs. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method for controlling the performance of a communication connector, a central node, and a storage medium, which can solve the above technical problems.

[0005] The first aspect of the embodiments of this application provides a method for controlling the performance of a communication connector, which is applied to a central node. The method for controlling the performance of the communication connector includes:

[0006] S1: For each edge node, determine the basic loss percentage of each functional module in the communication connector of this edge node;

[0007] S2: Retrieve all historical tasks completed by this edge node, and classify the historical tasks to obtain several task groups of different categories;

[0008] S3: For each category of task group, determine the change amount of the loss percentage of each functional module of the communication connector when this edge node executes the historical tasks of this task group;

[0009] S4: Determine the loss rate of each functional module of this edge node for this category of task based on the change amount of the loss percentage;

[0010] S5: When receiving a new task, identify each local task in the new task;

[0011] 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;

[0012] S7: Send each local task to the matched edge node so that the edge node completes the local task.

[0013] In a second aspect of the embodiments of the present application, a central node is provided, including a memory and a processor. A computer program is stored in the memory. When the computer program is executed by the processor, the processor executes the steps of the communication connector performance control method.

[0014] In a third aspect of the embodiments of the present application, a storage medium is provided. 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.

[0015] The beneficial effects of the embodiments of the present application compared with the prior art are as follows: 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 several task groups of different categories; for each category of task group, determining the change amount of the 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 for this category of task according to the change amount of the loss percentage; when receiving a new task, identifying each local task in the new task; for each local task, matching 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; sending each local task to the matched edge node so that the edge node completes the local task. In the present application, the local tasks that make up 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 matched task, the deviation of the loss percentage of each functional module of the corresponding communication connector is reduced. Furthermore, when a functional module of the communication connector of any edge node is completely lost, other functional modules of the communication connector are also close to complete loss. Therefore, each communication connector can be fully utilized, and the usage cost is greatly reduced. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a schematic flowchart of the implementation of the communication connector performance control method provided by the embodiments of the present application;

[0018] Figure 2 It is a schematic diagram of the implementation environment of the communication connector performance control method provided by the embodiments of the present application;

[0019] Figure 3 It is a schematic diagram of a proportional column of the communication connector performance control method provided by the embodiments of the present application;

[0020] Figure 4 It is a schematic diagram of the central node provided by the embodiments of the present application. Detailed implementation manners

[0021] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can 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 avoid unnecessary details from interfering with the description of the present application.

[0022] To illustrate the technical solutions described in the present application, the following will be described through specific embodiments.

[0023] Figure 1 The figure shows a communication connector performance control method provided by Embodiment 1 of the present application, which is applied to a central node. The communication connector performance control method includes:

[0024] S1: For each edge node, determine the basic loss percentage of each functional module in the communication connector of the edge node;

[0025] S2: Retrieve all historical tasks completed by the edge node, and classify the historical tasks to obtain several task groups of different categories;

[0026] S3: For each category of task group, determine the change amount of the loss percentage of each functional module of the communication connector when the edge node executes the historical tasks of the task group;

[0027] S4: Determine the loss rate of each functional module of this type of task for this edge node based on the change in the loss percentage;

[0028] S5: When receiving a new task, identify each local task in the new task;

[0029] S6: For each local task, match the local task to an edge node based on 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;

[0030] S7: Send each local task to the matched edge node so that the edge node completes the local task.

[0031] In this embodiment, as Figure 2 shown, this method is executed in the central node. The central node can be an independent physical server or terminal, or a server cluster composed of multiple physical servers, and can be 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, laptop computer, or tablet computer, or a server; a communication connector is installed on each edge node to achieve a good communication effect with the central node;

[0032] 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%, it is completely lost; for each functional module, the central node can monitor its status data in real time. Some status data can be directly monitored by the central node. For example, for the signal transmission module, its signal transmission response rate can be monitored, and the response efficiency can be compared with a pre-set 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; for another part of the status data, it is collected by a 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.

[0033] In this embodiment, the categories of tasks can be parameter calculation, image recognition, interaction with the client, etc.; when the node executes different categories of tasks and communicates, the way of mobilizing the functional modules in the communication connector is not exactly the same, and the mobilization frequency and intensity are also not exactly the same. Therefore, when the node executes different tasks, the functional modules of the communication connector will be damaged to different degrees; grouping historical tasks of the same category into the same task group, and then by determining the loss of each module of the communication connector caused by the historical tasks in the task group (that is, how much loss the module has suffered in a certain period of time), the loss rate of each functional module of the edge node caused by this category of tasks (including subsequent tasks of this type) can be obtained. Since different edge nodes have different degrees of loss to the communication connector when executing the same category of tasks, the above process needs to be performed for each edge node respectively to determine the loss rate of each functional module of each edge node caused by each category of tasks;

[0034] In this embodiment, each new task received by the central node is an overall task composed of several local tasks. After receiving it, the overall task can be split into local tasks for distribution; if the communication connectors of all edge nodes are new communication connectors, that is, the basic loss percentage of each functional module of each communication connector is 0, local tasks can be randomly distributed at this time; after each edge node completes the task, the corresponding functional module will be damaged. When receiving a new task next time, this method can be executed;

[0035] In this application, usually, no matter what type of task the node executes, each functional module in the communication connector will be applied, but the application degree 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 are not completely damaged are wasted, making the communication connector not fully utilized; and through this method, it is possible to match the local tasks that make up the new task 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. Furthermore, when a functional module of the communication connector of any edge node is completely damaged, other functional modules of the communication connector are also close to complete damage, so that each communication connector can be fully utilized, greatly reducing the usage cost.

[0036] As a preferred embodiment, determining the change amount of the loss percentage of each functional module of the communication connector when the edge node executes the historical tasks of the task group includes:

[0037] For each historical task in the task group, determine the start time and end time when the historical task is executed, and the first duration from the start time to the end time;

[0038] 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;

[0039] Subtract the loss percentage at the end time from the loss percentage at the start time to obtain the change amount of the loss percentage of the functional module after the edge node completes the historical task.

[0040] Determine the loss rate of each functional module of the edge node by the task of this category according to the change amount of the loss percentage, including:

[0041] S41: For each functional module of the edge node, calculate the single loss rate of each historical task in the task group of this category for the functional module;

[0042] S42: Obtain the average value of each single loss rate to get the loss rate of the task of this category for the functional module;

[0043] Calculate the single loss rate of each historical task in the task group of this category for the functional module through the following formula:

[0044]

[0045] Wherein, is the single loss rate, is the change amount of the loss percentage of the functional module after the edge node completes this historical task, is the first duration corresponding to this historical task.

[0046] In this embodiment, the historical task is the local task received by the edge node in the past; the edge node will synchronize the task progress to the central node in real time during the process of completing the task, so that the central node can identify when the edge node starts the task (start time) and when it completes the task (end time), so as to determine the first duration; in this embodiment, the average value of the loss rates caused by each historical task of the same category in the past is obtained to get the loss rate of the task of this category for each functional module of the edge node. Since the data samples used in the calculation are large, the result has high accuracy;

[0047] As a preferred embodiment, matching the local task to an edge node according to the determined loss rate and each basic loss percentage includes:

[0048] S61: For each edge node, generate a corresponding bar chart of loss percentages, where each bar chart of loss percentages 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;

[0049] S62: Select an edge node as a potential matching node;

[0050] S63: Determine the loss increase amount of each functional module corresponding to the communication connector of the potential matching node for this local task;

[0051] S64: Retrieve the bar chart of loss percentages of this potential matching node, and add the corresponding loss increase amount of each functional module to each proportion bar in the figure;

[0052] S65: Determine whether the loss percentage corresponding to at least one adjusted proportion bar reaches 100%;

[0053] S66: If not, calculate the deviation of each adjusted proportion bar through the following formula:

[0054]

[0055] where, is the deviation of each adjusted proportion bar, n is the number of proportion bars, is the loss percentage corresponding to the i-th proportion bar, is the average value of the loss percentages corresponding to each proportion bar;

[0056] S67: Match this local task to the potential matching node with the smallest corresponding deviation;

[0057] S68: If the loss percentage corresponding to at least one adjusted proportion bar reaches 100%, then exclude this potential matching node with loss, and execute steps S62 to S68 until this local task is matched to a potential matching node.

[0058] When identifying each local task, identify the task volume and category of this local task; determining the loss increase amount of each functional module corresponding to the communication connector of the potential matching node for this local task includes:

[0059] S631: Determine the second duration for completing this local task at this potential matching node according to the task volume and category of this local task;

[0060] S632: Retrieve all loss rates corresponding to the category of this local task for this potential matching node;

[0061] S633: Multiply each loss rate by the second duration to obtain the loss increase amount of this local task for the functional module corresponding to this loss rate.

[0062] In this embodiment, the increased loss amount is the increased loss percentage; the proportional column is the column corresponding to the basic loss percentage; as Figure 3 shown, the increased loss amount of the corresponding functional module is added to the proportional column, that is, the column corresponding to the increased loss amount is added to the column corresponding to the basic loss percentage; the new tasks received by the central node also include task information, including the task amounts and categories of each local task of the new task; in the central node, there is a task amount-completion duration comparison table for each edge node for different types of tasks, including the required duration for the corresponding edge node to complete this type of task with different task amounts. After comparing the corresponding task amount with the data in the table, the second duration can be determined; the data in the task amount-completion duration comparison table can be determined according to the duration of the edge node to complete the same historical task (supplemented by interpolation calculation).

[0063] As a preferred embodiment, after step S6, it further includes:

[0064] S601: For each edge node, split a subtask from the local tasks matched by the edge node;

[0065] S602: After the splitting is completed, determine whether the deviation corresponding to the edge node is reduced. If not, cancel the splitting;

[0066] S603: If so, determine whether there is an adaptation node for the subtask among other edge nodes. If not, cancel the splitting, where the adaptation node can reduce the corresponding deviation after completing the subtask;

[0067] S604: If so, match the subtask to the adaptation node;

[0068] In step S7, if any subtask has been matched to any edge node, first split the subtask from the corresponding local task, and then send the local task and the subtask to the matched edge node.

[0069] The proportion of the task amount of each split subtask in the corresponding split local task is a set proportion, and the category of the subtask is the same as that of the split local task;

[0070] Determining whether the deviation of the edge node is reduced includes:

[0071] Taking the deviation of the proportional column corresponding to the edge node after completing the corresponding local task without task splitting as the first deviation;

[0072] Determining the third duration for the edge node to complete the subtask according to the task amount and category of the subtask;

[0073] Retrieve all the loss rates corresponding to the category of the subtask for this edge node;

[0074] Multiply each retrieved loss rate by the third duration to obtain the first sub-loss increase for the subtask with respect to the functional module corresponding to the loss rate;

[0075] For each adjusted proportion column corresponding to this edge node, subtract the corresponding first sub-loss increase from the proportion column to obtain an updated proportion column;

[0076] Calculate the deviation of each updated proportion column to obtain the second deviation;

[0077] Determine whether the second deviation is less than the first deviation. If so, the deviation of this edge node is reduced; otherwise, the deviation of this edge node is not reduced.

[0078] Determining whether there is an adaptation node for this subtask among other edge nodes includes:

[0079] For each other edge node, use the deviation corresponding to the completion of the corresponding local task by this edge node as the third deviation;

[0080] Determine the fourth duration for this subtask to be completed at this edge node according to the task volume and category of the subtask;

[0081] Retrieve all the loss rates corresponding to the category of the subtask for this edge node;

[0082] Multiply each retrieved loss rate by the fourth duration to obtain the second sub-loss increase for the subtask with respect to the functional module corresponding to the loss rate;

[0083] For each adjusted proportion column corresponding to this edge node, add the corresponding first sub-loss increase to the proportion column to obtain an updated proportion column;

[0084] Calculate the deviation of each updated proportion column to obtain the fourth deviation;

[0085] Determine whether the fourth deviation is less than the third deviation. If so, this edge node is a potential adaptation node; otherwise, this edge node is not a potential adaptation node;

[0086] For each potential adaptation node, subtract the corresponding fourth deviation from the corresponding third deviation to obtain the target difference;

[0087] Determine the potential adaptation node with the largest target difference as the adaptation node.

[0088] In this embodiment, the set ratio can be 10% or other ratios. For example, if a local task is 'performing image processing on 10 photos', then a subtask of 'performing image processing on 1 photo' can be segmented. If any subtask has been matched to any edge node, then send the subtask and the local task originally matched by the edge node to the edge node. For other edge nodes that have not matched subtasks, still send the originally matched local tasks to the edge node (if the local task it matches is segmented, then send the segmented local task to the edge node). In this embodiment, the determination methods of the third duration and the fourth duration are the same as that of the second duration. The calculation methods of the first deviation, the second deviation, the third deviation, and the fourth deviation are the same as those in step S66, which will not be elaborated here. Through the solution of this embodiment, the loss deviation of the corresponding communication connectors after each edge node completes the task can be further reduced, and the utilization rate of the communication connectors can be further improved.

[0089] A central node provided in the second embodiment of the present application includes a memory and a processor. A computer program is stored in the memory. When the computer program is executed by the processor, the processor executes the steps of the communication connector performance control method, specifically including:

[0090] S1: For each edge node, determine the basic loss percentage of each functional module in the communication connector of the edge node;

[0091] S2: Retrieve all historical tasks completed by the edge node, and classify the historical tasks to obtain several task groups of different categories;

[0092] S3: For each category of task group, determine the change amount of the loss percentage of each functional module of the communication connector when the edge node executes the historical tasks of the task group;

[0093] S4: Determine the loss rate of each functional module of the edge node for the task of this category according to the change amount of the loss percentage;

[0094] S5: When receiving a new task, identify each local task in the new task;

[0095] 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;

[0096] S7: Send each local task to the matched edge node so that the edge node completes the local task.

[0097] A storage medium provided in the third embodiment of the present application, on which a computer program is stored. When the computer program is executed by a processor, the processor is caused to execute the steps of the communication connector performance control method, specifically including:

[0098] S1: For each edge node, determine the basic loss percentage of each functional module in the communication connector of the edge node;

[0099] S2: Retrieve all historical tasks completed by the edge node, and classify the historical tasks to obtain several task groups of different categories;

[0100] S3: For each category of task group, determine the change amount of the loss percentage of each functional module of the communication connector when the edge node executes the historical tasks of the task group;

[0101] S4: Determine the loss rate of each functional module of the edge node for this category of task based on the change amount of the loss percentage;

[0102] S5: When receiving a new task, identify each local task in the new task;

[0103] S6: For each local task, match the local task to an edge node based on 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;

[0104] S7: Send each local task to the matched edge node so that the edge node completes the local task.

[0105] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do 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 to the implementation process of the embodiments of the present application.

[0106] It should be understood that when used in the specification of the present application, the term "comprising" indicates the presence of the 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 their combinations.

[0107] It should also be understood that the term " / and" as 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.

[0108] As used in the specification of this application, the term "if" may be construed as "when" or "once" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be construed as meaning "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]" depending on the context.

[0109] In addition, in the description of the specification of this application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance. It should also be understood that although the terms "first", "second", etc. are used in the text in some embodiments of this application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first table may be named the second table, and similarly, the second table may be named 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.

[0110] Reference to "one embodiment" or "some embodiments" etc. described in the specification of this application means that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" etc. that appear in different places in this specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0111] Figure 4 It is a schematic structural diagram of a central node provided by an embodiment of this application. As Figure 4 shown, the central node of this embodiment includes: at least one processor ( Figure 4 only one is shown in the figure), a memory, and a computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the steps in the above-mentioned embodiments of various communication connector performance control methods are implemented, such as Figure 1 the steps S1 to S7 shown in the figure.

[0112] The central node may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The central node may include, but is not limited to, a processor and a memory. Those skilled in the art can understand, Figure 4These are merely examples of the central node and do not constitute a limitation thereto. It may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, the central node may further include an input sending device, a network access device, a bus, etc.

[0113] The so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0114] In some embodiments, the memory may be an internal storage unit of the central node, such as the 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. Further, the memory may also include both the internal storage unit and the external storage device of the central node. The memory is used to store an operating system, application programs, a boot loader, 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 will be sent.

[0115] In addition, in each embodiment of the present application, each functional unit may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0116] The embodiments of the present application provide a computer program product. When the computer program product runs on a mobile central node, the mobile central node is caused to execute to implement the steps in the above-mentioned method embodiments.

[0117] When 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, to implement all or part of the processes in the above-described embodiment methods of this application, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0118] In the above embodiments, the descriptions of the respective embodiments have their own focuses. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0119] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0120] 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 can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included in the protection scope of this application.

Claims

1. A method for controlling the performance of a communication connector, which is applied to a central node, and is characterized in that, The method for controlling the performance of the communication connector 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 task groups of different categories; S3: For each task group of a category, determine the change amount of the loss percentage of each functional module of the communication connector when the edge node executes the historical tasks of the task group; S4: Determine the loss rate of each functional module of the edge node by the task of this category according to the change amount of the loss percentage; S5: When receiving a new task, identify 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 the matched edge node so that the edge node completes the local task.

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

3. The method according to claim 2, wherein Determining the loss rate of each functional module of the edge node by the task of this category according to the change amount of the loss percentage includes: S41: For each functional module of the edge node, calculate the single loss rate of each historical task in the task group of this category for the functional module; S42: Calculate the average value of each single loss rate to obtain the loss rate of the task of this category for the functional module; Calculate the single loss rate of each historical task in the task group of this category for the functional module through the following formula: Among them, S is the single-loss rate, and C v is the change amount of the loss percentage of the function module after the edge node completes the historical task this time, and T1 is the first duration corresponding to the historical task this time.

4. The method according to claim 3, wherein 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 bar chart, where the loss percentage bar chart includes a proportional column corresponding to each functional module, and the loss percentage corresponding to the height of the proportional column is the basic loss percentage of the functional module; S62: Select an edge node as a potential matching node; S63: Determine the loss increase amount of each functional module corresponding to the communication connector of the potential matching node for the local task; S64: Retrieve the loss percentage bar chart of the potential matching node, and add the loss increase amount of the corresponding functional module to each proportional column in the chart; S65: Judge whether there is at least one loss percentage corresponding to the adjusted proportional column reaching 100%; S66: If not, calculate the deviation of each adjusted proportional column through the following formula: where σ is the deviation of each adjusted proportional column, n is the number of proportional columns, and x i is the loss percentage corresponding to the i-th proportional column, and is the mean value of the loss percentages corresponding to each proportional column; S67: Match the local task to the potential matching node with the smallest corresponding deviation; S68: If the loss percentage corresponding to at least one adjusted ratio column reaches 100%, exclude the potential matching node, and execute steps S62 to S68 until the local task is matched to a potential matching node.

5. The method according to claim 4, wherein When identifying each local task, identify the task volume and category of the local task; determining the increased loss of each functional module corresponding to the communication connector of the potential matching node for the local task includes: S631: Determine the second duration for completing the local task at the potential matching node according to the task volume and category of the local task; S632: Retrieve all loss rates corresponding to the category of the local task for the potential matching node; S633: Multiply each loss rate by the second duration to obtain the increased 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, it further includes: S601: For each edge node, split a subtask from the local tasks matched by the edge node; S602: After the splitting is completed, determine whether the deviation corresponding to the edge node is reduced. If not, cancel the splitting; S603: If so, determine whether there is an adaptation node for the subtask among other edge nodes. If not, cancel the splitting, where the adaptation node can reduce the corresponding deviation after completing the subtask; S604: If so, match the subtask to the adaptation node; In step S7, if any subtask has been matched to any edge node, first split the subtask from the corresponding local task, and then send the subtask to the matched edge node.

7. The method according to claim 6, characterized in that, The task volume of each split subtask accounts for a set ratio of the corresponding split local task, and the category of the subtask is the same as that of the split local task; Determining whether the deviation of the edge node is reduced includes: Taking the deviation of the ratio column corresponding to the edge node after completing the corresponding local task without task splitting as the first deviation; Determining the third duration for completing the subtask at the edge node according to the task volume and category of the subtask; Retrieving all loss rates corresponding to the category of the subtask for the edge node; Multiplying each retrieved loss rate by the third duration to obtain the first sub-increased loss of the subtask for the functional module corresponding to the loss rate; For each adjusted ratio column corresponding to the edge node, subtract the corresponding first sub-increased loss from the ratio column to obtain an updated ratio column; Calculating the deviation of each updated ratio column to obtain a second deviation; Determining 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 adaptation node for the subtask among other edge nodes includes: For each other edge node, taking the deviation corresponding to the edge node after completing the corresponding local task as the third deviation; Determining the fourth duration for completing the subtask at the edge node according to the task volume and category of the subtask; Retrieving all loss rates corresponding to the category of the subtask for the edge node; Multiply the loss rate retrieved each time by the fourth duration to obtain the second sub-loss increment of the subtask for the functional module corresponding to the loss rate; For each adjusted proportion column corresponding to the edge node, add the corresponding second sub-loss increment to the proportion column to obtain an updated proportion column; Calculate the deviation of each updated proportion column 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; Determine the potential adaptation node with the largest target difference as the adaptation node.

9. A central node, characterized in that, It includes a memory and a processor. A computer program is stored in the memory. When the computer program is executed by the processor, the processor executes the steps of the communication connector performance control method according to 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 according to any one of claims 1 to 8.

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