Multi-process dynamic collaborative scheduling method and system for steel plate cutting

Abstract

The invention provides a steel plate cutting-oriented multi-process dynamic collaborative scheduling method and system, and relates to the technical field of steel plate cutting, and the method comprises the steps: carrying out the historical data collection of each process node in a cutting process chain, carrying out the post-process node impact analysis of the fluctuation of key parameters according to the historical data collection result, and obtaining a multi-process node impact analysis result; establishing an instability transfer model; setting a monitoring sensor, performing process node monitoring by using the monitoring sensor, and establishing node monitoring data; uploading node monitoring data to an instability transmission model, and establishing an instability transmission result of the cutting process chain; performing compensation optimization based on an instability transfer result by using the target function; and dynamic cooperative scheduling management of the cutting process chain is carried out by using a compensation optimization result. The technical problem that in the prior art, due to instability in the production process, the cutting precision and the production efficiency are further affected can be solved, and the technical effect of improving the cutting precision and the production efficiency is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of steel plate cutting, and in particular to a multi-process dynamic collaborative scheduling method and system for steel plate cutting. Background Art

[0002] In modern manufacturing, steel plate cutting is a key process step and is widely used in industries such as automobiles, ships, aerospace, and construction. With the increasing requirements for precision and efficiency, traditional steel plate cutting technology faces more and more challenges. Although the existing technology has improved production efficiency to a certain extent, it still has many defects in precision control, process stability, cost control, etc.

[0003] At present, most of the existing steel plate cutting technologies rely on manual intervention for process control, mainly by manually setting process parameters to ensure cutting quality, but this method has great limitations. First, the manually set parameters often cannot adapt to the changes in equipment performance and material status during the production process in real time, which can easily lead to instability in the production process, thereby affecting the cutting accuracy. Secondly, traditional quality monitoring mainly relies on terminal detection methods, and fails to promptly detect and correct deviations in the process. Therefore, the detection of unqualified products is often in the post-tracing stage, which not only wastes materials, but also causes waste of resources and reduced production efficiency. Furthermore, the lack of an effective process data feedback mechanism in the existing technology leads to the lack of scientific basis for the adjustment of process parameters, resulting in unstable processes and the inability to effectively control production costs.

[0004] In summary, the prior art has a technical problem that the process parameter setting due to manual intervention cannot adapt in real time to changes in equipment performance and material status during the production process, resulting in instability in the production process, further affecting cutting accuracy and production efficiency. Summary of the invention

[0005] The purpose of this application is to provide a multi-process dynamic collaborative scheduling method and system for steel plate cutting, so as to solve the technical problem in the prior art that the process parameter setting due to manual intervention cannot adapt to the changes in equipment performance and material status in the production process in real time, resulting in instability in the production process, further affecting the cutting accuracy and production efficiency.

[0006] In view of the above problems, the present application provides a multi-process dynamic collaborative scheduling method and system for steel plate cutting.

[0007] In the first aspect, the present application provides a multi-process dynamic collaborative scheduling method for steel plate cutting, which is implemented through a multi-process dynamic collaborative scheduling system for steel plate cutting, including: establishing a cutting process chain for steel plate cutting, and collecting historical data for each process node in the cutting process chain, performing post-process node impact analysis on the fluctuation of key parameters based on the historical data collection results, and establishing an instability transfer model; setting a monitoring sensor at each process node in the cutting process chain, using the monitoring sensor to monitor the process node, and establishing node monitoring data; uploading the node monitoring data to the instability transfer model, and establishing an instability transfer result of the cutting process chain; configuring an objective function for control optimization, the objective function is an objective function for balancing optimization of yield rate, total process time and dynamic cost, using the objective function to perform compensation optimization based on the instability transfer result, and establishing a compensation optimization result; using the compensation optimization result to perform dynamic collaborative scheduling management of the cutting process chain.

[0008] In the second aspect, the present application also provides a multi-process dynamic collaborative scheduling system for steel plate cutting, which is used to execute the multi-process dynamic collaborative scheduling method for steel plate cutting as described in the first aspect, including: an impact analysis module, the impact analysis module is used to establish a cutting process chain for steel plate cutting, and to collect historical data for each process node in the cutting process chain, and to perform post-process node impact analysis on the fluctuation of key parameters according to the historical data collection results, and to establish an instability transfer model; a node monitoring module, the node monitoring module is used to set a monitoring sensor at each process node in the cutting process chain, and to use the monitoring sensor to monitor the process node and establish node monitoring data; a data upload module, the data upload module is used to upload the node monitoring data to the instability transfer model, and to establish an instability transfer result of the cutting process chain; a compensation optimization module, the compensation optimization module is used to configure the objective function of control optimization, the objective function is the objective function of yield rate, total process time and dynamic cost balance optimization, and the objective function is used to perform compensation optimization based on the instability transfer result to establish a compensation optimization result; a collaborative scheduling module, the collaborative scheduling module is used to use the compensation optimization result to perform dynamic collaborative scheduling management of the cutting process chain.

[0009] The technical solution provided in the present application has at least the following technical effects or advantages: by establishing a cutting process chain for steel plate cutting, and collecting historical data for each process node in the cutting process chain, performing post-process node impact analysis on the fluctuation of key parameters based on the historical data collection results, and establishing an instability transfer model; setting a monitoring sensor at each process node in the cutting process chain, using the monitoring sensor to monitor the process node, and establishing node monitoring data; uploading the node monitoring data to the instability transfer model, and establishing the instability transfer result of the cutting process chain; configuring the objective function of control optimization, the objective function is the objective function of yield rate, total process time and dynamic cost balance optimization, using the objective function to perform compensation optimization based on the instability transfer result, and establishing the compensation optimization result; using the compensation optimization result to perform dynamic collaborative scheduling management of the cutting process chain, that is, by achieving the technical goals of process adaptation and intelligent control, the technical effect of improving cutting accuracy and improving production efficiency is achieved.

[0010] 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 according to the contents of the specification, and 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 specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 A schematic diagram of the process flow of a multi-process dynamic collaborative scheduling method for steel plate cutting in this application;

[0013] Figure 2 This is a structural diagram of the multi-process dynamic collaborative scheduling system for steel plate cutting in this application.

[0014] Explanation of the accompanying drawings: impact analysis module 11, node monitoring module 12, data upload module 13, compensation optimization module 14, collaborative scheduling module 15. DETAILED DESCRIPTION

[0015] This application provides a multi-process dynamic collaborative scheduling method and system for steel plate cutting, which solves the technical problem in the prior art that the process parameter setting due to manual intervention cannot adapt to the changes in equipment performance and material status in the production process in real time, resulting in instability in the production process, further affecting cutting accuracy and production efficiency. The technical goal of process adaptation and intelligent control is achieved, and the technical effect of improving cutting accuracy and production efficiency is achieved.

[0016] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0017] For example, please refer to the attached Figure 1 The present application provides a multi-process dynamic collaborative scheduling method for steel plate cutting, which is applied to a multi-process dynamic collaborative scheduling system for steel plate cutting, and specifically includes the following steps:

[0018] Step 1: Establish a cutting process chain for steel plate cutting, and collect historical data for each process node in the cutting process chain. According to the historical data collection results, perform post-process node impact analysis on the fluctuation of key parameters and establish an instability transfer model.

[0019] Specifically, establishing a cutting process chain for steel plate cutting means decomposing the entire steel plate cutting process into several process nodes, each of which represents a specific operation link. For example, laser cutting of the steel plate is first performed, followed by subsequent processes such as bending, grinding and beveling. In this process, each node has its own independent processing requirements and operation steps, and the corresponding equipment and process parameters need to be selected according to different process requirements. Next, historical data collection for each process node in the cutting process chain means recording relevant data for analysis and optimization during the processing of each node. For example, during laser cutting, parameters such as laser power, cutting speed, and material thickness can be recorded, while during the bending process, data such as bending angle and bending force can be recorded. By collecting and recording the fluctuation data of key parameters (such as cutting speed, laser power, equipment accuracy, etc.) during the process, it is possible to evaluate how these fluctuations affect subsequent process nodes. For example, power fluctuations in laser cutting may affect the subsequent bending accuracy. If the laser power is low during cutting, there may be burrs on the edge of the steel plate, which will affect the stability of the bending process and the quality of the final product. By analyzing the impact of these fluctuations, we can help identify the fluctuations of process parameters that have a greater impact on the subsequent process nodes, so that corresponding control measures can be taken to reduce the negative impact of fluctuations. The mutual influence relationship between process nodes is quantified and formed. An instability transfer model is established to describe the instability transmitted between different process nodes, which helps to identify which links in the process chain are more likely to produce instability, and can also predict how fluctuations at different process nodes affect the final product quality, thereby providing a scientific basis for process optimization.

[0020] Step 2: Setting a monitoring sensor at each process node in the cutting process chain, using the monitoring sensor to monitor the process node, and establishing node monitoring data.

[0021] Specifically, sensors are installed at each process link to collect key data of the process node in real time, such as temperature, pressure, vibration, displacement, etc. Among them, in the laser cutting process, since excessive temperature fluctuations will affect the cutting quality, temperature sensors are installed to monitor the heating of the steel plate during the laser cutting process. Through the monitoring sensor, the operating status of each process node can be obtained in real time, helping to detect abnormalities in time and make adjustments. The monitoring sensor is used to continuously monitor the process nodes to ensure that each node is maintained in the best working state during the production process, thereby ensuring the quality of the product and processing efficiency. By collecting and analyzing these sensor data, node monitoring data can be established, including various parameters and state change records of the process nodes, which can not only help to monitor the stability of the current process in real time, but also serve as the basis for subsequent analysis and optimization, thereby achieving continuous improvement and quality control of the production process.

[0022] Step three: Upload the node monitoring data to the instability transfer model to establish the instability transfer result of the cutting process chain.

[0023] Specifically, the monitoring data collected by sensors at each process node is transmitted to the instability transfer model used to analyze and predict the transfer of instability. Based on the collected data, the instability transfer model can analyze and predict how the instability between different process nodes is transferred from one node to another, thereby affecting the stability of the overall production process. By uploading the monitoring data, the instability transfer model can be updated in real time and calculate the results of instability transfer, such as predicting whether the instability of laser cutting will cause errors in the subsequent bending process. If the instability of laser cutting is large, it is believed that the accuracy of the bending process will be greatly affected. Through the calculation of the instability transfer model, the transfer path and intensity of instability in the entire process chain are finally obtained, which helps to identify the key process nodes that affect the stability of the entire production process, so as to take targeted measures to control instability and improve overall production efficiency and product quality.

[0024] Step 4: Configure the objective function of control optimization, which is the objective function of yield rate, total process time and dynamic cost balance optimization. Use the objective function to perform compensation optimization based on the instability transfer result and establish the compensation optimization result.

[0025] Specifically, the objective function of configuration control optimization is to consider the yield rate, total process time and dynamic cost factors at the same time, and to achieve the balance of the three through reasonable configuration. For example, in the steel plate cutting process, the yield rate represents the quality level of the product, the total process time reflects the efficiency of the production process, and the dynamic cost is related to the fluctuation of production cost. By configuring the objective function, these factors can be optimized at the same time to avoid simply pursuing the optimization of one aspect while ignoring the influence of other aspects. The objective function is the objective function of the yield rate, total process time and dynamic cost balance optimization, which is used to find the best balance point, that is, to shorten the production time as much as possible while ensuring the yield rate, and reduce cost fluctuations. By analyzing and calculating the results of instability transmission, it is determined which process nodes in the production process have a negative impact on other links, and optimization adjustments are made accordingly. For example, if the instability of laser cutting leads to errors in the subsequent bending process, the objective function will consider how to compensate for this effect by adjusting the cutting process, thereby improving the stability of the overall process chain, and finally derive a specific optimization plan, which can achieve the best balance between yield rate, process time and cost while controlling instability, thereby improving overall production efficiency and product quality.

[0026] Step 5: Use the compensation optimization result to perform dynamic collaborative scheduling management of the cutting process chain.

[0027] Specifically, based on the compensation optimization results obtained in the optimization process, the cutting process chain is dynamically adjusted and coordinated. The compensation optimization results reflect how to control instability and improve production efficiency and quality by adjusting the parameters of each process node in the production process. By applying these results to production scheduling, the various process links can be more coordinated in actual production to avoid the instability of a certain link affecting the entire production process. For example, if the instability of the laser cutting process affects the accuracy of the subsequent bending process, the compensation optimization results may indicate that it is necessary to adjust the parameters of the laser cutting or arrange more time to complete the cutting to ensure that the bending process can proceed smoothly. Dynamic collaborative scheduling management refers to the flexible adjustment of the operation sequence, resource allocation and time arrangement of each process node according to real-time data and production needs to cope with possible changes and uncertainties in the production process, thereby minimizing the conflicts and instability between the process nodes, ensuring that each process node can work at the appropriate time and conditions, thereby improving overall production efficiency and product quality.

[0028] The multi-process dynamic collaborative scheduling method for steel plate cutting is applied to a multi-process dynamic collaborative scheduling system for steel plate cutting, which can achieve the technical goals of process adaptation and intelligent control, and achieve the technical effects of improving cutting accuracy and enhancing production efficiency.

[0029] Furthermore, the present application also includes: performing upstream and downstream relationship analysis between processes based on the cutting process chain, and establishing upstream and downstream constraints; creating a dependency graph based on the upstream and downstream constraints; using the historical data collection results to calculate the instability correlation between processes in the dependency graph, and normalizing the instability correlation into a process weight matrix; using the process weight matrix to perform cumulative instability transfer analysis and construct an instability transfer model.

[0030] Specifically, each process node in the cutting process chain is not isolated, but has a certain order relationship with each other, that is, the "upstream and downstream relationship". For example, laser cutting is an upstream process, and its completion directly affects the subsequent downstream processes such as bending and grinding. In the process chain, the order and processing conditions of each node will affect the efficiency and quality of the entire chain. Therefore, when establishing a cutting process chain, the upstream and downstream relationships between these process nodes are analyzed to identify which processes are precedent and which are dependent on subsequent processes, thereby providing a basis for subsequent optimization and scheduling.

[0031] A dependency graph is a visual tool used to show the interdependencies between different process nodes. In the cutting process chain, each process node can be regarded as a node in a graph, and the edges between the nodes represent the dependencies between the processes. Through the dependency graph, it is clear whether the completion of a certain process is a prerequisite for the start of another process. For example, laser cutting must be completed before bending the steel plate, so the laser cutting node depends on the upstream steel plate preparation work, and the bending process node depends on the completion of the laser cutting node. Through such a dependency graph, production schedulers can better understand the workflow and schedule of each process link.

[0032] Each process node may encounter different degrees of fluctuation and instability in the actual production process. For example, in the laser cutting process, due to changes in equipment accuracy or different materials, there may be certain errors in the cutting effect. Through historical data collection, the stability performance of different process nodes is recorded, and these data can be used to calculate the "instability correlation" between the processes. This correlation reflects how the instability of a certain process affects the stability of other processes. In order to facilitate analysis and comparison, these instability correlation data can be converted into a process weight matrix through normalization. Each value in the weight matrix represents the degree of instability between different processes. The higher the value, the greater the instability of the process on other processes.

[0033] Through the process weight matrix, the instability transfer analysis of the entire cutting process chain can be performed. Cumulative instability transfer analysis is a method that can help understand how process fluctuations are gradually transferred to downstream processes and ultimately affect the quality of the final product. For example, if the quality of laser cutting is unstable, it may cause errors in the bending process, thereby affecting the accuracy of the entire product. When constructing an instability transfer model, the analysis of the weight matrix can predict how the instability between each process node is transferred to each other, and help design reasonable intervention measures to reduce the instability of the overall system.

[0034] Furthermore, the present application also includes: the instability transmission model is as follows: Among them, U k Characterize the instability of the kth process node, W ik Characterizes the influence weight of the previous process node i on the current kth process node, P i Characterizes the actual parameter fluctuation value of process node i, f(P i ) is a nonlinear mapping function used to transform the actual parameter fluctuation value P i Transformed into the impact on instability, ∈ k Characterizes the inherent instability disturbance term of the kth process node, Utotal Characterizes the overall instability, n is the total number of process nodes in the cutting process chain, W k，total Characterizes the instability weight of the kth process node on the final product quality.

[0035] Specifically, the instability transfer model is used to describe the mutual influence between different process nodes in the cutting process chain and their impact on the final quality of the product. By establishing the instability transfer model, it is possible to accurately analyze how the fluctuations of each process node affect the subsequent process and thus affect the final product quality.

[0036] Among them, U k Characterize the instability of the kth process node, indicating the degree of fluctuation caused by factors such as equipment, environment or materials during the execution of a specific process node. These fluctuations may appear as parameter deviations, such as fluctuations in laser power during cutting, or unstable motion accuracy of machine equipment. By quantifying these instabilities, the contribution and impact of each process node on the overall process chain can be evaluated.

[0037] W ik Characterizes the weight of the impact of the previous process node i on the current kth process node. It is a parameter used to quantify the impact of the fluctuation of the previous process node on the instability of the current process node. For example, if the quality of laser cutting is unstable, this instability will affect the accuracy of the subsequent bending process. Therefore, a weight value needs to be set in the model to indicate the intensity of the impact of laser cutting on bending. The higher the weight value, the greater the impact of the previous process on the subsequent process.

[0038] Pi represents the actual parameter fluctuation value of process node i, and represents a nonlinear mapping function, which is used to convert the actual parameter fluctuation value into the influence of instability. Each process node has certain parameter fluctuations, such as the instability of cutting speed or power in laser cutting, the degree of abrasive wear in the grinding process, etc. Through the nonlinear mapping function, these specific parameter fluctuations can be converted into corresponding instability influences, which can more accurately analyze and quantify the contribution of each process node to instability.

[0039] ∈ k The disturbance term that characterizes the inherent instability of the kth process node indicates the source of fluctuation of each process node itself, which may be caused by factors such as equipment and personnel operation. For example, the fluctuation of the laser source of the laser cutting equipment or the mechanical failure of the bending machine may cause its instability. Therefore, this disturbance term is used to describe the fluctuation of the process node caused by its internal reasons when there is no external influence.

[0040] U totalCharacterizes the overall instability, indicating the comprehensive instability level of the entire cutting process chain, reflecting the cumulative instability of all process nodes from the initial process to the final product. The overall instability is usually the sum of the effects of instability at all process nodes, affecting the final quality and consistency of the product.

[0041] n is the total number of process nodes in the cutting process chain, indicating the number of all process nodes involved in the entire cutting process chain. The performance of each process node will affect the stability of the overall process chain.

[0042] W k,total Characterize the instability weight of the kth process node on the quality of the final product, which represents the weight value of the impact of a specific process node on the final quality of the product. Each process node has a different degree of impact on the quality of the final product. For example, the quality fluctuation of laser cutting may have a greater impact on the accuracy of the final product, while the fluctuation of the beveling process has a smaller impact on the product quality. By setting the weight of the impact of each node on product quality, it can help better manage process optimization and focus on the process nodes with greater impact.

[0043] By quantifying the instability of each process node and the mutual influence between the nodes, it helps to analyze how the instability is transmitted from one process node to another and ultimately affects the product quality. By establishing these parameters and their weights, it is possible to more accurately analyze and predict the instability transmission path in the cutting process chain, thereby effectively managing and optimizing the process flow and reducing the negative impact of instability on the quality of the final product.

[0044] Furthermore, the present application also includes: performing production monitoring on steel plate cutting and establishing production monitoring results; using the production monitoring results to calculate yield deviation and establish a first state variable; using the order deadline to perform time margin analysis and establish a second state variable; establishing a third state variable based on cost deviation, and calculating the yield weight, total process time weight, and dynamic cost weight according to the first state variable, the second state variable, and the third state variable respectively; performing weight normalization processing on the yield weight, the total process time weight, and the dynamic cost weight to establish an objective function.

[0045] Specifically, during the entire production process of steel plate cutting, the key parameters and status of each process link are tracked and recorded in real time, including cutting speed, laser power, material thickness, equipment status, etc., so that problems in production can be discovered in time, ensuring that the production process meets the predetermined quality standards, and establishing production monitoring results to analyze possible deviations and risks in the current production process.

[0046] Based on the data recorded in production monitoring, the gap between the actual yield rate and the target yield rate in the current production process is calculated. The yield rate deviation reflects the quality problems that occur in the production process, such as insufficient cutting accuracy, which leads to a decrease in the yield rate. By calculating the yield rate deviation, the stability of the production process can be evaluated and potential room for improvement can be found. The yield rate deviation is used as a key indicator to establish the first state variable for quantification and comparison in subsequent analysis and decision-making.

[0047] According to the delivery deadline of the customer's order, by analyzing the time requirements of each process in the production process, calculate whether there is enough time margin to deal with possible delays. For example, if the time of steel plate cutting exceeds expectations, it may affect the subsequent bending and grinding processes, resulting in delivery delays. Through time margin analysis, we can understand whether the order task can be completed on time in the actual production process, so as to adjust the production plan. Establishing the second state variable means using the time margin as a new variable to measure whether there is a risk of time pressure in the production process.

[0048] The cost deviation is calculated by analyzing the difference between the actual cost and the predetermined cost in the production process. For example, in the laser cutting process, if the production cost increases due to material waste or equipment failure, the cost deviation needs to be calculated. The cost deviation is converted into a quantifiable value as the cost deviation, which is used for analysis together with other state variables.

[0049] The yield rate weight, total process time weight, and dynamic cost weight are calculated based on the first state variable, the second state variable, and the third state variable to reflect their importance to the decision-making in the overall production process. The yield rate weight reflects the importance of quality to the production process, the total process time weight reflects the impact of time on production efficiency, and the dynamic cost weight reflects the urgency of cost control, which can ensure that each factor is given appropriate attention in the overall decision-making.

[0050] The yield rate weight, the total process time weight, and the dynamic cost weight are normalized to ensure that all weight values ​​are within the same standard range, so as to make unified comparisons and optimizations, avoid the influence of a certain weight being too large or too small on the final decision, and ensure that various decision factors are balanced. Finally, the objective function is established through the normalized weights as the basis for optimizing the production process.

[0051] Furthermore, the present application also includes: obtaining equipment response parameters of the process node; performing anomaly segmentation according to the equipment response parameters and the monitoring data set, and establishing equipment anomalies and steel plate anomalies; performing self-optimization of equipment control of the corresponding process node according to the equipment anomaly, and establishing a first optimization feedback; performing control feedback of the subsequent process node according to the steel plate anomaly, and establishing a second optimization feedback; and performing dynamic collaborative scheduling management of the cutting process chain according to the first optimization feedback and the second optimization feedback.

[0052] Specifically, by monitoring the operating status of the equipment in the process node, various response parameters of the equipment during operation are collected and recorded, including the temperature, pressure, movement speed, power output, etc. of the equipment, to analyze whether the equipment is in normal working condition. For example, during the laser cutting process, the power fluctuation of the laser may affect the cutting quality, so it is necessary to monitor the power response of the laser in real time to judge the working status of the equipment. By obtaining these equipment response parameters, basic data can be provided for subsequent abnormal analysis and optimization.

[0053] Using the acquired equipment response parameters and other monitoring data (such as steel plate cutting conditions, processing errors, etc.), data analysis methods are used to identify and differentiate abnormal conditions. For example, if the power response of the equipment fluctuates, but the cutting quality of the steel plate is not affected, it indicates that the equipment is abnormal, but the steel plate itself is not a problem. Through abnormal segmentation, equipment abnormalities and steel plate abnormalities can be identified separately, thereby more accurately locating the source of the problem.

[0054] After the abnormal segmentation, specific abnormal identification is carried out for the equipment and steel plate respectively. Equipment abnormality refers to the performance problems of the equipment itself, such as failure, wear, reduced precision, etc., which may lead to instability in the process; steel plate abnormality refers to the quality problems of the steel plate during the cutting process, such as cutting error, uneven surface, etc., which helps to clarify the specific links where the problem occurs and facilitates subsequent adjustments and optimizations.

[0055] Self-optimization of equipment control for corresponding process nodes based on equipment anomalies means that after discovering equipment anomalies, the performance of the equipment is improved by adjusting or optimizing the working parameters of the equipment, thereby restoring the stability of the process node. For example, if the power fluctuation of the laser cutting equipment is too large, it may be necessary to restore the stable working state by adjusting the laser power or cooling system. Through self-optimization of equipment control, the equipment can be quickly adjusted back to the normal working range after an abnormality occurs to ensure that subsequent processes are not affected. Establishing the first optimization feedback means that through this process, an optimization feedback mechanism is formed to help continuously monitor and adjust the equipment status.

[0056] Control feedback of subsequent process nodes based on steel plate anomalies means that when an abnormality occurs in the steel plate, the control strategy of subsequent process nodes is adjusted in time to compensate for the impact of the previous process. For example, if the cutting accuracy of the steel plate in the cutting process does not meet the requirements, it may be necessary to make adjustments in the subsequent bending or grinding process to ensure the quality of the final product. Establishing the second optimization feedback means that through this feedback mechanism, it is ensured that the subsequent process nodes can respond to the anomalies in the previous process in a timely manner and make necessary adjustments to avoid further deterioration of quality problems.

[0057] Dynamic collaborative scheduling management of the cutting process chain based on the first optimization feedback and the second optimization feedback refers to the comprehensive feedback of equipment abnormalities and steel plate abnormalities, and the dynamic adjustment of the scheduling and operation of each process node in the entire cutting process chain. For example, if an equipment abnormality occurs during the laser cutting process, it is necessary to adjust the schedule of subsequent bending and grinding or change the order of equipment use to ensure the efficient operation of the entire process chain. Through dynamic collaborative scheduling management, the coordination of each link in the process chain can be achieved to ensure that when an abnormality occurs in the equipment or steel plate, the entire production process can still proceed smoothly as planned.

[0058] Furthermore, the present application also includes: using the compensation optimization result to predict the compensation effect and establish a compensation effect prediction result; if the compensation effect prediction result cannot meet the preset effect threshold, an elimination command is generated, and the corresponding steel plate is scrapped according to the elimination command.

[0059] Specifically, based on the compensation optimization results, further predictive analysis of the compensation effect is performed. The compensation optimization results reflect the optimization of the production process by adjusting process parameters or equipment settings to reduce instability. Using these results, it is possible to predict whether the original problem can be effectively solved after adjustment in actual production, such as compensating for equipment instability by adjusting cutting parameters. The purpose of compensation effect prediction is to evaluate in advance whether the compensation measures can achieve the expected results and ensure that no larger problems will arise during the production process. Establishing compensation effect prediction results refers to forming evaluation results of compensation effects and quantifying these predicted effects for subsequent decision-making. For example, if the prediction results show that the laser cutting accuracy after compensation still does not meet the standard, other process parameters may need to be further adjusted.

[0060] After the compensation effect is predicted, if the predicted compensation effect is lower than the pre-set standard or threshold, for example, the cutting accuracy is lower than the required standard, an elimination command will be triggered to ensure that the number of defective products in the production process is controlled to avoid further processing that may lead to higher quality problems or waste of resources. By generating an elimination command, the steel plates that need to be discarded are identified to prevent them from continuing to enter the subsequent process flow, thereby reducing the negative impact on the quality of the final product.

[0061] According to the generated elimination order, the steel plates that fail to meet the quality requirements are scrapped in time. Scrap processing includes marking, isolating and removing these steel plates from the production line to avoid affecting other process steps or the quality of the final product. For example, if the laser cutting process produces steel plates that do not meet the requirements, the elimination order will instruct these steel plates to be scrapped to prevent them from entering the subsequent bending or grinding process, thereby affecting the overall production efficiency and product quality.

[0062] Furthermore, the present application also includes: establishing a self-learning iterative channel, and continuously recording dynamic collaborative scheduling management data through the self-learning iterative channel; and using the continuous recording results to update the instability transfer model.

[0063] Specifically, a self-learning iteration channel is established to enable continuous learning and adjustment based on real-time data and production feedback. For example, in the laser cutting process, by continuously collecting cutting accuracy and equipment status data, the parameter settings that cause instability are identified, so that these parameters can be adjusted in the next round of operations to optimize the production process. The purpose of the self-learning iteration channel is to continuously update and optimize production strategies so that processes and equipment can increasingly adapt to production needs. Through the self-learning iteration channel, dynamic collaborative scheduling management data is continuously recorded, and data related to dynamic collaborative scheduling is continuously recorded, including the time of process nodes, resource allocation, equipment status, etc., and will be continuously updated with each iteration. Through continuous recording, a large amount of data can be accumulated to provide a basis for subsequent optimization and decision-making.

[0064] The instability transfer model is updated and adjusted using the continuous recording results to determine the instability transfer method between the various process links in the production process. For example, if the instability of laser cutting causes errors in the bending process, the updated instability transfer model will be able to more accurately predict the extent and scope of this impact. Therefore, the instability in the production process can be more accurately controlled, and the optimized model will help engineers make more effective decisions.

[0065] In summary, the multi-process dynamic collaborative scheduling method for steel plate cutting provided in the present application has the following technical effects: by establishing a cutting process chain for steel plate cutting, and collecting historical data for each process node in the cutting process chain, the post-process node impact analysis of the fluctuation of key parameters is performed according to the historical data collection results, and an instability transfer model is established; a monitoring sensor is set at each process node in the cutting process chain, and the monitoring sensor is used to monitor the process node to establish node monitoring data; the node monitoring data is uploaded to the instability transfer model to establish the instability transfer result of the cutting process chain; the objective function of the control optimization is configured, and the objective function is the objective function of the yield rate, the total process time and the dynamic cost balance optimization, and the objective function is used to perform compensation optimization based on the instability transfer result to establish the compensation optimization result; the compensation optimization result is used to perform dynamic collaborative scheduling management of the cutting process chain, that is, by achieving the technical goals of process adaptation and intelligent control, the technical effect of improving cutting accuracy and improving production efficiency is achieved.

[0066] Embodiment 2: Based on the multi-process dynamic collaborative scheduling method for steel plate cutting in the previous embodiment, the present application also provides a multi-process dynamic collaborative scheduling system for steel plate cutting, as shown in the attached Figure 2 , including: an impact analysis module 11, the impact analysis module 11 is used to establish a cutting process chain for steel plate cutting, and collect historical data for each process node in the cutting process chain, perform post-process node impact analysis on the fluctuation of key parameters according to the historical data collection results, and establish an instability transfer model; a node monitoring module 12, the node monitoring module 12 is used to set a monitoring sensor at each process node in the cutting process chain, use the monitoring sensor to monitor the process node, and establish node monitoring data; a data upload module 13, the data upload module 13 is used to upload the node monitoring data to the instability transfer model, and establish the instability transfer result of the cutting process chain; a compensation optimization module 14, the compensation optimization module 14 is used to configure the objective function of control optimization, the objective function is the objective function of yield rate, total process time and dynamic cost balance optimization, and the objective function is used to perform compensation optimization based on the instability transfer result to establish a compensation optimization result; a collaborative scheduling module 15, the collaborative scheduling module 15 is used to use the compensation optimization result to perform dynamic collaborative scheduling management of the cutting process chain.

[0067] Furthermore, the multi-process dynamic collaborative scheduling system for steel plate cutting is also used to: analyze the upstream and downstream relationships between processes based on the cutting process chain, and establish upstream and downstream constraints; create a dependency graph based on the upstream and downstream constraints; use the historical data collection results to calculate the instability correlation between processes in the dependency graph, and normalize the instability correlation into a process weight matrix; use the process weight matrix to perform cumulative instability transfer analysis and construct an instability transfer model.

[0068] Furthermore, the multi-process dynamic collaborative scheduling system for steel plate cutting is also used for: the instability transfer model is as follows: Among them, U k Characterize the instability of the kth process node, W ik Characterizes the influence weight of the previous process node i on the current kth process node, P i Characterizes the actual parameter fluctuation value of process node i, f(P i ) is a nonlinear mapping function used to transform the actual parameter fluctuation value P i Transformed into the impact on instability, ∈ k Characterizes the inherent instability disturbance term of the kth process node, U total Characterizes the overall instability, n is the total number of process nodes in the cutting process chain, W k，total Characterizes the instability weight of the kth process node on the final product quality.

[0069] Furthermore, the multi-process dynamic collaborative scheduling system for steel plate cutting is also used to: perform production monitoring on steel plate cutting and establish production monitoring results; use the production monitoring results to calculate yield deviation and establish a first state variable; use the order deadline to perform time margin analysis and establish a second state variable; establish a third state variable based on cost deviation, and calculate the yield weight, total process time weight, and dynamic cost weight according to the first state variable, the second state variable, and the third state variable respectively; perform weight normalization processing on the yield weight, the total process time weight, and the dynamic cost weight to establish an objective function.

[0070] Furthermore, the multi-process dynamic collaborative scheduling system for steel plate cutting is also used to: obtain equipment response parameters of process nodes; perform anomaly segmentation according to the equipment response parameters and the monitoring data set, and establish equipment anomalies and steel plate anomalies; perform equipment control self-optimization of corresponding process nodes according to the equipment anomaly, and establish a first optimization feedback; perform control feedback of subsequent process nodes according to the steel plate anomaly, and establish a second optimization feedback; and perform dynamic collaborative scheduling management of the cutting process chain according to the first optimization feedback and the second optimization feedback.

[0071] Furthermore, the multi-process dynamic collaborative scheduling system for steel plate cutting is also used to: use the compensation optimization result to predict the compensation effect and establish the compensation effect prediction result; if the compensation effect prediction result cannot meet the preset effect threshold, an elimination command is generated, and the corresponding steel plate is scrapped according to the elimination command.

[0072] Furthermore, the multi-process dynamic collaborative scheduling system for steel plate cutting is also used to: establish a self-learning iterative channel, through which dynamic collaborative scheduling management data is continuously recorded; and use the continuous recording results to update the instability transfer model.

[0073] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The multi-process dynamic collaborative scheduling method for steel plate cutting and the specific examples in the aforementioned embodiment one are also applicable to the multi-process dynamic collaborative scheduling system for steel plate cutting in this embodiment. Through the aforementioned detailed description of the multi-process dynamic collaborative scheduling method for steel plate cutting, technical personnel in this field can clearly understand the multi-process dynamic collaborative scheduling system for steel plate cutting in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0074] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0075] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A multi-process dynamic collaborative scheduling method for steel plate cutting, characterized in that: include: Establish a cutting process chain for steel plate cutting, collect historical data for each process node in the cutting process chain, perform post-process node impact analysis on the fluctuation of key parameters based on the historical data collection results, and establish an instability transfer model; Setting a monitoring sensor at each process node in the cutting process chain, using the monitoring sensor to monitor the process node, and establishing node monitoring data; Uploading the node monitoring data to the instability transfer model to establish the instability transfer result of the cutting process chain; The objective function of the configuration control optimization is the objective function of the yield rate, the total process time and the dynamic cost balance optimization, and the objective function is used to perform compensation optimization based on the instability transfer result to establish the compensation optimization result; The compensation optimization result is used to perform dynamic collaborative scheduling management of the cutting process chain.

2. The multi-process dynamic collaborative scheduling method for steel plate cutting according to claim 1 is characterized in that: The post-process node impact analysis of the fluctuation of key parameters based on the historical data collection results and the establishment of an instability transfer model include: Based on the cutting process chain, analyze the upstream and downstream relationships between processes and establish upstream and downstream constraints; Creating a dependency graph based on the upstream and downstream constraints; Calculating the instability correlation between processes in the dependency graph using the historical data collection results, and normalizing the instability correlation into a process weight matrix; The process weight matrix is ​​used to perform cumulative instability transfer analysis and construct an instability transfer model.

3. The multi-process dynamic collaborative scheduling method for steel plate cutting according to claim 2 is characterized in that: The instability transmission model is as follows: Among them, U k Characterize the instability of the kth process node, W ik Characterizes the influence weight of the previous process node i on the current kth process node, P i Characterizes the actual parameter fluctuation value of process node i, f(P i ) is a nonlinear mapping function used to transform the actual parameter fluctuation value P i Transformed into the impact on instability, ∈ k Characterizes the inherent instability disturbance term of the kth process node, U total Characterizes the overall instability, n is the total number of process nodes in the cutting process chain, W k,total Characterizes the instability weight of the kth process node on the final product quality.

4. The multi-process dynamic collaborative scheduling method for steel plate cutting according to claim 1 is characterized in that: The objective function of the configuration control optimization includes: Conduct production monitoring of steel plate cutting and establish production monitoring results; Calculating the yield rate deviation using the production monitoring result to establish a first state variable; Use the order deadline to conduct time margin analysis and establish the second state variable; Establish a third state variable based on the cost deviation, and calculate the yield rate weight, the total process time weight, and the dynamic cost weight according to the first state variable, the second state variable, and the third state variable; The yield rate weight, the total process time weight, and the dynamic cost weight are weight normalized to establish an objective function.

5. The multi-process dynamic collaborative scheduling method for steel plate cutting according to claim 1, characterized in that: Also includes: Obtain equipment response parameters of process nodes; Perform anomaly segmentation according to the equipment response parameters and the monitoring data set to establish equipment anomalies and steel plate anomalies; Performing self-optimization of equipment control at a corresponding process node according to the equipment abnormality, and establishing a first optimization feedback; Establishing a second optimization feedback according to the control feedback of the process node after the abnormality of the steel plate; Dynamic collaborative scheduling management of the cutting process chain is performed according to the first optimization feedback and the second optimization feedback.

6. The multi-process dynamic collaborative scheduling method for steel plate cutting according to claim 1, characterized in that: Also includes: Using the compensation optimization result to predict the compensation effect, and establish the compensation effect prediction result; If the compensation effect prediction result cannot meet the preset effect threshold, an elimination command is generated, and the corresponding steel plate is scrapped according to the elimination command.

7. The multi-process dynamic collaborative scheduling method for steel plate cutting according to claim 1, characterized in that: Also includes: Establishing a self-learning iteration channel, through which dynamic collaborative scheduling management data is continuously recorded; Instability propagation model update using continuously recorded results.

8. A multi-process dynamic collaborative scheduling system for steel plate cutting, characterized by: The steps for implementing the multi-process dynamic collaborative scheduling method for steel plate cutting according to any one of claims 1 to 7 include: An impact analysis module, which is used to establish a cutting process chain for steel plate cutting, collect historical data for each process node in the cutting process chain, perform post-process node impact analysis on fluctuations of key parameters based on the historical data collection results, and establish an instability transfer model; A node monitoring module, wherein the node monitoring module is used to set a monitoring sensor at each process node in the cutting process chain, use the monitoring sensor to monitor the process node, and establish node monitoring data; A data uploading module, the data uploading module is used to upload the node monitoring data to the instability transfer model to establish the instability transfer result of the cutting process chain; A compensation optimization module, wherein the compensation optimization module is used to configure an objective function of control optimization, wherein the objective function is an objective function of balancing optimization of yield rate, total process time and dynamic cost, and the objective function is used to perform compensation optimization based on the instability transfer result to establish a compensation optimization result; A collaborative scheduling module is used to utilize the compensation optimization result to perform dynamic collaborative scheduling management of the cutting process chain.

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