Man-machine and object multi-core communication method and system based on 5G RedCap and medium

Abstract

The invention provides a man-machine and object multi-core communication method and system based on 5G RedCap and a medium. The method comprises the following steps: monitoring a target man-machine-object multi-core communication system, processing to obtain a communication load capacity index, a data priority index and an equipment performance index corresponding to each single core, further processing to obtain task allocation proportion data corresponding to each single core, and performing performance optimization on the target man-machine-object multi-core communication system to obtain a task allocation proportion data corresponding to each single core. Extracting equipment connection state data and inter-core cooperation characteristic data of the optimized man-machine and object multi-core communication system, processing to obtain a system stability index, processing to obtain a communication quality inspection index according to communication quality characteristic data, processing to obtain a data transmission efficiency index according to data transmission characteristic data, and performing data transmission on the data transmission efficiency index. The method comprises the following steps of: obtaining a system optimization effect evaluation index by combining a system stability index and a communication quality inspection index for processing, and correspondingly adjusting a performance optimization scheme, thereby realizing a 5G RedCap-based man-machine-object multi-core communication technology.

Description

Technical Field

[0001] The present application relates to the field of multi-core communication technology, and more specifically, to a human-machine-object multi-core communication method, system and medium based on 5G RedCap. Background Art

[0002] With the rise of the concept of smart construction sites, a large number of personnel (such as workers and managers), mechanical equipment (such as cranes and excavators) and materials (such as sensors for building materials) on construction sites need to communicate and work together in real time. Traditional communication technologies have problems such as insufficient bandwidth, high power consumption, and poor equipment compatibility when faced with complex construction site environments and diverse communication needs. The emergence of 5G RedCap technology provides a new solution for smart construction site communications, but how to effectively use 5G RedCap in smart construction sites to achieve efficient multi-core communication between humans, machines, and objects remains a challenge.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of this application is to provide a human-machine-object multi-core communication method, system and medium based on 5G RedCap. The method can monitor the target human-machine-object multi-core communication system and process the communication load capacity index, data priority index and equipment performance index corresponding to each single core, and further process to obtain the task allocation ratio data corresponding to each single core, optimize the performance of the target human-machine-object multi-core communication system, monitor the optimized human-machine-object multi-core communication system, extract the device connection status data and inter-core collaboration feature data of the optimized human-machine-object multi-core communication system, process to obtain the system stability index, obtain the communication quality inspection index according to the communication quality feature data, and obtain the data transmission efficiency index according to the data transmission feature data, and process in combination with the system stability index and the communication quality inspection index to obtain the system optimization effectiveness evaluation index, and adjust the performance optimization plan accordingly to realize the technology of human-machine-object multi-core communication based on 5G RedCap.

[0005] This application also provides a human-machine-object multi-core communication method based on 5G RedCap, including the following steps: Monitor the operating status information of the target human-machine-object multi-core communication system, extract the communication load characteristic data corresponding to each single core, and process it to obtain the communication load capacity index; Acquire data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, and process to obtain a data priority index; Acquire device performance characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, and process to obtain a device performance index; Processing is performed according to the communication load capacity index, the data priority index and the device performance index to obtain the task allocation ratio data corresponding to each single core, optimizing the performance of the target human-machine-object multi-core communication system, and monitoring the optimized human-machine-object multi-core communication system; Collecting the operation information of the optimized human-machine-object multi-core communication system within a preset time period, extracting device connection status data and inter-core collaboration feature data, and processing to obtain a system stability index; Acquire communication quality characteristic data of the optimized human-machine-object multi-core communication system, and process to obtain a communication quality inspection index; Acquire data transmission characteristic data of the optimized human-machine-object multi-core communication system, and process and obtain a data transmission efficiency index; The system stability index, communication quality inspection index and data transmission efficiency index are processed to obtain a system optimization effectiveness evaluation index, and the performance optimization plan is adjusted accordingly.

[0006] Optionally, in the 5G RedCap-based human-machine-object multi-core communication method described in the present application, the operation status information of the target human-machine-object multi-core communication system is monitored, and the communication load characteristic data corresponding to each single core is extracted, and the communication load capacity index is obtained by processing, including: Monitor the operating status information of the target human-machine-object multi-core communication system and extract the communication load characteristic data corresponding to each single core; The communication load characteristic data includes communication flow data, communication load peak data and communication load valley data; The communication load capacity index is obtained by processing the communication flow data, the communication load peak data and the communication load valley data.

[0007] Optionally, in the 5G RedCap-based human-machine-object multi-core communication method described in the present application, the step of obtaining data level feature data corresponding to each single core in the target human-machine-object multi-core communication system and processing to obtain a data priority index includes: Acquire data level feature data corresponding to each single core in the target human-machine-object multi-core communication system; The data level characteristic data includes data importance level data and data timeliness level data; The data priority index is obtained by processing the data importance level data and the data timeliness level data.

[0008] Optionally, in the human-machine-object multi-core communication method based on 5G RedCap described in the present application, the processing is performed according to the communication load capacity index, the data priority index and the device performance index, the task allocation ratio data corresponding to each single core is obtained, the performance of the target human-machine-object multi-core communication system is optimized, and the optimized human-machine-object multi-core communication system is monitored, including: Processing is performed according to the communication load capacity index, the data priority index and the device performance index to obtain task allocation ratio data corresponding to each single core; Optimizing the performance of the target human-machine-object multi-core communication system according to the task allocation ratio data corresponding to each single core; Monitor the operating status and performance status of the optimized human-machine-object multi-core communication system.

[0009] Optionally, in the human-machine-object multi-core communication method based on 5G RedCap described in the present application, the operation information of the optimized human-machine-object multi-core communication system within a preset time period is collected, and the device connection status data and inter-core collaboration feature data are extracted, and the system stability index is obtained by processing, including: Collecting the operation information of the optimized human-machine-object multi-core communication system within a preset time period, and extracting device connection status data and inter-core collaboration feature data; The device connection status data includes connection success rate data, connection interruption frequency data and connection interruption duration data; The inter-core collaboration feature data includes total communication data data and inter-core communication delay maximum value data; The connection success rate data, the connection interruption frequency data, and the connection interruption duration data are processed in combination with the total communication data data and the maximum value data of the inter-core communication delay to obtain a system stability index.

[0010] Optionally, in the human-machine-object multi-core communication method based on 5G RedCap described in the present application, the system stability index, the communication quality inspection index and the data transmission efficiency index are processed to obtain the system optimization effectiveness evaluation index, and the performance optimization plan is adjusted accordingly, including: Processing is performed according to the system stability index, communication quality inspection index and data transmission efficiency index to obtain a system optimization effectiveness evaluation index; Comparing the system optimization effectiveness evaluation index with a preset system optimization effectiveness evaluation threshold to obtain a comparison result; The performance optimization effect of the target human-machine-object multi-core communication system is evaluated based on the comparison results, and the performance optimization plan is adjusted accordingly.

[0011] In a second aspect, the present application provides a human-machine-object multi-core communication system based on 5G RedCap, the system comprising: a memory and a processor, the memory comprising a program of a human-machine-object multi-core communication method based on 5G RedCap, and the program of a human-machine-object multi-core communication method based on 5G RedCap is executed by the processor to implement the following steps: Monitor the operating status information of the target human-machine-object multi-core communication system, extract the communication load characteristic data corresponding to each single core, and process it to obtain the communication load capacity index; Acquire data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, and process to obtain a data priority index; Acquire device performance characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, and process to obtain a device performance index; Processing is performed according to the communication load capacity index, the data priority index and the device performance index to obtain the task allocation ratio data corresponding to each single core, optimizing the performance of the target human-machine-object multi-core communication system, and monitoring the optimized human-machine-object multi-core communication system; Collecting the operation information of the optimized human-machine-object multi-core communication system within a preset time period, extracting device connection status data and inter-core collaboration feature data, and processing to obtain a system stability index; Acquire communication quality characteristic data of the optimized human-machine-object multi-core communication system, and process to obtain a communication quality inspection index; Acquire data transmission characteristic data of the optimized human-machine-object multi-core communication system, and process and obtain a data transmission efficiency index; The system stability index, communication quality inspection index and data transmission efficiency index are processed to obtain a system optimization effectiveness evaluation index, and the performance optimization plan is adjusted accordingly.

[0012] Optionally, in the human-machine-object multi-core communication system based on 5G RedCap described in the present application, the operation status information of the target human-machine-object multi-core communication system is monitored, and the communication load characteristic data corresponding to each single core is extracted, and the communication load capacity index is obtained by processing, including: Monitor the operating status information of the target human-machine-object multi-core communication system and extract the communication load characteristic data corresponding to each single core; The communication load characteristic data includes communication flow data, communication load peak data and communication load valley data; The communication load capacity index is obtained by processing the communication flow data, the communication load peak data and the communication load valley data.

[0013] Optionally, in the human-machine-object multi-core communication system based on 5G RedCap described in the present application, the step of obtaining data level feature data corresponding to each single core in the target human-machine-object multi-core communication system and processing to obtain a data priority index includes: Acquire data level feature data corresponding to each single core in the target human-machine-object multi-core communication system; The data level characteristic data includes data importance level data and data timeliness level data; The data priority index is obtained by processing the data importance level data and the data timeliness level data.

[0014] In the third aspect, the present application also provides a computer-readable storage medium, which stores a human-machine-object multi-core communication method program based on 5G RedCap. When the human-machine-object multi-core communication method program based on 5G RedCap is executed by a processor, the steps of the human-machine-object multi-core communication method based on 5G RedCap as described in any one of the above items are implemented.

[0015] As can be seen from the above, the human-machine-object multi-core communication method, system and medium based on 5G RedCap provided by the present application monitor the operating status information of the target human-machine-object multi-core communication system, and extract the communication load characteristic data corresponding to each single core, process it to obtain the communication load capacity index, obtain the data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, process it to obtain the data priority index, obtain the device performance characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, process it to obtain the device performance index, and process it according to the communication load capacity index, the data priority index and the device performance index to obtain the task allocation ratio data corresponding to each single core, and perform the target human-machine-object multi-core communication system. Perform performance optimization and monitor the optimized human-machine-object multi-core communication system, collect the operating information of the optimized human-machine-object multi-core communication system within a preset time period, extract device connection status data and inter-core collaboration feature data, process to obtain the system stability index, obtain the communication quality feature data of the optimized human-machine-object multi-core communication system, process to obtain the communication quality inspection index, obtain the data transmission feature data of the optimized human-machine-object multi-core communication system, process to obtain the data transmission efficiency index, process according to the system stability index, communication quality inspection index and data transmission efficiency index, obtain the system optimization effectiveness evaluation index, and adjust the performance optimization plan accordingly, so as to realize the human-machine-object multi-core communication technology based on 5G RedCap.

[0016] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a human-machine-object multi-core communication method based on 5G RedCap provided in an embodiment of the present application; Figure 2 A flow chart of obtaining a communication load capacity index for a human-machine-object multi-core communication method based on 5G RedCap provided in an embodiment of the present application; Figure 3 A flow chart of obtaining a data priority index for a human-machine-object multi-core communication method based on 5G RedCap provided in an embodiment of the present application; Figure 4 A flowchart of obtaining a system stability index for a human-machine-object multi-core communication method based on 5G RedCap provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 1 is a flowchart of a 5G RedCap-based multi-core communication method for human-machine-objects in some embodiments of the present application. The 5G RedCap-based multi-core communication method for human-machine-objects is used in terminal devices, such as computers, mobile terminals, etc. The 5G RedCap-based multi-core communication method for human-machine-objects includes the following steps: S11, monitoring the operating status information of the target human-machine-object multi-core communication system, extracting the communication load characteristic data corresponding to each single core, and processing to obtain the communication load capacity index; S12, obtaining data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, and processing to obtain a data priority index; S13, obtaining device performance characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, and processing to obtain a device performance index; S14, performing processing according to the communication load capacity index, the data priority index and the device performance index to obtain the task allocation ratio data corresponding to each single core, optimizing the performance of the target human-machine-object multi-core communication system, and monitoring the optimized human-machine-object multi-core communication system; S15, collecting the operation information of the optimized human-machine-object multi-core communication system within a preset time period, extracting the device connection status data and the inter-core collaboration feature data, and processing to obtain the system stability index; S16, obtaining communication quality characteristic data of the optimized human-machine-object multi-core communication system, and processing to obtain a communication quality inspection index; S17, obtaining data transmission characteristic data of the optimized human-machine-object multi-core communication system, and processing to obtain a data transmission efficiency index; S18. Processing is performed according to the system stability index, communication quality inspection index and data transmission efficiency index to obtain a system optimization effectiveness evaluation index, and the performance optimization plan is adjusted accordingly.

[0022] It should be noted that with the rise of the concept of smart construction sites, a large number of personnel (such as workers, managers), mechanical equipment (such as cranes, excavators) and materials (such as sensors for building materials) on construction sites need to communicate and work together in real time. Traditional communication technologies have problems such as insufficient bandwidth, high power consumption, and poor equipment compatibility when facing complex construction site environments and diverse communication needs. The emergence of 5G RedCap technology provides a new solution for smart construction site communications, but how to effectively use 5G RedCap in smart construction sites? Realizing efficient communication among multiple cores of man, machine, and object is still a challenge. In the construction of smart construction sites, a reliable and efficient communication method is needed to realize efficient collaboration among personnel, mechanical equipment, and materials. In this embodiment, the operation status information of the target multi-core communication system of man, machine, and object is monitored first, and the communication load characteristic data corresponding to each single core is extracted, and the communication load capacity index is obtained by processing, and the data level characteristic data corresponding to each single core in the target multi-core communication system of man, machine, and object is obtained, and the data priority index is obtained, and the equipment performance characteristic data corresponding to each single core in the target multi-core communication system of man, machine, and object is obtained, and the equipment performance index is obtained. According to the communication load capacity index, the data priority index, and the equipment performance index, the data of each single core is obtained. Corresponding task allocation ratio data is used to optimize the performance of the target human-machine-object multi-core communication system, and the optimized human-machine-object multi-core communication system is monitored, and the operation information of the optimized human-machine-object multi-core communication system within a preset time period is collected, and the device connection status data and inter-core collaboration feature data are extracted, and the system stability index is obtained, and the communication quality feature data of the optimized human-machine-object multi-core communication system is obtained, and the communication quality inspection index is obtained, and the data transmission feature data of the optimized human-machine-object multi-core communication system is obtained, and the data transmission efficiency index is obtained. According to the system stability index, the communication quality inspection index and the data transmission efficiency index, the system optimization effectiveness evaluation index is obtained, and the performance optimization plan is adjusted accordingly, so as to realize the human-machine-object multi-core communication technology based on 5G RedCap.

[0023] Please refer to Figure 2 , Figure 2 The present invention is a flowchart of obtaining a communication load capacity index of a human-machine-object multi-core communication method based on 5G RedCap in some embodiments of the present application. According to an embodiment of the present invention, the operation status information of the target human-machine-object multi-core communication system is monitored, and the communication load characteristic data corresponding to each single core is extracted, and the communication load capacity index is obtained by processing, including: S21, monitoring the operating status information of the target human-machine-object multi-core communication system, and extracting the communication load characteristic data corresponding to each single core; S22, the communication load characteristic data includes communication flow data, communication load peak data and communication load valley data; S23. Process the communication flow data, the communication load peak data, and the communication load valley data to obtain a communication load capacity index.

[0024] It should be noted that in the multi-core communication system of man-machine-object, it is key to count the communication flow of each core (which can be a processor core, a device core, etc.), and mastering the peak and valley values ​​of the communication load of each core is helpful for dynamic resource allocation. Therefore, the operation status information of the target multi-core communication system of man-machine-object is monitored and the communication load characteristic data corresponding to each single core is extracted, wherein the communication load characteristic data includes the communication flow, the communication load peak value and the communication load valley value data, and the communication load capacity index is obtained by processing the above data; The calculation formula of the communication load capacity index is: ; in, is the communication load capacity index corresponding to the ith core among the n single cores, , , They are the communication flow data, communication load peak data, and communication load valley data corresponding to the i-th core among the n single cores, , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0025] Please refer to Figure 3 , Figure 3 The flowchart of obtaining the data priority index of the human-machine-object multi-core communication method based on 5G RedCap in some embodiments of the present application. According to an embodiment of the present invention, the data level feature data corresponding to each single core in the target human-machine-object multi-core communication system is obtained, and the data priority index is obtained by processing, including: S31, obtaining data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system; S32, the data level characteristic data includes data importance level data and data timeliness level data; S33: Process the data importance level data and the data timeliness level data to obtain a data priority index.

[0026] It should be noted that clarifying the importance level of different data is an important basis for the reasonable allocation of communication resources, and different data have different timeliness. Therefore, the data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system is obtained, wherein the data level characteristic data includes data importance level data and data timeliness level data, and then the data is processed according to the above data to obtain the data priority index; The calculation formula of the data priority index is: ; in, is the data priority index corresponding to the i-th core among the n single cores, , They are the data importance level data and data timeliness level data corresponding to the i-th core in n single cores, , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0027] According to an embodiment of the present invention, the processing is performed according to the communication load capacity index, the data priority index and the device performance index to obtain the task allocation ratio data corresponding to each single core, the performance of the target human-machine-object multi-core communication system is optimized, and the optimized human-machine-object multi-core communication system is monitored, including: Processing is performed according to the communication load capacity index, the data priority index and the device performance index to obtain task allocation ratio data corresponding to each single core; Optimizing the performance of the target human-machine-object multi-core communication system according to the task allocation ratio data corresponding to each single core; Monitor the operating status and performance status of the optimized human-machine-object multi-core communication system.

[0028] It should be noted that the performance of the entire human-machine-object multi-core communication system can be optimized according to the task allocation ratio shared by each single core, so that the resource allocation of the entire system is more balanced and reasonable. First, the task allocation ratio data corresponding to each single core is obtained according to the obtained communication load capacity index, data priority index and equipment performance index, and then the performance of the human-machine-object multi-core communication system is optimized according to these ratio data. At the same time, the system operation status and performance status after optimization are monitored. If it is found that the performance allocation does not reach the ideal state, it can be adjusted later; The calculation formula of the task allocation ratio data is: ; in, Allocate proportional data for the i-th task corresponding to n single cores, , , are the communication load capacity index, data priority index, and device performance index corresponding to the ith core among the n single cores, , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0029] Please refer to Figure 4 , Figure 4 It is a flowchart of obtaining a system stability index of a human-machine-object multi-core communication method based on 5G RedCap in some embodiments of the present application. According to an embodiment of the present invention, the operation information of the optimized human-machine-object multi-core communication system within a preset time period is collected, and device connection status data and inter-core collaboration feature data are extracted, and the system stability index is obtained by processing, including: S41, collecting operation information of the optimized human-machine-object multi-core communication system within a preset time period, and extracting device connection status data and inter-core collaboration feature data; S42, the device connection status data includes connection success rate data, connection interruption frequency data and connection interruption duration data; S43, the inter-core collaboration feature data includes total communication data data and inter-core communication delay maximum value data; S44. Process the connection success rate data, the connection interruption frequency data, and the connection interruption duration data in combination with the communication data total amount data and the inter-core communication delay maximum value data to obtain a system stability index.

[0030] It should be noted that the system stability of the optimized human-machine-object multi-core communication system needs to be evaluated, so it is very important to understand the connection status of the device and the coordination between each single core. Therefore, the operation information of the optimized human-machine-object multi-core communication system within a preset time period is collected, and the device connection status data and the inter-core coordination feature data are extracted, wherein the device connection status data includes the connection success rate, the connection interruption frequency and the connection interruption duration data, the connection success rate data refers to the ratio of the number of successfully connected devices to the total number of devices trying to connect, and the inter-core coordination feature data includes the total amount of communication data and the maximum value of the inter-core communication delay data. The system stability index is obtained by processing the above data; The calculation formula of the system stability index is: ; in, is the system stability index, , , They are connection success rate data, connection interruption frequency data, and connection interruption duration data. , They are the total amount of communication data and the maximum value of inter-core communication delay. , , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0031] According to an embodiment of the present invention, the processing according to the system stability index, the communication quality inspection index and the data transmission efficiency index to obtain the system optimization effectiveness evaluation index and adjust the performance optimization scheme accordingly includes: Processing is performed according to the system stability index, communication quality inspection index and data transmission efficiency index to obtain a system optimization effectiveness evaluation index; Comparing the system optimization effectiveness evaluation index with a preset system optimization effectiveness evaluation threshold to obtain a comparison result; The performance optimization effect of the target human-machine-object multi-core communication system is evaluated based on the comparison results, and the performance optimization plan is adjusted accordingly.

[0032] It should be noted that it is necessary to monitor the operating status and performance of the optimized human-machine-object multi-core communication system, and to readjust it in time when the status is not ideal, so that the optimization effect is more efficient and the entire communication system is more reasonable. To this end, the obtained system stability index, communication quality inspection index and data transmission efficiency index are processed to obtain the system optimization effect evaluation index, which is then compared with the preset system optimization effect evaluation threshold to obtain the comparison result. According to the comparison result, the performance optimization effect of the target human-machine-object multi-core communication system is evaluated, and the performance optimization plan is adjusted accordingly; The calculation formula of the system optimization effectiveness evaluation index is: ; in, To evaluate the effectiveness of system optimization, , , They are system stability index, communication quality inspection index, and data transmission efficiency index. , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0033] According to an embodiment of the present invention, it also includes: The obtaining of device performance characteristic data corresponding to each single core in the target human-machine-object multi-core communication system and processing to obtain a device performance index specifically includes: Acquire device performance characteristic data corresponding to each single core in the target human-machine-object multi-core communication system; The device performance characteristic data includes core processing capability data and device communication interface capability data; The core processing capability data includes clock frequency data, core quantity data, and cache size data; The device communication interface capability data includes interface bandwidth data and maximum connection number data; Processing the clock frequency data, the core quantity data, and the cache size data in combination with the interface bandwidth data and the maximum connection number data to obtain a device performance index; The calculation formula of the equipment performance index is: ; in, is the equipment performance index, , , They are the clock frequency data, core quantity data, and cache size data corresponding to the i-th core among the n single cores. , They are the interface bandwidth data and maximum connection number data corresponding to the i-th core among n single cores. , , , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0034] It should be noted that the difference in processing power of different cores will affect the allocation of communication resources. In addition, the capabilities of the device communication interface of each single core will also affect the performance of the system. Among them, the core processing power data includes clock frequency, core data and cache size data, and the device communication interface capability data includes interface bandwidth data and maximum number of connections data. The above data is then processed to obtain the device performance index.

[0035] According to an embodiment of the present invention, it also includes: The obtaining of the communication quality characteristic data of the optimized human-machine-object multi-core communication system and processing to obtain the communication quality inspection index specifically includes: Acquire communication quality characteristic data of the optimized human-machine-object multi-core communication system; The communication quality characteristic data includes signal strength data, coverage data, signal-to-noise ratio data, bit error rate data and bandwidth utilization data; Processing the signal strength data, coverage data, signal-to-noise ratio data, bit error rate data, and bandwidth utilization rate data to obtain a communication quality inspection index; The calculation formula of the communication quality inspection index is: ; in, is the communication quality inspection index, , , , , They are signal strength data, coverage data, signal-to-noise ratio data, bit error rate data, and bandwidth utilization data. , , , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0036] It should be noted that one of the indicators for testing the performance of the optimized human-machine-object multi-core communication system is communication quality. The communication quality characteristic data include signal strength, coverage, signal-to-noise ratio, bit error rate and bandwidth utilization data. In smart construction sites, signal strength is affected by many factors, such as the power of communication equipment, antenna gain, obstruction and interference of the surrounding environment, etc. The coverage range refers to the construction site area that the communication signal can effectively cover. The signal-to-noise ratio (SNR) is an important indicator for measuring signal quality. The bit error rate is directly related to the reliability of communication, and the bandwidth utilization reflects the efficiency of the use of communication resources. The above data are then processed to obtain the communication quality inspection index.

[0037] According to an embodiment of the present invention, it also includes: The step of obtaining the data transmission characteristic data of the optimized human-machine-object multi-core communication system and processing to obtain the data transmission efficiency index specifically includes: Acquire data transmission characteristic data of the optimized human-machine-object multi-core communication system, including data transmission rate average value data and throughput data; Processing the data transmission rate average value data and the throughput data to obtain a data transmission efficiency index; The calculation formula of the data transmission efficiency index is: ; in, is the data transmission efficiency index, , They are the average data of data transmission rate and throughput data respectively. , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0038] It should be noted that the data transmission characteristic data of the optimized human-machine-object multi-core communication system are obtained, including the average data transmission rate data and throughput data, among which the average data transmission rate refers to the average value of the uplink rate (data transmission speed from device to server) and the downlink rate (command transmission speed from server to device), and the throughput refers to the amount of effective data passing through the communication network or device per unit time. The above data are then processed to obtain the data transmission efficiency index.

[0039] In a second aspect, the present invention further discloses a human-machine-object multi-core communication system based on 5G RedCap, including a memory and a processor, wherein the memory includes a human-machine-object multi-core communication method program based on 5G RedCap, and when the human-machine-object multi-core communication method program based on 5GRedCap is executed by the processor, the following steps are implemented: Monitor the operating status information of the target human-machine-object multi-core communication system, extract the communication load characteristic data corresponding to each single core, and process it to obtain the communication load capacity index; Acquire data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, and process to obtain a data priority index; Acquire device performance characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, and process to obtain a device performance index; Processing is performed according to the communication load capacity index, the data priority index and the device performance index to obtain the task allocation ratio data corresponding to each single core, optimizing the performance of the target human-machine-object multi-core communication system, and monitoring the optimized human-machine-object multi-core communication system; Collecting the operation information of the optimized human-machine-object multi-core communication system within a preset time period, extracting device connection status data and inter-core collaboration feature data, and processing to obtain a system stability index; Acquire communication quality characteristic data of the optimized human-machine-object multi-core communication system, and process to obtain a communication quality inspection index; Acquire data transmission characteristic data of the optimized human-machine-object multi-core communication system, and process and obtain a data transmission efficiency index; The system stability index, communication quality inspection index and data transmission efficiency index are processed to obtain a system optimization effectiveness evaluation index, and the performance optimization plan is adjusted accordingly.

[0040] It should be noted that with the rise of the concept of smart construction sites, a large number of personnel (such as workers, managers), mechanical equipment (such as cranes, excavators) and materials (such as sensors for building materials) on construction sites need to communicate and work together in real time. Traditional communication technologies have problems such as insufficient bandwidth, high power consumption, and poor equipment compatibility when facing complex construction site environments and diverse communication needs. The emergence of 5G RedCap technology provides a new solution for smart construction site communications, but how to effectively use 5G RedCap in smart construction sites? Realizing efficient communication among multiple cores of man, machine, and object is still a challenge. In the construction of smart construction sites, a reliable and efficient communication method is needed to realize efficient collaboration among personnel, mechanical equipment, and materials. In this embodiment, the operation status information of the target multi-core communication system of man, machine, and object is monitored first, and the communication load characteristic data corresponding to each single core is extracted, and the communication load capacity index is obtained by processing, and the data level characteristic data corresponding to each single core in the target multi-core communication system of man, machine, and object is obtained, and the data priority index is obtained, and the equipment performance characteristic data corresponding to each single core in the target multi-core communication system of man, machine, and object is obtained, and the equipment performance index is obtained. According to the communication load capacity index, the data priority index, and the equipment performance index, the data of each single core is obtained. Corresponding task allocation ratio data is used to optimize the performance of the target human-machine-object multi-core communication system, and the optimized human-machine-object multi-core communication system is monitored, and the operation information of the optimized human-machine-object multi-core communication system within a preset time period is collected, and the device connection status data and inter-core collaboration feature data are extracted, and the system stability index is obtained, and the communication quality feature data of the optimized human-machine-object multi-core communication system is obtained, and the communication quality inspection index is obtained, and the data transmission feature data of the optimized human-machine-object multi-core communication system is obtained, and the data transmission efficiency index is obtained. According to the system stability index, the communication quality inspection index and the data transmission efficiency index, the system optimization effectiveness evaluation index is obtained, and the performance optimization plan is adjusted accordingly, so as to realize the human-machine-object multi-core communication technology based on 5G RedCap.

[0041] According to an embodiment of the present invention, the monitoring of the operating status information of the target human-machine-object multi-core communication system, extracting the communication load characteristic data corresponding to each single core, and processing to obtain the communication load capacity index includes: Monitor the operating status information of the target human-machine-object multi-core communication system and extract the communication load characteristic data corresponding to each single core; The communication load characteristic data includes communication flow data, communication load peak data and communication load valley data; The communication load capacity index is obtained by processing the communication flow data, the communication load peak data and the communication load valley data.

[0042] It should be noted that in the multi-core communication system of man-machine-object, it is key to count the communication flow of each core (which can be a processor core, a device core, etc.), and mastering the peak and valley values ​​of the communication load of each core is helpful for dynamic resource allocation. Therefore, the operation status information of the target multi-core communication system of man-machine-object is monitored and the communication load characteristic data corresponding to each single core is extracted, wherein the communication load characteristic data includes the communication flow, the communication load peak value and the communication load valley value data, and the communication load capacity index is obtained by processing the above data; The calculation formula of the communication load capacity index is: ; in, is the communication load capacity index corresponding to the ith core among the n single cores, , , They are the communication flow data, communication load peak data, and communication load valley data corresponding to the i-th core among the n single cores, , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0043] According to an embodiment of the present invention, the step of obtaining data level feature data corresponding to each single core in the target human-machine-object multi-core communication system and processing to obtain a data priority index includes: Acquire data level feature data corresponding to each single core in the target human-machine-object multi-core communication system; The data level characteristic data includes data importance level data and data timeliness level data; The data priority index is obtained by processing the data importance level data and the data timeliness level data.

[0044] It should be noted that clarifying the importance level of different data is an important basis for the reasonable allocation of communication resources, and different data have different timeliness. Therefore, the data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system is obtained, wherein the data level characteristic data includes data importance level data and data timeliness level data, and then the data is processed according to the above data to obtain the data priority index; The calculation formula of the data priority index is: ; in, is the data priority index corresponding to the i-th core among the n single cores, , They are the data importance level data and data timeliness level data corresponding to the i-th core in n single cores, , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0045] According to an embodiment of the present invention, the processing is performed according to the communication load capacity index, the data priority index and the device performance index to obtain the task allocation ratio data corresponding to each single core, the performance of the target human-machine-object multi-core communication system is optimized, and the optimized human-machine-object multi-core communication system is monitored, including: Processing is performed according to the communication load capacity index, the data priority index and the device performance index to obtain task allocation ratio data corresponding to each single core; Optimizing the performance of the target human-machine-object multi-core communication system according to the task allocation ratio data corresponding to each single core; Monitor the operating status and performance status of the optimized human-machine-object multi-core communication system.

[0046] It should be noted that the performance of the entire human-machine-object multi-core communication system can be optimized according to the task allocation ratio shared by each single core, so that the resource allocation of the entire system is more balanced and reasonable. First, the task allocation ratio data corresponding to each single core is obtained according to the obtained communication load capacity index, data priority index and equipment performance index, and then the performance of the human-machine-object multi-core communication system is optimized according to these ratio data. At the same time, the system operation status and performance status after optimization are monitored. If it is found that the performance allocation does not reach the ideal state, it can be adjusted later; The calculation formula of the task allocation ratio data is: ; in, Allocate proportional data for the i-th task corresponding to n single cores, , , are the communication load capacity index, data priority index, and device performance index corresponding to the ith core among the n single cores, , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0047] According to an embodiment of the present invention, the collecting of operating information of the optimized human-machine-object multi-core communication system within a preset time period, extracting device connection status data and inter-core collaboration feature data, and processing to obtain a system stability index includes: Collecting the operation information of the optimized human-machine-object multi-core communication system within a preset time period, and extracting device connection status data and inter-core collaboration feature data; The device connection status data includes connection success rate data, connection interruption frequency data and connection interruption duration data; The inter-core collaboration feature data includes total communication data data and inter-core communication delay maximum value data; The connection success rate data, the connection interruption frequency data, and the connection interruption duration data are processed in combination with the total communication data data and the maximum value data of the inter-core communication delay to obtain a system stability index.

[0048] It should be noted that the system stability of the optimized human-machine-object multi-core communication system needs to be evaluated, so it is very important to understand the connection status of the device and the coordination between each single core. Therefore, the operation information of the optimized human-machine-object multi-core communication system within a preset time period is collected, and the device connection status data and the inter-core coordination feature data are extracted, wherein the device connection status data includes the connection success rate, the connection interruption frequency and the connection interruption duration data, the connection success rate data refers to the ratio of the number of successfully connected devices to the total number of devices trying to connect, and the inter-core coordination feature data includes the total amount of communication data and the maximum value of the inter-core communication delay data. The system stability index is obtained by processing the above data; The calculation formula of the system stability index is: ; in, is the system stability index, , , They are connection success rate data, connection interruption frequency data, and connection interruption duration data. , They are the total amount of communication data and the maximum value of inter-core communication delay. , , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0049] According to an embodiment of the present invention, the processing according to the system stability index, the communication quality inspection index and the data transmission efficiency index to obtain the system optimization effectiveness evaluation index and adjust the performance optimization scheme accordingly includes: Processing is performed according to the system stability index, communication quality inspection index and data transmission efficiency index to obtain a system optimization effectiveness evaluation index; Comparing the system optimization effectiveness evaluation index with a preset system optimization effectiveness evaluation threshold to obtain a comparison result; The performance optimization effect of the target human-machine-object multi-core communication system is evaluated based on the comparison results, and the performance optimization plan is adjusted accordingly.

[0050] It should be noted that it is necessary to monitor the operating status and performance of the optimized human-machine-object multi-core communication system, and to readjust it in time when the status is not ideal, so that the optimization effect is more efficient and the entire communication system is more reasonable. To this end, the obtained system stability index, communication quality inspection index and data transmission efficiency index are processed to obtain the system optimization effect evaluation index, which is then compared with the preset system optimization effect evaluation threshold to obtain the comparison result. According to the comparison result, the performance optimization effect of the target human-machine-object multi-core communication system is evaluated, and the performance optimization plan is adjusted accordingly; The calculation formula of the system optimization effectiveness evaluation index is: ; in, To evaluate the effectiveness of system optimization, , , They are system stability index, communication quality inspection index, and data transmission efficiency index. , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0051] According to an embodiment of the present invention, it also includes: The obtaining of device performance characteristic data corresponding to each single core in the target human-machine-object multi-core communication system and processing to obtain a device performance index specifically includes: Acquire device performance characteristic data corresponding to each single core in the target human-machine-object multi-core communication system; The device performance characteristic data includes core processing capability data and device communication interface capability data; The core processing capability data includes clock frequency data, core quantity data, and cache size data; The device communication interface capability data includes interface bandwidth data and maximum connection number data; Processing the clock frequency data, the core quantity data, and the cache size data in combination with the interface bandwidth data and the maximum connection number data to obtain a device performance index; The calculation formula of the equipment performance index is: ; in, is the equipment performance index, , , They are the clock frequency data, core quantity data, and cache size data corresponding to the i-th core among the n single cores. , They are the interface bandwidth data and maximum connection number data corresponding to the i-th core among n single cores. , , , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0052] It should be noted that the difference in processing power of different cores will affect the allocation of communication resources. In addition, the capabilities of the device communication interface of each single core will also affect the performance of the system. Among them, the core processing power data includes clock frequency, core data and cache size data, and the device communication interface capability data includes interface bandwidth data and maximum number of connections data. The above data is then processed to obtain the device performance index.

[0053] According to an embodiment of the present invention, it also includes: The obtaining of the communication quality characteristic data of the optimized human-machine-object multi-core communication system and processing to obtain the communication quality inspection index specifically includes: Acquire communication quality characteristic data of the optimized human-machine-object multi-core communication system; The communication quality characteristic data includes signal strength data, coverage data, signal-to-noise ratio data, bit error rate data and bandwidth utilization data; Processing the signal strength data, coverage data, signal-to-noise ratio data, bit error rate data, and bandwidth utilization rate data to obtain a communication quality inspection index; The calculation formula of the communication quality inspection index is: ; in, is the communication quality inspection index, , , , , They are signal strength data, coverage data, signal-to-noise ratio data, bit error rate data, and bandwidth utilization data. , , , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0054] It should be noted that one of the indicators for testing the performance of the optimized human-machine-object multi-core communication system is communication quality. The communication quality characteristic data include signal strength, coverage, signal-to-noise ratio, bit error rate and bandwidth utilization data. In smart construction sites, signal strength is affected by many factors, such as the power of communication equipment, antenna gain, obstruction and interference of the surrounding environment, etc. The coverage range refers to the construction site area that the communication signal can effectively cover. The signal-to-noise ratio (SNR) is an important indicator for measuring signal quality. The bit error rate is directly related to the reliability of communication, and the bandwidth utilization reflects the efficiency of the use of communication resources. The above data are then processed to obtain the communication quality inspection index.

[0055] According to an embodiment of the present invention, it also includes: The step of obtaining the data transmission characteristic data of the optimized human-machine-object multi-core communication system and processing to obtain the data transmission efficiency index specifically includes: Acquire data transmission characteristic data of the optimized human-machine-object multi-core communication system, including data transmission rate average value data and throughput data; Processing the data transmission rate average value data and the throughput data to obtain a data transmission efficiency index; The calculation formula of the data transmission efficiency index is: ; in, is the data transmission efficiency index, , They are the average data of data transmission rate and throughput data respectively. , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset human-machine-object multi-core communication database).

[0056] It should be noted that the data transmission characteristic data of the optimized human-machine-object multi-core communication system are obtained, including the average data transmission rate data and throughput data, among which the average data transmission rate refers to the average value of the uplink rate (data transmission speed from device to server) and the downlink rate (command transmission speed from server to device), and the throughput refers to the amount of effective data passing through the communication network or device per unit time. The above data are then processed to obtain the data transmission efficiency index.

[0057] The third aspect of the present invention provides a readable storage medium, which stores a human-machine-object multi-core communication method program based on 5GRedCap. When the human-machine-object multi-core communication method program based on 5G RedCap is executed by a processor, the steps of the human-machine-object multi-core communication method based on 5G RedCap as described in any one of the above items are implemented.

[0058] The human-machine-object multi-core communication method, system and medium based on 5G RedCap disclosed in the present invention monitor the operating status information of the target human-machine-object multi-core communication system, extract the communication load characteristic data corresponding to each single core, process to obtain the communication load capacity index, obtain the data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, process to obtain the data priority index, obtain the device performance characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, process to obtain the device performance index, process according to the communication load capacity index, the data priority index and the device performance index, obtain the task allocation ratio data corresponding to each single core, and perform the target human-machine-object multi-core communication system. Perform performance optimization and monitor the optimized human-machine-object multi-core communication system, collect the operating information of the optimized human-machine-object multi-core communication system within a preset time period, extract device connection status data and inter-core collaboration feature data, process to obtain the system stability index, obtain the communication quality feature data of the optimized human-machine-object multi-core communication system, process to obtain the communication quality inspection index, obtain the data transmission feature data of the optimized human-machine-object multi-core communication system, process to obtain the data transmission efficiency index, process according to the system stability index, communication quality inspection index and data transmission efficiency index, obtain the system optimization effectiveness evaluation index, and adjust the performance optimization plan accordingly, so as to realize the human-machine-object multi-core communication technology based on 5G RedCap.

[0059] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0060] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0061] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0062] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0063] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. The human-machine-object multi-core communication method based on 5G RedCap is characterized by: The following steps are involved: Monitor the operating status information of the target human-machine-object multi-core communication system, extract the communication load characteristic data corresponding to each single core, and process it to obtain the communication load capacity index; Acquire data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, and process to obtain a data priority index; Acquire device performance characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, and process to obtain a device performance index; Processing is performed according to the communication load capacity index, the data priority index and the device performance index to obtain the task allocation ratio data corresponding to each single core, optimizing the performance of the target human-machine-object multi-core communication system, and monitoring the optimized human-machine-object multi-core communication system; Collecting the operation information of the optimized human-machine-object multi-core communication system within a preset time period, extracting device connection status data and inter-core collaboration feature data, and processing to obtain a system stability index; Acquire communication quality characteristic data of the optimized human-machine-object multi-core communication system, and process to obtain a communication quality inspection index; Acquire data transmission characteristic data of the optimized human-machine-object multi-core communication system, and process and obtain a data transmission efficiency index; The system stability index, communication quality inspection index and data transmission efficiency index are processed to obtain a system optimization effectiveness evaluation index, and the performance optimization plan is adjusted accordingly.

2. The human-machine-object multi-core communication method based on 5G RedCap according to claim 1 is characterized in that: The monitoring target human-machine-object multi-core communication system operation status information, extracting the communication load characteristic data corresponding to each single core, and processing to obtain the communication load capacity index include: Monitor the operating status information of the target human-machine-object multi-core communication system and extract the communication load characteristic data corresponding to each single core; The communication load characteristic data includes communication flow data, communication load peak data and communication load valley data; The communication load capacity index is obtained by processing the communication flow data, the communication load peak data and the communication load valley data.

3. The human-machine-object multi-core communication method based on 5G RedCap according to claim 2 is characterized in that: The step of obtaining data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system and processing to obtain a data priority index includes: Acquire data level feature data corresponding to each single core in the target human-machine-object multi-core communication system; The data level characteristic data includes data importance level data and data timeliness level data; The data priority index is obtained by processing the data importance level data and the data timeliness level data.

4. The human-machine-object multi-core communication method based on 5G RedCap according to claim 3 is characterized in that: The processing is performed according to the communication load capacity index, the data priority index and the device performance index to obtain the task allocation ratio data corresponding to each single core, the performance of the target human-machine-object multi-core communication system is optimized, and the optimized human-machine-object multi-core communication system is monitored, including: Processing is performed according to the communication load capacity index, the data priority index and the device performance index to obtain task allocation ratio data corresponding to each single core; Optimizing the performance of the target human-machine-object multi-core communication system according to the task allocation ratio data corresponding to each single core; Monitor the operating status and performance status of the optimized human-machine-object multi-core communication system.

5. The human-machine-object multi-core communication method based on 5G RedCap according to claim 4 is characterized in that: The collecting of the operation information of the optimized human-machine-object multi-core communication system within a preset time period, extracting the device connection status data and the inter-core collaboration feature data, and processing to obtain the system stability index includes: Collecting the operation information of the optimized human-machine-object multi-core communication system within a preset time period, and extracting device connection status data and inter-core collaboration feature data; The device connection status data includes connection success rate data, connection interruption frequency data and connection interruption duration data; The inter-core collaboration feature data includes total communication data data and inter-core communication delay maximum value data; The connection success rate data, the connection interruption frequency data, and the connection interruption duration data are processed in combination with the total communication data data and the maximum value data of the inter-core communication delay to obtain a system stability index.

6. The human-machine-object multi-core communication method based on 5G RedCap according to claim 5 is characterized in that: The processing according to the system stability index, the communication quality inspection index and the data transmission efficiency index to obtain the system optimization effectiveness evaluation index and adjust the performance optimization plan accordingly includes: Processing is performed according to the system stability index, communication quality inspection index and data transmission efficiency index to obtain a system optimization effectiveness evaluation index; Comparing the system optimization effectiveness evaluation index with a preset system optimization effectiveness evaluation threshold to obtain a comparison result; The performance optimization effect of the target human-machine-object multi-core communication system is evaluated based on the comparison results, and the performance optimization plan is adjusted accordingly.

7. The human-machine-object multi-core communication system based on 5G RedCap is characterized by: The system includes: a memory and a processor, wherein the memory includes a program of a human-machine-object multi-core communication method based on 5G RedCap, and when the program of the human-machine-object multi-core communication method based on 5G RedCap is executed by the processor, the following steps are implemented: Monitor the operating status information of the target human-machine-object multi-core communication system, extract the communication load characteristic data corresponding to each single core, and process it to obtain the communication load capacity index; Acquire data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, and process to obtain a data priority index; Acquire device performance characteristic data corresponding to each single core in the target human-machine-object multi-core communication system, and process to obtain a device performance index; Processing is performed according to the communication load capacity index, the data priority index and the device performance index to obtain the task allocation ratio data corresponding to each single core, optimizing the performance of the target human-machine-object multi-core communication system, and monitoring the optimized human-machine-object multi-core communication system; Collecting the operation information of the optimized human-machine-object multi-core communication system within a preset time period, extracting device connection status data and inter-core collaboration feature data, and processing to obtain a system stability index; Acquire communication quality characteristic data of the optimized human-machine-object multi-core communication system, and process to obtain a communication quality inspection index; Acquire data transmission characteristic data of the optimized human-machine-object multi-core communication system, and process and obtain a data transmission efficiency index; The system stability index, communication quality inspection index and data transmission efficiency index are processed to obtain a system optimization effectiveness evaluation index, and the performance optimization plan is adjusted accordingly.

8. The human-machine-object multi-core communication system based on 5G RedCap according to claim 7 is characterized in that: The monitoring target human-machine-object multi-core communication system operation status information, extracting the communication load characteristic data corresponding to each single core, and processing to obtain the communication load capacity index include: Monitor the operating status information of the target human-machine-object multi-core communication system and extract the communication load characteristic data corresponding to each single core; The communication load characteristic data includes communication flow data, communication load peak data and communication load valley data; The communication load capacity index is obtained by processing the communication flow data, the communication load peak data and the communication load valley data.

9. The human-machine-object multi-core communication system based on 5G RedCap according to claim 8 is characterized in that: The step of obtaining data level characteristic data corresponding to each single core in the target human-machine-object multi-core communication system and processing to obtain a data priority index includes: Acquire data level feature data corresponding to each single core in the target human-machine-object multi-core communication system; The data level characteristic data includes data importance level data and data timeliness level data; The data priority index is obtained by processing the data importance level data and the data timeliness level data.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a human-machine-object multi-core communication method program based on 5GRedCap. When the human-machine-object multi-core communication method program based on 5G RedCap is executed by a processor, the steps of the human-machine-object multi-core communication method based on 5G RedCap as described in any one of claims 1 to 6 are implemented.

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