Implementation method and device of 5G edge engine and storage medium

Abstract

The invention provides a 5G edge engine implementation method and device and a storage medium, and the method comprises the steps that respective performance data of N edge engines are monitored, the N edge engines are registered in a cloud engine, and N is an integer greater than or equal to 2; according to the performance data of any edge engine, calculating to obtain a performance evaluation score of any edge engine; comparing the performance evaluation score with a preset target performance evaluation score, and determining the running state of any edge engine; and when the running state of any edge engine is determined to be busy or faulty, disassembling the data acquisition task of any edge engine, and allocating the data acquisition task obtained by disassembling to the idle edge engine in the N edge engines according to a PID (Proportion Integration Differentiation) algorithm. According to the invention, the computing power of the 5G edge engine can be maximized, and the stability of system operation in an industrial environment can be guaranteed.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device and storage medium for implementing a 5G edge engine. Background Art

[0002] With the rapid development of communication technology, the application of the fifth generation mobile communication technology (5th Generation Mobile Communication Technology, 5G) edge computing engine is becoming more and more widespread. The 5G edge engine can manage the network communication of the 5G terminal engine to effectively determine the networking status of the 5G terminal engine and the networking status of the 5G terminal engine and the device, so as to control and switch the 5G terminal engine with better connection and timely alarm the offline device.

[0003] In an industrial environment, 5G edge engines usually need to process more or less data acquisition tasks, and the industrial network environment has high requirements for network stability. Based on this, how to maximize the computing power of the 5G edge engine and ensure the stability of system operation in the industrial environment has become a problem that needs to be solved at present. Summary of the invention

[0004] The embodiments of the present application provide a method, device and storage medium for implementing a 5G edge engine to solve the problems existing in related technologies. The technical solutions are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for implementing a 5G edge engine, including:

[0006] Monitor the performance data of each of N edge engines, where the N edge engines are registered under one cloud engine, and N is an integer greater than or equal to 2;

[0007] Calculate the performance evaluation score of any edge engine according to the performance data of any edge engine;

[0008] Comparing the performance evaluation score with a preset target performance evaluation score to determine the operating status of any edge engine;

[0009] When it is determined that the running state of any edge engine is busy or faulty, the data acquisition task of any edge engine is disassembled, and the disassembled data acquisition task is allocated to an idle edge engine among the N edge engines according to the PID algorithm.

[0010] In one embodiment, calculating the performance evaluation score of any edge engine according to the performance data of any edge engine includes:

[0011] According to the performance data of any of the edge engines, respectively calculate the comprehensive performance evaluation score, performance occupancy score and performance efficiency score of any of the edge engines;

[0012] The performance evaluation score is calculated according to the comprehensive performance evaluation score, the performance occupancy score and the performance efficiency score.

[0013] In one embodiment, according to the performance data of any of the edge engines, respectively calculating the comprehensive performance evaluation score, the performance occupancy score and the performance efficiency score of any of the edge engines comprises:

[0014] Calculate the performance score of each device data unit of any edge engine according to the actual value of the arrival time of each device data in the performance data of any edge engine; calculate the comprehensive performance evaluation score according to the performance score of each device data unit;

[0015] The performance occupancy score is calculated based on the average memory usage rate in minutes, the average memory usage rate in hours, the average hard disk read and write rate in minutes, the average hard disk read and write rate in hours, the average CPU usage rate in minutes, and the average CPU usage rate in hours in the performance data of any edge engine;

[0016] The performance efficiency score is calculated based on the average data reading time per minute, the average data reading time per hour, the average number of access requests per minute, the average number of access requests per hour, the average task completion time per minute, and the average task completion time per hour in the performance data of any of the edge engines.

[0017] In one implementation, calculating the performance evaluation score according to the comprehensive performance evaluation score, the performance occupancy score, and the performance efficiency score includes:

[0018] Calculate the working performance evaluation score of any edge engine according to the performance occupancy score and the performance efficiency score;

[0019] The performance evaluation score is calculated based on the comprehensive performance evaluation score and the work performance evaluation score.

[0020] In one embodiment, comparing the performance evaluation score with a preset target performance evaluation score to determine the operating status of any edge engine includes:

[0021] Comparing the performance evaluation score with the target performance evaluation score, and determining that the performance evaluation score is less than the target performance evaluation score, and when the performance evaluation score is greater than a set dead zone, determining that the operating state of any edge engine is idle, wherein the conditional judgment within the numerical range of the dead zone is not effective;

[0022] Comparing the performance evaluation score with the target performance evaluation score, and determining that the performance evaluation score is greater than the target performance evaluation score, and when the performance evaluation score is greater than a set dead zone, determining that the operating state of any edge engine is busy;

[0023] The performance evaluation score is compared with the target performance evaluation score, and when it is determined that the performance evaluation score is greater than the target performance evaluation score and the performance evaluation score is less than a set dead zone, it is determined that the operating state of any edge engine is a fault.

[0024] In one embodiment, decomposing the data acquisition tasks of any edge engine, and allocating the decomposed data acquisition tasks to idle edge engines among the N edge engines according to the PID algorithm includes:

[0025] According to the data collection points, decompose the data collection task of any edge engine into multiple point data collection tasks;

[0026] When the running state of any of the edge engines is busy, according to the PID algorithm, randomly allocate some of the point data collection tasks among the multiple point data collection tasks to an idle edge engine among the N edge engines; determine whether any of the edge engines has reached a performance balance state; if any of the edge engines has not reached a performance balance state, replace the remaining point data collection tasks in any of the edge engines with multiple point data collection tasks, and return to execution according to the PID algorithm, randomly allocate some of the point data collection tasks among the multiple point data collection tasks to an idle edge engine among the N edge engines, until any of the edge engines reaches a performance balance state;

[0027] When the operating status of any of the edge engines is faulty, according to the PID algorithm, a plurality of the point data collection tasks are randomly allocated to idle edge engines among the N edge engines; it is determined whether any of the idle edge engines has reached a performance balance state; if any of the idle edge engines has not reached a performance balance state, the number of point data collection tasks allocated to any of the idle edge engines is adjusted, and the process returns to determine whether any of the idle edge engines has reached a performance balance state until any of the idle edge engines has reached a performance balance state.

[0028] In one embodiment, the method further comprises:

[0029] When it is determined that the running state of any edge engine is idle, any edge engine is assigned to collect data from a nearby device, and the collection tasks of the nearby devices of the remaining edge engines among the N edge engines are controlled to be in a sleep state.

[0030] In a second aspect, the embodiment of the present application further provides a device for implementing a 5G edge engine, including:

[0031] A monitoring unit, used to monitor the performance data of each of N edge engines, where the N edge engines are registered under one cloud engine, and N is an integer greater than or equal to 2;

[0032] A calculation unit, configured to calculate a performance evaluation score of any of the edge engines based on the performance data of any of the edge engines;

[0033] A dispatching unit is used to compare the performance evaluation score with a preset target performance evaluation score to determine the operating status of any of the edge engines; when it is determined that the operating status of any of the edge engines is busy or faulty, the data acquisition tasks of any of the edge engines are disassembled, and the disassembled data acquisition tasks are dispatched to an idle edge engine among the N edge engines according to a PID algorithm.

[0034] In a third aspect, an embodiment of the present application further provides a communication device, comprising: a memory and a processor, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement a method in any one of the above-mentioned aspects, wherein the memory and the processor communicate with each other through an internal connection path.

[0035] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. When the computer program runs on a computer, the method in any one of the above-mentioned embodiments is implemented.

[0036] The advantages or beneficial effects of the above technical solution include at least:

[0037] By monitoring the performance data of each of N edge engines registered under a cloud engine, the present application can conveniently monitor which edge engine is about to reach or has reached a busy state at all times; further, by calculating the performance evaluation score of any edge engine based on the performance data of any edge engine, it is convenient to timely judge the operating status of any edge engine; further, by comparing the performance evaluation score with the pre-set target performance evaluation score to determine the operating status of any edge engine, it is convenient to timely confirm the operating status of any edge engine; further, when it is determined that the operating status of any edge engine is busy or faulty, the data acquisition tasks of any edge engine are disassembled, and part of the disassembled data acquisition tasks are allocated to the idle edge engines among the N edge engines according to the PID algorithm, thereby maximizing the computing power of the 5G edge engine and ensuring the stability of system operation in an industrial environment.

[0038] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present application will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the multiple drawings represent the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings only depict some embodiments disclosed in the present application and should not be regarded as limiting the scope of the present application.

[0040] Figure 1 A flowchart of a method for implementing a 5G edge engine provided in this application;

[0041] Figure 2 A schematic diagram of a process for executing step S120 provided in this application;

[0042] Figure 3 A schematic diagram of a process for executing step S140 provided in this application;

[0043] Figure 4 Another flowchart of executing step S140 provided by the present application;

[0044] Figure 5 A structural block diagram of a 5G edge engine implementation device provided in this application;

[0045] Figure 6 A structural block diagram of a communication device provided in this application. DETAILED DESCRIPTION

[0046] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present application. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0047] In related technologies, the commonly used edge computing engine data acquisition and calculation implementation solutions are as follows:

[0048] Solution 1: Collect data to the cloud through a gateway or communication device, then analyze and calculate customer needs, and present and display the data.

[0049] Solution 2: Deploy the data collection and processing system to the local server, and deploy the data acquisition and control system to the local terminal. Each local terminal is an independent system. Combine 5G and wired communications to solve stability and high latency problems. Complete basic data collection and control through local terminals, and transmit the data collection results to the local server for calculation. The local server reads, writes and calculates the device data for the device terminal.

[0050] However, in the above solution 1, on the one hand, even if 5G technology is adopted, the public cloud delay is still relatively long, and in industrial scenarios with high requirements for network stability, it can only perform calculations for data collection and data presentation, and cannot have equipment control capabilities. It is difficult to apply to high-frequency equipment control scenarios and cannot guarantee the stability of system operation in industrial environments. On the other hand, although analysis and calculation are performed on the cloud, when resource bottlenecks occur and need to be expanded, it still needs to be paused for several minutes. It is not suitable for scenarios with 24-hour uninterrupted computing and continuously increasing demand, and cannot maximize the computing power of the 5G edge engine.

[0051] In the above-mentioned solution 2, the hardware resources of each local terminal are relatively fixed. In this case, the following three problems usually arise: First, when the data acquisition task is busy, the local terminal will become a bottleneck, which may easily lead to abnormal data acquisition. Even if two wired redundant terminals are established, the two redundant terminals will be shut down due to overload, and the stability of system operation in the industrial environment cannot be guaranteed; second, when the data acquisition task is idle, the hardware resources of the local terminal will be wasted, and the computing power of the 5G edge engine cannot be maximized; third, when the local terminal fails, it will directly lead to abnormal data acquisition and control of the relevant equipment that the terminal is responsible for, resulting in data interruption, and the stability of system operation in the industrial environment cannot be guaranteed.

[0052] In order to solve the above problems, an embodiment of the present application provides an implementation solution of a 5G edge engine, which is used to maximize the computing power of the 5G edge engine and ensure the stability of system operation in an industrial environment.

[0053] In order to facilitate the understanding of the technical solution of the present application, the 5G edge engine involved in the present application will be explained below.

[0054] The 5G edge engine mainly includes: 5G terminal engine, mobile edge computing (MEC) engine (hereinafter referred to as edge engine), cloud engine and engine control application (Application, APP), etc.

[0055] 5G terminal engine: connected to the operator's edge engine through the 5G network, and connected to the control device through the device protocol. The 5G terminal engine reports the device readings, alarms, operating status, instrument data and other information to the edge engine, and accepts the control commands of the edge engine, such as device start and stop, to control the device in real time. Therefore, it requires extremely high stability, and each device will be connected to multiple redundant 5G terminal engines to prepare for effective switching when the network status is poor or offline, without affecting normal production.

[0056] Edge engine: used to implement mobility management and session management of 5G terminal engines, configure equipment collection and control strategies, and manage; establish user plane bearers for 5G edge terminals to transmit uplink and downlink service data. Control and switch redundant 5G terminal engines, determine and switch the 5G terminal engine with better network status as the main control device, and use the 5G terminal engine with poor status as an offline device to ensure that production can proceed in an orderly and stable manner under the 5G environment.

[0057] Cloud engine: used to achieve unified management of data of various 5G terminal connection devices and manage the edge engine. At the same time, it opens interfaces to third-party application systems so that various applications can quickly build their own 5G industrial business.

[0058] Engine control APP: used to implement business management of the engine, including engine start and stop, business issuance, business control and presentation, etc.

[0059] Figure 1 FIG. 1 is a flow chart showing a method for implementing a 5G edge engine according to an embodiment of the present application. Figure 1 As shown, the method may include the following steps:

[0060] S110. Monitor the performance data of each of the N edge engines.

[0061] In a specific implementation, N edge engines are registered under one cloud engine, where N is an integer greater than or equal to 2. Among them, the N edge engines can be used to perform data acquisition tasks.

[0062] In the present application, by executing step S110, it is possible to conveniently monitor at all times which edge engine is about to reach or has reached a busy state, so as to timely allocate the data acquisition tasks of the edge engine so that each edge engine can achieve a performance balance state.

[0063] S120. Calculate a performance evaluation score of any edge engine based on the performance data of any edge engine.

[0064] In one embodiment, if Figure 2 As shown, step S120 may include the following steps:

[0065] S121. Calculate the comprehensive performance evaluation score, performance occupancy score, and performance efficiency score of any edge engine based on the performance data of any edge engine.

[0066] In specific implementation, the performance score of each device data unit of any edge engine can be calculated based on the actual value of the arrival time of each device data in the performance data of any edge engine; based on the performance score of each device data unit, the comprehensive performance evaluation score of any edge engine can be calculated.

[0067] For example, the performance score SA of each device data unit of any edge engine can be calculated according to the actual value of the arrival time of each device data in the performance data of any edge engine according to the following formula (1): i ; Then according to the performance score SA of each device data unit i , the comprehensive performance evaluation score Y1 of any edge engine is calculated according to the following formula (2).

[0068]

[0069] Among them, the device data arrival time reference value can be set according to actual needs, such as being calculated by the three-value estimation method, that is, the device data arrival time reference value = (3*maximum value of actual value of device data arrival time+2*average value of actual value of device data arrival time+minimum value of actual value of device data arrival time) / 6.

[0070]

[0071] Where n is the number of device data units in any edge engine, KA i Represented as the weight value of the i-th device data unit.

[0072] In specific implementation, the performance occupancy score of any edge engine can be calculated based on the average memory occupancy rate in minutes, the average memory occupancy rate in hours, the average hard disk read and write rate in minutes, the average hard disk read and write rate in hours, the average CPU usage rate in minutes and the average CPU usage rate in hours in the performance data of any edge engine.

[0073] For example, the average minute memory occupancy, the average hour memory occupancy, the average minute hard disk read / write rate, the average hour hard disk read / write rate, the average minute CPU usage rate, and the average hour CPU usage rate can be assigned scores SB1, SB2, SB3, SB4, SB5, and SB6, respectively. Then, based on the pre-set weight values ​​corresponding to the average minute memory occupancy, the average hour memory occupancy, the average minute hard disk read / write rate, the average hour hard disk read / write rate, the average minute CPU usage rate, and the average hour CPU usage rate, i.e., KB1, KB2, KB3, KB4, KB5, and KB6, the performance occupancy score Y2 of any edge engine can be calculated according to the following formula (3).

[0074]

[0075] It should be understood that the scores assigned to the average memory minute occupancy, the average memory hourly occupancy, the average hard disk read and write minute rate, the average hard disk read and write hourly rate, the average CPU minute usage rate and the average CPU hourly usage rate can be based on the specific values ​​of these performance data. For example, if the specific value of the average memory minute occupancy is 68%, then it can be assigned a score of 68. It can also be based on the numerical range of these performance data. For example, the numerical range of the average memory minute occupancy of 68% is 60%-70%, and the score corresponding to this numerical range is 70, then it can be assigned a score of 70. It can also be based on the same or similar method as the above-mentioned method of calculating the performance score of each device data unit of any edge engine to assign scores to these performance data, and so on. This application is not limited to this.

[0076] In specific implementation, the performance efficiency score of any edge engine can be calculated based on the average data reading time per minute, the average data reading time per hour, the average number of access requests per minute, the average number of access requests per hour, the average task completion time per minute, and the average task completion time per hour in the performance data of any edge engine.

[0077] For example, the average data reading time per minute, the average data reading time per hour, the average number of access requests per minute, the average number of access requests per hour, the average task completion time per minute, and the average task completion time per hour can be assigned scores SC1, SC2, SC3, SC4, SC5, and SC6, respectively. Then, based on the preset weight values ​​corresponding to the average data reading time per minute, the average data reading time per hour, the average number of access requests per minute, the average number of access requests per hour, the average task completion time per minute, and the average task completion time per hour, i.e., KC1, KC2, KC3, KC4, KC5, and KC6, the performance efficiency score Y3 of any edge engine can be calculated according to the following formula (4).

[0078]

[0079] It should be understood that assigning scores to the average data reading time by minute, the average data reading time by hour, the average number of access requests by minute, the average number of access requests by hour, the average task completion time by minute, and the average task completion time by hour can be done in the same or similar manner as the above-mentioned method of assigning scores to the average memory occupancy by minute, the average memory occupancy by hour, the average hard disk read and write rate by minute, the average hard disk read and write rate by hour, the average CPU usage by minute, and the average CPU usage by hour, which will not be repeated in this application.

[0080] S122. Calculate the performance evaluation score of any edge engine based on the comprehensive performance evaluation score, the performance occupancy score, and the performance efficiency score.

[0081] In specific implementation, the working performance evaluation score of any edge engine can be calculated based on the performance occupancy score and performance efficiency score of any edge engine.

[0082] For example, the working performance evaluation score Y4 of any edge engine can be calculated according to the pre-set weight values ​​corresponding to the performance occupancy score and the performance efficiency score, namely KD1 and KD2, combined with the above formula (3) and the above formula (4), according to the following formula (5).

[0083]

[0084] In specific implementation, the performance evaluation score of any edge engine can be calculated based on the comprehensive performance evaluation score and work performance evaluation score of any edge engine.

[0085] For example, the performance evaluation score Y of any edge engine can be calculated according to the pre-set weight values ​​corresponding to the comprehensive performance evaluation score and the work performance evaluation score, i.e., KE1 and KE2, combined with the above formula (2) and the above formula (5), according to the following formula (6).

[0086]

[0087] In the present application, by executing step S120, it is possible to timely determine the operating status of any edge engine, so as to facilitate timely allocation of data acquisition tasks of busy edge engines, so that each edge engine can achieve a performance balance state.

[0088] S130: Compare the performance evaluation score with a preset target performance evaluation score to determine the operating status of any edge engine.

[0089] In specific implementation, the performance evaluation score is compared with the target performance evaluation score. When it is determined that the performance evaluation score is less than the target performance evaluation score and the performance evaluation score is greater than the set dead zone, it can be determined that the operating state of any edge engine is idle.

[0090] In specific implementation, the performance evaluation score is compared with the target performance evaluation score. When it is determined that the performance evaluation score is greater than the target performance evaluation score and the performance evaluation score is greater than the set dead zone, it can be determined that the operating status of any edge engine is busy.

[0091] In specific implementation, the performance evaluation score is compared with the target performance evaluation score. When it is determined that the performance evaluation score is greater than the target performance evaluation score and the performance evaluation score is less than the set dead zone, the operating state of any edge engine is determined to be faulty.

[0092] Among them, the conditional judgment within the numerical range of the above-mentioned dead zone is not effective.

[0093] In the present application, by executing step S130, the operating status of any edge engine can be conveniently and timely confirmed, so as to timely allocate the data acquisition tasks of the busy edge engine, so that each edge engine can achieve a performance balance state. At the same time, the performance bottlenecks and problems of the 5G edge computing engine can be discovered in time, and relevant personnel can be reminded to perform operation and maintenance in real time.

[0094] S140: When it is determined that the running status of any edge engine is busy or faulty, the data acquisition tasks of any edge engine are disassembled, and the disassembled data acquisition tasks are allocated to idle edge engines among the N edge engines according to the PID algorithm.

[0095] In one embodiment, if Figure 3 As shown, when the running state of any edge engine is busy, step S140 may include the following steps:

[0096] S141. Decompose the data collection task of any edge engine into multiple point data collection tasks according to the data collection points.

[0097] In a specific implementation, the data acquisition task of any edge engine may include data acquisition tasks for multiple points, where the points can be distinguished by data type or device type.

[0098] Based on this, the data collection task of any edge engine can be decomposed into multiple point data collection tasks according to the data collection points.

[0099] S142. Randomly allocate some of the multiple point data collection tasks to idle edge engines among the N edge engines according to the PID algorithm.

[0100] In specific implementation, the PID algorithm can be represented by the following formula (7):

[0101]

[0102] Where t represents the number of data collection tasks at some points, u(t) represents the desired balance performance, e(t) represents the difference between the performance evaluation score Y of any edge engine and the target performance evaluation score, and K p Expressed as proportional gain, it is inversely proportional to the degree of proportionality, T t Expressed as the integration time constant, T D Expressed as the differential time constant.

[0103] S143: Determine whether any edge engine has reached a performance balance state. If any edge engine has not reached a performance balance state, execute step S144; otherwise, execute step S145.

[0104] In specific implementation, the performance evaluation score of any edge engine can be calculated by combining the above formulas (2) to (6), and then combined with the above formula (7) to determine whether any edge engine has reached a performance balance state.

[0105] S144. Replace the remaining point data collection tasks in any edge engine with multiple point data collection tasks, and return to execute step S142.

[0106] In a specific implementation, step S144 may be executed to continuously adjust the input value of the PID algorithm until the performance balance of any edge engine reaches a steady state.

[0107] S145. End the process.

[0108] In another embodiment, if Figure 4 As shown, when the operating status of any edge engine is faulty, step S140 may include the following steps:

[0109] S141a. Decompose the data collection task of any edge engine into multiple point data collection tasks according to the data collection points.

[0110] In specific implementation, the execution of step S141a may refer to the execution of the above-mentioned step S141, and this application will not go into details here.

[0111] S142a. According to the PID algorithm, randomly allocate multiple point data collection tasks to idle edge engines among the N edge engines.

[0112] In specific implementation, the execution of step S142a may refer to the execution of the above-mentioned step S142, and this application will not go into details here.

[0113] S143a, determine whether any idle edge engine has reached a performance balance state. If any idle edge engine has not reached a performance balance state, execute step S144a, otherwise execute step S145a.

[0114] In specific implementation, the execution of step S143a may refer to the execution of the above-mentioned step S143, and this application will not go into details here.

[0115] S144a, adjust the number of point data collection tasks assigned to any idle edge engine, and return to execute step S143a.

[0116] In a specific implementation, step S144a may be executed to continuously adjust the input value of the PID algorithm until the performance balance of any idle edge engine reaches a steady state.

[0117] S145a, end the process.

[0118] In the present application, since the number, difficulty and performance of data acquisition tasks of any edge engine are not in a linear relationship, by executing step S140, hardware resources can be utilized to the maximum extent and investment costs can be saved. At the same time, the number of tasks that balance the performance of any edge engine can be found, so as to form a leveling of the performance utilization of all edge engines under the same computing task of the same 5G network slice, so as to dynamically allocate data acquisition tasks under the same 5G network slice and always keep each edge engine in a state of dynamic performance balance. At the same time, it can also be applied to scenarios with 24-hour uninterrupted computing and continuously increasing demand, thereby maximizing the computing power of the 5G edge engine and ensuring the stability of system operation in industrial environments.

[0119] That is, this application introduces the PID algorithm and uses the edge engine computing power under a cloud engine as several algorithm sharing nodes. That is, each edge engine can be used to execute data acquisition tasks, so as to comprehensively allocate data acquisition computing units and facilitate timely deployment of data acquisition tasks of busy edge engines, so that each edge engine can achieve a performance balance state.

[0120] In one implementation, the implementation method of the 5G edge engine provided in the present application may further include the following steps:

[0121] S150: When it is determined that the running state of any edge engine is idle, any edge engine is assigned to collect data from a nearby device, and the remaining edge engines among the N edge engines are controlled to be in a sleep state for the collection task of the nearby device.

[0122] In specific implementation, when the running state of any edge engine is idle, in principle, any edge engine collects data from nearby devices, which helps to maximize the computing power of the 5G edge engine and ensure the stability of system operation in industrial environments. When the edge engine collects data from the nearby device, the remaining edge engines among the N edge engines are in a sleep state for the collection tasks of the nearby device, and at the same time, the connection to the nearby device can also be in a sleep state to ensure that any edge engine collects data from the nearby device in an orderly manner.

[0123] It should be noted that the above-mentioned edge engine can refer to edge terminals and access terminals, where the edge terminal can refer to the operator's MEC server or the edge cloud terminal purchased by the user customer, and the access terminal can refer to the local field station, industrial computer or embedded system, etc.

[0124] From the above description, it can be seen that the present application can conveniently monitor which edge engine is about to reach or has reached a busy state at all times by monitoring the performance data of each of the N edge engines registered under a cloud engine; further, by calculating the performance evaluation score of any edge engine based on the performance data of any edge engine, it is convenient to timely judge the operating status of any edge engine; further, by comparing the performance evaluation score with the pre-set target performance evaluation score to determine the operating status of any edge engine, it is convenient to timely confirm the operating status of any edge engine; further, when it is determined that the operating status of any edge engine is busy or faulty, the data acquisition tasks of any edge engine are disassembled, and according to the PID algorithm, part of the disassembled data acquisition tasks are allocated to the idle edge engines among the N edge engines, thereby maximizing the computing power of the 5G edge engine and ensuring the stability of the system operation in an industrial environment.

[0125] Figure 5FIG. 5 is a block diagram showing a device for implementing a 5G edge engine according to an embodiment of the present application. Figure 5 As shown, the device may include:

[0126] A monitoring unit 210, configured to monitor the performance data of each of N edge engines, where the N edge engines are registered under one cloud engine, and N is an integer greater than or equal to 2;

[0127] A calculation unit 220, configured to calculate a performance evaluation score of any edge engine based on the performance data of any edge engine;

[0128] The allocation unit 230 is used to compare the performance evaluation score with a preset target performance evaluation score to determine the operating status of any edge engine; when it is determined that the operating status of any edge engine is busy or faulty, the data acquisition tasks of any edge engine are disassembled, and the disassembled data acquisition tasks are allocated to the idle edge engines among the N edge engines according to the PID algorithm.

[0129] The functions of each unit in the implementation device of the 5G edge engine of the embodiment of the present application can be found in the corresponding description in the above method and will not be repeated here.

[0130] Figure 6 FIG. 2 shows a structural block diagram of a communication device according to an embodiment of the present application. Figure 6 As shown, the communication device includes: a memory 310 and a processor 320, wherein the memory 310 stores instructions, and the instructions are loaded and executed by the processor 320 to implement the implementation method of the 5G edge engine in the above embodiment. The number of the memory 310 and the processor 320 can be one or more.

[0131] The communication device also includes:

[0132] The communication interface 330 is used to communicate with external devices and perform data exchange transmission.

[0133] If the memory 310, the processor 320 and the communication interface 330 are implemented independently, the memory 310, the processor 320 and the communication interface 330 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0134] Optionally, in a specific implementation, if the memory 310, the processor 320 and the communication interface 330 are integrated on a chip, the memory 310, the processor 320 and the communication interface 330 can communicate with each other through an internal interface.

[0135] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program runs on a computer, the method provided in the embodiment of the present application is implemented.

[0136] An embodiment of the present application also provides a chip, which includes a processor for calling and executing instructions stored in a memory, so that a communication device equipped with the chip executes the method provided by the embodiment of the present application.

[0137] An embodiment of the present application also provides a chip, including: an input interface, an output interface, a processor and a memory, wherein the input interface, the output interface, the processor and the memory are connected via an internal connection path, and the processor is used to execute the code in the memory. When the code is executed, the processor is used to execute the method provided in the embodiment of the application.

[0138] It should be understood that the processor may be a central processing unit (CPU), or 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. It is worth noting that the processor may be a processor supporting the Advanced RISC Machines (ARM) architecture.

[0139] Further, optionally, the above-mentioned memory may include a read-only memory and a random access memory, and may also include a non-volatile random access memory. The memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct rambus RAM (DR RAM).

[0140] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0141] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0142] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0143] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0144] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0145] It should be understood that the various parts of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium, and when the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0146] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0147] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for implementing a 5G edge engine, characterized in that: include: Monitor the performance data of each of N edge engines, where the N edge engines are registered under one cloud engine, and N is an integer greater than or equal to 2; Calculate the performance evaluation score of any edge engine according to the performance data of any edge engine; Comparing the performance evaluation score with a preset target performance evaluation score to determine the operating status of any edge engine; When it is determined that the running state of any edge engine is busy or faulty, the data acquisition task of any edge engine is disassembled, and the disassembled data acquisition task is allocated to an idle edge engine among the N edge engines according to the PID algorithm.

2. The method according to claim 1, characterized in that: According to the performance data of any of the edge engines, the performance evaluation score of any of the edge engines is calculated to include: According to the performance data of any of the edge engines, respectively calculate the comprehensive performance evaluation score, performance occupancy score and performance efficiency score of any of the edge engines; The performance evaluation score is calculated according to the comprehensive performance evaluation score, the performance occupancy score and the performance efficiency score.

3. The method according to claim 2, characterized in that According to the performance data of any of the edge engines, the comprehensive performance evaluation score, performance occupancy score and performance efficiency score of any of the edge engines are calculated respectively, including: Calculate the performance score of each device data unit of any edge engine according to the actual value of the arrival time of each device data in the performance data of any edge engine; calculate the comprehensive performance evaluation score according to the performance score of each device data unit; The performance occupancy score is calculated based on the average memory usage rate in minutes, the average memory usage rate in hours, the average hard disk read and write rate in minutes, the average hard disk read and write rate in hours, the average CPU usage rate in minutes, and the average CPU usage rate in hours in the performance data of any edge engine; The performance efficiency score is calculated based on the average data reading time per minute, the average data reading time per hour, the average number of access requests per minute, the average number of access requests per hour, the average task completion time per minute, and the average task completion time per hour in the performance data of any of the edge engines.

4. The method according to claim 2, characterized in that: The performance evaluation score calculated according to the comprehensive performance evaluation score, the performance occupancy score and the performance efficiency score includes: Calculate the working performance evaluation score of any edge engine according to the performance occupancy score and the performance efficiency score; The performance evaluation score is calculated based on the comprehensive performance evaluation score and the work performance evaluation score.

5. The method according to claim 1, characterized in that Comparing the performance evaluation score with a preset target performance evaluation score to determine the operating status of any edge engine includes: Comparing the performance evaluation score with the target performance evaluation score, and determining that the performance evaluation score is less than the target performance evaluation score, and when the performance evaluation score is greater than a set dead zone, determining that the operating state of any edge engine is idle, wherein the conditional judgment within the numerical range of the dead zone is not effective; Comparing the performance evaluation score with the target performance evaluation score, and determining that the performance evaluation score is greater than the target performance evaluation score, and when the performance evaluation score is greater than a set dead zone, determining that the operating state of any edge engine is busy; The performance evaluation score is compared with the target performance evaluation score, and when it is determined that the performance evaluation score is greater than the target performance evaluation score and the performance evaluation score is less than a set dead zone, it is determined that the operating state of any edge engine is a fault.

6. The method according to claim 1, characterized in that Decomposing the data acquisition tasks of any edge engine, and allocating the decomposed data acquisition tasks to idle edge engines among the N edge engines according to the PID algorithm includes: According to the data collection points, decompose the data collection task of any edge engine into multiple point data collection tasks; When the running state of any of the edge engines is busy, according to the PID algorithm, randomly allocate some of the point data collection tasks among the multiple point data collection tasks to an idle edge engine among the N edge engines; determine whether any of the edge engines has reached a performance balance state; if any of the edge engines has not reached a performance balance state, replace the remaining point data collection tasks in any of the edge engines with multiple point data collection tasks, and return to execution according to the PID algorithm, randomly allocate some of the point data collection tasks among the multiple point data collection tasks to an idle edge engine among the N edge engines, until any of the edge engines reaches a performance balance state; When the operating status of any of the edge engines is faulty, according to the PID algorithm, a plurality of the point data collection tasks are randomly allocated to idle edge engines among the N edge engines; it is determined whether any of the idle edge engines has reached a performance balance state; if any of the idle edge engines has not reached a performance balance state, the number of point data collection tasks allocated to any of the idle edge engines is adjusted, and the process returns to determine whether any of the idle edge engines has reached a performance balance state until any of the idle edge engines has reached a performance balance state.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: When it is determined that the running state of any edge engine is idle, any edge engine is assigned to collect data from a nearby device, and the collection tasks of the nearby devices of the remaining edge engines among the N edge engines are controlled to be in a sleep state.

8. A device for implementing a 5G edge engine, characterized in that: include: A monitoring unit, used to monitor the performance data of each of N edge engines, where the N edge engines are registered under one cloud engine, and N is an integer greater than or equal to 2; A calculation unit, configured to calculate a performance evaluation score of any of the edge engines based on the performance data of any of the edge engines; A deployment unit, configured to compare the performance evaluation score with a preset target performance evaluation score to determine the operating status of any edge engine; When it is determined that the running state of any edge engine is busy or faulty, the data acquisition task of any edge engine is disassembled, and the disassembled data acquisition task is allocated to an idle edge engine among the N edge engines according to the PID algorithm.

9. A communication device, characterized in that: include: A memory and a processor, wherein the memory stores instructions, and the instructions are loaded and executed by the processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed on a computer, the method according to any one of claims 1 to 7 is implemented.

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