Production control method and equipment for reinforcing ring of automobile commutator

Abstract

The invention is suitable for the technical field of automobile commutator reinforcing rings, and particularly relates to an automobile commutator reinforcing ring production control method and equipment, and the method comprises the steps: obtaining all to-be-processed order information in a reinforcing ring order management system in real time; order analysis is carried out on the to-be-processed order information to obtain priority processing information; performing pre-production analysis based on the priority processing information to obtain production line model information of the reinforcing ring in the production workshop; performing performance simulation analysis according to the production line model information to obtain performance result information; performing control parameter optimization on the performance result information to obtain production control information; wherein the production control information is used for controlling the commutator reinforcing ring production equipment to produce the reinforcing ring. According to the automobile commutator reinforcing ring production control method provided by the invention, the performance of the automobile commutator reinforcing ring can be predicted, so that the utilization rate and the production speed of automobile commutator reinforcing ring production equipment are increased, and the structural stability and durability are improved.

Description

Technical Field

[0001] The present application belongs to the technical field of automobile commutator reinforcement rings, and in particular, relates to a production control method and equipment for automobile commutator reinforcement rings. Background Art

[0002] The automotive commutator reinforcement ring is a reinforcement component used for the motor commutator (commutator). The automotive commutator reinforcement ring can enhance the structural stability and durability of the commutator.

[0003] When producing automobile commutator reinforcement rings, the automobile commutator reinforcement ring production equipment in the related technology produces automobile commutator reinforcement rings one by one, so the utilization rate of the automobile commutator reinforcement ring production equipment is low, the production speed is slow, and the performance of the automobile commutator reinforcement rings is unpredictable, resulting in poor structural stability and durability. Summary of the invention

[0004] The embodiments of the present application provide a method and device for controlling the production of automobile commutator reinforcement rings, which can solve the problems of slow production speed and unpredictable performance of production equipment caused by producing reinforcement rings one by one.

[0005] In a first aspect, an embodiment of the present application provides a method for controlling the production of a reinforcement ring of an automobile commutator, comprising: Acquire all pending order information in the reinforcement ring order management system in real time; wherein the pending order information includes the time production information and product production information of the automobile commutator reinforcement ring, and the time production information is used to indicate the order formation time and the target production completion time of the reinforcement ring; Performing order analysis on the pending order information to obtain priority processing information; wherein the priority processing information is any one of the pending order information among all the pending order information; Performing pre-production analysis based on the priority processing information to obtain production line model information of the reinforcement ring in the production workshop; wherein the production line model information is used to indicate a plurality of virtual production equipment that can be used when producing the reinforcement ring; Perform performance simulation analysis according to the production line model information to obtain performance result information; wherein the performance result information is used to indicate the performance value of the virtual production equipment obtained after simulating and analyzing the reinforcement ring simulated by the virtual production equipment; The control parameters of the performance result information are optimized to obtain production control information; wherein the production control information is used to control the commutator reinforcement ring production equipment to produce the reinforcement ring.

[0006] The automobile commutator reinforcement ring production control method provided by the present application obtains all pending order information in the reinforcement ring order management system in real time, improves the real-time and accuracy of the order management system, grasps the order status in real time, and provides a basis for subsequent production scheduling and resource allocation; performs order analysis on the pending order information to obtain priority processing information, and can determine the processing order of the orders through analysis, reasonably arrange the production plan, and improve production efficiency; performs pre-production analysis based on the priority processing information to obtain the production line model information of the reinforcement ring in the production workshop, and by simulating the operation of the production line, potential problems can be discovered in advance, the performance of the automobile commutator reinforcement ring can be predicted, the production process can be optimized, and the risks and waste in actual production can be reduced; then, a performance simulation analysis is performed based on the production line model information to obtain performance result information, and the performance of the virtual production equipment is evaluated through simulation analysis to improve the stability of the production process and product quality; finally, the control parameters of the performance result information are optimized to obtain production control information, and the precise control of the production process is achieved by optimizing the control parameters, the production efficiency and product quality are improved, the utilization rate and production speed of the automobile commutator reinforcement ring production equipment are enhanced, and the stability and durability of the automobile commutator reinforcement ring structure are improved.

[0007] In a second aspect, an embodiment of the present application provides a production control system for a reinforcement ring of an automobile commutator, comprising: An acquisition unit is used to acquire all pending order information in the reinforcement ring order management system in real time; wherein the pending order information includes time production information and product production information of the automobile commutator reinforcement ring, and the time production information is used to indicate the order formation time and target production completion time of the reinforcement ring; The first analysis unit is used to perform order analysis on the order information to be processed to obtain priority processing information; wherein the priority processing information is any one of the order information to be processed among all the order information to be processed; A second analysis unit is used to perform pre-production analysis based on the priority processing information to obtain production line model information of the reinforcement ring in the production workshop; wherein the production line model information is used to indicate a plurality of virtual production equipment that can be used when producing the reinforcement ring; A third analysis unit is used to perform a performance simulation analysis based on the production line model information to obtain performance result information; wherein the performance result information is used to indicate the performance value of the virtual production equipment obtained after the simulation analysis of the reinforcement ring simulated by the virtual production equipment; The control unit is used to optimize the control parameters of the performance result information to obtain production control information; wherein the production control information is used to control the commutator reinforcement ring production equipment to produce the reinforcement ring.

[0008] In a third aspect, an embodiment of the present application provides an automobile commutator reinforcement ring production device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements a method as described in any one of the first aspects above.

[0009] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on automobile commutator reinforcement ring production equipment, the automobile commutator reinforcement ring production equipment is enabled to execute the automobile commutator reinforcement ring production control method described in any one of the above-mentioned first aspects.

[0010] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0012] Figure 1 It is a flow chart of a method for controlling the production of a reinforcement ring of an automobile commutator provided in one embodiment of the present application; Figure 2 It is a schematic diagram of the implementation process of step S200 in the automobile commutator reinforcement ring production control method provided in one embodiment of the present application; Figure 3 This is another implementation flow diagram of step S200 in the automobile commutator reinforcement ring production control method provided in one embodiment of the present application; Figure 4 It is a schematic diagram of the implementation process of step S300 in the automobile commutator reinforcement ring production control method provided in one embodiment of the present application; Figure 5 It is a schematic diagram of the implementation process of step S400 in the automobile commutator reinforcement ring production control method provided in one embodiment of the present application; Figure 6 It is a schematic diagram of the implementation process of step S420 in the automobile commutator reinforcement ring production control method provided in one embodiment of the present application; Figure 7 It is a schematic diagram of the implementation process of step S430 in the automobile commutator reinforcement ring production control method provided in one embodiment of the present application; Figure 8This is another implementation flow diagram of step S400 in the automobile commutator reinforcement ring production control method provided in one embodiment of the present application; Fig. 9 It is a schematic diagram of the implementation process after step S432 in the automobile commutator reinforcement ring production control method provided in one embodiment of the present application; Fig.10 It is a structural schematic diagram of a production control system for a vehicle commutator reinforcement ring provided in an embodiment of the present application; Fig.11 It is a structural schematic diagram of a control device for automobile commutator reinforcement ring production equipment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0015] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0016] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0017] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0019] The automotive commutator reinforcement ring is a reinforcement component used for the motor commutator (commutator). The automotive commutator reinforcement ring can enhance the structural stability and durability of the commutator.

[0020] When producing automobile commutator reinforcement rings, the automobile commutator reinforcement ring production equipment in the related technology produces automobile commutator reinforcement rings one by one, so the utilization rate of the automobile commutator reinforcement ring production equipment is low, the production speed is slow, and the performance of the automobile commutator reinforcement rings is unpredictable, resulting in poor structural stability and durability.

[0021] In order to solve the above problems, the embodiments of the present application provide a method and device for controlling the production of a reinforcement ring of an automobile commutator. In the method, all pending order information in the reinforcing ring order management system is obtained in real time, the real-time and accuracy of the order management system is improved, the order status is mastered in real time, and a basis is provided for subsequent production scheduling and resource allocation; order analysis is performed on the pending order information to obtain priority processing information, and the processing sequence of the orders can be determined by analysis, the production plan can be reasonably arranged, and the production efficiency can be improved; pre-production analysis is performed based on the priority processing information to obtain the production line model information of the reinforcing ring in the production workshop, and by simulating the operation of the production line, potential problems can be discovered in advance, the performance of the automobile commutator reinforcing ring can be predicted, the production process can be optimized, and the risks and waste in actual production can be reduced; then, performance simulation analysis is performed based on the production line model information to obtain performance result information, and the performance of the virtual production equipment is evaluated through simulation analysis to improve the stability of the production process and product quality; finally, the control parameter optimization of the performance result information is performed to obtain production control information, and by optimizing the control parameters, accurate control of the production process is achieved, the production efficiency and product quality are improved, the utilization rate and production speed of the automobile commutator reinforcing ring production equipment are enhanced, and the stability and durability of the automobile commutator reinforcing ring structure are improved.

[0022] The automobile commutator reinforcement ring production control method provided in the embodiment of the present application can be applied to the automobile commutator reinforcement ring production equipment. At this time, the automobile commutator reinforcement ring production equipment is the executor of the automobile commutator reinforcement ring production control method provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the automobile commutator reinforcement ring production equipment.

[0023] For example, the automobile commutator reinforcement ring production equipment may include a production device and a control device, and the production device and the control device are communicatively connected; the production device is any production device that can produce automobile commutator reinforcement rings, for example, the production device may be an automated production line, including but not limited to stamping machines, welding machines, cutting machines, heat treatment furnaces, etc., and these devices can work together through instructions issued by the control device; the control device may be a control panel in the production device, may be a control platform or controller in the control room, or may be a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a smart large screen, a computer, a laptop computer, etc.

[0024] In order to better understand the automobile commutator reinforcement ring production control method provided in the embodiment of the present application, the specific implementation process of the automobile commutator reinforcement ring production control method provided in the embodiment of the present application is exemplarily introduced below.

[0025] Figure 1 A schematic flow chart of a method for controlling the production of a reinforcing ring for a commutator of an automobile provided in an embodiment of the present application is shown. The method for controlling the production of a reinforcing ring for a commutator of an automobile comprises: S100, obtaining all pending order information in the reinforcement ring order management system in real time; wherein the pending order information includes the time production information and product production information of the automobile commutator reinforcement ring, and the time production information is used to indicate the order formation time and target production completion time of the reinforcement ring.

[0026] For example, real-time acquisition of all pending order information can be achieved through application program interface integration technology, which connects the order management system with the production control system. The program interface integration technology allows the production control system to directly access the database of the order management system and capture the information of pending orders in real time; in detail, a scheduled task (for example, executed every minute or every few seconds) can be set, and the task pulls the latest pending order data from the database of the order management system by calling the API interface. Message queues (such as RabbitMQ, Kafka, etc.) can also be used as middleware to achieve real-time data exchange. The order management system encapsulates the pending order information into messages and pushes them to the message queue. The production control system subscribes to the queue and processes new order information messages immediately when they arrive. In some special cases, such as when both the order management system and the production control system support the Webhooks mechanism, this feature can be used to achieve real-time synchronization of orders. Webhooks allow users to customize HTTP callbacks. When the order status in the order management system changes (for example, a new order is generated), a POST request will be automatically sent to the specified URL with the latest order information. The production control system can obtain the data of pending orders in real time by listening to these requests.

[0027] S200, performing order analysis on the order information to be processed to obtain priority processing information; wherein the priority processing information is the order information to be processed that is prioritized for production among all the order information to be processed.

[0028] It can be understood that order analysis of pending order information refers to priority query of pending order information or comparison of urgency of pending order information according to order requirements; wherein, the urgency can be determined by the urgency of delivery time or the difficulty of production and processing.

[0029] Exemplarily, an order scoring system can be established, and the order scoring system can score each pending order according to preset rules, and the rules may include factors such as the urgency of the order, customer level, order quantity, delivery deadline, etc., which are not described in detail in this embodiment. For example, urgent orders can be given higher scores, and large orders or orders from important customers can also get higher scoring weights. Secondly, historical order data is analyzed using data mining technology to find out the key factors affecting order priority; through machine learning algorithms, such as decision trees, random forests or neural networks, it is possible to automatically identify which factors have a high impact on the priority of order processing, and adjust the weights in the scoring system accordingly, and score all pending orders according to the order scoring system, and sort them according to the scores. The sorting results will directly determine the processing order of the orders, and the orders with the highest scores will be given priority.

[0030] In one possible implementation, see Figure 2 , S200, performing order analysis on the order information to be processed, and obtaining priority processing information, including: S210A, performing a first acquisition operation on the enhanced ring order management system, if the pending order information obtained from the enhanced ring order management system is one, extracting the time production information and product production information in the pending order information.

[0031] For example, you can send a request to obtain pending order information by strengthening the API interface of the ring order management system. The interface can return detailed order data, including order number, customer information, order status, production requirements, etc. After obtaining the data, you can parse and verify the data to improve the completeness and accuracy of the information, and filter and classify the obtained order information. Time production information usually includes key time points such as the scheduled production time and delivery deadline of the order, while product production information includes required materials, specification parameters, quality requirements, etc. Orders can be classified for subsequent processing.

[0032] S220A, performing time calculation on the order formation time and the target production completion time indicated by the time production information to obtain time remaining information; wherein the time remaining information is used to indicate the remaining time of the production reinforcement loop.

[0033] Exemplarily, the time when the order is formed, that is, the time when the customer places the order, can be extracted from the order information, and the target production completion time, that is, the delivery deadline required by the customer, can be obtained. Through these two time points, the remaining time from the current time to the target production completion time can be calculated. In order to ensure the accuracy of time calculation, various possible influencing factors in the production process can be considered. For example, it can be the current load situation of the production line, the maintenance plan of the equipment, the work schedule of the employees, etc., which are incorporated into the time calculation model to improve the accuracy and practicality of the remaining time information, and the time series analysis method is used to predict the remaining time information; according to the analysis of historical order data, the periodic fluctuations and trends in the production process can be identified, so as to make a more accurate prediction of future production time. For example, the efficiency changes of the production line in a specific time period can be identified, and the predicted value of the remaining time information can be adjusted; the automobile commutator reinforcement ring production control system provided in this embodiment can accurately calculate the remaining time of the production reinforcement ring, and carry out effective production scheduling and production planning according to the remaining time information.

[0034] S230A, use the reinforcement ring prediction model to process the product production information to obtain production result test information; wherein the production result test information is used to indicate the production speed and equipment matching degree of the reinforcement ring, and the reinforcement ring prediction model is a machine learning model pre-trained using the product production information of the sample reinforcement ring and the corresponding production result test information.

[0035] Exemplarily, the product production information of the sample reinforcement ring can be collected and sorted, and the product production information can include key data such as material type, specification parameters, production difficulty, etc., but not limited to this. At the same time, the corresponding production result test information will be recorded, such as actual production speed, equipment operation status, failure rate, etc., but not limited to this. A large amount of product production information and production result test information will be used as training data sets to build a reinforcement ring prediction model, and then a machine learning algorithm will be used to analyze and train the training data set. The algorithm can be one of linear regression, support vector machine, and random forest. Through the algorithm, the relationship between product production information and production result test information can be identified, and a model that can predict production results can be constructed, and then the model is evaluated and optimized using a cross-validation method. By dividing the training data set into multiple subsets and training and verifying on different subsets, the performance of the model on unknown data can be evaluated, so as to select the best model parameters and structure, and the reinforcement ring prediction model can be updated and maintained in real time. With the continuous accumulation of new data in the production process, the model is retrained regularly to enable it to adapt to changes in the production environment. In addition, an anomaly detection mechanism can be introduced to identify and process abnormal data to prevent it from having a negative impact on model performance. Finally, the product production information of new orders will be processed using the reinforcement ring prediction model to obtain production result test information.

[0036] S240A analyzes the remaining time information and the production result test information to obtain priority processing information.

[0037] For example, the remaining time information and the production result test information can be integrated to form a comprehensive data set, which includes key indicators such as the remaining time, production speed, and equipment matching degree of each order, and the comprehensive data set is analyzed using a multi-factor analysis method. By considering multiple factors in the remaining time information and the production result test information, it is possible to identify which orders need to be processed first. For example, the urgency (remaining time), production efficiency (production speed), and equipment utilization (equipment matching degree) of the order are evaluated, and the priority is determined based on the comprehensive score of these factors.

[0038] With such a configuration, the automobile commutator reinforcement ring production control system provided in this embodiment can accurately analyze the remaining time information and production result test information, generate priority processing information, thereby optimizing production scheduling and production arrangements, enabling timely delivery of orders, and helping to improve production efficiency.

[0039] In one possible implementation, see Figure 3 , S200, performing order analysis on the order information to be processed, and obtaining priority processing information, including: S210B, performing a first acquisition operation on the enhanced ring order management system, if there are multiple pending order information obtained from the enhanced ring order management system, extracting the time production information and product production information in each pending order information.

[0040] Exemplarily, a request is sent to the enhanced ring order management system through the system's built-in API interface or data query module to obtain pending order information. After the system responds to the request, the pending order information is returned in the form of a data packet. At this time, the returned data packet is parsed to extract the time production information and product production information of each order; the time production information may include key time points such as the order time, estimated production time, actual production start time, and production completion time. The product production information may include detailed information such as product name, specification model, production quantity, and production batch. The extracted time production information and product production information are sorted and classified for subsequent analysis and processing.

[0041] S220B, calculating and processing the order formation time and the target production completion time indicated by each time production information to obtain a plurality of time margin information; wherein the time margin information is used to reflect the remaining time of the production reinforcement loop.

[0042] Exemplarily, the formation time and target production completion time are extracted from each pending order information. The formation time usually refers to the time point when the order starts production, while the target production completion time is the time point when the production is expected to be completed. The difference between the two time points is the preliminary time margin. In order to calculate the time margin information more accurately, the delay factors that may occur in the actual production process can be used. For example, historical production data can be introduced to analyze the difference between the actual time and the expected time of previous orders in the production process, so as to adjust the preliminary time margin. Further, the time margin can be dynamically adjusted in combination with the real-time status of the current production line, such as equipment operation, personnel configuration, material supply, etc. If the production line is currently in a high-load state, the time margin may need to be reduced accordingly, otherwise it can be appropriately relaxed, and then the time margin can be optimized using analytical methods. For example, linear programming, genetic algorithm and other optimization algorithms can be used to optimize the time margin so that the resources in the production process are most reasonably allocated and multiple time margin information is obtained.

[0043] S230B, performing time comparison and analysis on multiple time margin information to obtain time priority information; wherein the time priority information is the time margin information with the shortest remaining production time of the reinforcement ring.

[0044] Exemplarily, the time margin information of all pending orders can be summarized into a list, which will contain the formation time and target production completion time of each order, as well as the time margin adjusted according to the actual production situation, for time comparison analysis. By comparing the time margin of each order in the list, the order with the shortest remaining production time can be found. This order is the time priority information, which proves that production resources are prioritized and completed within the specified time. In order to ensure the accuracy of time comparison analysis, a variety of analysis methods can be used. For example, a sorting algorithm can be used to sort the time margin from short to long, so as to quickly identify the time priority information; data visualization tools, such as Gantt charts or timelines, can also be used to intuitively display the time margin of each order to help production managers make decisions.

[0045] S240B, use the reinforced ring test model to analyze the product production information respectively to obtain priority production information; wherein the priority production information is the product production information that is processed first, and the reinforced ring test model is a machine learning model pre-trained using the sample reinforced ring product production information and the corresponding priority production information.

[0046] Exemplarily, the product production information and corresponding priority production information of the sample reinforcement ring can be collected and sorted. The data will be used as a training data set to train the reinforcement ring test model, so that the data set contains various production situations so that the model can learn the priority judgment in different scenarios, and select a suitable machine learning algorithm to build the reinforcement ring test model. In this embodiment, decision trees, random forests, support vector machines, and neural networks can be selected; according to the characteristics of production information and the needs of priority judgment, a decision tree algorithm is selected for model training, and the training data set is input into the selected machine learning algorithm for model training; wherein, during the training process, the decision tree algorithm automatically adjusts the model parameters to minimize the prediction error, and then the generalization ability of the model is evaluated through the cross-validation method to improve its performance on unknown data. After the training is completed, the reinforcement ring prediction model is tested, and the accuracy and reliability of the model can be evaluated using an independent test data set. According to the test results, the model is fine-tuned to further improve its prediction performance. The trained reinforcement ring prediction model is deployed to the automotive commutator reinforcement ring production control system. When new product production information is input into the system, the model will automatically analyze and output the priority production information. In order to continuously optimize the reinforcement ring test model, new production information and priority data are collected regularly. Use this data to retrain the model to adapt to changes in the production environment and new production requirements. Through continuous iteration, the accuracy and effectiveness of the model can be maintained, and more accurate priority production information can be obtained.

[0047] S250B, weight matching is performed on the time priority information and the priority production information to obtain priority processing information.

[0048] It can be understood that weight matching of the time priority information and the priority production information means selecting the time priority information or the priority production information that needs to be produced first according to the weight calculation of the time priority information and the priority production information; Exemplarily, the weight values ​​of various factors can be determined, and the weight values ​​can be set based on historical data or actual production needs. For example, the weight of the time margin can be set to 0.6, and the weight of the priority production information can be set to 0.4. These weight values ​​can be adjusted according to actual conditions to make the final priority processing information more in line with actual needs, and the time priority information and the priority production information are standardized. Since these two information may have different dimensions and numerical ranges, they need to be converted into a unified dimension and numerical range for weighted calculation. Common standardization methods include minimum-maximum standardization, Z-score standardization, etc. Weighted calculation is performed to obtain priority processing information.

[0049] The specific calculation formula is as follows: priority processing information = time priority information × time priority information weight + priority production information × priority production information weight; among them, time priority information and priority production information are respectively standardized and then multiplied by the corresponding weight value, and finally the two are added together to obtain the final priority processing information.

[0050] S300, performing pre-production analysis based on the priority processing information to obtain production line model information of the reinforcement ring in the production workshop; wherein the production line model information is used to indicate a plurality of virtual production equipment that can be used when producing the reinforcement ring.

[0051] Exemplarily, pre-production analysis is performed based on priority information, and various data related to production are collected and sorted, including but not limited to equipment status, material supply, etc. The data will provide necessary input information for the construction of the production line model, and the collected data are processed and analyzed using data mining and statistical analysis methods. By identifying patterns and trends in the data, problems and bottlenecks that may occur in the production process can be predicted. For example, cluster analysis methods can be used to classify equipment status data and find equipment with similar performance so that they can be reasonably allocated during production scheduling. When constructing production line model information, simulation technology can be used to simulate the production process, and then by establishing a virtual production equipment model, the operation of the production line can be simulated to evaluate the effects of different production scheduling schemes. The simulation model can include key parameters such as equipment failure rate, production efficiency, and material flow to improve the accuracy and practicality of the model, and then the priority information is combined with the simulation model to optimize production scheduling. By adjusting the operating parameters of the virtual production equipment and the allocation order of production tasks, the optimal production scheduling scheme can be obtained. Heuristic algorithms such as genetic algorithms and particle swarm optimization can be used to optimize the production scheduling scheme to shorten the production cycle and improve resource utilization. The optimized production scheduling scheme can generate production line model information.

[0052] In one possible implementation, see Figure 4 , S300, performs pre-production analysis based on the priority processing information to obtain the production line model information of the reinforcement ring in the production workshop, including: S310, performing analysis based on the priority processing information to obtain multiple pieces of production equipment information; wherein the production equipment information is used to reflect the production equipment model and technical specifications of the reinforcement ring corresponding to the production priority processing information.

[0053] Exemplarily, the priority information is used as input, combined with the equipment database stored in the production management system for matching and screening. By analyzing the key parameters involved in the priority information, such as time margin, production priority, and resource requirements, the equipment model and technical specifications that best suit the current production needs can be identified. Using data mining technology, historical production data is analyzed to find production scenarios similar to the current priority information. By comparing the performance of different devices in similar scenarios, the equipment selection can be further optimized, and the equipment status can be monitored in real time, including the equipment's operating efficiency, failure rate, and maintenance records. Combined with these real-time data, the equipment information can be dynamically adjusted, and potential problems can be discovered and resolved in a timely manner during the production process to avoid production interruptions. Ultimately, multiple production equipment information will be generated.

[0054] S320, obtaining first modeling information according to the equipment model of each piece of production equipment information; wherein the first modeling information is used to indicate a size value and a weight value corresponding to the production equipment information.

[0055] Exemplarily, the equipment model in the production equipment information can be used as a keyword to query and extract in the equipment database (the equipment database can contain detailed specification information of various equipment models, including size and weight, etc. After obtaining the production equipment information, the corresponding size value and weight value can be obtained by querying this database to improve the accuracy of the query result. Fuzzy matching technology can be used to deal with slight errors that may occur when the model is input. After retrieving the matching equipment model from the database, the size value and weight value corresponding to the model are extracted and stored in the form of structured data to facilitate automatic reading and processing by the program) to verify the extracted size value and weight value. It can be compared with the measurement data of the actual equipment, or cross-validated using historical data. The verified size values ​​and weight values ​​are sorted according to a predetermined format to form the first modeling information. During the production process, the specification information of the equipment may change. Therefore, the equipment database can be updated regularly to ensure the timeliness and accuracy of the first modeling information.

[0056] S330, obtaining second modeling information according to the technical specifications of each piece of production equipment information; wherein the second modeling information is used to indicate technical parameters and process parameters corresponding to the production equipment information.

[0057] For example, the technical specifications contained in the production equipment information are analyzed. The technical specifications may include key parameters such as the maximum processing capacity, precision requirements, and energy consumption level of the equipment. Through the key parameters, the second modeling information can be constructed to guide the specific operations and process flows in the production process. The key parameters in the technical specifications can be matched with the process database, which stores the technical parameters and process parameters corresponding to various equipment. By querying the database, detailed process guidance information matching the production equipment information can be obtained. For example, for a specific model of reinforcing ring production equipment, its applicable processing technology, coolant usage and other parameters can be queried.

[0058] S340: Perform modeling according to the first modeling information and the second modeling information to obtain production line model information.

[0059] Exemplarily, the specific contents of the first modeling information and the second modeling information are clarified. If the first modeling information includes the equipment layout, equipment parameters and connection relationship between the equipment of the production line, and the second modeling information covers the process flow, material flow and production rhythm in the production process, after acquisition, the first modeling information and the second modeling information are integrated to keep all data consistent and complete, clean the data and convert the format so that the two types of information can be seamlessly connected; based on the integrated data, a three-dimensional model of the production line is constructed. Professional modeling software, such as AutoCAD, SolidWorks, etc., can be used to visualize the equipment layout and process flow. In the three-dimensional model, each device can be represented by a geometric body, and the connection relationship between the devices is reflected by elements such as pipelines and conveyor belts. According to the equipment parameters in the first modeling information, the corresponding attributes are set for the equipment in the three-dimensional model, such as processing speed, energy consumption, failure rate, etc. At the same time, according to the production rhythm and material flow in the second modeling information, the production parameters in the model, such as material processing time, waiting time, etc., are set, and the production line model information is finally obtained.

[0060] S400, performing performance simulation analysis according to the production line model information to obtain performance result information; wherein the performance result information is used to indicate the performance value of the virtual production equipment obtained after simulating and analyzing the reinforcement ring produced by the virtual production equipment.

[0061] Exemplarily, the production line model information is imported into the performance simulation analysis software. By using the simulation tool, a virtual production environment can be created so that each virtual production equipment and process step can work together under optimal conditions. According to the imported production line model information, the parameters and conditions of the simulation analysis are set, including determining key parameters such as production speed, equipment load, and material supply. By using data analysis technology, the parameter settings of the simulation analysis can be matched with the actual production environment, improving the credibility of the simulation results, and performing performance simulation analysis to evaluate the performance of the virtual production equipment under different production conditions, including production efficiency, equipment utilization, and smoothness of material flow. Through the simulation software, each link in the actual production process can be simulated, potential bottlenecks can be identified and eliminated, and the production line model can be optimized and adjusted according to the simulation analysis results. The optimization adjustment includes fine-tuning of equipment layout, process flow, and material flow, so that the production line model reaches the optimal state. Multiple iterations can be performed to achieve the best configuration of all relevant parameters. Based on the optimized and adjusted production line model information, performance result information is obtained.

[0062] In one possible implementation, see Figure 5 , S400, perform performance simulation analysis based on the production line model information to obtain performance result information, including: S410, obtaining a three-dimensional model product of the reinforcement ring; wherein the three-dimensional model product is modeled after a virtual production test based on production line model information.

[0063] For example, by defining the boundary conditions and loading conditions of the three-dimensional model product of the reinforcement ring, the boundary conditions refer to the constraints imposed on the model in the simulation analysis, such as fixed points, sliding supports, etc. The loading conditions describe the forces and moments that the model may bear in the actual working environment, such as pressure, tension, torque, etc. The conditions and loading conditions simulate the actual working state as much as possible to improve the reliability of the results; perform finite element analysis, and after defining the material properties, boundary conditions and loading conditions, use finite element analysis software to simulate the performance of the three-dimensional model product of the reinforcement ring; wherein, finite element analysis is a numerical calculation method that can analyze stress, strain, vibration, etc. of models with complex geometric shapes and material properties, obtain analysis simulation results and optimize; the performance result information obtained by finite element analysis will include key data such as stress distribution diagrams, deformation diagrams, and safety factors. According to the results, the performance of the reinforcement ring under the current design can be evaluated, and the three-dimensional model product can be imported into the production line model for overall performance simulation, so that the three-dimensional model product of the reinforcement ring has excellent performance in the production line model, providing a reliable design basis for actual production.

[0064] S420, performing life prediction analysis on the three-dimensional model product to obtain a first test result; wherein the first test result is used to indicate a fatigue life value of the three-dimensional model product.

[0065] For example, one or more fatigue life prediction models are selected according to the material type, use environment and working load of the reinforcement ring. The models may include stress-based SN curve, strain-based EN curve, Paris law, etc.; fatigue life calculation is performed using the fatigue life prediction model in combination with the geometric shape, material properties, boundary conditions and loading conditions of the three-dimensional model product. With the help of fatigue analysis software, the stress-strain response of the reinforcement ring under different load cycles can be calculated through numerical simulation to obtain the fatigue life value. According to the calculated fatigue life value, the reliability and durability of the reinforcement ring under the expected use conditions are evaluated; if the prediction results show that the fatigue life of the reinforcement ring does not meet the design requirements, it needs to be adjusted, such as changing the material, optimizing the geometry or adjusting the working load; finally, the first test result is obtained.

[0066] In one possible implementation, see Figure 6 , S420, performing life prediction analysis on the three-dimensional model product to obtain a first test result, including: S421, obtaining fatigue characteristic information of the three-dimensional model product; wherein the fatigue characteristic information is used to indicate a stress-life curve of the three-dimensional model product.

[0067] Exemplarily, the fatigue characteristic information of the three-dimensional model product can be obtained by conducting material fatigue tests, and fatigue tests can be conducted on the materials used for the reinforcement ring; wherein, fatigue tests generally include tensile and compression cycle tests, rotational bending tests, etc., to simulate the stress state in actual work, and the test results will provide fatigue life data of the material at different stress levels, and establish a stress life curve. According to the results of the material fatigue test, a stress life (SN) curve is drawn. Through the SN curve, the fatigue life of the material at different stress levels can be intuitively understood, thereby providing reliable data support for the design; fatigue analysis software such as ANSYS, ABAQUS, etc. is used to import the three-dimensional model product into the software. Combining the SN curve of the material and the load conditions in the actual working environment, a fatigue life analysis is performed, and the stress-strain state of the reinforcement ring in actual work is simulated to calculate the fatigue life.

[0068] S422, performing a life simulation test on the three-dimensional model product, and collecting the load borne by the three-dimensional model product during the simulation process, the load including amplitude, frequency and number of cycles.

[0069] For example, it is necessary to create an accurate three-dimensional model product model in computer-aided engineering (CAE) software, including defining material properties, geometric shapes and boundary conditions, setting simulation test parameters such as load amplitude, frequency and number of cycles according to the actual use environment and working load, and using fatigue analysis software for life simulation testing. One or more load conditions can be set in the software to simulate the load changes in the actual working environment; for example, a cyclic load with an amplitude of 1000 Newtons and a frequency of 1 Hz can be set for 1 million cycles to simulate the load conditions that the reinforcement ring may encounter in actual use. During the simulation process, the software records and analyzes the stress-strain response of the three-dimensional model product under different loads in real time. Through these data, the damage accumulation of the model under each load cycle can be calculated. Finally, the software outputs the life prediction results, including the fatigue life of the model and the possible initiation and extension of fatigue cracks.

[0070] S423, simulate the loads and boundary conditions under actual working conditions on the three-dimensional model product, and perform stress analysis to obtain a stress distribution diagram, identify the stress concentration area in the stress distribution diagram, calculate the stress concentration factor in the stress concentration area, use the fatigue analysis method to determine the corresponding fatigue life, and then use the cumulative damage calculation to determine the predicted fatigue life information.

[0071] For example, the optimized three-dimensional model product can be imported into the computer-aided engineering (CAE) software, and the corresponding boundary conditions and load conditions can be set according to the actual working environment and load conditions; for example, the performance of the reinforcing ring under complex conditions such as cyclic loads, temperature changes and vibrations can be simulated. After applying the loads and boundary conditions under the actual working conditions, the stress analysis is performed using the finite element analysis (FEA) technology to obtain the stress distribution diagram of the entire three-dimensional model product. After the stress concentration factor is calculated, the fatigue life corresponding to the stress concentration area can be determined by the fatigue analysis method in combination with the SN curve of the material. Finally, the cumulative damage theory, such as Miner's law, can be used to calculate the predicted fatigue life information of the entire three-dimensional model product. The cumulative damage theory assumes that the damage of the material under different load levels is cumulative. By calculating the damage ratio under different load cycles, the fatigue life of the model under the expected use conditions can be predicted, and the simulation results can be compared with the actual test data for verification. If it is found that there is a large difference between the simulation results and the actual test data, it is necessary to readjust the model parameters or improve the analysis method to improve the reliability of the prediction so that the three-dimensional model product meets the fatigue life requirements.

[0072] S424, obtaining a first test result according to the fatigue life information.

[0073] For example, the data previously obtained through simulation tests and stress analysis, including stress concentration area data, stress concentration coefficient and fatigue life prediction results extracted from CAE software, can be sorted and analyzed, and these data can be integrated into a comprehensive analysis report for further processing and interpretation. All data obtained from simulation tests and stress analysis are sorted, including load amplitude, frequency, number of cycles, stress value of stress concentration area and corresponding fatigue life prediction value, so that all data are accurate and consistent in format, which is convenient for subsequent analysis, and then the sorted data are analyzed using statistical analysis software or self-compiled programs; wherein, the purpose of the analysis is to identify the key factors affecting fatigue life and evaluate the fatigue life change trend under different load conditions. Regression analysis, variance analysis and other methods can be used to determine the degree of influence of each factor on fatigue life. According to the results of data analysis, identify which areas or load conditions have the shortest fatigue life, so as to determine the weak links of the product, and evaluate the overall reliability of the product according to the fatigue life prediction results, interpret the fatigue life information, and generate the first test result.

[0074] S430, based on the first test result, performing crack prediction analysis on the three-dimensional model product to obtain a second test result; wherein the second test result is used to reflect the location where initial cracks may appear in the three-dimensional model product and the location where cracks extend after appearing.

[0075] For example, key data can be extracted from the first test results, including parameters such as elastic modulus, yield strength, Poisson's ratio, etc. of the material. The parameters will be used as the basic input of finite element analysis. Finite element analysis software, such as ANSYS or ABAQUS, is used to build a geometric model of a three-dimensional model product. Boundary conditions and loading conditions are defined in the model. The conditions simulate the loads and constraints that the product may encounter in actual use. After the finite element model is established, meshing is performed; wherein, meshing is to divide the continuous geometric model into a finite number of small units for numerical calculation. The density of the mesh has an important influence on the accuracy of the analysis results. Therefore, the use of adaptive meshing technology can make the stress concentration area have a finer mesh, while the mesh in the area with small stress changes is relatively sparse, and then the stress and strain are calculated. By solving the control equation, the software can calculate the stress and strain distribution of the model under given load and constraint conditions. By focusing on those areas with higher stress values, because these areas are potential locations for crack initiation, in order to predict the crack propagation path, the extended finite element method (XFEM) in fracture mechanics is used. XFEM can simulate crack propagation without re-meshing. Through XFEM, the simulation path of crack propagation can be obtained, thereby predicting the crack propagation behavior in the product and obtaining the second test result.

[0076] In one possible implementation, see Figure 7 , S430, based on the first test result, performing crack prediction analysis on the three-dimensional model product to obtain a second test result, including: S431, determining the location where the initial crack of the three-dimensional model product appears based on the first test result, and then obtaining the initial crack information according to the stress concentration area; wherein the initial crack information is used to indicate the size and shape of the initial crack of the three-dimensional model product.

[0077] For example, the stress distribution data recorded in the first test result can be analyzed. By identifying stress concentration areas, potential crack initiation points can be determined. Stress concentration areas usually appear at geometric discontinuities, such as holes, notches, sharp corners, or places where material properties suddenly change. Numerical analysis methods, such as finite element analysis (FEA), are used to simulate the stress and strain distribution of the three-dimensional model under actual working conditions. The stress value of each unit in the model can be obtained through FEA, and the data can be visualized through post-processing software. In the stress distribution diagram, the areas with the highest stress values ​​can be identified, which are the potential initial crack locations. After obtaining the potential initial crack locations, the stress concentration level of the area is analyzed to determine the size and shape of the initial crack. The shape of the initial crack can be elliptical or semi-elliptical, and the size can be determined based on the stress concentration level and the fracture toughness of the material. In order to more accurately evaluate the size and shape of the initial crack, experimental data, such as crack growth rate and fatigue life test results, can be combined. After determining the initial crack information, it is input into the extended finite element method (XFEM) model to simulate the crack growth behavior. XFEM can simulate the crack growth path and the change of stress intensity factor at the crack tip without re-meshing to predict the impact of crack growth on the structural integrity of the product, that is, to obtain the initial crack information.

[0078] S432, using the initial crack information as the analysis condition, simulating the load under the actual working condition, calculating the stress intensity factor information of the three-dimensional model product, and calculating the crack growth rate information based on the crack growth law formula; wherein the stress intensity factor information is used to reflect the stress concentration degree near the crack tip of the three-dimensional model product, and the crack growth rate information is used to indicate the speed value at which the crack length increases with the increase in the number of load cycles under the action of the load.

[0079] Exemplarily, the initial crack information is input into the finite element model as a boundary condition to simulate the load under actual working conditions, including static load, cyclic load or impact load, etc. By applying the load, the stress and strain distribution of the model under different load conditions can be calculated; the stress intensity factor (K) is calculated by expressing the degree of stress concentration near the crack tip; the stress intensity factor is a parameter to measure the strength of the stress field at the crack tip, which is closely related to the crack growth rate. In order to calculate the stress intensity factor, numerical analysis methods such as finite element analysis (FEA) are used, and combined with the theoretical solution of the crack tip stress field, the stress intensity factor distribution at the crack tip can be obtained; the crack growth rate information can be calculated based on the crack growth law formula. The crack growth rate is related to the stress intensity factor range (ΔK) and follows the Paris formula (Paris formula: Da / Dn =C(ΔK) m, where a represents crack depth or width, N represents the number of stress cycles, C and m are material-related parameters, and ΔK is the range of variation of the stress intensity factor. The material constants c and m are constants in the second stage of crack growth, and can predict the growth rate of crack length under a given number of load cycles. In order to more accurately evaluate the crack growth rate, combined with experimental data, such as the fatigue crack growth curve of the material, the stress intensity factor information and crack growth rate information can be obtained.

[0080] In one possible implementation, see Fig. 9 , S432, using the initial crack information as an analysis condition, simulating the load under actual working conditions, calculating the stress intensity factor information of the three-dimensional model product, and calculating the crack growth rate information based on the crack growth law formula, the method includes: S43201, determine the crack type by performing non-destructive testing on the three-dimensional model product in the simulation, and after determining the crack type on the three-dimensional model product, determine the crack positions of the point cracks and linear cracks on the three-dimensional model product; wherein the crack type includes point cracks or linear cracks, and the crack positions include an inner ring and an outer ring, the inner ring is the side in contact with the commutator, and the outer ring is the side not in contact with the commutator.

[0081] For example, high-resolution 3D scanning technology can be used to perform a comprehensive scan of the surface and internal structure of the 3D model product. The scanned data will be imported into professional analysis software for subsequent crack identification and location work; advanced image processing algorithms are used in the software to analyze the scanned 3D model; abnormal areas on the surface and inside of the model can be identified through comparative analysis. Abnormal areas may appear as slight changes in color, texture or shape, which may indicate the presence of cracks. When potential crack areas are identified, the areas are analyzed. For point cracks, a high-power magnification function can be used to analyze each suspected point crack. By measuring the length, width and depth of the crack, its specific characteristics can be determined and compared with known point crack standards. For linear cracks, continuous scanning and tracking technology can be used to analyze along the direction of the crack. In this way, the starting point, direction, length and change of crack width can be determined. The analysis of linear cracks will pay special attention to its potential impact on the structural integrity of the product, because linear cracks may extend along stress concentration areas and pose a threat to product safety performance, in order to obtain the crack type and determine the crack location.

[0082] S43202, after the crack type and crack position are determined, the directional cracks of the point cracks or linear cracks in the crack type are determined; wherein the directional cracks include circumferential directional cracks and width directional cracks.

[0083] It can be understood that the directional crack is the next crack direction of the crack type on the reinforcement ring; to determine the directional crack of the point crack or linear crack in the crack type, the identified crack can be analyzed to determine its directional characteristics; for point cracks, directional analysis will identify the crack expansion trend, and by simulating the crack expansion path in different directions on the three-dimensional model, it is helpful to determine whether the crack has a circumferential direction or width direction expansion tendency (for example, if the point crack shows obvious expansion characteristics in the circumferential direction of the model, such as the crack edge presents a relatively smooth curve in the circumferential direction Line, but relatively rough in width direction, which indicates that the crack has a tendency to expand in the circumferential direction. On the contrary, if the crack shows a more obvious expansion feature in the width direction, it can be judged that it has a tendency to expand in the width direction). Directional analysis is used for linear cracks. Linear cracks may expand in multiple directions. By analyzing the direction of the crack and the stress distribution at the crack tip, the main direction of the linear crack can be obtained; for example, the crack expands along the circumferential direction of the model, and the stress concentration area is mainly distributed in the circumferential direction, then it can be judged that the linear crack has a tendency to expand in the circumferential direction. On the contrary, if the crack expands along the width direction, and the stress concentration area is mainly distributed in the width direction, then it can be judged that the linear crack has a tendency to expand in the width direction; after determining the directional characteristics of the crack, the model can also be used to simulate the crack expansion behavior. By introducing the crack directional parameters in the model, the crack expansion rate and path in different directions can be more accurately predicted, which is helpful to evaluate the impact of the crack on the structural integrity of the product and provide an important basis for formulating repair and maintenance strategies. Then it can be classified as a circumferential direction crack.

[0084] S43203: If it is determined that the crack direction of the point crack or the linear crack in the crack type is a circumferential crack, feedback information is generated; wherein the feedback information is used to reflect the situation of sending the crack direction to the operator.

[0085] For example, the identified circumferential cracks can be marked using specific functions in the analysis software. This will make the directional characteristics of the cracks clearly visible on the three-dimensional model and can be used for subsequent analysis and processing. Detailed information containing crack direction information will be generated, including a three-dimensional image of the crack, size parameters, location coordinates, and a detailed description of the crack direction; in addition, the report can provide repair or replacement suggestions for circumferential cracks to ensure safe operation of the product; after the report is generated, the feedback mechanism will be automatically triggered. The generated feedback information will be sent to the relevant operators through the internal communication system, which will enable the operators to receive detailed information about the crack direction in a timely manner, so as to take corresponding repair or replacement measures; the feedback information will be sent in a variety of forms, including emails, text messages, system notifications, etc., and the information includes a three-dimensional image of the crack, location coordinates, directional characteristics, and repair suggestions.

[0086] S43204: If the crack direction of the point crack or the linear crack in the crack type is determined to be a wide-band directional crack, a reminder message is generated; wherein the reminder message is used to remind the operator that the three-dimensional model product has a risk of fracture.

[0087] For example, the identified width cracks can be marked using specific functions in the analysis software, which will make the directional characteristics of the cracks clearly visible on the three-dimensional model and can be used for subsequent analysis and processing. A detailed report containing crack direction information will be generated. The report will include a three-dimensional image of the crack, size parameters, position coordinates, and a detailed description of the crack direction; after the report is generated, the reminder mechanism will be automatically triggered. The generated reminder information is sent to the relevant operators through the internal communication system. This will enable the operator to receive detailed information about the crack direction in a timely manner, so as to take corresponding maintenance or replacement measures. The reminder information can be sent by email, text message, system notification, etc. The information can have a three-dimensional image of the crack, position coordinates, directional characteristics, and maintenance suggestions, which can ensure the safe operation of the product, thereby improving the reliability and service life of the product.

[0088] In this embodiment, the difference between the feedback information of step S4323 and the reminder information of step S43204 is that the risk level of product fracture indicated in the reminder information of step S43204 is greater than the risk level of product fracture indicated in the feedback information of step S4323, so the feedback information is for the operator to take relevant measures according to the actual fracture level of the reinforcement ring, while the reminder information requires the operator to immediately check the fracture situation and take corresponding remedial measures; in this embodiment, the difference between circumferential cracks and width cracks is that when the crack direction is circumferential, the shortest distance required for the crack to crack is the circumferential distance of the reinforcement ring, and when the crack direction is to break in the width direction of the reinforcement ring, the breakable distance is less than the circumferential distance, the shortest crack distance of the width crack is the shortest straight line distance on the width, the shortest distance of the circumferential crack is the shortest circumference of all circumferences of the reinforcement ring, and the other crack distances of the width crack and the other crack distances and shortest distances of the circumferential crack are the same, and are not described in detail here.

[0089] S433, predicting the results according to the stress intensity factor and the crack growth rate to obtain a second test result.

[0090] For example, the calculated stress intensity factor (K) and crack growth rate information are integrated into a comprehensive analysis framework, and specialized fatigue analysis software or custom calculation programs can be used to accurately simulate the crack growth process. According to the stress intensity factor range (ΔK) and the crack growth rate formula, the crack growth under different load cycle numbers is predicted. This method can generate a crack growth prediction curve showing the change of crack length with the number of load cycles; these prediction data can be combined with the number of load cycles under actual working conditions to simulate the actual use of the product. In this way, the crack growth within a specific service life can be predicted, so as to evaluate the durability and reliability of the product. In order to improve the accuracy of the prediction, experimental data should be combined, such as fatigue crack growth curves and fracture toughness of materials; by combining experimental data with numerical analysis results, the prediction model can be adjusted and calibrated to reflect the actual crack growth behavior; after obtaining the crack growth prediction curve, it is also necessary to evaluate the impact of crack growth on the structural integrity of the product. This usually involves analyzing the stress and strain distribution of the model when the crack propagates to different stages to determine the specific impact of crack propagation on product performance, such as reduced load-bearing capacity, reduced stiffness, etc. Ultimately, the results are integrated to obtain a second test result.

[0091] S440: Summarize the first test result and the second test result to obtain performance result information.

[0092] For example, after the first test result and the second test result, the crack length, stress intensity factor, crack growth rate, number of load cycles, etc. may be included; the first test result and the second test result are compared and analyzed to identify the differences, similarities and factors that may affect the performance results. Through analysis, the impact of crack growth behavior on product performance can be understood, and a comprehensive evaluation is performed on the basis of the comparative analysis. The key data in the first test result and the second test result are integrated into a unified analysis framework. In this way, the overall performance of the product under different working conditions can be evaluated, and performance result information is generated based on the results of the comprehensive evaluation, which includes the specific impact of crack growth on performance indicators such as product load-bearing capacity, stiffness, and fatigue life; the performance result information will describe in detail the performance of the product under actual use conditions, and provide improvement and optimization suggestions. Finally, comprehensive performance result information is obtained based on the analysis results.

[0093] In one possible implementation, see Figure 8 , while performing performance simulation analysis based on the production line model information, the method also includes: S401, performing a second acquisition operation on the enhanced ring order management system, acquiring pending order information in the enhanced ring order management system, and extracting product production information from the pending order information.

[0094] For example, after executing the second acquisition operation, the acquisition of pending order information in the enhanced ring order management system can be to establish a database connection so that data can be extracted from the enhanced ring order management system in real time, and the system can access the database safely and efficiently by configuring the database driver and connection parameters. It can also be to develop a data extraction module, which is responsible for extracting pending order information from the enhanced ring order management system, and then use SQL query statements to filter out the required order data according to predefined rules; for example, the WHERE clause can be used to filter out orders within a specific time period, or to filter out pending orders according to order status; it can be to design a data parsing engine for parsing the order information extracted from the enhanced ring order management system, which will format and convert the extracted data so that it can be used by the subsequent performance simulation analysis module. During the parsing process, various data formats may need to be processed, such as CSV, JSON or XML; it can also be to develop a data mapping tool to map the parsed order information to the production line model information. This step involves defining data mapping rules so that product production information can accurately correspond to various parameters of the production line model.

[0095] S402, disassembling the product production information to obtain a plurality of independent production information; wherein the independent production information is used to reflect that the production of the reinforcement ring is divided into a plurality of separate production links, and each production link corresponds to a virtual production device.

[0096] For example, a virtual production equipment model corresponding to each production link can be constructed, but the performance parameters of each virtual equipment should be defined in detail, such as processing speed, failure rate, maintenance cycle, etc. The actual working conditions of each production link can be simulated, and virtual equipment performance simulation can be implemented. Using the virtual production equipment model, each production link can be independently simulated. Simulation software, such as Simul8 or AnyLogic, can be used to simulate the performance of each virtual equipment under different production conditions. Through simulation, the impact of potential bottleneck links and equipment failures on production efficiency can be discovered. All virtual production equipment models are integrated into a production line model for overall collaborative simulation. The collaborative working conditions of the production line in the actual production process can be evaluated, including material flow, equipment collaboration, production scheduling, etc. The overall simulation can discover the bottleneck links and optimization space of the entire production line. According to the results of the virtual equipment performance simulation and the overall production line collaborative simulation, the production disassembly strategy can be optimized, which may involve adjusting the division of production links, optimizing production processes, improving equipment configuration, etc., and then improving the overall performance and production efficiency of the production line based on the optimization.

[0097] S403, analyzing the usage status of the plurality of virtual production devices indicated by the production line model information to obtain at least one piece of idle device information; wherein the idle device information is used to reflect the virtual production device that is not working.

[0098] For example, a real-time monitoring system can be established, which can collect and process production line model information, and obtain the operating status of each virtual production equipment in real time through sensors and data acquisition equipment, including but not limited to the equipment's on / off status, production progress, fault alarm, etc., and transmit the collected data to the central processing unit, and analyze the information through data processing algorithms; a variety of methods can be used to identify idle equipment, for example, by setting equipment status thresholds, when a virtual production equipment is in a non-working state for a long time, it is judged as an idle equipment; it can be if a device has not produced any products or received any production tasks within a set time. The real-time monitoring system can mark a virtual production device as "idle" if it is idle. It can also analyze the task queue of each virtual production device. If the task queue of a device is empty or the number of tasks in the task queue has not changed for a long time, it means that the device currently has no tasks to execute, and it can be judged as an idle device. Use historical data and machine learning algorithms to predict the future use status of each virtual production device. It can also be through the prediction results that the real-time monitoring system can identify the equipment that will be idle in advance and provide its information to the production device. After obtaining the idle equipment information, this information can be passed to the production scheduling module. The scheduling module arranges production tasks reasonably according to production needs and equipment status so that the production line can run efficiently. For example, if a product requires multiple processes to complete, the scheduling module can give priority to assigning tasks to idle devices, reducing production cycles and improving equipment utilization. In addition, the real-time monitoring system can also perform preventive maintenance based on idle equipment information. When a device is idle for a long time, it can be automatically arranged for inspection and maintenance to avoid production interruptions caused by equipment failure.

[0099] S404, matching and determining multiple independent production information with at least one idle equipment information to obtain at least one separate matching information; wherein the separate matching information is used to indicate that the production matching value of at least one non-working virtual production equipment and one of the production links meets the preset production matching value.

[0100] Exemplarily, detailed information of all production links is collected, including performance parameters of required equipment, priority of production tasks, production time window, etc.; at the same time, information of idle equipment is sorted, including equipment type, performance parameters, current status, etc.; matching rules are established according to the requirements of production links and performance parameters of equipment, and the rules are used to evaluate whether the equipment meets the requirements of specific production links. For example, the rules may include the processing capacity, accuracy, speed, etc. of the equipment; a matching algorithm can be designed to traverse all idle equipment and compare them with the requirements of each production link, and calculate the matching value of each equipment and production link, which is based on the matching degree of equipment performance parameters and production requirements. After matching the values, the matching results are sorted according to the priority and time window of the production task, giving priority to equipment that can meet high-priority tasks and is available within the appropriate time window, and then the matching results are sorted into a report, which lists the matching situation of each production link and idle equipment, and the report includes information such as equipment number, production link number, matching value, and whether the preset production matching value is met, and finally one or more separate matching information is obtained; the description in this embodiment is one case, and the operation mode of multiple is the same.

[0101] S405, performing simulated production based on the individual matching information.

[0102] For example, a virtual production line environment can be created, which should contain detailed models of all production links and equipment, and individual matching information can be input into the simulation environment. This information includes the matching of each production link with idle equipment, equipment performance parameters and production task priority. Set simulation parameters according to actual production needs, such as the time window, production volume, quality requirements, etc. of the production task; start the simulated production process, and automatically simulate the production process according to the input matching information and the set simulation parameters. The simulation system will monitor the use of equipment and the progress of production tasks in real time; after the simulated production is completed, a production report is generated, including the completion status of each production link, the use efficiency of equipment, the completion time of production tasks, etc.; through analysis, the effectiveness of matching information and the operating efficiency of the production line can be evaluated, and the matching information can be optimized according to the simulation results; if some production links fail to complete the task within the expected time, or the equipment is inefficient, the matching information can be readjusted to improve productivity. In the simulated production process, if the matching information and scheduling strategy meet the expected requirements, this information and strategy can be applied to actual production.

[0103] Such settings can make the simulated production reflect the actual production situation and provide effective guidance for actual production. By optimizing matching information and scheduling strategies, production performance and efficiency can be improved to meet production needs.

[0104] S500, optimizing control parameters of the performance result information to obtain production control information; wherein the production control information is used to control the commutator reinforcement ring production equipment to produce the reinforcement ring.

[0105] Exemplarily, performance result information (including equipment operation data, product quality inspection data, production efficiency data, etc.) is used as the basis for optimizing control parameters; the performance result information is analyzed in detail to identify bottlenecks and problem points in the production process; for example, the equipment operation data is analyzed to determine whether there is unstable equipment performance or high failure rate, and the product quality inspection data is analyzed to determine whether there is a high rate of defective products. An optimization model is established based on the performance data analysis results, and various factors in the model production process, such as equipment parameters, production environment, raw material characteristics, etc., are used to determine the key control parameters that affect production efficiency and product quality, and the effect of parameter optimization is verified after optimizing the control parameters; the simulation process will use the optimized control parameters for production, record the production results, and evaluate the optimization effect.

[0106] Such a setting can realize the control of the commutator reinforcement ring production equipment, improve production efficiency and product quality, and meet market demand; by optimizing the control parameters, the performance and production efficiency of the production line can be improved, potential problems can be discovered in advance, the performance of the automobile commutator reinforcement ring can be predicted, the production efficiency and product quality can be improved, the utilization rate and production speed of the reinforcement ring production equipment can be improved, and the stability and durability of the reinforcement ring can be enhanced.

[0107] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0108] Corresponding to the automobile commutator reinforcement ring production control method described in the above embodiment, the embodiment of the present application also provides an automobile commutator reinforcement ring production control system, and each unit of the system can implement each step of the automobile commutator reinforcement ring production control method. Fig.10 A structural block diagram of an automobile commutator reinforcement ring production control system provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0109] Reference Fig.10 The automotive commutator reinforcing ring production control system includes: An acquisition unit is used to acquire all pending order information in the reinforcement ring order management system in real time; wherein the pending order information includes time production information and product production information of the automobile commutator reinforcement ring, and the time production information is used to indicate the order formation time and target production completion time of the reinforcement ring; The first analysis unit is used to perform order analysis on the order information to be processed to obtain priority processing information; wherein the priority processing information is any one of the order information to be processed among all the order information to be processed; A second analysis unit is used to perform pre-production analysis based on the priority processing information to obtain production line model information of the reinforcement ring in the production workshop; wherein the production line model information is used to indicate a plurality of virtual production equipment that can be used when producing the reinforcement ring; A third analysis unit is used to perform a performance simulation analysis based on the production line model information to obtain performance result information; wherein the performance result information is used to indicate the performance value of the virtual production equipment obtained after the simulation analysis of the reinforcement ring simulated by the virtual production equipment; The control unit is used to optimize the control parameters of the performance result information to obtain production control information; wherein the production control information is used to control the commutator reinforcement ring production equipment to produce the reinforcement ring.

[0110] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0111] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0112] The embodiment of the present application also provides a production device for a reinforcement ring of an automobile commutator, Fig.11 This is a schematic diagram of the structure of a control device for a vehicle commutator reinforcement ring production device provided in one embodiment of the present application. Fig.11 As shown, the control device 6 of this embodiment includes: at least one processor 60 ( Fig.11 Only one is shown), at least one memory 61 ( Fig.11Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the control device 6 implements the steps in any of the above-mentioned embodiments of the automobile commutator reinforcement ring production control method, or implements the functions of each module / unit in the above-mentioned system embodiments.

[0113] Exemplarily, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the control device 6.

[0114] The control device 6 may be a computing device such as a desktop computer, a notebook, or a PDA. The control device 6 of the automobile commutator reinforcement ring production equipment may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will appreciate that Fig.11 It is only an example of the control device 6 and does not constitute a limitation on the control device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0115] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0116] In some embodiments, the memory 61 may be an internal storage unit of the control device 6, such as a hard disk or memory of the control device 6. In other embodiments, the memory 61 may also be an external storage device of the control device 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the control device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the control device 6. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0117] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0118] An embodiment of the present application provides a computer program product. When the computer program product is run on an automobile commutator reinforcement ring production device, the automobile commutator reinforcement ring production device is enabled to implement the steps in any of the above-mentioned method embodiments.

[0119] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the automotive commutator reinforcement ring production equipment, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0120] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0121] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0122] In the embodiments provided in the present application, it should be understood that the disclosed automobile commutator reinforcement ring production control system, equipment and method can be implemented in other ways. For example, the automobile commutator reinforcement ring production control system and equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0123] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A production control method for a reinforcing ring of an automobile commutator, characterized in that: include: Acquire all pending order information in the reinforcement ring order management system in real time; wherein the pending order information includes the time production information and product production information of the automobile commutator reinforcement ring, and the time production information is used to indicate the order formation time and the target production completion time of the reinforcement ring; Performing order analysis on the pending order information to obtain priority processing information; wherein the priority processing information is the pending order information that is prioritized for production among all the pending order information; Performing pre-production analysis based on the priority processing information to obtain production line model information of the reinforcement ring in the production workshop; wherein the production line model information is used to indicate a plurality of virtual production equipment that can be used when producing the reinforcement ring; Perform performance simulation analysis according to the production line model information to obtain performance result information; wherein the performance result information is used to indicate the performance value of the virtual production equipment obtained after simulating and analyzing the reinforcement ring simulated by the virtual production equipment; The control parameters of the performance result information are optimized to obtain production control information; wherein the production control information is used to control the commutator reinforcement ring production equipment to produce the reinforcement ring.

2. The automobile commutator reinforcement ring production control method according to claim 1, characterized in that: The step of performing order analysis on the pending order information to obtain priority processing information includes: Performing a first acquisition operation on the enhanced ring order management system, if the pending order information acquired from the enhanced ring order management system is one, extracting the time production information and the product production information in the pending order information; Performing time calculation on the order formation time and the target production completion time indicated by the time production information to obtain time remaining information; wherein the time remaining information is used to indicate the remaining time for the production reinforcement ring; The product production information is processed using a reinforcement ring prediction model to obtain production result test information; wherein the production result test information is used to indicate the production speed and equipment matching degree of the reinforcement ring, and the reinforcement ring prediction model is a machine learning model pre-trained using the product production information of the sample reinforcement ring and the corresponding production result test information; Priority processing information is obtained by analyzing the remaining time information and the production result test information.

3. The automobile commutator reinforcement ring production control method according to claim 1, characterized in that: The step of performing order analysis on the pending order information to obtain priority processing information includes: Performing a first acquisition operation on the enhanced ring order management system, if the pending order information obtained from the enhanced ring order management system is multiple, extracting the time production information and the product production information in each pending order information; Calculate and process the order formation time and the target production completion time indicated by each of the time production information to obtain a plurality of time margin information; wherein the time margin information is used to reflect the remaining time of the production reinforcement ring; Perform time comparison and analysis on multiple time margin information to obtain time priority information; wherein the time priority information is the time margin information of the reinforcement ring with the shortest remaining production time; The product production information is analyzed respectively using the reinforcement ring test model to obtain priority production information; wherein the priority production information is the product production information that is processed first, and the reinforcement ring test model is a machine learning model that is pre-trained using the product production information of the sample reinforcement ring and the corresponding priority production information; The time priority information and the priority production information are weighted and matched to obtain priority processing information.

4. The automobile commutator reinforcement ring production control method according to claim 1, characterized in that: The pre-production analysis based on the priority processing information to obtain the production line model information of the reinforcement ring in the production workshop includes: Based on the priority processing information, multiple pieces of production equipment information are obtained by analyzing; wherein the production equipment information is used to reflect the production equipment model and technical specifications of the reinforcement ring corresponding to the priority processing information; Obtaining first modeling information according to the equipment model of each of the production equipment information; wherein the first modeling information is used to indicate the size value and weight value corresponding to the production equipment information; Obtain second modeling information according to the technical specifications of each of the production equipment information; wherein the second modeling information is used to indicate technical parameters and process parameters corresponding to the production equipment information; Modeling is performed according to the first modeling information and the second modeling information to obtain the production line model information.

5. The automobile commutator reinforcement ring production control method according to claim 1, characterized in that: The performing of performance simulation analysis according to the production line model information to obtain performance result information includes: Acquire a three-dimensional model product of the reinforcement ring; wherein the three-dimensional model product is modeled after a virtual production test based on the production line model information; Performing life prediction analysis on the three-dimensional model product to obtain a first test result; wherein the first test result is used to indicate a fatigue life value of the three-dimensional model product; Based on the first test result, a crack prediction analysis is performed on the three-dimensional model product to obtain a second test result; wherein the second test result is used to reflect the location where an initial crack may appear in the three-dimensional model product and the location where the crack propagates after the crack appears; The first test result and the second test result are aggregated to obtain performance result information.

6. The automobile commutator reinforcement ring production control method according to claim 5, characterized in that: The performing of life prediction analysis on the three-dimensional model product to obtain a first test result includes: Acquiring fatigue characteristic information of the three-dimensional model product; wherein the fatigue characteristic information is used to indicate a stress-life curve of the three-dimensional model product; Performing a life simulation test on the three-dimensional model product, and collecting the load borne by the three-dimensional model product during the simulation process, wherein the load includes amplitude, frequency and number of cycles; Applying loads and boundary conditions under actual working conditions to the three-dimensional model product, and performing stress analysis to obtain a stress distribution diagram, identifying stress concentration areas in the stress distribution diagram, calculating stress concentration factors in the stress concentration areas, using fatigue analysis methods to determine corresponding fatigue life, and then using cumulative damage calculation to determine predicted fatigue life information; The first test result is obtained according to the fatigue life information.

7. The automobile commutator reinforcement ring production control method according to claim 6, characterized in that: The method of performing crack prediction analysis on the three-dimensional model product based on the first test result to obtain a second test result includes: Determine the location where the initial crack of the three-dimensional model product appears based on the first test result, and then obtain the initial crack information according to the stress concentration area; wherein the initial crack information is used to indicate the size and shape of the initial crack of the three-dimensional model product; The initial crack information is used as an analysis condition to simulate the load under actual working conditions, calculate the stress intensity factor information of the three-dimensional model product, and calculate the crack growth rate information based on the crack growth law formula; wherein the stress intensity factor information is used to reflect the stress concentration degree near the crack tip of the three-dimensional model product, and the crack growth rate information is used to indicate the speed value at which the crack length increases with the increase of the number of load cycles under the action of the load; Predicting a result according to the stress intensity factor and the crack growth rate to obtain a second test result; And / or, the method further comprises: By performing nondestructive testing on the three-dimensional model product in the simulation, the crack type is determined, and after the crack type on the three-dimensional model product is determined, the crack position of the crack type on the three-dimensional model product is determined; wherein the crack type includes a point crack or a linear crack, and the crack position includes an inner ring and an outer ring, the inner ring is a side in contact with the commutator, and the outer ring is a side not in contact with the commutator; After the crack type and the crack position are determined, the directional crack of the point crack or the linear crack in the crack type is determined; wherein the directional crack is a circumferential directional crack or a width directional crack; If it is determined that the directional crack of the point crack or the linear crack in the crack type is the circumferential directional crack, feedback information is generated; wherein the feedback information is used to reflect the situation of sending the crack direction to the operator; If it is determined that the directional crack of the point crack or the linear crack in the crack type is the width direction crack, a reminder message is generated; wherein the reminder message is used to reflect and remind the operator that the three-dimensional model product has the risk of fracture.

8. The automobile commutator reinforcement ring production control method according to claim 1, characterized in that: While performing performance simulation analysis according to the production line model information, the method further includes: Performing a second acquisition operation on the enhanced ring order management system to acquire the pending order information in the enhanced ring order management system, and extracting product production information from the pending order information; The product production information is disassembled to obtain a plurality of independent production information; wherein the independent production information is used to reflect that the production of the reinforcement ring is divided into a plurality of separate production links, and each of the production links corresponds to one of the virtual production devices; Analyze the usage status of the plurality of virtual production devices indicated by the production line model information to obtain at least one piece of idle device information; wherein the idle device information is used to reflect the virtual production device that is not working; Matching and determining the plurality of independent production information with at least one idle equipment information to obtain at least one separate matching information; wherein the separate matching information is used to indicate that the production matching value of at least one non-working virtual production equipment and one of the production links meets the preset production matching value; A simulated production is performed based on the individual matching information.

9. A production control system for automobile commutator reinforcement ring, characterized in that: include: An acquisition unit is used to acquire all pending order information in the reinforcement ring order management system in real time; wherein the pending order information includes time production information and product production information of the automobile commutator reinforcement ring, and the time production information is used to indicate the order formation time and target production completion time of the reinforcement ring; A first analysis unit is used to perform order analysis on the pending order information to obtain priority processing information; wherein the priority processing information is any one of the pending order information among all the pending order information; A second analysis unit is used to perform pre-production analysis based on the priority processing information to obtain production line model information of the reinforcement ring in the production workshop; wherein the production line model information is used to indicate a plurality of virtual production equipment that can be used when producing the reinforcement ring; A third analysis unit is used to perform a performance simulation analysis according to the production line model information to obtain performance result information; wherein the performance result information is used to indicate the performance value of the virtual production equipment obtained after simulating and analyzing the reinforcement ring simulated by the virtual production equipment; A control unit is used to optimize control parameters of the performance result information to obtain production control information; wherein the production control information is used to control the commutator reinforcement ring production equipment to produce the reinforcement ring.

10. A device for producing a reinforcing ring for a commutator of an automobile, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

Images