A production scheduling control method based on industrial edge gateway

Abstract

The present invention discloses a production scheduling control method based on an industrial edge gateway, which relates to the technical field of production management. The production status data is collected through industrial edge devices, and the product deviation factor on each production line is constructed to effectively identify the initial abnormal state of production and realize accurate production status monitoring. Then, the edge effect of the gateway is analyzed, and by calculating the edge effect coefficient, the system can quickly perform task transfer when abnormal nodes are detected. Based on the monitoring data after the task transfer, the health status index of the selected gateway is constructed to ensure efficient task allocation and screen out the target gateway with relatively good performance. By constructing the production stability evaluation index of the target gateway and combining it with the set stability range, it is possible to further identify production line abnormalities in a timely manner, and perform intelligent scheduling based on the evaluation results, optimize the production scheduling strategy, and improve production efficiency and resource utilization.

Description

Technical Field

[0001] The present invention relates to the technical field of production management, and in particular to a production scheduling control method based on an industrial edge gateway. Background Art

[0002] Industrial Internet of Things is the specific application of Internet of Things technology in the manufacturing and industrial fields. With the complexity and automation of industrial production processes, the demand for production scheduling control is increasing. As an important edge computing device, the industrial edge gateway can support production scheduling decisions through real-time monitoring, data collection and local processing. The production scheduling control method based on the industrial edge gateway is a solution produced by the combination of industrial automation and Internet of Things technology, which is particularly suitable for multi-production line coordination and production efficiency optimization.

[0003] Traditional production scheduling and control methods are difficult to achieve efficient scheduling in terms of real-time and system response, because most of them still rely on cloud processing. Especially when equipment abnormalities occur, they often cannot respond quickly, resulting in production stagnation or resource waste. Although edge computing using industrial edge gateways can alleviate the lag problem of cloud processing, there are still some shortcomings. When edge computing is performed based on industrial edge gateways, although edge computing can reduce latency, the computing and storage capabilities of edge gateways are still limited compared to cloud computing, and may be difficult to handle complex analysis tasks, affecting the accuracy of real-time decision-making. In addition, the industrial data collected by the edge gateway may be due to the defects of edge computing, resulting in the authenticity of industrial data. Therefore, how to effectively deal with the edge effect of equipment, the stability of the production line and the balance of resource allocation needs further discussion. Of course, in the case of multiple groups of edge gateways, if a fault occurs, it may not be discovered in time, thereby increasing the downtime frequency of the production line, and ultimately affecting the overall operational efficiency and cost control of the enterprise. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a production scheduling control method based on an industrial edge gateway, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A production scheduling control method based on an industrial edge gateway comprises the following steps:

[0006] S1. Several groups of industrial edge gateways are distributed in the industrial Internet of Things system in advance, and each industrial edge gateway collects the production status data in the corresponding production line through the edge device, constructs the product deviation factor Cpyz in each production line according to the production status data, and issues a troubleshooting instruction based on the value of the product deviation factor Cpyz;

[0007] S2. After receiving the troubleshooting instruction, use Pacemaker to analyze whether the corresponding industrial edge gateway has an edge effect, so as to analyze and calculate the edge effect degree coefficient Bxxs. If the edge effect degree coefficient Bxxs exceeds the preset second difference threshold, the task transfer operation is executed;

[0008] S3, based on the task transfer operation in S2, monitor the relevant operation data of each industrial edge gateway in real time, and build a health status index Jkzs based on the relevant operation data to screen the target gateway;

[0009] S4. After the task is transferred, the production status data in the production line corresponding to the target gateway is monitored, and the industrial edge gateway corresponding to the task transfer operation in S2 is combined to analyze and judge the consistency of the results. If the judgment results are inconsistent, the stability of the production in the corresponding production line is analyzed to construct the production stability evaluation index Swzs;

[0010] S5. Use Sampling Theory to pre-set the stability range , and compare it with the production stability assessment index Swzs to comprehensively determine the stability of the corresponding production line, and execute corresponding scheduling measures based on the determination results.

[0011] Preferably, the specific steps of S1 include:

[0012] S11, the industrial edge gateway connects the Modbus RTU interface to the edge device by integrating the Modbus protocol to collect the production status data in the corresponding production line in real time, wherein the edge device includes industrial sensors and programmable logic controllers;

[0013] S111, the production status data includes the production rate Cpz in different monitoring periods in the production line corresponding to each industrial edge gateway, and the average production rate is calculated by combining statistical algorithms .

[0014] Preferably, the specific step S1 also includes:

[0015] S12. According to the production status data, construct the output deviation factor Cpyz in each production line. The output deviation factor Cpyz is obtained in the following manner:

[0016] ;

[0017] In the formula, It is expressed as the output rate at the i-th monitoring time point, Expressed as the average yield, It is expressed as the standard deviation of the yield;

[0018] S13. By comparing the product deviation factor Cpyz in each production line with the preset No. 1 difference threshold, it is determined whether a troubleshooting instruction needs to be issued. The specific determination content is as follows:

[0019] If the product deviation factor Cpyz exceeds the preset No. 1 difference threshold, a troubleshooting instruction will be issued externally and transmitted to S2 for industrial edge gateway inspection;

[0020] If the product deviation factor Cpyz does not exceed the preset No. 1 difference threshold, no troubleshooting instructions will be issued at this time.

[0021] Preferably, the specific steps of S2 include:

[0022] S21. After receiving the troubleshooting instruction, according to Pacemaker, automatically detect several groups of industrial edge gateways distributed in the industrial Internet of Things system to identify the relevant signal status information of several groups of industrial edge gateways regularly sending active signals to Pacemaker, wherein the relevant signal status information includes the time interval Jz of each industrial edge gateway receiving the active signal at different monitoring intervals, and calculates and obtains the average receiving time interval by combining statistical algorithms. ;

[0023] S22. Based on the relevant signal status information, analyze whether the corresponding industrial edge gateway has an edge effect, so as to analyze and calculate the edge effect degree coefficient Bxxs, which is obtained specifically in the following manner:

[0024] ;

[0025] Where m is the number of monitoring intervals, j=1, 2, 3, ..., m, Expressed as the signal jitter rate at the jth monitoring interval, Expressed as the average signal jitter rate, and are weight values, among which, and The specific value is set by the user according to the situation;

[0026] S221, wherein the signal jitter rate at the jth monitoring interval Obtained by the following formula:

[0027] ;

[0028] In the formula, It is represented as the time interval of receiving active signal at the jth monitoring interval, Expressed as the average reception time interval.

[0029] Preferably, the specific step S2 also includes:

[0030] S23. Preset the second difference threshold, and compare the edge effect degree coefficient Bxxs with the second difference threshold to identify abnormal gateways and candidate gateways to perform task transfer operations. The specific identification content is as follows:

[0031] If the edge effect degree coefficient Bxxs exceeds the second difference threshold, the corresponding industrial edge gateway will be judged to be in an abnormal state and marked as an abnormal gateway;

[0032] If the edge effect degree coefficient Bxxs does not exceed the second difference threshold, the corresponding industrial edge gateway is judged to be not in an abnormal state and is marked as a candidate gateway;

[0033] S24: construct a candidate set based on the candidate gateways identified in S23.

[0034] Preferably, the specific steps of S3 include:

[0035] S31: Real-time monitoring of the relevant operating data of each candidate gateway in the candidate set, wherein the relevant operating data includes the CPU usage rate of each candidate gateway. And the failure rate .

[0036] Preferably, the specific step S3 also includes:

[0037] S32. Based on the relevant operation data and in combination with the edge effect degree coefficient Bxxs, after linear normalization processing, a health status index Jkzs is constructed. The health status index Jkzs is obtained by the following formula:

[0038] ;

[0039] In the formula, Expressed as the failure rate, Expressed as CPU usage, Expressed as the first correction constant, and are weight values, among which, and The specific value is set by the user according to the situation;

[0040] S33, based on the method of obtaining the health status index Jkzs in S32, respectively obtain the health status index Jkzs of each candidate gateway in the candidate set, and extract the minimum value of the health status index Jkzs through feature extraction, and use the candidate gateway corresponding to the health status index Jkzs of the minimum value as the target gateway;

[0041] S34. According to the acquired target gateway, the task of the abnormal gateway in S23 is executed instead.

[0042] Preferably, the specific steps of S4 include:

[0043] S41. After the task is transferred, the production status data of the production line corresponding to the target gateway is monitored, wherein the production status data of the production line corresponding to the target gateway includes the output rate Cpz and the production rate Cpz of the production line corresponding to the target gateway in different monitoring periods. ;

[0044] S42, according to the production status data of the production line corresponding to the target gateway, and in combination with the method of obtaining the output deviation factor Cpyz in each production line in S12, calculate and obtain the output deviation factor Mpyz of the production line corresponding to the target gateway;

[0045] S43. Analyze and judge the consistency of the output deviation factor Mpyz of the production line corresponding to the target gateway and the output deviation factor Cpyz of the corresponding abnormal gateway in S12. If the results are consistent, it is determined that the production line corresponding to the current abnormal gateway has an abnormal risk, and a scheduling instruction will be issued at this time.

[0046] Preferably, the specific step S4 also includes:

[0047] S44. If the judgment results are inconsistent, the stability of production in the corresponding production line is analyzed based on the production status data in the production line corresponding to the target gateway, and after linear normalization, a production stability evaluation index Swzs is constructed. The production stability evaluation index Swzs is obtained by the following formula:

[0048] ;

[0049] Where n represents the monitoring period, i=1, 2, 3, ..., n, It is represented as the output deviation factor of the production line corresponding to the target gateway at the i-th monitoring time point, It is expressed as the average output deviation factor of the production line corresponding to the target gateway. It is represented as the production rate of the production line corresponding to the target gateway at the i-th monitoring time point, It is represented by the average production rate of the production line corresponding to the target gateway. and are weight values, Expressed as the second correction constant, and The specific value is set by the user according to the situation.

[0050] Preferably, the specific steps of S5 include:

[0051] S51, stable range Compare with the production stability assessment index Swzs to comprehensively determine the stability of the corresponding production line. The specific contents are as follows:

[0052] If the production stability assessment index Swzs falls into the stable range At this time, it will be comprehensively determined that the production line corresponding to the current abnormal gateway does not have abnormal risks;

[0053] If the production stability assessment index Swzs does not fall into the stable range At this time, it will be comprehensively determined that the production line corresponding to the current abnormal gateway has abnormal risks, and a scheduling instruction will be issued;

[0054] S52. After receiving the dispatch instruction, manually intervene in the production line corresponding to the current abnormal gateway.

[0055] The present invention provides a production scheduling control method based on an industrial edge gateway, which has the following beneficial effects:

[0056] (1) Collect production status data through industrial edge devices, build the product deviation factor Cpyz on each production line, effectively identify the initial abnormal state of production, and realize accurate production status monitoring. Use Pacemaker to analyze the edge effect of the gateway. By calculating the edge effect coefficient Bxxs, when abnormal nodes are detected, the system can quickly perform task transfer, further avoiding production interruptions. At the same time, it is also to prevent inaccurate parameter collection of the corresponding production line caused by system interruptions, thereby misissuing scheduling instructions, and further improving the reliability of the system. Based on the monitoring data after task transfer, the health status index Jkzs of the candidate gateway is constructed to ensure efficient task allocation and screen out the target gateway with relatively better performance. By constructing the production stability evaluation index Swzs of the target gateway and combining the stability range set by Sampling Theory , can further identify production line anomalies in a timely manner, and perform intelligent scheduling based on the evaluation results, optimize production scheduling strategies, and improve production efficiency and resource utilization. In short, this method integrates real-time monitoring, edge effect evaluation, health status monitoring, and production line stability evaluation, which maximizes the flexibility and accuracy of production scheduling, and ensures the high availability of the system through an intelligent task transfer mechanism.

[0057] (2) By integrating the Modbus protocol through the industrial edge gateway, edge devices can collect production line data in real time, ensuring that data is transmitted in a standardized format, improving the reliability and stability of production status data collection. By calculating the average yield rate and standard deviation through statistical algorithms, the system can quickly construct the yield deviation factor to ensure accurate judgment of production deviations, effectively improving the accuracy of production monitoring. By comparing with the preset difference threshold, the system can issue troubleshooting instructions in a timely manner and quickly locate abnormal production lines, further realizing early fault detection and automatic scheduling optimization, reducing production interruptions, and improving the continuity and efficiency of the production line.

[0058] (3) By automatically detecting the active signal status of industrial edge gateways through Pacemaker, the system can monitor the health status of the gateways in real time to ensure that each gateway in the industrial Internet of Things is continuously monitored. Through statistical analysis of signal jitter rate and time interval data, the edge effect degree coefficient Bxxs is calculated to effectively identify the edge effect degree and improve the accuracy of fault prediction and processing. When an edge gateway is identified as an abnormal state, the system can quickly identify the abnormal gateway and perform task transfer by analyzing the edge effect coefficient to ensure uninterrupted production. The identification process of abnormal gateways and candidate gateways ensures the stability and flexibility of production scheduling. By constructing a set of candidate gateways, the system can flexibly select available gateways when an abnormality occurs, improve resource utilization efficiency, and avoid excessive load.

[0059] (4) While analyzing and judging abnormalities on the production line, this method can also check edge gateways with abnormalities or faults. While reducing the frequency of production line downtime, it can also transfer the tasks of abnormal gateways in a timely manner, thereby further achieving the accuracy of production line data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The present invention is a flowchart of a production scheduling control method based on an industrial edge gateway. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Example 1

[0063] See also Figure 1 The present invention provides a production scheduling control method based on an industrial edge gateway, comprising the following steps:

[0064] S1. Several groups of industrial edge gateways are distributed in the industrial Internet of Things system in advance, and each industrial edge gateway collects the production status data in the corresponding production line through the edge device, constructs the product deviation factor Cpyz in each production line according to the production status data, and issues a troubleshooting instruction based on the value of the product deviation factor Cpyz;

[0065] S2. After receiving the troubleshooting instruction, use Pacemaker to analyze whether the corresponding industrial edge gateway has an edge effect, so as to analyze and calculate the edge effect degree coefficient Bxxs. If the edge effect degree coefficient Bxxs exceeds the preset second difference threshold, the task transfer operation is executed;

[0066] S3, based on the task transfer operation in S2, monitor the relevant operation data of each industrial edge gateway in real time, and build a health status index Jkzs based on the relevant operation data to screen the target gateway;

[0067] S4. After the task is transferred, the production status data in the production line corresponding to the target gateway is monitored, and the industrial edge gateway corresponding to the task transfer operation in S2 is combined to analyze and judge the consistency of the results. If the judgment results are inconsistent, the stability of the production in the corresponding production line is analyzed to construct the production stability evaluation index Swzs;

[0068] S5. Use Sampling Theory to pre-set the stability range , and compare it with the production stability assessment index Swzs to comprehensively determine the stability of the corresponding production line, and execute corresponding scheduling measures based on the determination results.

[0069] In this embodiment, the method further realizes real-time judgment and scheduling optimization of the production line status by monitoring the industrial edge gateway. First, the edge device is used to collect production data and construct the product deviation factor, so that the abnormality can be quickly located and the troubleshooting instruction can be issued. Secondly, Pacemaker is used to analyze the gateway edge effect, and the edge effect degree coefficient is used to determine whether to perform task transfer to ensure production stability. Finally, the stability of the production line is accurately evaluated through the health status index and the production stability evaluation index, thereby improving production efficiency, reducing downtime and resource waste.

[0070] Example 2

[0071] Please refer to Figure 1 , specifically: S1 specific steps include:

[0072] S11. The industrial edge gateway integrates the Modbus protocol and connects the Modbus RTU interface to the edge device to collect the production status data in the corresponding production line in real time, thereby realizing real-time data collection in the production line. The edge device includes industrial sensors and programmable logic controllers (PLCs);

[0073] S111, the production status data includes the production rate Cpz in different monitoring periods in the production line corresponding to each industrial edge gateway, and the average production rate is calculated by combining statistical algorithms .

[0074] As an open and standardized protocol, Modbus protocol ensures smooth communication between devices. Data can be transmitted to the edge gateway in a standardized format for local processing and analysis, thereby optimizing the monitoring and management of the production line.

[0075] The specific steps of S1 also include:

[0076] S12. According to the production status data, construct the output deviation factor Cpyz in each production line. The output deviation factor Cpyz is obtained in the following manner:

[0077] ;

[0078] In the formula, It is expressed as the output rate at the i-th monitoring time point, Expressed as the average yield, It is expressed as the standard deviation of the yield;

[0079] The above yield The number of finished products can be monitored by quality inspection instruments, barcode scanners or weighing equipment to calculate the yield rate.

[0080] S13. By comparing the product deviation factor Cpyz in each production line with the preset No. 1 difference threshold, it is determined whether a troubleshooting instruction needs to be issued. The specific determination content is as follows:

[0081] If the product deviation factor Cpyz exceeds the preset No. 1 difference threshold, a troubleshooting instruction will be issued externally and transmitted to S2 for industrial edge gateway inspection;

[0082] If the product deviation factor Cpyz does not exceed the preset No. 1 difference threshold, no troubleshooting instructions will be issued at this time.

[0083] In this embodiment, the method integrates the Modbus protocol to achieve smooth communication between the industrial edge gateway and sensors, PLC and other equipment, ensuring real-time and accurate collection of production data. Specifically, by calculating the yield rate, average yield rate and yield rate standard deviation, the yield deviation factor Cpyz is constructed to achieve dynamic monitoring of the production line status. This method further improves the accuracy and analyzability of the data. When the yield deviation factor Cpyz exceeds the preset threshold, the troubleshooting instruction is automatically triggered to ensure that the problem can be detected and handled at an early stage, further improving production efficiency and system stability.

[0084] Example 3

[0085] Please refer to Figure 1 , specifically: S2 specific steps include:

[0086] S21. After receiving the troubleshooting instruction, according to Pacemaker, automatically detect several groups of industrial edge gateways distributed in the industrial Internet of Things system to identify the relevant signal status information of several groups of industrial edge gateways regularly sending active signals to Pacemaker, wherein the relevant signal status information includes the time interval Jz of each industrial edge gateway receiving the active signal at different monitoring intervals, and calculates and obtains the average receiving time interval by combining statistical algorithms. ;

[0087] S22. Based on the relevant signal status information, analyze whether the corresponding industrial edge gateway has an edge effect, so as to analyze and calculate the edge effect degree coefficient Bxxs, which is obtained specifically in the following manner:

[0088] ;

[0089] Where m is the number of monitoring intervals, j=1, 2, 3, ..., m, Expressed as the signal jitter rate at the jth monitoring interval, Expressed as the average signal jitter rate, and are weight values, among which, and The specific value is set by the user according to the situation;

[0090] S221, wherein the signal jitter rate at the jth monitoring interval Obtained by the following formula:

[0091] ;

[0092] In the formula, It is represented as the time interval of receiving active signal at the jth monitoring interval, Expressed as the average reception time interval.

[0093] The time interval Jz at which each of the above-mentioned industrial edge gateways receives active signals at different monitoring intervals is monitored by a network monitoring tool, such as a built-in network adapter or a communication module, to monitor the signal reception time.

[0094] The specific steps of S2 also include:

[0095] S23. Preset the second difference threshold, and compare the edge effect degree coefficient Bxxs with the second difference threshold to identify abnormal gateways and candidate gateways to perform task transfer operations. The specific identification content is as follows:

[0096] If the edge effect degree coefficient Bxxs exceeds the second difference threshold, the corresponding industrial edge gateway will be judged to be in an abnormal state and marked as an abnormal gateway;

[0097] If the edge effect degree coefficient Bxxs does not exceed the second difference threshold, the corresponding industrial edge gateway is judged to be not in an abnormal state and is marked as a candidate gateway;

[0098] S24: construct a candidate set based on the candidate gateways identified in S23.

[0099] In this embodiment, the system can obtain and analyze the signal status information in time, accurately calculate the signal jitter rate and time interval of each gateway, ensure the real-time monitoring of the health status of the industrial edge gateway, and further avoid the inaccurate parameter collection of the corresponding production line caused by system interruption, thereby sending the scheduling instruction by mistake. By calculating the edge effect degree coefficient, the industrial edge gateway with abnormalities can be quickly identified and marked as abnormal nodes, and the set of gateways to be selected can be constructed at the same time. This mechanism further improves the accuracy of fault detection and makes the task transfer operation more efficient. According to the comparison of the edge effect degree coefficient with the preset threshold, the system can automatically identify abnormal gateways and perform task transfer, and assign tasks to relatively healthy gateways to be selected. This scheduling method based on real-time status improves the fault tolerance and resource utilization efficiency of the system as much as possible, and further avoids production interruptions caused by node failures. By monitoring the signal jitter rate of the gateway, the communication quality between gateways can be accurately grasped, and the edge effect can be analyzed according to the signal jitter. This monitoring mechanism effectively improves the responsiveness of the system in the face of different network conditions, making it more stable. In short, this method further improves the stability, flexibility and intelligence of the production scheduling system through multi-level monitoring and data analysis. It can cope with real-time response and task scheduling of various abnormalities in the production process, ensuring efficient operation and continuous production of the system.

[0100] Example 4

[0101] Please refer to Figure 1 , specifically: S3 specific steps include:

[0102] S31: Real-time monitoring of the relevant operating data of each candidate gateway in the candidate set, wherein the relevant operating data includes the CPU usage rate of each candidate gateway. And the failure rate .

[0103] The specific steps of S3 also include:

[0104] S32. Based on the relevant operation data and in combination with the edge effect degree coefficient Bxxs, after linear normalization processing, a health status index Jkzs is constructed. The health status index Jkzs is obtained by the following formula:

[0105] ;

[0106] In the formula, Expressed as the failure rate, Expressed as CPU usage, Expressed as the first correction constant, and are weight values, among which, and The specific value is set by the user according to the situation;

[0107] The above failure rate Fault diagnostic sensors or condition monitoring systems are used to calculate the failure rate in combination with historical data.

[0108] CPU usage The CPU usage of edge devices can be monitored in real time through system monitoring software (such as the top command in Linux or other embedded monitoring tools).

[0109] S33, based on the method of obtaining the health status index Jkzs in S32, respectively obtain the health status index Jkzs of each candidate gateway in the candidate set, and extract the minimum value of the health status index Jkzs through feature extraction, and use the candidate gateway corresponding to the health status index Jkzs of the minimum value as the target gateway;

[0110] S34. According to the acquired target gateway, the task of the abnormal gateway in S23 is executed instead.

[0111] In this embodiment, by real-time monitoring of the CPU usage and failure rate of the gateways in the candidate set, the system can detect potential problems in advance, ensure that the equipment is handled in time before failure, and improve the reliability of the system. Through the edge effect coefficient and operation data, the health status index is constructed through linear normalization, and the minimum value is extracted to accurately identify the relatively healthy target gateway, thereby replacing the abnormal gateway to perform tasks. By dynamically selecting the target gateway with a relatively good health status, the smooth transfer of production tasks is further ensured, the utilization of system resources is optimized, and production losses are reduced. The introduction of the health status index ensures that when the equipment fails or is overloaded, the task can be automatically transferred to a relatively suitable gateway, which improves the efficiency of resource utilization and the stability of the system. This system improves the intelligence of production scheduling as much as possible, enhances fault tolerance, and effectively avoids the imbalance problem in task allocation, ultimately improving the overall production efficiency and system robustness.

[0112] Example 5

[0113] Please refer to Figure 1 , specifically: S4 specific steps include:

[0114] S41. After the task is transferred, the production status data of the production line corresponding to the target gateway is monitored, wherein the production status data of the production line corresponding to the target gateway includes the output rate Cpz and the production rate Cpz of the production line corresponding to the target gateway in different monitoring periods. ;

[0115] S42, according to the production status data of the production line corresponding to the target gateway, and in combination with the method of obtaining the output deviation factor Cpyz in each production line in S12, calculate and obtain the output deviation factor Mpyz of the production line corresponding to the target gateway;

[0116] S43. Analyze and judge the consistency of the output deviation factor Mpyz of the production line corresponding to the target gateway and the output deviation factor Cpyz of the corresponding abnormal gateway in S12. If the results are consistent, it is determined that the production line corresponding to the current abnormal gateway has an abnormal risk, and a scheduling instruction will be issued at this time.

[0117] In this embodiment, by monitoring the production status data of the target gateway after task transfer, the operating status of the production line can be understood in time, ensuring that the system has dynamic adjustment capabilities. By calculating the output deviation factor of the target gateway and performing consistency analysis with the output deviation factor of the abnormal gateway, it is possible to accurately determine whether the production line has abnormal risks, further avoiding losses caused by potential problems not being handled in time. When the results are consistent, the system can automatically issue scheduling instructions to achieve rapid response and optimize the production scheduling process. The automatic generation of instructions reduces manual intervention and improves the automation and intelligence level of the production line. Based on the analysis of production status data, the system not only improves the response speed to abnormal conditions, but also effectively reduces the probability of misjudgment, ensures the accuracy and reliability of production line scheduling, and further improves the robustness and efficiency of the overall production system. In short, this method monitors the output deviation factors of the target gateway and the abnormal gateway, combined with consistency analysis, to ensure that the system can quickly and accurately identify production risks and optimize the efficiency and stability of production scheduling.

[0118] Example 6

[0119] Please refer to Figure 1 Specifically: S4 includes the following steps:

[0120] S44. If the judgment results are inconsistent, the stability of production in the corresponding production line is analyzed based on the production status data in the production line corresponding to the target gateway, and after linear normalization, a production stability evaluation index Swzs is constructed. The production stability evaluation index Swzs is obtained by the following formula:

[0121] ;

[0122] Where n represents the monitoring period, i=1, 2, 3, ..., n, It is represented as the output deviation factor of the production line corresponding to the target gateway at the i-th monitoring time point, It is expressed as the average output deviation factor of the production line corresponding to the target gateway. It is represented as the production rate of the production line corresponding to the target gateway at the i-th monitoring time point, It is represented by the average production rate of the production line corresponding to the target gateway. and are weight values, Expressed as the second correction constant, and The specific value is set by the user according to the situation, and the weight value can be obtained by referring to the hierarchical analysis method;

[0123] The above production rate A line speed sensor or encoder can be used to measure the actual production rate of the production line.

[0124] The specific steps of S5 include:

[0125] S51, stable range Compare with the production stability assessment index Swzs to comprehensively determine the stability of the corresponding production line. The specific contents are as follows:

[0126] If the production stability assessment index Swzs falls into the stable range At this time, it will be comprehensively determined that the production line corresponding to the current abnormal gateway does not have abnormal risks;

[0127] If the production stability assessment index Swzs does not fall into the stable range At this time, it will be comprehensively determined that the production line corresponding to the current abnormal gateway has abnormal risks, and a scheduling instruction will be issued;

[0128] The above stability range The setting method is as follows: Based on the method of obtaining the production stability evaluation index Swzs, obtain the production stability evaluation index Swzs of each gateway, and combine the statistical algorithm to calculate the average production stability evaluation index and the standard deviation of the production stability assessment index , based on the average production stability assessment index and the standard deviation of the production stability assessment index , set the stability range ;in, is a constant, usually with a value of 1-3, corresponding to different confidence levels, and the specific value is set by the user (according to the actual situation); according to the setting method of the stability range, similarly, the thresholds in S1 and S2 above are also set in this way, which sets the upper limit of the stability range;

[0129] S52. After receiving the dispatch instruction, manually intervene in the production line corresponding to the current abnormal gateway.

[0130] It should be noted that the hierarchical analysis method is a combination of qualitative and quantitative analysis methods. It can decompose complex problems into multiple levels. By comparing the importance of factors at each level, it can help decision makers make decisions on complex problems and determine the final decision plan. In this process, the hierarchical analysis method can be used to determine the weight values ​​of these indicators.

[0131] In this embodiment, by analyzing the data of the target gateway production line and combining key indicators such as product deviation factor and production rate, a production stability evaluation index Swzs is constructed after linear normalization processing, which can more accurately evaluate the stability of the production line and ensure the dynamic adjustment of the production process. By comparing, the system can quickly identify the abnormal risks of the production line and make judgments, reducing losses caused by production fluctuations. Through the analysis of the production stability index, the system can comprehensively determine the stability of the production line, and take manual intervention or automatic scheduling measures based on the judgment results to ensure the efficient and stable operation of the production line, and further avoid unnecessary downtime or waste of resources due to data errors. Through systematic data analysis and production stability evaluation, the intelligent early warning capability of the production line can be effectively improved, so that factory managers can understand the health status of the production line in a timely manner and make adjustments quickly, thereby ensuring production continuity and product quality. In short, this method further improves the accuracy of production line stability evaluation and the level of automated management, making production scheduling more intelligent and flexible, and effectively reducing the impact of abnormalities on production efficiency.

[0132] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A production scheduling control method based on an industrial edge gateway, characterized in that: The following steps are included: S1. Several groups of industrial edge gateways are distributed in the industrial Internet of Things system in advance, and each industrial edge gateway collects the production status data in the corresponding production line through the edge device, builds the product deviation factor Cpyz in each production line according to the production status data, and issues a troubleshooting instruction based on the value of the product deviation factor Cpyz; The specific steps of S1 include: S11, the industrial edge gateway connects the Modbus RTU interface to the edge device by integrating the Modbus protocol to collect the production status data in the corresponding production line in real time, wherein the edge device includes industrial sensors and programmable logic controllers; S111, the production status data includes the production rate Cpz in different monitoring periods in the production line corresponding to each industrial edge gateway, and the average production rate is calculated by combining statistical algorithms ; The specific steps of S1 also include: S12. According to the production status data, construct the output deviation factor Cpyz in each production line. The output deviation factor Cpyz is obtained in the following manner: ； In the formula, It is expressed as the output rate at the i-th monitoring time point, Expressed as the average yield, It is expressed as the standard deviation of the yield; S13. By comparing the product deviation factor Cpyz in each production line with the preset No. 1 difference threshold, it is determined whether a troubleshooting instruction needs to be issued. The specific determination content is as follows: If the product deviation factor Cpyz exceeds the preset No. 1 difference threshold, a troubleshooting instruction will be issued externally and transmitted to S2 for industrial edge gateway inspection; If the product deviation factor Cpyz does not exceed the preset No. 1 difference threshold, no troubleshooting instructions will be issued at this time; S2. After receiving the troubleshooting instruction, use Pacemaker to analyze whether the corresponding industrial edge gateway has an edge effect, so as to analyze and calculate the edge effect degree coefficient Bxxs. If the edge effect degree coefficient Bxxs exceeds the preset second difference threshold, the task transfer operation is executed; The specific steps of S2 include: S21. After receiving the troubleshooting instruction, according to Pacemaker, automatically detect several groups of industrial edge gateways distributed in the industrial Internet of Things system to identify the relevant signal status information of several groups of industrial edge gateways regularly sending active signals to Pacemaker, wherein the relevant signal status information includes the time interval Jz of each industrial edge gateway receiving the active signal at different monitoring intervals, and calculates and obtains the average receiving time interval by combining statistical algorithms. ; S22. Based on the relevant signal status information, analyze whether the corresponding industrial edge gateway has an edge effect, so as to analyze and calculate the edge effect degree coefficient Bxxs, which is obtained specifically in the following manner: ； Where m is the number of monitoring intervals, j=1, 2, 3, ..., m, Expressed as the signal jitter rate at the jth monitoring interval, Expressed as the average signal jitter rate, and are weight values, among which, and The specific value is set by the user according to the situation; S221, wherein the signal jitter rate at the jth monitoring interval Obtained by the following formula: ； In the formula, It is represented as the time interval of receiving active signal at the jth monitoring interval, It is expressed as the average receiving time interval; The specific steps of S2 also include: S23. Preset the second difference threshold, and compare the edge effect degree coefficient Bxxs with the second difference threshold to identify abnormal gateways and candidate gateways to perform task transfer operations. The specific identification content is as follows: If the edge effect degree coefficient Bxxs exceeds the second difference threshold, the corresponding industrial edge gateway will be judged to be in an abnormal state and marked as an abnormal gateway; If the edge effect degree coefficient Bxxs does not exceed the second difference threshold, the corresponding industrial edge gateway is judged to be not in an abnormal state and is marked as a candidate gateway; S24, constructing a candidate set based on the candidate gateways identified in S23; S3, based on the task transfer operation in S2, monitor the relevant operation data of each industrial edge gateway in real time, and build a health status index Jkzs based on the relevant operation data to screen the target gateway; The specific steps of S3 include: S31: Real-time monitoring of the relevant operating data of each candidate gateway in the candidate set, wherein the relevant operating data includes the CPU usage rate of each candidate gateway. And the failure rate ; The specific steps of S3 also include: S32. Based on the relevant operation data and in combination with the edge effect degree coefficient Bxxs, after linear normalization processing, a health status index Jkzs is constructed. The health status index Jkzs is obtained by the following formula: ； In the formula, Expressed as the failure rate, Expressed as CPU usage, Expressed as the first correction constant, and are weight values, among which, and The specific value is set by the user according to the situation; S33, based on the method of obtaining the health status index Jkzs in S32, respectively obtain the health status index Jkzs of each candidate gateway in the candidate set, and extract the minimum value of the health status index Jkzs through feature extraction, and use the candidate gateway corresponding to the health status index Jkzs of the minimum value as the target gateway; S34, according to the acquired target gateway, replace the task of the abnormal gateway in S23; S4. After the task is transferred, the production status data in the production line corresponding to the target gateway is monitored, and the industrial edge gateway corresponding to the task transfer operation in S2 is combined to analyze and judge the consistency of the results. If the judgment results are inconsistent, the stability of the production in the corresponding production line is analyzed to construct the production stability evaluation index Swzs; The specific steps of S4 include: S41. After the task is transferred, the production status data of the production line corresponding to the target gateway is monitored, wherein the production status data of the production line corresponding to the target gateway includes the output rate Cpz and the production rate Cpz of the production line corresponding to the target gateway in different monitoring periods. ; S42, according to the production status data of the production line corresponding to the target gateway, and in combination with the method of obtaining the output deviation factor Cpyz in each production line in S12, calculate and obtain the output deviation factor Mpyz of the production line corresponding to the target gateway; S43, by analyzing and judging the consistency of the output deviation factor Mpyz of the production line corresponding to the target gateway and the output deviation factor Cpyz of the corresponding abnormal gateway in S12, if the results are consistent, it is determined that the production line corresponding to the current abnormal gateway has an abnormal risk, and a scheduling instruction will be issued at this time; The specific steps of S4 also include: S44. If the judgment results are inconsistent, the stability of production in the corresponding production line is analyzed based on the production status data in the production line corresponding to the target gateway, and after linear normalization, a production stability evaluation index Swzs is constructed. The production stability evaluation index Swzs is obtained by the following formula: ； Where n represents the monitoring period, i=1, 2, 3, ..., n, It is represented as the output deviation factor of the production line corresponding to the target gateway at the i-th monitoring time point, It is expressed as the average output deviation factor of the production line corresponding to the target gateway. It is represented as the production rate of the production line corresponding to the target gateway at the i-th monitoring time point, It is represented by the average production rate of the production line corresponding to the target gateway. and are weight values, Expressed as the second correction constant, and The specific value is set by the user according to the situation; S5. Use Sampling Theory to pre-set the stability range , and compare it with the production stability assessment index Swzs to comprehensively determine the stability of the corresponding production line, and execute corresponding scheduling measures based on the determination results.

2. According to claim 1, a production scheduling control method based on an industrial edge gateway is characterized in that: The specific steps of S5 include: S51, stable range Compare with the production stability assessment index Swzs to comprehensively determine the stability of the corresponding production line. The specific contents are as follows: If the production stability assessment index Swzs falls into the stable range At this time, it will be comprehensively determined that the production line corresponding to the current abnormal gateway does not have abnormal risks; If the production stability assessment index Swzs does not fall into the stable range At this time, it will be comprehensively determined that the production line corresponding to the current abnormal gateway has abnormal risks, and a scheduling instruction will be issued; S52. After receiving the dispatch instruction, manually intervene in the production line corresponding to the current abnormal gateway.

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