Intelligent scheduling method and system for mold processing stations based on historical data prediction

Abstract

The invention relates to the technical field of station scheduling, in particular to a mold processing station intelligent scheduling method and system based on historical data prediction, and the method comprises the steps: obtaining processing drawing information; corresponding to-be-processed parameters are determined according to a preset prediction model and the processing drawing information, the to-be-processed parameters comprise the to-be-processed machine tool and the operation duration of a to-be-used station in the to-be-processed machine tool, the preset prediction model is constructed by historical processing data, and the historical processing data comprises performance data of each processing machine tool in a historical time period; determining real-time production data corresponding to the to-be-processed machine tool based on the to-be-processed parameters, wherein the real-time production data comprises a current operation state of the to-be-processed machine tool, a to-be-processed task queue and a material supply state; and generating a station scheduling instruction based on the to-be-processed parameters and the real-time production data, and binding the station scheduling instruction with the processing drawing information. According to the method, the precision in the mold machining station scheduling process is improved conveniently.

Description

Technical Field

[0001] The present application relates to the technical field of workstation scheduling, and in particular to a method and system for intelligent scheduling of mold processing workstations based on historical data prediction. Background Art

[0002] In modern manufacturing, the scheduling accuracy of mold processing stations during product processing directly affects the quality and production cost of the product. High-precision mold processing station scheduling can ensure that every product produced is consistent with the design specifications, thereby avoiding problems such as product size deviation and shape discrepancy caused by insufficient scheduling accuracy of mold processing stations.

[0003] Traditional mold processing station scheduling is usually based on a fixed schedule or manual judgment, which easily ignores dynamic changes in the actual processing process, such as fluctuations in processing equipment performance and differences in operator efficiency. In addition, since traditional mold processing station scheduling methods often rely on manual experience, it is difficult to make optimal scheduling decisions in real time and accurately based on the complex conditions of the production site. Therefore, with the increase in product types and changes in the production environment, traditional scheduling methods may be difficult to meet the needs of high-precision product processing. Summary of the invention

[0004] In order to improve the accuracy of the mold processing station scheduling process and thus meet the high-precision product processing requirements, the present application provides a mold processing station intelligent scheduling method and system based on historical data prediction.

[0005] In the first aspect, the present application provides a method for intelligent scheduling of mold processing stations based on historical data prediction, which adopts the following technical solutions: A method for intelligent scheduling of mold processing stations based on historical data prediction, comprising: Acquire processing drawing information, wherein the processing drawing information includes processing process flow, processing workload and parameters of the part to be processed, wherein the parameters of the part to be processed include part size, part material and processing accuracy requirements; Determine the corresponding parameters to be processed according to the preset prediction model and the processing drawing information, wherein the parameters to be processed include the machine tool to be processed and the operation time of the workstation to be used in the machine tool to be processed, and the preset prediction model is constructed by historical processing data, and the historical processing data includes the performance data of each processing machine tool in the historical time period; Determine the real-time production data corresponding to the machine tool to be processed based on the parameters to be processed, wherein the real-time production data includes the current operation status of the machine tool to be processed, the queue of tasks to be processed, and the material supply status; A workstation scheduling instruction is generated based on the parameters to be processed and the real-time production data, and the workstation scheduling instruction is bound to the processing drawing information.

[0006] By adopting the above technical solution, the preset prediction model constructed according to the historical processing data is convenient for accurately predicting the parameters to be processed corresponding to the processing drawing information, so as to provide effective data support for the subsequent generation of workstation scheduling instructions. In addition, by considering and analyzing the real-time production data of the machine tool to be processed in the process of determining the workstation scheduling instructions, the adaptability of the workstation scheduling instructions to the actual processing conditions of the current production environment can be improved, so as to improve the real-time and accuracy of the workstation scheduling instructions. Finally, by binding the workstation scheduling instructions with the corresponding processing drawing information, it is convenient to improve the correlation between the execution of the scheduling instructions and the specific processing tasks, so as to avoid the disconnection between the scheduling instructions and the actual processing needs, so as to improve the accuracy of the workstation scheduling instructions and meet the high-precision product processing needs.

[0007] In a possible implementation, when the parameters to be processed include multiple machine tools to be processed, the method further includes: When a scheduling execution instruction is detected, a central machine tool to be processed is determined from a plurality of machine tools to be processed based on the processing parameters, wherein the central machine tool to be processed is a machine tool to be processed with the most associated machine tools; Acquire the associated machine tools of the machine tools to be processed in the center, and determine the association type of each associated machine tool according to the preset prediction model, wherein the association type includes active association and passive association; Determine an associated machine tool whose association type is active association as a target associated machine tool, and determine a simulated processing flow of each target associated machine tool according to the preset prediction model; Based on the processing flow of each target associated machine tool, the predicted landing form of the corresponding part to be processed when it arrives at the corresponding workstation to be executed in the central machine tool to be processed is determined, and based on the predicted landing form of the part to be processed, the clamping form of the corresponding workstation to be executed in the central machine tool to be processed is determined, wherein each workstation to be executed corresponds to a part to be processed.

[0008] By adopting the above technical scheme, the central machine tool to be processed among multiple machine tools to be processed is determined by the processing parameters, and the target associated machine tool of the central machine tool to be processed is determined at the same time, so as to improve the accuracy of the resource allocation process, that is, to ensure the efficient use of key machine tools to be processed. In addition, by predicting the landing shape of the parts to be processed leaving the target associated machine tool, and adjusting the clamping shape of the central machine tool to be processed based on the prediction result, it is convenient to improve the efficiency of transferring the parts to be processed between different machine tools to be processed, and can also effectively avoid processing errors or parts damage caused by improper clamping, thereby facilitating the improvement of processing quality and production efficiency in the production process.

[0009] In one possible implementation, it also includes: Counting the clamping form value corresponding to each workstation to be executed in the central machine tool to be processed within a preset time period, and determining the form data axis of each workstation to be executed based on the clamping form value of each workstation to be executed within the preset time period; Identify whether there is a periodic form in each form data axis, and determine the form data axis with the periodic form as the first focus form data axis, and determine the to-be-executed workstation corresponding to the first focus form data axis as the first focus to-be-executed workstation; Identify all periodic patterns contained in each first-focused pattern data axis, and determine the predicted pattern maintenance duration of the periodic pattern in the corresponding first-focused pattern data axis based on the change trends of all periodic patterns; Based on the periodic form and predicted form maintenance duration corresponding to each first focus form data axis, the clamping instructions for each first focus workstation to be executed within the predicted maintenance time period are determined, wherein the predicted maintenance time period corresponds to the predicted form maintenance duration.

[0010] By adopting the above technical solution, by quantifying the clamping forms determined within a preset time period, that is, by converting different clamping forms into clamping form values, it is convenient to intuitively view or judge whether the clamping forms corresponding to different workstations to be executed are consistent. In addition, by constructing a form data axis of the workstations to be executed within a preset time period, it is convenient to horizontally compare and analyze the changes in the clamping forms at the same workstation to be executed, so as to provide effective data support for the subsequent determination of clamping instructions. By analyzing the changing trend of the periodic form and predicting the maintenance time of the periodic form, it is convenient to improve the effectiveness and accuracy of determining the clamping instructions. Finally, by predicting the clamping instructions within a period of time in the future, instead of determining the clamping form of each first-focus workstation to be executed after real-time data analysis, it is convenient to reduce data processing pressure.

[0011] In one possible implementation, it also includes: Determine the morphological data axis without periodic morphology as the second focused morphological data axis, and determine the to-be-executed workstation corresponding to the second focused morphological data axis as the second focused to-be-executed workstation; Acquire the transfer-associated machine tool corresponding to each second focus workstation to be executed, and identify the setting position and performance data of each transfer-associated machine tool from the preset prediction model; Based on the setting position and performance data of each transfer-associated machine tool, determining a transfer area of ​​interest corresponding to a second workstation to be executed of interest; When it is detected that the part to be processed enters the attention transfer area corresponding to the second attention to be executed workstation, the clamping form adjustment time corresponding to the second attention to be executed workstation is determined.

[0012] By adopting the above technical scheme, by analyzing the setting positions and performance data of different transfer-associated machine tools, it is convenient to analyze the rate at which each transfer-associated machine tool transfers the parts to be processed to the central machine tool to be processed, and the clamping form adjustment time of the second focus workstation to be executed in the central machine tool to be processed is determined by the transfer rate, so as to reduce the waiting time for the parts to be processed to be clamped, thereby facilitating improving the efficiency of the parts clamping process. In addition, by adjusting the clamping form in advance, it is also convenient to improve the stability of the clamping process, thereby facilitating reducing the probability of the parts to be processed falling during the clamping process, and then facilitating improving the accuracy of the workstation scheduling work.

[0013] In one possible implementation, it also includes: Obtain the scheduling log of each machine tool to be processed in the current operation cycle, and identify the cumulative scheduling workload and scheduling operation difficulty level corresponding to each scheduling log; Based on each accumulated scheduling workload and the scheduling workload difficulty level, determine whether there is a hidden danger load machine tool, the hidden danger load machine tool corresponding to the hidden danger accumulated scheduling workload is higher than the preset standard scheduling workload, and the corresponding hidden danger scheduling workload difficulty level is higher than the preset standard difficulty level; Based on the accumulated hidden danger scheduling operation volume of the hidden danger load machine tool and the difficulty level of the hidden danger scheduling operation, the operation parameters of the hidden danger load machine tool in the next operation cycle are adjusted, and the operation parameters include operation depth and operation speed.

[0014] By adopting the above technical solution, the load situation of each machine tool to be processed is understood by analyzing the scheduling log of the machine tools to be processed. When a machine tool with hidden danger load is determined, the operating parameters of the machine tool with hidden danger load in the future are adjusted in time to reduce the operating burden of the machine tool with hidden danger load, thereby reducing the probability of interruption of part processing due to damage to the machine tool.

[0015] In one possible implementation, it also includes: Execute the loop step, and generate a shutdown instruction based on the hidden danger load machine tool when the preset conditions are met, wherein the loop step is: Analyze the accumulated scheduling workload and the difficulty level of the scheduling workload of the hidden danger load machine tool in the current operation cycle in real time, determine a first real-time load value corresponding to the hidden danger load machine tool, and update the displayed load value of the first axis interval in the load value coordinate axis in real time based on the first real-time load value, wherein the displayed load value corresponds to the first real-time load value; When the displayed load value corresponding to the first axis interval is higher than the preset load value, the second real-time load value in the second axis interval is determined based on the load value conversion mapping relationship, and the parameter adjustment value of the hidden danger load machine tool is determined based on the second real-time load value and the adjustment mapping relationship, the load value conversion mapping relationship is the conversion correspondence relationship between the first real-time load value and the second real-time load value, the adjustment mapping relationship is the correspondence relationship between the second real-time load value and the parameter adjustment value, and the parameter adjustment value includes an operation depth adjustment value and an operation speed adjustment value; Clearing the displayed load value corresponding to the first axis interval; The preset conditions are: The current operation cycle ends; or the number of clearing times exceeds a preset threshold.

[0016] By adopting the above technical scheme, by real-time viewing and analyzing the first real-time load value of the hidden danger load machine tool, it is convenient to understand the operating load change trend of the hidden danger load machine tool, and update the adjustment strategy of the operating parameters based on the load change trend, instead of adopting a fixed parameter adjustment strategy to adjust the operating parameters of the hidden danger load machine tool at different first real-time load values, so as to improve the real-time and accuracy of the adjustment strategy, thereby avoiding overload operation or waste of resources of the hidden danger load machine tool, realizing reasonable utilization of resources and load balancing, and then facilitating improving the efficiency and stability of the parts processing process.

[0017] In a second aspect, the present application provides a scheduling system, which adopts the following technical solution: A scheduling system, comprising: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned intelligent scheduling method for mold processing stations based on historical data prediction.

[0018] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program that can be loaded by a processor and execute the above-mentioned intelligent scheduling method for mold processing stations based on historical data prediction.

[0019] In a fourth aspect, the present application provides a computer program product, which adopts the following technical solution: A computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements the above-mentioned intelligent scheduling method for mold processing stations based on historical data prediction.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: The preset prediction model constructed based on historical processing data is convenient for accurately predicting the parameters to be processed corresponding to the processing drawing information, thereby facilitating the provision of effective data support for the subsequent generation of workstation scheduling instructions. In addition, by considering and analyzing the real-time production data of the machine tool to be processed in the process of determining the workstation scheduling instructions, the adaptability of the workstation scheduling instructions to the actual processing conditions of the current production environment can be improved, thereby facilitating the improvement of the real-time and accuracy of the workstation scheduling instructions. Finally, by binding the workstation scheduling instructions with the corresponding processing drawing information, it is convenient to improve the correlation between the execution of the scheduling instructions and the specific processing tasks, so as to avoid the disconnection between the scheduling instructions and the actual processing needs, thereby facilitating the improvement of the accuracy of the workstation scheduling instructions, and also facilitating the satisfaction of high-precision product processing needs.

[0021] By quantifying the clamping forms determined within a preset time period, that is, by converting different clamping forms into clamping form values, it is convenient to intuitively view or judge whether the clamping forms corresponding to different workstations to be executed are consistent. In addition, by constructing a form data axis of the workstations to be executed within a preset time period, it is convenient to horizontally compare and analyze the changes in the clamping forms at the same workstation to be executed, so as to provide effective data support for the subsequent determination of clamping instructions. By analyzing the changing trend of the periodic form and predicting the maintenance duration of the periodic form, it is convenient to improve the effectiveness and accuracy of determining the clamping instructions. Finally, by predicting the clamping instructions within a period of time in the future, instead of determining the clamping form of each first-focus workstation to be executed after real-time data analysis, it is convenient to reduce data processing pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a method for intelligent scheduling of mold processing stations based on historical data prediction in an embodiment of the present application; Figure 2 It is a schematic diagram of a process for determining a clamping instruction in an embodiment of the present application; Figure 3 It is a structural diagram of a scheduling system in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following is combined with Figure 1-3 This application is described in further detail.

[0024] After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed, but such modifications are protected by patent law as long as they are within the scope of the claims of this application.

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0027] Specifically, the embodiment of the present application provides an intelligent scheduling method for mold processing stations based on historical data prediction, which is executed by a scheduling system, and the scheduling system can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited to this. The terminal device and the server can be directly or indirectly connected through wired or wireless communication, and the embodiment of the present application does not limit this.

[0028] refer to Figure 1 , Figure 1 : is a flow chart of a mold processing station intelligent scheduling method based on historical data prediction in an embodiment of the present application, the method includes steps S110 to S140, wherein: Step S110: Acquire processing drawing information, which includes processing process flow, processing workload and parameters of the part to be processed, and the parameters of the part to be processed include part size, part material and processing accuracy requirements.

[0029] Specifically, the processing drawing information is a guiding document in the process of parts processing and manufacturing, ensuring that the parts are processed according to the design requirements and quality standards, and is the basis for realizing production planning and quality control. The processing drawing information can be uploaded to the scheduling system in real time through the client, and can also be entered in advance. The specific acquisition method is not specifically limited in the embodiment of this application. The processing process is the processing steps and processing sequence of the parts to be processed, wherein different processing steps correspond to different processing machines, and the corresponding processing machine can be determined by the name or serial number of the processing step. The processing workload is the demand for part processing. Based on the processing workload, it is convenient to plan the part processing plan and resource allocation, and it is also convenient to predict the material supply. Due to the different designs, structures and processing capabilities of different processing machines, different processing machines have differences in the operable part size, part material and processing accuracy requirements. For example, a large gantry milling machine can process larger parts, while a precision grinder is more suitable for processing smaller parts or parts with high precision requirements.

[0030] Step S120: Determine the corresponding parameters to be processed according to the preset prediction model and the processing drawing information. The parameters to be processed include the machine tool to be processed and the operation time of the workstation to be used in the machine tool to be processed. The preset prediction model is constructed by historical processing data, and the historical processing data includes the performance data of each processing machine tool in the historical time period.

[0031] Specifically, the historical time period is a period of time before the current moment, and the historical time period can be 3 days before the current moment, or 5 days before the current moment. The specific duration is not specifically limited in the embodiment of the present application, as long as it can provide effective data support for determining the parameters to be processed. In order to improve the accuracy of the prediction preset model, after the historical processing data is acquired, the historical processing data can be preprocessed. The data preprocessing includes but is not limited to data cleaning, data normalization and other operations. After data preprocessing, the model is constructed based on the processed historical processing data. Since the design and structure of different processing machines are different, each processing machine tool corresponds to a preset prediction model. The simulation design in each preset prediction model is consistent with the design of the corresponding processing machine tool, and the simulation structure is consistent with the structure of the corresponding processing machine tool. Driving the corresponding preset prediction model can simulate the processing operation of the processing machine tool.

[0032] The performance data of the processing machine tool may include the machine tool model, processing capacity, current status and historical fault records, wherein the processing capacity includes but is not limited to the spindle speed, feed speed and processing accuracy, etc. The current status may include idle state, processing state and maintenance state. The performance data of the processing machine tool can be uploaded by the relevant operator, or obtained from the production monitoring log of the CNC machine tool. The specific acquisition method is not specifically limited in the embodiment of this application. Due to the different complexity of the processing drawing information, it may be necessary for multiple machine tools to be processed to work together to complete the entire drawing processing information. Since some machine tools to be processed may be equipped with a variety of processing tools and accessories, such as milling cutters, drill bits, boring tools, etc., it is possible to complete drilling, milling, boring and other operations in the same machine tool to be processed, that is, different operations can be performed on different parts to be processed at different stations in the same machine tool to be processed. Therefore, when determining the processing parameters corresponding to the processing drawing information, it will be specific to the operating time period of the station to be used.

[0033] Step S130: determining the real-time production data corresponding to the machine tool to be processed based on the parameters to be processed, the real-time production data including the current operation status of the machine tool to be processed, the queue of tasks to be processed and the material supply status.

[0034] Specifically, the parameters to be processed are the prerequisite for completing the processing drawing information. If all the processing content corresponding to the processing drawing information needs to be completed, actual production or processing needs to be carried out based on the parameters to be processed. However, different machine tools to be processed have different tasks to perform. The machine tools to be processed included in the parameters to be processed may be in an idle state or in a non-idle state. Therefore, it is necessary to analyze the real-time production data of the machine tools to be processed to ensure that the processing drawing information can be executed smoothly.

[0035] The machine tool to be processed required to complete the processing drawing information can be determined based on the parameters to be processed. The machine tool to be processed included in the parameters to be processed can be characterized by the machine tool function or the machine tool name. For example, the machine tool to be processed included in the parameters to be processed may be a drilling machine tool, wherein the specific machine tool serial number of the drilling machine tool is not limited, that is, as long as the machine tool to be processed is a drilling machine tool, then the corresponding operation time of the workstation to be used may only be the use time of a certain workstation in a certain machine tool, which is related to the processing workload in the processing drawing information.

[0036] The real-time production data corresponding to the machine tool to be processed is the real-time production data of the machine tools of the same type as the machine tool to be processed. For example, if the machine tool to be processed is a drilling machine tool, the real-time production data corresponding to the machine tool to be processed is the real-time production data of each drilling machine tool in the production environment. The current operating status of the machine tool to be processed may include idle status, processing status and maintenance status. The queue of tasks to be processed is a list of parts waiting to be processed by the drilling machine tool or a production plan. The queue of tasks to be processed may contain multiple processing tasks for different parts to be processed, and each processing task has its specific processing requirements. The material supply status of the machine tool to be processed usually refers to the inventory status, supply channels and whether the materials required by the machine tool to be processed to process the parts to be processed meet the needs of current and future processing tasks. The material supply status is crucial to the execution of the production plan.

[0037] Step S140: Generate a workstation scheduling instruction based on the parameters to be processed and the real-time production data, and bind the workstation scheduling instruction with the processing drawing information.

[0038] Specifically, when generating workstation scheduling instructions, it may be necessary to analyze the delivery date contained in the processing drawing information, and use an optimization algorithm to generate workstation scheduling instructions based on the delivery date, part material, processing accuracy requirements, parameters to be processed, and real-time production data to complete the processing tasks corresponding to the processing drawing information. The optimization algorithm can be a genetic algorithm, a simulated annealing algorithm, or a linear programming algorithm. The specific optimization algorithm is not specifically limited in the embodiments of the present application.

[0039] After determining the workstation scheduling instructions, the workstation scheduling instructions can be bound to the processing drawing information to ensure that each processing task of the processing station has clear drawing guidance. The bound scheduling instructions and drawing information are sent to each machine tool to be processed through the scheduling system to control each machine tool to be processed to perform the processing task.

[0040] For the embodiments of the present application, the preset prediction model constructed based on historical processing data is convenient for accurately predicting the parameters to be processed corresponding to the processing drawing information, thereby facilitating the provision of effective data support for the subsequent generation of workstation scheduling instructions. In addition, by considering and analyzing the real-time production data of the machine tool to be processed in the process of determining the workstation scheduling instructions, the adaptability of the workstation scheduling instructions to the actual processing conditions of the current production environment is improved, thereby facilitating the improvement of the real-time and accuracy of the workstation scheduling instructions. Finally, by binding the workstation scheduling instructions with the corresponding processing drawing information, it is convenient to improve the correlation between the execution of the scheduling instructions and the specific processing tasks, and to avoid the disconnection between the scheduling instructions and the actual processing needs, thereby facilitating the improvement of the accuracy of the workstation scheduling instructions, and also facilitating the satisfaction of high-precision product processing needs.

[0041] Furthermore, in order to improve the efficiency of transferring the parts to be processed between different processing machines, when the processing parameters include multiple processing machines, the method provided in the embodiment of the present application further includes: When a scheduling execution instruction is detected, a central machine tool to be processed is determined from a plurality of machine tools to be processed based on the processing parameters, and the central machine tool to be processed is the machine tool to be processed with the most associated machine tools.

[0042] Specifically, when the parameters to be processed contain multiple machine tools to be processed, it indicates that when completing the processing task corresponding to the processing drawing information, multiple machine tools to be processed are required to participate in the processing task. When a scheduling execution instruction is detected, it indicates that the corresponding machine tool to be processed has started to execute the processing task according to the station scheduling instruction. In the production and manufacturing process of the parts to be processed, there may be multiple processes, each of which may need to be completed by a different machine tool to be processed. For example, a part to be processed may need to be rough-processed on machine tool A, then fine-processed on machine tool B, and finally drilled on machine tool C. Therefore, there may be an association between different machine tools to be processed. When determining the central machine tool to be processed, multiple machine tools to be processed can be determined based on the parameters to be processed, and then the processing order corresponding to all machine tools to be processed can be determined based on the processing process flow. Finally, a processing association network is constructed according to each machine tool to be processed and the processing order of each machine tool to be processed. The machine tools to be processed that have an associated relationship in the processing association network are connected by connecting lines. The number of associated machine tools for each machine tool to be processed can be determined by identifying the number of connecting lines of each machine tool to be processed in the processing association network, and the machine tool to be processed with the largest number of associated machine tools is determined as the central machine tool to be processed. The method of determining the central machine tool to be processed is not specifically limited in the embodiments of the present application and can be set by relevant technical personnel.

[0043] The associated machine tools of the machine tools to be processed in the center are obtained, and the association type of each associated machine tool is determined according to a preset prediction model, and the association type includes active association and passive association.

[0044] Specifically, since the preset prediction model can simulate the actual processing task of the corresponding machine tool, the input execution steps and output execution steps of different preset prediction models can be determined through the preset prediction model corresponding to each associated machine tool and the preset prediction model corresponding to the central machine tool to be processed. The input execution steps and output execution steps of each associated machine tool can also be determined based on the processing process flow. The association type of each associated machine tool can be determined based on the input execution steps and the output execution steps. For example, the input execution step of the central machine tool to be processed is "connecting the operation result of the associated machine tool a", that is, the output execution step of the associated machine tool a is "transferring the operation result to the central machine tool to be processed", at this time, the association type of the associated machine tool a can be determined to be active association; if the output execution step of the central machine tool to be processed is "transferring the operation result to the associated machine tool b", at this time, the association type of the associated machine tool b can be determined to be passive association. When the association type of the associated machine tool is active association, it indicates that the associated machine tool is located at the upstream processing node of the central machine tool to be processed; when the association type of the associated machine tool is passive association, it indicates that the associated machine tool is located at the downstream processing node of the central machine tool to be processed.

[0045] An associated machine tool whose association type is active association is determined as a target associated machine tool, and a simulation processing flow of each target associated machine tool is determined according to a preset prediction model.

[0046] Based on the processing flow of each target associated machine tool, the predicted landing form of the corresponding part to be processed when it arrives at the corresponding workstation to be executed in the central machine tool to be processed is determined, and based on the predicted landing form of the part to be processed, the clamping form of the corresponding workstation to be executed in the central machine tool to be processed is determined, wherein each workstation to be executed corresponds to a part to be processed.

[0047] Specifically, since the target associated machine tool is located at the upstream processing node of the central machine tool to be processed, the central machine tool to be processed may need to adjust the clamping form to undertake the parts to be processed transferred from the target associated machine tool to the central machine tool to be processed. Therefore, it is necessary to analyze the simulated processing process of the target associated machine tool to determine the predicted landing form of the parts to be processed when the target associated machine tool transfers the corresponding parts to be processed to the central machine tool to be processed. The predicted landing form is the form presented by the parts to be processed in the last link of the simulated processing process. Since the central machine tool to be processed may be equipped with multiple accessories, that is, the processing operations corresponding to different processing stations in the central machine tool to be processed may be different. Therefore, when determining the predicted landing form of the corresponding parts to be processed of the target associated machine tool arriving at the central machine tool to be processed, it is based on the corresponding to-be-executed stations in the central machine tool to be processed. The to-be-executed stations are the to-be-processed stations of the target associated machine tool in the central machine tool to be processed. When the target associated machine tool contains at least two target associated processing stations, it is necessary to determine the to-be-executed stations of each target associated processing station in the central part to be processed based on the above method.

[0048] After determining the predicted landing shape when arriving at each workstation to be executed in the central processing machine tool, the clamping shape of the corresponding workstation to be executed is adjusted based on each predicted landing shape to facilitate accurate grasping of the processed parts. It can also effectively avoid processing errors or part damage caused by improper clamping, thereby facilitating the improvement of processing quality and production efficiency during the production process.

[0049] Further, in order to improve the effectiveness and accuracy of determining the clamping instruction, the method provided in the embodiment of the present application further includes steps S1 to S4, such as Figure 2 As shown, where: Step S1: Count the clamping morphology values ​​corresponding to each workstation to be executed in the central processing machine tool within a preset time period, and determine the morphology data axis of each workstation to be executed based on the clamping morphology value of each workstation to be executed within the preset time period.

[0050] Specifically, different clamping forms correspond to different clamping form values. The clamping form value is a parameter used to quantify and describe the clamping form. The clamping form value corresponding to each clamping form can be determined according to the clamping form value table. The clamping form value table contains clamping form values ​​corresponding to different clamping forms. The clamping form table can be determined by relevant staff based on experimental data or historical operation data and uploaded to the scheduling system. The specific content is not specifically limited in the embodiments of this application and can be adjusted by relevant technical personnel according to actual needs. By converting different clamping forms into corresponding clamping form values, it is convenient to perform unified processing in the production scheduling process.

[0051] The preset time period is 3 minutes before the current moment, or 5 minutes before the current moment. The duration corresponding to the preset time period is not specifically limited in the embodiment of the present application. Since the operation process of the part to be processed at a certain station to be executed may be relatively rapid, it may be transferred multiple times within the preset time period, that is, the station to be executed may perform multiple clamping actions within the preset time period. The morphological data corresponding to each station to be executed is constructed based on the duration corresponding to the preset time period and the clamping morphological value corresponding to each moment within the preset time period. Based on the morphological data axis, it is convenient to intuitively view the number of clamping times of the station to be executed in the preset time period and the clamping morphological value corresponding to each clamping.

[0052] Step S2: Identify whether there is a periodic morphology in each morphological data axis, and determine the morphological data axis with a periodic morphology as the first focus morphological data axis, and determine the to-be-executed workstation corresponding to the first focus morphological data axis as the first focus to-be-executed workstation.

[0053] Specifically, the periodic form is the consistent or repeated clamping form value segments that appear in different time periods of a preset time period. The form data axis with the periodic form is determined as the first focus form data axis, and the number of periodic forms contained in the first focus form data axis is at least two.

[0054] Based on any first focus morphological data axis, the periodic time period corresponding to each periodic morphology is determined from the morphological data axis, and different periodic time periods are sampled based on a preset sampling frequency to obtain a sampling value corresponding to each periodic time period, that is, a sampling periodic morphological value corresponding to each periodic time period is obtained, and all sampling values ​​corresponding to the same periodic time period are summed up to obtain a periodic sampling value corresponding to each periodic time period. If the sampling difference between the periodic sampling value corresponding to periodic time period a and the periodic sampling value corresponding to periodic time period b is not higher than the preset difference, then it indicates that there are consistent or repeated clamping morphological value segments between periodic time period a and periodic time period b. The specific preset difference is not specifically limited in the embodiment of the present application and can be set by relevant technical personnel.

[0055] Step S3: Identify all periodic forms contained in each first focus form data axis, and determine the predicted form maintenance duration of the periodic form in the corresponding first focus form data axis based on the change trend of all periodic forms.

[0056] Specifically, for any first focus morphology data axis, the periodic sampling value corresponding to each periodic morphology is obtained, and the changing trend of all periodic morphologies is obtained by comparing the periodic sampling values ​​corresponding to different periodic time periods, that is, the changing rate of the periodic sampling value can be obtained, and the predicted morphology maintenance duration can be determined based on the periodic sampling value changing rate and the preset difference. For example, the preset difference is 5, and the periodic sampling value corresponding to the first periodic time period in the first focus morphology data axis is 15, the periodic sampling value corresponding to the second periodic time period is 14, the periodic sampling value corresponding to the third periodic time period is 13, and the periodic sampling value corresponding to the fourth periodic time period is 12. Based on this, the periodic sampling value corresponding to the fifth periodic time period may be 11, the periodic sampling value corresponding to the sixth periodic time period is 10, and the periodic sampling value corresponding to the seventh periodic time period is 9. Since the preset difference is 5, the periodic morphology will no longer exist in the seventh cycle. Therefore, it can be determined that the predicted morphology maintenance duration of the periodic morphology in the first focus morphology data axis is two periodic time periods. When each periodic time period is 5 minutes, the predicted morphology maintenance duration of the periodic morphology in the first focus morphology data axis is 10 minutes.

[0057] Based on the above method, the predicted shape maintenance time corresponding to each first focus shape data axis can be determined.

[0058] Step S4: Based on the periodic form and predicted form maintenance duration corresponding to each first focus form data axis, determine the clamping instructions for each first focus workstation to be executed within the predicted maintenance time period, wherein the predicted maintenance time period corresponds to the predicted form maintenance duration.

[0059] Specifically, for any concerned form data axis, the periodic clamping form corresponding to the periodic form can be determined according to the periodic form, the predicted maintenance time period is a period of time before the periodic form disappears, and the duration corresponding to the predicted maintenance time period is consistent with the predicted form maintenance duration, that is, within the predicted maintenance time period, the corresponding first concerned workstation to be executed is controlled to clamp the workpiece to be processed using the periodic clamping form. Based on the above method, the clamping instructions of each first concerned workstation to be executed within the corresponding predicted maintenance time period can be determined.

[0060] By constructing a morphological data axis for the workstation to be executed within a preset time period, it is convenient to compare and analyze the changes in the clamping morphology at the same workstation to be executed horizontally, so as to provide effective data support for the subsequent determination of the clamping instructions. By analyzing the changing trend of the periodic morphology and predicting the maintenance duration of the periodic morphology, it is convenient to improve the effectiveness and accuracy of determining the clamping instructions. Finally, by predicting the clamping instructions within a period of time in the future, instead of determining the clamping morphology of each first-focus workstation to be executed after real-time data analysis, it is convenient to reduce data processing pressure.

[0061] Furthermore, in order to improve the efficiency of the parts clamping process, the method provided in the embodiment of the present application also includes: Determine a morphological data axis without a periodic morphology as a second focus morphological data axis, and determine the workstation to be executed corresponding to the second focus morphological data axis as the second focus workstation to be executed; obtain the transfer-associated machine tool corresponding to each second focus workstation to be executed, and identify the setting position and performance data of each transfer-associated machine tool from a preset prediction model; determine the focus transfer area corresponding to the second focus workstation to be executed based on the setting position and performance data of each transfer-associated machine tool; when it is detected that the part to be processed enters the focus transfer area corresponding to the second focus workstation to be executed, determine the clamping morphology adjustment moment corresponding to the second focus workstation to be executed.

[0062] Specifically, the clamping form of the second focus workstation to be executed corresponding to the second focus form data axis does not have periodic changes, so there is no periodic form in the second focus form data axis. At this time, it can be characterized that the second focus workstation to be executed may undertake parts to be processed from different target associated machine tools, and the predicted landing forms corresponding to different transfer associated machine tools may be different. Therefore, when the second focus workstation to be executed clamps the parts to be processed transferred from different transfer associated machine tools, the clamping forms that need to be provided are also different.

[0063] Since different transfer-associated machine tools need to perform different processing tasks, and different transfer-associated machine tools have different corresponding performance data, different transfer-associated machine tools have different transfer rates when transferring different parts to be processed. Since it takes a certain amount of time to switch the clamping form of the second concerned workstation to be executed, it is necessary to determine the moment of adjusting the clamping form of the second concerned workstation to be executed when dealing with parts to be processed from different transfer-associated machine tools. In the embodiment of the present application, according to the setting position and performance data of each transfer-associated machine tool, a focus transfer area of ​​the second concerned workstation to be executed is constructed. Since the focus transfer area is related to the setting position and performance data of each transfer-associated machine tool, the focus transfer area does not have a standard shape. For example, the smaller the interval between the setting position of the transfer-associated machine tool a and the second concerned workstation to be executed, and the higher the performance data of the transfer-associated machine tool a, the closer the distance between the transfer-associated machine tool a and the boundary line of the focus transfer area.

[0064] Once it is detected that there are parts to be processed in the transfer area of ​​interest, the clamping form of the second workstation to be executed is adjusted immediately. Each transfer-associated machine tool has a fixed transfer route when transferring parts to the central machine tool to be processed. Therefore, the corresponding transfer-associated machine tool can be determined by identifying the transfer position of the parts to be processed in the transfer area of ​​interest, and then the clamping form is determined based on the determined transfer-associated machine tool. The clamping form adjustment time of the second workstation to be executed in the central machine tool to be processed is determined by the transfer rate, so as to reduce the waiting time for the parts to be processed to be clamped, thereby facilitating the improvement of the efficiency of the parts clamping process.

[0065] In addition, without limiting the focus transfer area, the clamping form switching rate of the second focus workstation for different transfer-associated machine tools can be determined according to different transfer-associated machine tools to reduce the probability of the parts to be processed falling during the clamping process.

[0066] Furthermore, in order to reduce the probability of interruption of part processing due to damage to the machine tool, the method provided in the embodiment of the present application also includes: Obtain the scheduling log of each machine tool to be processed in the current operation cycle, and identify the cumulative scheduling workload and scheduling operation difficulty level corresponding to each scheduling log; based on each cumulative scheduling workload and scheduling operation difficulty level, determine whether there is a hidden danger load machine tool, the hidden danger load machine tool corresponding to the hidden danger cumulative scheduling workload is higher than the preset standard scheduling workload, and the corresponding hidden danger scheduling operation difficulty level is higher than the preset standard difficulty level; based on the hidden danger cumulative scheduling workload and hidden danger scheduling operation difficulty level of the hidden danger load machine tool, adjust the operation parameters of the hidden danger load machine tool in the next operation cycle, the operation parameters include operation depth and operation speed.

[0067] Specifically, the duration corresponding to the current operation cycle can be 4 hours, 8 hours or 10 hours. The specific duration is not specifically limited in the embodiment of the present application. The scheduling log of each machine tool to be processed records the operation information of each station to be executed in the machine tool to be processed. For any machine tool to be processed, the cumulative scheduling operation volume corresponding to the scheduling log is the operation volume of each station to be executed in the machine tool to be processed in the current operation cycle. The difficulty level of the scheduling operation is related to the continuous operation time. When the continuous operation time is higher than the preset time, the continuous operation time can be determined as the difficulty operation time. Different difficulty operation times correspond to different scheduling operation difficulty levels. When determining the scheduling operation difficulty level corresponding to the machine tool to be processed, all the difficulty operation times of each station to be executed in the machine tool to be processed in the current operation cycle can be summed to obtain the summary difficulty operation time, and then the scheduling operation difficulty level corresponding to the summary difficulty operation time is determined based on the level mapping relationship, wherein the level mapping relationship is the corresponding relationship between the summary difficulty operation time and the scheduling operation difficulty level, which can be determined by relevant staff according to historical experimental data and uploaded to the scheduling system. The specific content is not specifically limited in the embodiment of the present application.

[0068] Based on the above method, the corresponding cumulative scheduling workload and scheduling workload difficulty level of each machine tool to be processed in the current operation cycle can be determined. According to the preset standard scheduling workload and the preset standard difficulty level, it is judged whether there is a hidden danger load machine tool among the multiple machine tools to be processed, and the hidden danger load machine tool corresponding to the hidden danger cumulative scheduling workload is higher than the preset standard scheduling workload, and the hidden danger scheduling workload difficulty level is higher than the preset standard difficulty level.

[0069] When a machine tool with hidden danger load appears, it is necessary to promptly adjust the operating depth and operating speed of the machine tool with hidden danger load in the next operating cycle in order to reduce the operating load of the machine tool with hidden danger load, thereby reducing the probability of interruption of parts processing due to machine tool damage. Based on the cumulative scheduling workload of hidden dangers and the difficulty level of hidden danger scheduling operations of hidden danger-loaded machine tools, when adjusting the operating parameters of hidden danger-loaded machine tools in the next operating cycle, the first score corresponding to the cumulative scheduling workload of hidden dangers can be determined through the first corresponding relationship, and then the second score corresponding to the difficulty level of hidden danger scheduling operations can be determined based on the second corresponding relationship. Finally, the adjustment score of the hidden danger-loaded machine tool is determined based on the first score and the second score, and then the adjustment strategy corresponding to each adjustment score is determined based on the third corresponding relationship, wherein the adjustment strategy includes an operating depth adjustment value and an operating speed adjustment value, wherein the first corresponding relationship is the corresponding relationship between the cumulative scheduling workload of hidden dangers and the first score, the second corresponding relationship is the corresponding relationship between the difficulty level of hidden danger scheduling operations and the second score, and the third corresponding relationship is the corresponding relationship between the adjustment score and the adjustment strategy. The specific contents of the first corresponding relationship, the second corresponding relationship and the third corresponding relationship are not specifically limited in the embodiments of the present application, and can be determined by relevant staff based on experimental data and uploaded to the scheduling system.

[0070] Furthermore, the method provided in the embodiment of the present application also includes: Execute the loop steps, and generate a shutdown instruction based on the hidden danger load machine tool when the preset conditions are met, wherein the loop steps are: Real-time analysis of the accumulated scheduling workload and difficulty level of the hidden danger scheduling operations of the hidden danger load machine tool in the current operation cycle, determining the first real-time load value corresponding to the hidden danger load machine tool, and updating the displayed load value of the first axis interval in the load value coordinate axis in real time based on the first real-time load value, the displayed load value corresponds to the first real-time load value; when the displayed load value corresponding to the first axis interval is higher than the preset load value, determining the second real-time load value in the second axis interval based on the load value conversion mapping relationship, and determining the parameter adjustment value of the hidden danger load machine tool based on the second real-time load value and the adjustment mapping relationship, the load value conversion mapping relationship is the conversion correspondence relationship between the first real-time load value and the second real-time load value, the adjustment mapping relationship is the correspondence relationship between the second real-time load value and the parameter adjustment value, the parameter adjustment value includes the operation depth adjustment value and the operation speed adjustment value; clearing the displayed load value corresponding to the first axis interval; The preset conditions are: the current operation cycle ends; or the number of clearing times exceeds the preset threshold.

[0071] Specifically, the method for determining the first real-time load value corresponding to the hidden danger load machine tool can refer to the method for determining the adjustment score in the above embodiment, except that when determining the adjustment score, it is necessary to count the corresponding cumulative scheduling workload and scheduling workload difficulty level in the current operation cycle, and when determining the first real-time load value, it is necessary to count the cumulative scheduling workload and scheduling workload difficulty level in the current analysis period, wherein the analysis period can be determined by a preset analysis frequency, and the hidden danger load machine tool can adjust the corresponding operation parameters according to the preset analysis frequency. Since frequent adjustment of the operation parameters of the hidden danger load machine tool may cause the control program of the hidden danger load machine tool to be disordered, the operation parameters are generally adjusted according to the preset analysis frequency, and the preset analysis frequency can be every 3 minutes, or every 5 minutes, that is, the operation parameters of the hidden danger load machine tool can be adjusted every 3 minutes or every 5 minutes.

[0072] The first real-time load value represents the load condition of the hidden danger load machine tool in the current analysis period. In order to more intuitively view and analyze the load condition of the hidden danger load machine tool in different analysis periods, the load value data axis can be constructed based on the first real-time load value corresponding to each analysis period and the initial data axis, wherein the initial data axis includes two axis intervals, namely the first axis interval and the second axis interval. The first axis interval is used to describe the corresponding relationship between the analysis period and the first real-time load value, and the second axis interval is used to describe the corresponding relationship between the analysis period and the second real-time load value. The first real-time load value in the first axis interval can also be called the display load value.

[0073] The numerical value of the specific preset load value is not specifically limited in the embodiment of the present application and can be set by relevant technical personnel. In the initial data axis, the first axis interval and the second axis interval are empty. The displayed load value in the first axis interval is updated according to the first real-time load value corresponding to the current analysis period. When the displayed load value is not higher than the preset load value, the displayed load value in the second axis interval is 0. When the displayed load value is higher than the preset load value, the second real-time load value in the second axis interval is determined according to the load value conversion mapping relationship and the first real-time load value. At this time, the displayed load value in the second axis interval corresponds to the second real-time load value, and the displayed load value in the first axis interval is cleared at the same time, that is, the displayed load value in the first axis interval is updated to 0. Finally, the parameter adjustment value corresponding to the next analysis period is determined based on the second real-time load value and the adjustment mapping relationship.

[0074] The load value conversion mapping relationship is the conversion ratio of the first real-time load value to the second real-time load value. For example, the conversion ratio is 10:1. If the first real-time load value corresponding to the current analysis period is 10, the corresponding second real-time load value after the load value conversion is 1. At this time, the displayed load value of the second axis interval is updated to 1, and the displayed load value in the first axis interval is cleared at the same time, and then the parameter adjustment value corresponding to the next analysis period is determined according to the displayed load value of the second axis interval; if the first real-time load value corresponding to the next analysis period is 20, and the corresponding second real-time load value after the load value conversion is 2, at this time, it is updated on the basis of the displayed load value of the original second axis interval, and the updated displayed load value of the second axis interval is 3, and the displayed load value in the first axis interval is cleared at the same time, and then the parameter adjustment value corresponding to the next analysis period is determined according to the displayed load value of the second axis interval, and so on. The adjustment mapping relationship is the corresponding relationship between the second real-time load value and the parameter adjustment value. The parameter adjustment values ​​corresponding to different second real-time load values ​​may be the same or different. For example, when the second real-time load value is in the range of 0-5, the corresponding parameter adjustment value is A.

[0075] When adjusting the hidden danger load machine tool, it is also necessary to analyze and determine whether the current analysis period belongs to the last analysis period of the current operation cycle. If the current analysis period belongs to the last analysis period of the current operation cycle, it indicates that the current operation cycle has ended. At this time, there is no need to adjust the operation parameters of the hidden danger load machine tool, and a shutdown command can be directly generated. In addition, the number of times the load value displayed in the first axis interval is cleared indicates the number of times the first real-time load value is higher than the preset load value. When the number of clearing times is higher than the preset threshold, it indicates that the overload operation situation in the current operation cycle is more serious. At this time, a shutdown command can be directly generated to put the hidden danger load machine tool in a shutdown state to avoid more serious damage to the hidden danger load machine tool during subsequent processing, thereby facilitating the avoidance of overload operation or waste of resources of the hidden danger load machine tool, realizing reasonable utilization of resources and load balancing, and thus facilitating the improvement of efficiency and stability in the parts processing process.

[0076] The present application embodiment provides a scheduling system, such as Figure 3 As shown, Figure 3 The scheduling system 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the scheduling system 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the scheduling system 300 does not constitute a limitation on the embodiments of the present application.

[0077] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0078] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The fact that only one line is used in the diagram does not mean that there is only one bus or only one type of bus.

[0079] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0080] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0081] The dispatching system includes but is not limited to: mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The scheduling system shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0082] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0083] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method in any of the above embodiments is implemented.

[0084] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0085] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for intelligent scheduling of mold processing stations based on historical data prediction, characterized in that: include: Acquire processing drawing information, wherein the processing drawing information includes processing process flow, processing workload and parameters of the part to be processed, wherein the parameters of the part to be processed include part size, part material and processing accuracy requirements; Determine the corresponding parameters to be processed according to the preset prediction model and the processing drawing information, wherein the parameters to be processed include the machine tool to be processed and the operation time of the workstation to be used in the machine tool to be processed, and the preset prediction model is constructed by historical processing data, and the historical processing data includes the performance data of each processing machine tool in the historical time period; Determine the real-time production data corresponding to the machine tool to be processed based on the parameters to be processed, wherein the real-time production data includes the current operation status of the machine tool to be processed, the queue of tasks to be processed, and the material supply status; A workstation scheduling instruction is generated based on the parameters to be processed and the real-time production data, and the workstation scheduling instruction is bound to the processing drawing information.

2. The intelligent scheduling method for mold processing stations based on historical data prediction according to claim 1 is characterized in that: When the parameters to be processed include multiple machine tools to be processed, it also includes: When a scheduling execution instruction is detected, a central machine tool to be processed is determined from a plurality of machine tools to be processed based on the processing parameters, wherein the central machine tool to be processed is a machine tool to be processed with the most associated machine tools; Acquire the associated machine tools of the machine tools to be processed in the center, and determine the association type of each associated machine tool according to the preset prediction model, wherein the association type includes active association and passive association; Determine an associated machine tool whose association type is active association as a target associated machine tool, and determine a simulated processing flow of each target associated machine tool according to the preset prediction model; Based on the processing flow of each target associated machine tool, the predicted landing form of the corresponding part to be processed when it arrives at the corresponding workstation to be executed in the central machine tool to be processed is determined, and based on the predicted landing form of the part to be processed, the clamping form of the corresponding workstation to be executed in the central machine tool to be processed is determined, wherein each workstation to be executed corresponds to a part to be processed.

3. The intelligent scheduling method for mold processing stations based on historical data prediction according to claim 2 is characterized in that: Also includes: Counting the clamping form value corresponding to each workstation to be executed in the central machine tool to be processed within a preset time period, and determining the form data axis of each workstation to be executed based on the clamping form value of each workstation to be executed within the preset time period; Identify whether there is a periodic form in each form data axis, and determine the form data axis with the periodic form as the first focus form data axis, and determine the to-be-executed workstation corresponding to the first focus form data axis as the first focus to-be-executed workstation; Identify all periodic patterns contained in each first-focused pattern data axis, and determine the predicted pattern maintenance duration of the periodic pattern in the corresponding first-focused pattern data axis based on the change trends of all periodic patterns; Based on the periodic form and predicted form maintenance duration corresponding to each first focus form data axis, the clamping instructions for each first focus workstation to be executed within the predicted maintenance time period are determined, wherein the predicted maintenance time period corresponds to the predicted form maintenance duration.

4. The intelligent scheduling method for mold processing stations based on historical data prediction according to claim 3 is characterized in that: Also includes: Determine the morphological data axis without periodic morphology as the second focused morphological data axis, and determine the to-be-executed workstation corresponding to the second focused morphological data axis as the second focused to-be-executed workstation; Acquire the transfer-associated machine tool corresponding to each second focus workstation to be executed, and identify the setting position and performance data of each transfer-associated machine tool from the preset prediction model; Based on the setting position and performance data of each transfer-associated machine tool, determining a transfer area of ​​interest corresponding to a second workstation to be executed of interest; When it is detected that the part to be processed enters the attention transfer area corresponding to the second attention to be executed workstation, the clamping form adjustment time corresponding to the second attention to be executed workstation is determined.

5. The intelligent scheduling method for mold processing stations based on historical data prediction according to claim 1 is characterized in that: Also includes: Obtain the scheduling log of each machine tool to be processed in the current operation cycle, and identify the cumulative scheduling workload and scheduling operation difficulty level corresponding to each scheduling log; Based on each accumulated scheduling workload and the scheduling workload difficulty level, determine whether there is a hidden danger load machine tool, the hidden danger load machine tool corresponding to the hidden danger accumulated scheduling workload is higher than the preset standard scheduling workload, and the corresponding hidden danger scheduling workload difficulty level is higher than the preset standard difficulty level; Based on the accumulated hidden danger scheduling operation volume of the hidden danger load machine tool and the difficulty level of the hidden danger scheduling operation, the operation parameters of the hidden danger load machine tool in the next operation cycle are adjusted, and the operation parameters include operation depth and operation speed.

6. The intelligent scheduling method for mold processing stations based on historical data prediction according to claim 5 is characterized in that: Also includes: Execute the loop step, and generate a shutdown instruction based on the hidden danger load machine tool when the preset conditions are met, wherein the loop step is: Analyze the accumulated scheduling workload and the difficulty level of the scheduling workload of the hidden danger load machine tool in the current operation cycle in real time, determine a first real-time load value corresponding to the hidden danger load machine tool, and update the displayed load value of the first axis interval in the load value coordinate axis in real time based on the first real-time load value, wherein the displayed load value corresponds to the first real-time load value; When the displayed load value corresponding to the first axis interval is higher than the preset load value, the second real-time load value in the second axis interval is determined based on the load value conversion mapping relationship, and the parameter adjustment value of the hidden danger load machine tool is determined based on the second real-time load value and the adjustment mapping relationship, the load value conversion mapping relationship is the conversion correspondence relationship between the first real-time load value and the second real-time load value, the adjustment mapping relationship is the correspondence relationship between the second real-time load value and the parameter adjustment value, and the parameter adjustment value includes an operation depth adjustment value and an operation speed adjustment value; Clearing the displayed load value corresponding to the first axis interval; The preset conditions are: The current operation cycle ends; or the number of clearing times exceeds a preset threshold.

7. A scheduling system, characterized in that: The dispatch system includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a mold processing station intelligent scheduling method based on historical data prediction as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that: include: A computer program is stored which can be loaded by a processor and executes a method for intelligently scheduling mold processing stations based on historical data prediction as described in any one of claims 1 to 6.

9. A computer program product, characterized in that It includes a computer program, which, when executed by a processor, implements the steps of a mold processing station intelligent scheduling method based on historical data prediction as described in any one of claims 1 to 6.

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