Production management platform based on 5G RedCap
Through the production management platform based on 5G RedCap, dynamically match terminal equipment and optimize transmission paths, the problems of high delay and high power consumption in traditional production management platforms are solved, and more efficient data transmission and management are achieved.
Patent Information
- Application Number
- CN202411090571.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-09
- Publication Date
- 2024-12-10
- Estimated Expiration
- 2044-08-09
AI Technical Summary
When traditional production management platforms face complex and changeable production environments, it is difficult for traditional production management platforms to reduce equipment complexity and transmission power consumption, resulting in high delay, low speed, poor efficiency and high power consumption of production data transmission.
The production management platform based on 5G RedCap, dynamically matches terminal equipment matching module, weight coefficient judgment module, feature parameter analysis module, optimal space establishment module and optimal transmission path acquisition module, dynamically matches terminal equipment, analyzes weight coefficients , equipped with edge data centers, optimize transmission paths, reduce energy consumption and latency.
It realizes the reduction of energy consumption in data transmission, improves the efficiency of production data transmission, solves the problems of large transmission power consumption and delay, and improves the real-time and reliability of production data.
Smart Images
Figure CN118627758B_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field related to production management, and specifically to a production management platform based on 5G RedCap. Background Art
[0002] With the continuous development of smart industry, the requirements for intelligent, efficient and low-cost production management systems are increasing. 5G RedCap technology has enabled the production management platform to be upgraded. As a lightweight 5G network, 5G RedCap meets the real-time, stability and economy requirements of the production management platform by reducing the complexity, cost, size and power consumption of terminals and modules. However, when traditional production management platforms are facing complex and changeable production environments and transmitting and processing production data, it is difficult to reduce the complexity of equipment and transmission power consumption and energy consumption by simplifying configurations and requirements. The long-term high power consumption operation of processing equipment increases the energy consumption and operating costs of production management. In addition, the data transmission delay is high and the data transmission rate is low, which makes the real-time and reliability of production data management problematic.
[0003] Therefore, in the current production management related technologies, there are technical problems such as large production data transmission power and delay, which in turn lead to poor production data transmission efficiency and high power consumption and energy consumption. Summary of the invention
[0004] This application solves the technical problems of large production data transmission power and delay in existing production management, which in turn leads to poor transmission efficiency of production data and high power consumption and energy consumption of transmission, by providing a production management platform based on 5G RedCap. It achieves the technical effect of reducing data transmission energy consumption and improving production data transmission efficiency.
[0005] The present application provides a production management platform based on 5G RedCap, the platform comprising: a node set building module, used to build a target production and manufacturing node set, the target production and manufacturing node set comprising a plurality of key nodes in production and manufacturing; a terminal device matching module, used to match the target distributed 5G terminal device with the first predetermined production and manufacturing feature of the first key node among the plurality of key nodes; A first terminal device is matched in a RedCap terminal device; a weight coefficient judgment module is used to match the first weight coefficient of the first key node in the predetermined node weight allocation scheme, and to judge whether the first weight coefficient meets the first weight limit; a characteristic parameter analysis module is used to equip the first terminal device with a first edge data center if it does not meet the requirements, and to analyze the first predetermined production and manufacturing characteristic parameters dynamically collected by the first terminal device through the first edge data center to obtain a first real-time management index of the first key node; an optimization space establishment module is used to dynamically collect the first predetermined production and manufacturing characteristics as the optimization constraint, take the minimum transmission loss as the optimization evaluation index, and take the physical space of the production and manufacturing as the optimization space when the first real-time management index does not meet the management limit; an optimal transmission path acquisition module is used to obtain the optimal transmission path in the optimization space under the optimization constraint and in combination with the optimization evaluation index; a production control management module is used to transmit the first predetermined production and manufacturing characteristic parameters to the cloud data center based on the optimal transmission path, and perform production control management on the first key node through the cloud data center.
[0006] In a possible implementation, the weight coefficient judgment module also performs the following processing: extracting a first historical management record from the historical production and manufacturing management records, wherein the first historical management record includes multiple nodes with historical node quality identifications and a first historical finished product quality; performing a correlation analysis on the first historical node quality of a first node among the multiple nodes with historical node quality identifications and the first historical finished product quality to obtain a first correlation coefficient; and generating the predetermined node weight allocation scheme based on the first weight of the first node obtained by analyzing the first correlation coefficient.
[0007] In a possible implementation, the weight coefficient judgment module also performs the following processing: reading a preset correlation value threshold, the preset correlation value threshold including a preset 0.05 level threshold and a preset 0.01 level threshold; judging whether the first 0.01 level correlation coefficient in the first correlation coefficient satisfies the preset 0.01 level threshold to obtain a first judgment result; judging whether the first 0.05 level correlation coefficient in the first correlation coefficient satisfies the preset 0.05 level threshold to obtain a second judgment result; and determining the first weight in combination with the first judgment result and the second judgment result.
[0008] In a possible implementation, the characteristic parameter analysis module also performs the following processing: determining whether the first weight coefficient meets the second weight limit, wherein the second weight limit is less than the first weight limit; if so, starting the normal operating mode of the first terminal device, and based on the normal operating mode, collecting the first predetermined production and manufacturing characteristics in real time to obtain the first predetermined production and manufacturing characteristic parameters.
[0009] In a possible implementation, the characteristic parameter analysis module also performs the following processing: if it does not comply, start the energy-saving operation mode of the first terminal device, and based on the energy-saving operation mode, perform feature collection at predetermined intervals on the first predetermined production and manufacturing feature to obtain the first predetermined production and manufacturing feature parameter.
[0010] In a possible implementation, the characteristic parameter analysis module also performs the following processing: the first edge data center pre-stores a first predetermined production and manufacturing characteristic parameter benchmark; the first predetermined production and manufacturing characteristic parameter is compared with the first predetermined production and manufacturing characteristic parameter benchmark to obtain first comparison information; and a weighted analysis is performed on multiple parameter deviation values of multiple features in the first comparison information after normalization to obtain the first real-time management index.
[0011] In a possible implementation, the optimal transmission path acquisition module also performs the following processing: based on the optimization constraint, obtaining a first effective deployment range of the first terminal device, the first effective deployment range includes multiple deployment points; arranging the multiple deployment points in ascending order according to the straight-line distance from each deployment point to the cloud data center to obtain a deployment point sequence; obtaining a first transmission path from the first target point in the deployment point sequence to the cloud data center, wherein the first target point refers to the first point in the deployment point sequence; obtaining a first obstacle length of the first transmission path in combination with the physical space of the production and manufacturing; adjusting the first path length of the first transmission path with the first obstacle length as the transmission loss feedback adjustment coefficient to obtain a first target path length; according to the optimization evaluation index, combining the transmission loss coefficient and the first target path length to obtain a first transmission loss index, and determining the optimal transmission path based on the first transmission loss index.
[0012] The present application also provides a production management method based on 5G RedCap, the method comprising: forming a target production and manufacturing node set, the target production and manufacturing node set comprising multiple key nodes in production and manufacturing; matching a first terminal device in a target distributed 5G RedCap terminal device according to a first predetermined production and manufacturing feature of a first key node among the multiple key nodes; matching a first weight coefficient of the first key node in a predetermined node weight allocation scheme, and determining whether the first weight coefficient meets a first weight limit; if not, equipping the first terminal device with a first edge data center, and analyzing the first predetermined production and manufacturing feature parameters dynamically collected by the first terminal device through the first edge data center to obtain a first real-time management index of the first key node; when the first real-time management index does not meet the management limit, dynamically collecting the first predetermined production and manufacturing feature as an optimization constraint, taking the minimum transmission loss as an optimization evaluation index, and taking the physical space of the production and manufacturing as an optimization space; under the optimization constraint, obtaining an optimal transmission path in the optimization space in combination with the optimization evaluation index; transmitting the first predetermined production and manufacturing feature parameters to the cloud data center based on the optimal transmission path, and performing production regulation and management on the first key node through the cloud data center.
[0013] It is planned to form a target production and manufacturing node set through the production management platform based on 5G RedCap proposed in this application; match the first terminal device in the terminal device; determine whether the first weight coefficient meets the first weight limit; if not, analyze the first predetermined production and manufacturing characteristic parameters collected dynamically; use the minimum transmission loss as the optimization evaluation index, and use the production and manufacturing space as the optimization space; obtain the optimal transmission path; transmit to the cloud data center for production control management. It solves the technical problems of large production data transmission power and delay in existing production management, which leads to poor transmission efficiency of production data and high power consumption and energy consumption of transmission, and achieves the technical effect of reducing data transmission energy consumption and improving production data transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure are briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the platform according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.
[0015] Figure 1 A schematic diagram of the structure of a production management platform based on 5G RedCap provided in an embodiment of the present application;
[0016] Figure 2 A flow chart of a production management method based on 5G RedCap provided in an embodiment of the present application.
[0017] Explanation of the accompanying drawings: node set assembly module 10, terminal device matching module 20, weight coefficient judgment module 30, characteristic parameter analysis module 40, optimization space establishment module 50, optimal transmission path acquisition module 60, production control management module 70. DETAILED DESCRIPTION
[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.
[0019] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.
[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.
[0021] The embodiment of the present application provides a production management platform based on 5G RedCap, such as Figure 1 As shown, the platform includes:
[0022] The node set building module 10 is used to build a target production and manufacturing node set, and the target production and manufacturing node set includes multiple key nodes in production and manufacturing; when building the target production and manufacturing node set, it is necessary to identify and include multiple key nodes in the production and manufacturing process. These key nodes usually include the entire life cycle of the product from raw materials to finished products, as well as various production and quality control activities related thereto, such as product design, production preparation, production and manufacturing (equipment operation, production data collection, etc.), quality inspection, inventory management, logistics and transportation, etc.
[0023] The terminal device matching module 20 is used to match the first terminal device in the target distributed 5G RedCap terminal device according to the first predetermined production and manufacturing characteristics of the first key node among the multiple key nodes. Among them, the first key node is a node randomly selected from multiple key nodes, and the first predetermined production and manufacturing feature is the production and manufacturing feature corresponding to the first key node. For example, if the first key node is the production line setting and startup, then the first predetermined production and manufacturing feature may include the type, quantity, configuration requirements, production capacity, etc. of the production line equipment. Specifically, the technical characteristics of the 5G RedCap terminal device include limited spectrum bandwidth (communication capability), simplified antenna configuration (communication range and performance of the equipment), lower data rate (medium and low performance requirements such as production and manufacturing management) and cost, lower complexity and power consumption (large-scale deployment of equipment, long-term operation). Based on the production and manufacturing features of the first key node, the first terminal device corresponding to the production and manufacturing features of the first key node is matched in the target distributed 5G RedCap terminal device, for example, ensuring that the spectrum bandwidth, antenna configuration and data rate of the 5G RedCap terminal device can meet the communication requirements required during the production line setting and startup process. Considering the scale and cost budget of the production line, a 5G RedCap terminal device with high cost-effectiveness and moderate complexity is selected. For production lines that need to run for a long time, a 5G RedCap terminal device with low power consumption and long running time is selected.
[0024] The weight coefficient judgment module 30 is used to match the first weight coefficient of the first key node in the predetermined node weight distribution scheme, and to determine whether the first weight coefficient meets the first weight limit. The importance of different key nodes in the production process is quantitatively evaluated, and the weight coefficient of each node is set and adjusted based on these evaluation results. The first weight limit is a preset threshold for judging whether the first weight coefficient is reasonable. Specifically, the node weight distribution scheme is a pre-set framework, which is used to determine the importance of each key node in the production process. In this scheme, each key node will be assigned a weight coefficient, which reflects the degree of influence of the node on the entire production process. In the node weight distribution scheme, it is necessary to find the first weight coefficient corresponding to the first key node. The weight coefficient is determined comprehensively based on the characteristics of the first key node, its importance in production, and its influence on other nodes. If the first weight coefficient does not meet this weight limit, it means that there may be problems with the current weight distribution scheme and it needs to be adjusted.
[0025] The characteristic parameter analysis module 40 is used to equip the first terminal device with a first edge data center if there is no compliance, and analyze the first predetermined production and manufacturing characteristic parameters dynamically collected by the first terminal device through the first edge data center to obtain the first real-time management index of the first key node. When the first weight coefficient of the first key node does not meet the preset first weight limit, the first terminal device is equipped with a first edge data center. The edge data center is located at the edge of the network, that is, between the user end and the traditional centralized cloud data center, close to the data source and the user, which can significantly reduce the transmission distance and time of data in the network, and improve the data processing speed and efficiency. For production management applications that require rapid response, such as real-time monitoring, remote control, etc., the edge data center can provide low-latency services. The first edge data center can ensure that the data of the node can be processed and analyzed in time, thereby realizing real-time management and optimization of the node; the production and manufacturing characteristic parameters of the first key node, such as the operating status of the production line, the performance data of the equipment, the quality indicators of the product, etc., are collected in real time and dynamically through the first terminal device (already equipped with a 5G RedCap terminal device), and the collected first predetermined production and manufacturing characteristic parameters are deeply analyzed to identify the problems or hidden dangers that may exist in the operation of the first key node, evaluate the performance and efficiency of the node, and calculate the first real-time management index of the first key node based on the analysis results, which is used to quantitatively reflect the real-time operating status and management level of the node.
[0026] The optimization space establishment module 50 is used to dynamically collect the first predetermined production and manufacturing characteristics as the optimization constraint, take the minimum transmission loss as the optimization evaluation index, and take the physical space of the production and manufacturing as the optimization space when the first real-time management index does not meet the management limit. If the first real-time management index does not meet the management limit, the dynamically collected real-time data (the first predetermined production and manufacturing characteristics) will be used as the constraint condition of the optimization process to ensure that the optimization result meets the actual production situation. Taking the minimum transmission loss as the optimization evaluation index means that in the optimization process, priority will be given to reducing the loss during data transmission to improve the efficiency and stability of data transmission; taking the physical space of production and manufacturing as the optimization space means optimizing the physical space in the production and manufacturing process, including the production line layout, equipment location, material storage, etc., and adjusting and optimizing the layout and configuration of the physical space to achieve the purpose of improving the operating status or management level of the first key node.
[0027] The optimal transmission path acquisition module 60 is used to obtain the optimal transmission path in the optimization space under the optimization constraint and in combination with the optimization evaluation index. Acquiring the optimal transmission path in the optimization space in combination with the optimization evaluation index refers to finding an optimal data transmission path in the physical space of manufacturing to meet the actual production needs of manufacturing. Specifically, by using an optimization algorithm (such as an ant colony algorithm, a particle swarm optimization algorithm, etc.), combined with the optimization constraint and the evaluation index, a possible transmission path is searched and calculated in the optimization space, and a series of possible transmission paths and their corresponding evaluation index values (such as transmission loss) can be obtained. According to the optimization evaluation index (minimal transmission loss), these possible paths are evaluated and compared, and finally the optimal transmission path is selected.
[0028] The production control management module 70 transmits the first predetermined production and manufacturing characteristic parameter to the cloud data center based on the optimal transmission path, and performs production control management on the first key node through the cloud data center. The first terminal device (such as a sensor, a controller, etc.) will transmit the first predetermined production and manufacturing characteristic parameter (such as equipment operation status, production data, etc.) of the first key node along this optimal path. The first predetermined production and manufacturing characteristic parameter is sent to the cloud data center through the optimal transmission path. After receiving the data, the cloud data center analyzes and processes it. Based on the analysis result, that is, according to the importance and real-time requirements of the data, it determines whether it needs to be sent to the cloud, thereby reducing the amount of data processed by the cloud. At the same time, for the data that really needs to be transmitted to the cloud, its transmission path is optimized to reduce the power consumption loss of the transmission. The cloud data center generates a production control instruction for the first key node, which may include adjusting the operating parameters of the equipment, optimizing the layout of the production line, predicting equipment failure, etc., and the generated control instruction is sent back to the first terminal device or the corresponding production management platform to realize real-time control and management of the first key node, realize real-time transmission and processing of data, and ensure timely monitoring and control of the first key node. Real-time transmission and processing of data are realized, and timely monitoring and control of the first key node are ensured. Among them, the cloud data center is a place where a large amount of data is centrally stored and processed. It has powerful computing and storage capabilities and can receive, process and analyze data from various 5G RedCap terminal devices in real time.
[0029] The production management platform based on 5G RedCap according to the embodiment of the present invention is used to solve the technical problems of large production data transmission power and delay in existing production management, which leads to poor transmission efficiency of production data and high power consumption and energy consumption of transmission, and achieves the technical effect of reducing data transmission energy consumption and improving production data transmission efficiency. The production management platform based on 5G RedCap includes: a node set assembly module 10, a terminal device matching module 20, a weight coefficient judgment module 30, a characteristic parameter analysis module 40, an optimization space establishment module 50, an optimal transmission path acquisition module 60, and a production control management module 70.
[0030] The specific configuration of the weight coefficient judgment module 30 will be described in detail below. The weight coefficient judgment module 30 may further include: extracting a first historical management record from the historical production and manufacturing management record, wherein the first historical management record includes multiple nodes with historical node quality identifiers and a first historical finished product quality. Retrieve and select specific historical data records from the existing production and manufacturing management database, the first historical management record usually refers to one of these historical records, and contains key information related to the production and manufacturing process. In the production and manufacturing process, the entire process can be decomposed into several stages or steps, called nodes, each node represents a key link in the production process, for each historical node, there will be one or more quality identifiers to reflect the quality status of the node in history, the quality identifier may include the node's qualified rate, failure rate, production speed, energy consumption and other indicators, through these identifiers, the performance of the node in the past production can be understood, the finished product quality refers to the quality status of the final product after the entire production and manufacturing process, and the first historical finished product quality is the quality of the finished product corresponding to the first historical management record. It also includes performing a correlation analysis on the first historical node quality of the first node among the multiple nodes with historical node quality identifiers and the first historical finished product quality to obtain a first correlation coefficient. The first correlation coefficient refers to the result of correlation analysis between the first historical node quality of the first node among multiple nodes with historical node quality identifiers and the first historical finished product quality, which is used to quantify the strength and direction of the association between two variables. Usually, the value range of the correlation coefficient (such as the Pearson correlation coefficient) is between -1 and 1. If the coefficient is close to 1, it means that there is a strong positive correlation between the two variables; if the coefficient is close to -1, it means that there is a strong negative correlation between the two variables; if the coefficient is close to 0, it means that there is no obvious linear relationship between the two variables. It also includes generating the predetermined node weight allocation scheme based on the first weight of the first node obtained by analyzing the first correlation coefficient.
[0031] The specific configuration of the weight coefficient judgment module 30 will be described in detail below. The weight coefficient judgment module 30 may further include: reading a preset correlation value threshold, the preset correlation value threshold including a preset 0.05 level threshold and a preset 0.01 level threshold; judging whether the first 0.01 level correlation coefficient in the first correlation coefficient meets the preset 0.01 level threshold, and obtaining a first judgment result; judging whether the first 0.05 level correlation coefficient in the first correlation coefficient meets the preset 0.05 level threshold, and obtaining a second judgment result; and determining the first weight in combination with the first judgment result and the second judgment result. In general, if both judgment results are met, the weight is at the first level and meets the first weight limit. Correspondingly, the characteristic parameters of the node are very important and need to be transmitted to the cloud data center in real time. 0.01 meets the requirement, but 0.05 does not meet the requirement. The weight is at the second level and does not meet the first weight limit. However, the characteristic parameters of the node are relatively important, so the edge data center of the node analyzes the data in real time. If neither of them is met, the weight is at the third level and does not meet the first weight limit. At the same time, the characteristic parameters of the node are relatively unimportant, so the terminal device of the node collects data at a certain period and frequency without real-time collection and analysis.
[0032] The specific configuration of the characteristic parameter analysis module 40 will be described in detail below. The characteristic parameter analysis module 40 may further include: determining whether the first weight coefficient meets the second weight limit, wherein the second weight limit is less than the first weight limit; if so, starting the normal operating mode of the first terminal device, and based on the normal operating mode, performing real-time collection of the first predetermined production and manufacturing feature to obtain the first predetermined production and manufacturing characteristic parameter. The first weight coefficient is compared with the second weight limit. If the first weight coefficient meets the second weight limit, it is considered to meet the conditions, indicating that the first node has sufficient importance or influence in the production process. Therefore, the normal operating mode of the first terminal device can be safely started, that is, normal production and manufacturing activities and data collection are carried out to obtain the first predetermined production and manufacturing characteristic parameters, such as the operating status of the equipment, the efficiency of the production line, the quality parameters of the product, etc.
[0033] The specific configuration of the characteristic parameter analysis module 40 will be described in detail below. The characteristic parameter analysis module 40 may further include: if it does not meet the requirements, start the energy-saving operation mode of the first terminal device, and based on the energy-saving operation mode, perform feature collection of the first predetermined production and manufacturing feature at a predetermined interval to obtain the first predetermined production and manufacturing characteristic parameter. When the first weight coefficient does not meet the second weight limit, it means that the importance or influence of the first node in the production process does not meet the preset standard or requirement. In order to reduce energy consumption and reduce unnecessary operating costs, the energy-saving operation mode of the first terminal device will be started. In the energy-saving operation mode, the frequency of feature collection of the first predetermined production and manufacturing feature will be reduced, and it will no longer be real-time collection, but will be collected at a predetermined time interval to obtain the first predetermined production and manufacturing characteristic parameter, wherein the energy-saving operation mode is a special working mode, which aims to reduce energy consumption by reducing the operating time of the equipment, reducing the operating speed of the equipment or adopting other energy-saving measures. The predetermined interval is a pre-set time interval, which can be adjusted according to the actual situation and needs of production and manufacturing. The length of the interval will affect the frequency and accuracy of data collection. In general, when the importance or impact of the first node is low, real-time data collection may not be necessary, or the cost of data collection is high. The energy-saving operation mode can reduce energy consumption and operating costs while ensuring that the production process is basically controllable.
[0034] The specific configuration of the characteristic parameter analysis module 40 will be described in detail below. The characteristic parameter analysis module 40 may further include: the first edge data center pre-stores a first predetermined manufacturing characteristic parameter benchmark. It also includes comparing the first predetermined manufacturing characteristic parameter with the first predetermined manufacturing characteristic parameter benchmark to obtain first comparison information. The first predetermined manufacturing characteristic parameter benchmark is a reference value or standard preset for comparison based on historical data, representing the ideal state or expected level of a certain feature in the manufacturing process. The first predetermined manufacturing characteristic parameter is compared with the first predetermined manufacturing characteristic parameter benchmark one by one to obtain a comparison result (i.e., the first comparison information), for example, the specific percentage of the actual production efficiency lower than the expected production efficiency, the specific percentage of the actual production efficiency lower than the expected production efficiency, the positive difference of the actual value higher than the benchmark value or the negative difference of the actual value lower than the benchmark value, etc. It also includes weighted analysis of multiple parameter deviation values of multiple features in the first comparison information after normalization to obtain the first real-time management index. Normalization processing is applied to multiple features and parameters in the first comparison information to ensure that they can be evaluated on the same dimension. Multiple parameter deviation values of multiple features in the first comparison information are combined to measure the size of the first real-time management index in the form of weights (i.e., variable coefficients). The first real-time management index is obtained, which can take into account the different degrees of influence of different features and parameters on the production process, and reflects the overall deviation of multiple features and parameters from the benchmark values in the production process.
[0035] The specific configuration of the optimal transmission path acquisition module 60 will be described in detail below. The optimal transmission path acquisition module 60 may further include: obtaining a first effective deployment range of the first terminal device based on the optimization constraint, and the first effective deployment range includes multiple deployment points. The first effective deployment range refers to a set of all possible locations where the first terminal device can be deployed under the condition that all optimization constraints are met, and includes multiple potential deployment points, each of which satisfies all optimization constraints, and the terminal device can achieve the expected business effect when deployed at this point. It also includes arranging the multiple deployment points in ascending order according to the straight-line distance from each deployment point in the multiple deployment points to the cloud data center to obtain a deployment point sequence. The calculated straight-line distance is used as the basis for sorting, and all deployment points are arranged in ascending order, that is, the deployment point with the shortest straight-line distance is placed at the front of the sequence, and then the deployment points with gradually increasing straight-line distances are arranged in sequence to obtain an ordered deployment point sequence, each position in this sequence corresponds to a deployment point, and they are arranged from small to large according to their straight-line distances to the cloud data center. The method further includes obtaining a first transmission path from a first target point in the deployment point sequence to the cloud data center, wherein the first target point refers to the first point in the deployment point sequence. The first target point refers to the point with the shortest straight-line distance to the cloud data center.
[0036] The specific configuration of the optimal transmission path acquisition module 60 will be described in detail below. The optimal transmission path acquisition module 60 may further include: obtaining the first obstacle length of the first transmission path in combination with the physical space of the manufacturing. Obstacles that hinder data collection and transmission on the first transmission path are identified according to the physical space of the manufacturing, and the degree of obstruction of the obstacles to the production data transmission is determined. The larger the first obstacle length, the greater the power consumption of the data transmission. It also includes adjusting the first path length of the first transmission path with the first obstacle length as the transmission loss feedback adjustment coefficient to obtain the first target path length. The first obstacle length is used as the transmission loss feedback adjustment coefficient, because the presence of obstacles will cause the signal to attenuate during the transmission process, thereby affecting the signal quality and transmission efficiency. It is necessary to set an adjustment coefficient according to the size or length of the obstacle to reflect the degree of its influence on the signal transmission loss. The first path length refers to the initial data transmission path. The transmission loss feedback adjustment coefficient (i.e., the first obstacle length) is applied to the calculation or adjustment of the first path length to obtain a new transmission path length, i.e., the first target path length. It also includes, according to the optimization evaluation index, combining the transmission loss coefficient and the first target path length to obtain a first transmission loss index, and determining the optimal transmission path based on the first transmission loss index. The first transmission loss index is a quantitative representation of the total transmission loss on the first transmission path, and intuitively reflects the loss of the path during the transmission process. By comparing the transmission loss indexes of different paths, the path with the smallest transmission loss index is selected as the optimal transmission path, which means that when data is transmitted on the path, the signal attenuation is small, thereby maintaining a high signal quality and transmission efficiency.
[0037] In the above, refer to Figure 1 The production management platform based on 5G RedCap according to an embodiment of the present invention is described in detail. Figure 2 A production management method based on 5G RedCap according to an embodiment of the present invention is described.
[0038] Production management methods based on 5G RedCap, such as Figure 2As shown, the method includes: forming a target production and manufacturing node set, the target production and manufacturing node set includes multiple key nodes in production and manufacturing; matching a first terminal device in a target distributed 5G RedCap terminal device according to a first predetermined production and manufacturing feature of a first key node among the multiple key nodes; matching a first weight coefficient of the first key node in a predetermined node weight allocation scheme, and determining whether the first weight coefficient meets a first weight limit; if not, equipping the first terminal device with a first edge data center, and analyzing the first predetermined production and manufacturing feature parameters dynamically collected by the first terminal device through the first edge data center to obtain a first real-time management index of the first key node; when the first real-time management index does not meet the management limit, dynamically collecting the first predetermined production and manufacturing feature as an optimization constraint, taking the minimum transmission loss as an optimization evaluation index, and taking the physical space of the production and manufacturing as an optimization space; under the optimization constraint, obtaining an optimal transmission path in the optimization space in combination with the optimization evaluation index; transmitting the first predetermined production and manufacturing feature parameters to the cloud data center based on the optimal transmission path, and performing production regulation and management on the first key node through the cloud data center.
[0039] In a possible implementation, the production management method based on 5G RedCap also includes: extracting a first historical management record from the historical production and manufacturing management records, wherein the first historical management record includes multiple nodes with historical node quality identifications and a first historical finished product quality; performing a correlation analysis on the first historical node quality of a first node among the multiple nodes with historical node quality identifications and the first historical finished product quality to obtain a first correlation coefficient; and generating the predetermined node weight allocation scheme based on the first weight of the first node obtained by analyzing the first correlation coefficient.
[0040] In a possible implementation, the production management method based on 5G RedCap also includes: reading a preset correlation value threshold, the preset correlation value threshold including a preset 0.05 level threshold and a preset 0.01 level threshold; judging whether the first 0.01 level correlation coefficient in the first correlation coefficient meets the preset 0.01 level threshold, and obtaining a first judgment result; judging whether the first 0.05 level correlation coefficient in the first correlation coefficient meets the preset 0.05 level threshold, and obtaining a second judgment result; and determining the first weight in combination with the first judgment result and the second judgment result.
[0041] In a possible implementation, the production management method based on 5G RedCap also includes: determining whether the first weight coefficient meets the second weight limit, wherein the second weight limit is less than the first weight limit; if so, starting the normal operating mode of the first terminal device, and based on the normal operating mode, collecting the first predetermined production and manufacturing features in real time to obtain the first predetermined production and manufacturing feature parameters.
[0042] In a possible implementation, the production management method based on 5G RedCap also includes: if it does not comply, starting the energy-saving operation mode of the first terminal device, and based on the energy-saving operation mode, performing feature collection at predetermined intervals on the first predetermined production and manufacturing feature to obtain the first predetermined production and manufacturing feature parameters.
[0043] In a possible implementation, the production management method based on 5G RedCap also includes: the first edge data center pre-stores a first predetermined production and manufacturing feature parameter benchmark; comparing the first predetermined production and manufacturing feature parameter with the first predetermined production and manufacturing feature parameter benchmark to obtain first comparison information; performing weighted analysis on multiple parameter deviation values of multiple features in the first comparison information after normalization to obtain the first real-time management index.
[0044] In a possible implementation, the production management method based on 5G RedCap also includes: obtaining a first effective deployment range of the first terminal device based on the optimization constraint, the first effective deployment range including multiple deployment points; arranging the multiple deployment points in ascending order according to the straight-line distance from each deployment point in the multiple deployment points to the cloud data center to obtain a deployment point sequence; obtaining a first transmission path from the first target point in the deployment point sequence to the cloud data center, wherein the first target point refers to the first point in the deployment point sequence; obtaining a first obstacle length of the first transmission path in combination with the physical space of the production and manufacturing; adjusting the first path length of the first transmission path with the first obstacle length as the transmission loss feedback adjustment coefficient to obtain a first target path length; according to the optimization evaluation index, combining the transmission loss coefficient and the first target path length to obtain a first transmission loss index, and determining the optimal transmission path based on the first transmission loss index.
[0045] The production management platform based on 5G RedCap provided in the embodiment of the present invention can execute the production management method based on 5G RedCap provided in any embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method.
[0046] Although the present application makes various references to certain modules in the platform according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.
[0047] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.
Claims
1. The production management platform based on 5G RedCap is characterized by: The platform includes: A node set building module, used to build a target production and manufacturing node set, wherein the target production and manufacturing node set includes a plurality of key nodes in production and manufacturing; A terminal device matching module, configured to match a first terminal device in a target distributed 5G RedCap terminal device according to a first predetermined manufacturing feature of a first key node among the plurality of key nodes; A weight coefficient determination module, used to match a first weight coefficient of the first key node in a predetermined node weight allocation scheme, and determine whether the first weight coefficient meets a first weight limit; A characteristic parameter analysis module, configured to, if not in compliance, equip the first terminal device with a first edge data center, and analyze the first predetermined production and manufacturing characteristic parameter dynamically collected by the first terminal device through the first edge data center to obtain a first real-time management index of the first key node; an optimization space establishment module, used for, when the first real-time management index does not meet the management limit, dynamically collecting the first predetermined production and manufacturing characteristics as an optimization constraint, taking the minimum transmission loss as an optimization evaluation index, and taking the physical space of the production and manufacturing as an optimization space; An optimal transmission path acquisition module, used to acquire an optimal transmission path in the optimization space under the optimization constraint and in combination with the optimization evaluation index; a production control management module, which transmits the first predetermined production manufacturing characteristic parameter to a cloud data center based on the optimal transmission path, and performs production control management on the first key node through the cloud data center; The platform includes: Extracting a first historical management record from the historical production and manufacturing management record, wherein the first historical management record includes a plurality of nodes with historical node quality identifiers and a first historical finished product quality; Performing a correlation analysis on a first historical node quality of a first node among the multiple nodes having historical node quality identifiers and the first historical finished product quality to obtain a first correlation coefficient; Generating the predetermined node weight allocation scheme according to the first weight of the first node obtained by analyzing the first correlation coefficient; Reading a preset correlation value threshold, wherein the preset correlation value threshold includes a preset 0.05 level threshold and a preset 0.01 level threshold; Determine whether a first 0.01 level correlation coefficient in the first correlation coefficient meets the preset 0.01 level threshold, and obtain a first determination result; Determine whether a first 0.05 level correlation coefficient in the first correlation coefficient meets the preset 0.05 level threshold, and obtain a second determination result; Determine the first weight by combining the first judgment result and the second judgment result; Determining whether the first weight coefficient meets a second weight limit, wherein the second weight limit is less than the first weight limit; If it is in compliance, starting the normal operation mode of the first terminal device, and collecting the first predetermined production and manufacturing feature in real time based on the normal operation mode to obtain the first predetermined production and manufacturing feature parameter; The platform includes: The first edge data center pre-stores a first predetermined production and manufacturing characteristic parameter benchmark; Comparing the first predetermined manufacturing characteristic parameter with the first predetermined manufacturing characteristic parameter benchmark to obtain first comparison information; Performing weighted analysis on multiple parameter deviation values of multiple features in the first comparison information after normalization to obtain the first real-time management index; The platform includes: Acquire a first effective deployment range of the first terminal device based on the optimization constraint, where the first effective deployment range includes multiple deployment points; Arrange the multiple deployment points in ascending order according to the straight-line distance from each deployment point in the multiple deployment points to the cloud data center to obtain a deployment point sequence; Acquire a first transmission path from a first target point in the deployment point sequence to the cloud data center, wherein the first target point refers to the first point in the deployment point sequence; Obtaining a first obstacle length of the first transmission path in combination with the physical space of the manufacturing; adjusting a first path length of the first transmission path by taking the first obstacle length as a transmission loss feedback adjustment coefficient to obtain a first target path length; According to the optimization evaluation index, a first transmission loss index is obtained by combining the transmission loss coefficient and the first target path length, and the optimal transmission path is determined based on the first transmission loss index.
2. The production management platform based on 5G RedCap according to claim 1, characterized in that: If it does not comply, the energy-saving operation mode of the first terminal device is started, and based on the energy-saving operation mode, the feature collection of the first predetermined production and manufacturing feature is performed at a predetermined interval to obtain the first predetermined production and manufacturing feature parameter.