A production intelligent management method and system for equipment parts

Abstract

The present invention discloses a production intelligent management method and system for equipment parts, wherein the method is applied to a production intelligent management system for equipment parts, the system is communicatively connected with an image acquisition and fitting device, and the method includes: obtaining a first configuration instruction; configuring a plurality of image acquisition nodes; obtaining production process detection data; outputting a first sample data; outputting a first biased distribution result; obtaining a first abnormal distribution result; and realizing production process management of the first equipment parts according to the first abnormal distribution result. The technical problem that the production abnormality results of equipment parts have a low degree of adaptability to the production process data and the abnormal points of the production process steps are difficult to reasonably evaluate is solved. Through the production process data of the production process of equipment parts, based on the parameter distribution bias analysis, the abnormal points of the production process steps are intelligently evaluated, the abnormal distribution results are determined, and the technical effect of realizing automated management of the production process is achieved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a production intelligent management method and system for equipment parts. Background Art

[0002] The continuous development of industrialization and the widespread demand for automation equipment have promoted technological progress and innovation, the industry's independent innovation capabilities have been significantly enhanced, breakthroughs have been made in key technologies of key industries, and innovation has been achieved through independent research and development and introduction, digestion, absorption and re-innovation. However, the progress and innovation of engineering machinery with automation equipment is restricted by the lack of equipment parts and components. Key equipment parts and components are the foundation, support and bottleneck for the development of engineering machinery products. When engineering machinery develops to a certain stage, the industry's high-tech research is mainly concentrated on key equipment parts and components such as engines, hydraulics, transmission and control technologies. The abnormal points of the production process steps of equipment parts and components are difficult to reasonably evaluate and accurately locate, the production abnormality results of equipment parts and components have a low degree of compatibility with the production process data, and the production management of equipment parts and components is difficult to effectively implement.

[0003] There are technical problems in the prior art that the abnormal production results of equipment parts and components have low compatibility with the production process data and the abnormal points of the production process steps are difficult to reasonably evaluate. Summary of the invention

[0004] The present application provides a method and system for intelligent production management of equipment parts and components, thereby solving the technical problems that the production abnormality results of equipment parts and components have low adaptability to the production process data and the abnormal points of the production process steps are difficult to reasonably evaluate. This application achieves the technical effect of intelligently evaluating the abnormal points of the production process steps, determining the abnormal distribution results, and realizing automated management of the production process.

[0005] In view of the above problems, the present application provides a method and system for intelligent production management of equipment parts.

[0006] In a first aspect, the present application provides a method for intelligent production management of equipment parts, wherein the method is applied to an intelligent production management system for equipment parts, the system is communicatively connected to an image acquisition and fitting device, and the method includes: obtaining a first configuration instruction according to production process information of a first equipment part; configuring multiple image acquisition nodes according to the first configuration instruction; performing data acquisition on the multiple image acquisition nodes based on the image acquisition and fitting device to obtain production process detection data, wherein the production process detection data includes multiple groups of detection distribution data, wherein the multiple groups of detection distribution data correspond one-to-one to the multiple image acquisition nodes; inputting the production process detection data into a data sampler, performing data sampling according to the data sampler, and outputting first sample data, wherein a data sampling model is embedded in the data sampler; performing parameter distribution bias analysis on the first sample data, and outputting a first bias distribution result; performing production anomaly point evaluation according to the first bias distribution result to obtain a first abnormal distribution result; and realizing production process management of the first equipment part according to the first abnormal distribution result.

[0007] In a second aspect, the present application provides a production intelligent management system for equipment parts, wherein the system is communicatively connected to an image acquisition and fitting device, and the system includes: a first acquisition unit, the first acquisition unit is used to obtain a first configuration instruction according to production process information of a first equipment part; a first execution unit, the first execution unit is used to configure multiple image acquisition nodes according to the first configuration instruction; a second acquisition unit, the second acquisition unit is used to perform data acquisition on the multiple image acquisition nodes based on the image acquisition and fitting device to obtain production process detection data, wherein the production process detection data includes multiple groups of detection distribution data, wherein the multiple groups of detection distribution data are consistent with the multiple image acquisition nodes. The acquisition nodes correspond one to one; a second execution unit, the second execution unit is used to input the production process detection data into a data sampler, perform data sampling according to the data sampler, and output first sample data, wherein a data sampling model is embedded in the data sampler; a first output unit, the first output unit is used to output a first biased distribution result by performing parameter distribution bias analysis on the first sample data; a third acquisition unit, the third acquisition unit is used to evaluate production anomalies according to the first biased distribution result to obtain a first abnormal distribution result; a third execution unit, the third execution unit is used to implement production process management of the first equipment parts according to the first abnormal distribution result.

[0008] In a third aspect, the present application provides an intelligent production management system for equipment parts, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the methods described in the first aspect when executing the program.

[0009] In a fourth aspect, the present application provides a computer program product, comprising a computer program and / or instructions, wherein the computer program and / or instructions, when executed by a processor, implement the steps of any method described in the first aspect.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] According to the production process information of the first equipment component, a first configuration instruction is obtained; according to the first configuration instruction, multiple image acquisition nodes are configured; based on the image acquisition fitting device, data is collected on the multiple image acquisition nodes to obtain production process detection data, wherein the production process detection data includes multiple groups of detection distribution data, wherein the multiple groups of detection distribution data correspond to the multiple image acquisition nodes one by one; the production process detection data is input into a data sampler, data sampling is performed according to the data sampler, and the first sample data is output, wherein a data sampling model is embedded in the data sampler; by performing parameter distribution bias analysis on the first sample data, a first bias distribution result is output; according to the first bias distribution result, production abnormal point evaluation is performed to obtain a first abnormal distribution result; according to the first abnormal distribution result, the production process management of the first equipment component is realized. The embodiment of the present application solves the technical problem that the production abnormal results of equipment components are poorly adapted to the production process data and the abnormal points of the production process steps are difficult to reasonably evaluate. Through the production process data of the production process of equipment components, based on the parameter distribution bias analysis, the abnormal points of the production process steps are intelligently evaluated, the abnormal distribution results are determined, and the technical effect of realizing automatic management of the production process is achieved.

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic diagram of a process for intelligent production management of equipment parts for this application;

[0014] Figure 2A schematic diagram of the flow chart of the data sampling logic function of the execution data sampler of the intelligent production management method of equipment parts in the present application;

[0015] Figure 3 A schematic diagram of a process for obtaining a first abnormal distribution result of a production intelligent management method for equipment parts of the present application;

[0016] Figure 4 A schematic diagram of a process for locating management targets according to the acceptance relationship between the first and second abnormal acceptance nodes of a production intelligent management method for equipment parts of the present application;

[0017] Figure 5 This is a schematic diagram of the structure of an intelligent production management system for equipment parts in this application;

[0018] Figure 6 This is a schematic diagram of the structure of an exemplary electronic device of the present application.

[0019] Explanation of the figure marks: first obtaining unit 11, first executing unit 12, second obtaining unit 13, second executing unit 14, first output unit 15, third obtaining unit 16, third executing unit 17, electronic device 300, memory 301, processor 302, communication interface 303, bus architecture 304. DETAILED DESCRIPTION

[0020] The present application provides a method and system for intelligent production management of equipment parts and components, thereby solving the technical problems that the production abnormality results of equipment parts and components have low adaptability to the production process data and the abnormal points of the production process steps are difficult to reasonably evaluate. This application achieves the technical effect of intelligently evaluating the abnormal points of the production process steps, determining the abnormal distribution results, and realizing automated management of the production process.

[0021] Application Overview

[0022] It is difficult to reasonably evaluate and accurately locate abnormal points in the production process steps of equipment parts and components. The abnormal production results of equipment parts and components have a low degree of compatibility with the production process data, and the production management of equipment parts and components is difficult to effectively implement.

[0023] There are technical problems in the prior art that the abnormal production results of equipment parts and components have low compatibility with the production process data and the abnormal points of the production process steps are difficult to reasonably evaluate.

[0024] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows:

[0025] The present application provides a production intelligent management method and system for equipment parts, wherein the method is applied to a production intelligent management system for equipment parts, the system is communicatively connected with an image acquisition and fitting device, and the method comprises: obtaining a first configuration instruction according to production process information of a first equipment part; configuring a plurality of image acquisition nodes according to the first configuration instruction; performing data acquisition on the plurality of image acquisition nodes based on the image acquisition and fitting device to obtain production process detection data, wherein the production process detection data comprises a plurality of groups of detection distribution data, wherein the plurality of groups of detection distribution data correspond one-to-one to the plurality of image acquisition nodes; inputting the production process detection data into a data sampler, performing data sampling according to the data sampler, and outputting first sample data, wherein a data sampling model is embedded in the data sampler; performing parameter distribution bias analysis on the first sample data, and outputting a first bias distribution result; performing production abnormal point evaluation according to the first bias distribution result, and obtaining a first abnormal distribution result; and realizing production process management of the first equipment part according to the first abnormal distribution result.

[0026] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0027] Embodiment 1

[0028] like Figure 1 As shown, the present application provides a production intelligent management method for equipment parts, wherein the method is applied to a production intelligent management system for equipment parts, the system is communicatively connected with an image acquisition and fitting device, and the method includes:

[0029] S100: Obtaining a first configuration instruction according to production process information of a first device component;

[0030] Specifically, the first equipment component may be a screw, a transmission bearing, a spring, a gear or other related component product, and there is no specific restriction on the material, shape and function of the first equipment component. The production process information is determined based on the first equipment component. The production process information may be production and processing process data composed of grinding, polishing, rough milling, fine milling or other related production process steps. The production and processing process data is specifically arranged in combination with the standard processing production process steps of the first equipment component. Based on the production process information, the first configuration instruction is obtained. The first configuration instruction is used to determine the processing flow of the first equipment component. The first configuration instruction is a real-time production and processing operation instruction of the first equipment component. The first configuration instruction is obtained to facilitate the process positioning of the production process information.

[0031] S200: configuring a plurality of image acquisition nodes according to the first configuration instruction;

[0032] Specifically, the first configuration instruction includes operation steps which may be operation steps corresponding to an automated machine or operation steps corresponding to manual processing. The operation process of the operation steps determines the first configuration instruction, and no specific process limitation is imposed on the operation steps of the first configuration instruction. The multiple acquisition nodes are configured in combination with the first configuration instruction. The first configuration instruction is an instruction for determining the operation process of the production and processing operation steps of the real-time first equipment components. The first configuration instruction does not represent a single processing instruction.

[0033] Combined with the production process of the first equipment component, a specific explanation is given. The first step of the production process information of the first equipment component is a grinding operation. The grinding standard of the first equipment component is that the surface of the first equipment component is uniform and free of graininess. The operation process of the grinding operation is also divided into an early stage and a late stage. Rough grinding is an early stage operation to remove the burr part of the first equipment component, and fine grinding is a late stage operation to remove the detailed concave and convex distribution part of the first equipment component. The first configuration instruction can locate the operation process of the actual production and processing operation step. The first configuration instruction is a set of instruction data. The instruction data of the first configuration instruction includes instruction data corresponding to the real-time operation process of multiple production and processing operation steps of the first equipment component. The multiple image acquisition nodes are image information of operation nodes corresponding to the real-time operation process of the multiple production and processing operation steps. In the above example, grinding is used for specific explanation. The example defines the grinding as the first step of multiple production and processing operation steps of the first equipment component. The description in the above example is for step explanation, and the actual first configuration instruction is not specifically limited.

[0034] S300: performing data acquisition on the plurality of image acquisition nodes based on the image acquisition and fitting device to obtain production process detection data, wherein the production process detection data includes a plurality of groups of detection distribution data, wherein the plurality of groups of detection distribution data correspond one-to-one to the plurality of image acquisition nodes;

[0035] Specifically, the image acquisition and fitting device includes multiple image acquisition devices, and the image acquisition devices can be cameras, camcorders or other image acquisition devices. There is no specific limitation on the image acquisition devices. The distribution of the multiple image acquisition devices is determined in correspondence with the production process information of the first equipment parts. The multiple image acquisition devices perform data acquisition on the multiple image acquisition nodes. The image acquisition and fitting device can integrate the image data collected to obtain production process detection data. The production process detection data includes multiple groups of detection distribution data, wherein the multiple groups of detection distribution data correspond one-to-one to the multiple image acquisition nodes, and the distribution rules of the multiple groups of detection distribution data correspond to the production process information of the first equipment parts. The production process detection data is obtained to provide a reliable data basis for production process detection and abnormal point evaluation.

[0036] S400: inputting the production process detection data into a data sampler, performing data sampling according to the data sampler, and outputting first sample data, wherein a data sampling model is embedded in the data sampler;

[0037] Specifically, the data sampling model performs functional training according to the data sampling layers and the data sampling weights. The data sampling weights correspond to the two groups of data of the data sampling layers one-to-one. The data sampling weights are actually determined in combination with the complexity of the production process corresponding to the data sampling layers. The data sampling logic function of the data sampler is specifically executed through the data sampling model. The production process detection data is input into the data sampler, and data sampling is performed according to the data sampler to output first sample data. The first sample data includes multiple groups of data samples. The data volume of the first group of data samples is related to the complexity of the production process corresponding to the data sampling layers. The number of groups of the data samples corresponds to the process steps of the production process information of the first equipment parts. The first sample data is output. When the data reliability is basically consistent, the complexity of the data is reduced. Directly using the production process detection data will make the speed of subsequent data analysis unable to be guaranteed. The first sample data includes the basic data characteristics of the production process detection data.

[0038] S500: Outputting a first biased distribution result by performing parameter distribution bias analysis on the first sample data;

[0039] Specifically, based on the partial correlation analysis, the parameter distribution bias analysis is performed on the first sample data. The partial correlation analysis can analyze the linear correlation between two variables under the condition of controlling the linear influence of other variables. The interference terms of the interference variables can be eliminated through the partial correlation analysis, so that the reliability of the data samples in the data analysis is improved. The first biased distribution result is determined by the parameter distribution bias analysis of the first sample data, which ensures the reliability of the data of the first biased distribution result.

[0040] S600: Evaluate production abnormality points according to the first bias distribution result to obtain a first abnormal distribution result;

[0041] S700: Implement production process management of the first equipment component according to the first abnormal distribution result.

[0042] Specifically, the production anomaly point indicates a production anomaly in a production process step determined in the production process when the production process detection result does not meet the standard. The production anomaly point does not represent a certain data group or a certain data amount. The production anomaly point can be represented as an abnormal identification information point. The specific data type should be determined in combination with the production process step type. The production anomaly point is evaluated according to the first biased distribution result. The evaluation standard is determined in combination with the partial correlation test. No specific details are given here. The evaluation result of the evaluation is determined as the first abnormal distribution result, and the first abnormal distribution result is obtained. According to the first abnormal distribution result, the production process management of the first equipment parts is realized, the production process of the first equipment parts is optimized, the production anomaly points of the first equipment parts in the production and processing are reduced, and the reliability and rationality of the production process management plan of the first equipment parts are improved.

[0043] Further, such as Figure 2 As shown, the method also includes:

[0044] S710: Determine the execution process level for production and processing of the first equipment component by performing process level analysis on the production process information of the first equipment component;

[0045] S720: Determine the number of data sampling levels according to the number of levels of the execution process level;

[0046] S730: Analyzing the execution complexity of each level in the execution process hierarchy, weighting the sampled data of each level according to the complexity index, and determining the data sampling weight;

[0047] S740: Performing function training on the data sampling model according to the number of data sampling layers and the data sampling weights to generate the data sampling model;

[0048] S750: Execute the data sampling logic function of the data sampler based on the data sampling model.

[0049] Specifically, the process level analysis is analyzed in combination with the production process information. The production process of the first equipment component includes multiple production process steps. Different production process steps correspond to different process layers. The process level is determined based on the multiple production process steps. By performing process level analysis on the production process information of the first equipment component, the execution process level for the production and processing of the first equipment component is determined. By determining the execution process level, the number of levels of the execution process level can be determined, and the number of data sampling layers can be determined. The execution complexity corresponds to the weight value determined by the weight allocation. The execution complexity of each level in the program level is analyzed, and the weight of the sampled data of each level is allocated according to the complexity index to determine the data sampling weight; the model basis of the data sampling model is a neural network model, and the number of data sampling layers and the data sampling weights are training data. The neural network model is functionally trained according to the number of data sampling layers and the data sampling weights, and the data sampling model is determined when the output tends to be stable. The function of the data sampling model is a data sampling logic function; the data sampler includes the data sampling model, and the data sampling model is used to execute the data sampling logic function of the data sampler.

[0050] To further specify, the execution complexity is linearly related to the weight value determined by the weight allocation. In simple terms, if the weight value data of the corresponding weight allocation with high execution difficulty is large, and the weight value data of the corresponding weight allocation with low execution difficulty is small, the data sampling model is obtained to provide a model basis for data sampling, thereby ensuring the reliability of the data sampling step plan.

[0051] Furthermore, by analyzing the execution complexity of each level in the execution process hierarchy, weight allocation of sampled data at each level is performed according to the complexity index to determine the data sampling weight, step S730 further includes:

[0052] S731: Obtain multiple groups of complexity indicators of the execution process level;

[0053] S732: Determine whether the plurality of groups of complexity indicators are within a preset complexity indicator threshold, and obtain a first determination result;

[0054] S733: classifying the multiple groups of complexity indicators according to the first judgment result, and outputting a first classification indicator and a second classification indicator, wherein the first classification indicator is an indicator set within the preset complexity indicator threshold, and the second classification indicator is an indicator set not within the preset complexity indicator threshold;

[0055] S734: using a first preset weight to classify the first classification indicator;

[0056] S735: Determine the data sampling weight by allocating weights to the second classification index by ratio and allocating weights to the second classification index by fixed value.

[0057] Specifically, multiple groups of complexity indicators of the execution process level are obtained, and the complexity indicators correspond to the execution complexity. Generally, the complexity indicators of high execution difficulty and high operation complexity are high, and the complexity indicators of low execution difficulty and low operation complexity are low. The high and low judgments are used to explain the understanding of complexity indicators. The specific customized quantitative analysis of the complexity indicators needs to be determined in combination with the data type characteristics of the multiple groups of complexity indicators; the preset complexity indicator threshold is a certain value, and the preset complexity corresponds to a certain value of weight. The fixed value of the weight must be less than the minimum value of the assigned weight. It is judged whether the multiple groups of complexity indicators are within the preset complexity index. The first judgment result is obtained in the threshold value of the standard; the multiple groups of complexity indicators are classified according to the first judgment result, and the first classification indicator and the second classification indicator are output, wherein the first classification indicator is a set of indicators within the preset complexity indicator threshold, and the second classification indicator is a set of indicators not within the preset complexity indicator threshold; the first preset weight is the weight of the fixed value, and the first classification indicator is assigned with the first preset weight; the first preset weight is used as the basic data for ratio allocation weight, and the data sampling weight is determined by performing ratio allocation weight on the second classification indicator and performing fixed value allocation weight on the second classification indicator.

[0058] To further specify, it is assumed that the calculation complexity corresponding to the preset complexity index threshold is set to 50, such as the calculation complexity: 40, 60, 82, 12, the complexity of the execution process level corresponding to the complexity index 40 and 12 below the preset complexity index threshold value 50 is not high, and the probability of anomaly is small, all are set to a fixed weight, the fixed weight is the first preset weight, the complexity of the execution process level corresponding to the complexity index 60 and 82 above the preset complexity index threshold value 50 is not high, and the probability of anomaly is high. Necessarily, the first preset weight must be less than the minimum value of the allocated weight, and the weight allocation needs to be performed according to the ratio. If the first preset weight corresponding to the preset complexity index threshold 50 is 1, and the weight corresponding to the complexity index 60 is 1.2, further optimization can be performed in combination with actual data, and no specific details are given here.

[0059] Further, such as Figure 3 As shown, the production abnormal point evaluation is performed according to the first biased distribution result to obtain the first abnormal distribution result, and step S600 also includes:

[0060] S610: Obtaining first production environment information for processing parts of the first equipment;

[0061] S620: Obtain the first bias distribution result, wherein the first bias distribution result includes a first bias domain and a second bias domain, the first bias is an automated production process domain, and the second bias is a real-time manual production process domain;

[0062] S630: performing partial correlation analysis using the first production environment information and two biased information in the first biased distribution result as three groups of variables to obtain a first partial correlation coefficient;

[0063] S640: Evaluate production anomaly points based on the first partial correlation coefficient to obtain the first anomaly distribution result.

[0064] Specifically, the first production environment information is real-time environmental data of the process of processing the first equipment parts, and the first production environment information includes environmental information corresponding to temperature, humidity or other relevant environmental indicators, and the first production environment information of the processing of the first equipment parts is obtained; the first biased distribution result is obtained, wherein the first biased distribution result includes a first bias domain and a second bias domain, the first bias is an automated production process domain, and the second bias is a real-time manual production process domain; the first production environment information and the two biased information in the first biased distribution result are used as three groups of variables for partial correlation analysis, and the partial correlation analysis can analyze the linear correlation between the first bias domain and the second bias domain under the condition of controlling the linear influence of the first production environment information, and the linear correlation is the first partial correlation coefficient; based on the first partial correlation coefficient, the production anomaly point evaluation is performed to obtain the first abnormal distribution result, to ensure the objectivity of the first abnormal distribution result, and to provide the reliability of the first abnormal distribution result.

[0065] Furthermore, the method further comprises:

[0066] S650: Perform a partial correlation test based on the first partial correlation coefficient to obtain a first partial correlation test result;

[0067] S660: If the first partial correlation test result is a success, determine whether the first partial correlation coefficient is greater than a preset partial correlation coefficient;

[0068] S670: If the first partial correlation coefficient is greater than the preset partial correlation coefficient, the process flow of the first equipment component is marked to determine a receiving process node;

[0069] S680: If the production anomaly point assessment includes the receiving process node, a second anomaly distribution result is obtained.

[0070] Specifically, the partial correlation test is a test determined in combination with the partial correlation analysis. Generally, if the partial correlation coefficient is large, it means that the current production anomaly may be caused by human factors, or the production anomaly may cause manual operation anomalies in the subsequent process. The partial correlation test is a test scheme for refining the partial correlation coefficient. The partial correlation test is performed based on the first partial correlation coefficient to obtain a first partial correlation test result; the preset partial correlation coefficient is a data threshold, and the data size of the preset partial correlation coefficient needs to be specifically determined in combination with the partial correlation analysis. If the first partial correlation test result is a successful test, it is determined whether the first partial correlation coefficient is greater than the preset partial correlation coefficient; the degree of correlation of the production process steps determined at the receiving process node is large, and an error within the threshold in the previous production process step may also cause anomalies in the next production process step. If the first partial correlation coefficient is greater than the preset partial correlation coefficient, the process flow of the first equipment component is identified to determine the receiving process node; if the receiving process node is included in the production anomaly point evaluation, a second abnormal distribution result is obtained, which is convenient for locating the production process steps that need to be optimized abnormally, which is conducive to improving the execution effect of the production process management plan.

[0071] To further explain, generally, if the partial correlation coefficient is large, it means that the current production anomaly may be caused by human factors, or the production anomaly may cause the abnormal manual operation of the subsequent process. In simple terms, the node of the process to be taken over is that the error within the threshold value of the previous production process step may also cause the abnormality of the next production process step. The value of the first partial correlation coefficient determined by the production anomaly associated with the manual operation is generally large. The error within the threshold value of the previous production process step may also cause the abnormality of the next production process step. Specifically, for example, the first processing step is divided into three small steps a, b, and c. The operations of a, b, and c are normal, and the execution result of the first processing step is normal; the operation of a is wrong, the operation of b and the operation of c are normal, and the execution result of the first processing step is normal; the operation of a and the operation of c are normal, the operation of b is wrong, but the error of the operation of b is within the threshold value, and the detection result indicates that the operation of b is normal, and the output of the execution result of the first processing step is abnormal. The operations of b and c are the node of the process to be taken over. The node of the process to be taken over should be determined first in the abnormality determination process, which can effectively improve the accuracy of abnormality positioning.

[0072] Furthermore, the method further comprises:

[0073] S760: Building a first conversion function module, wherein the first conversion function module includes a first recognition unit and a first conversion unit, the first recognition unit is used to perform screen recognition on the multiple image acquisition nodes, and the first conversion unit is used to perform conversion between image acquisition and device interface acquisition according to the screen recognition result;

[0074] S770: Logically configure the fitting image acquisition device according to the first conversion function module.

[0075] Specifically, a first conversion function module is constructed, wherein the first conversion function module includes a first recognition unit and a first conversion unit, wherein the first recognition unit is used to perform screen recognition on the multiple image acquisition nodes, and the first conversion unit is used to convert between the two functions of image acquisition and device interface acquisition according to the screen recognition result, and the conversion needs to be flexibly converted between the acquisition functions of the fitting image acquisition device according to different situations. Generally, for the case where only equipment is automated, image recognition is not required, and data acquisition can be realized by directly accessing the device interface; the fitting image acquisition device is logically configured according to the first conversion function module, and the logical configuration is not specifically limited. The specific logical configuration should be optimized in combination with the actual usage scenario, and no limitation is made here.

[0076] Further, such as Figure 4 As shown, the production process management of the first equipment component is realized according to the first abnormal distribution result, and further includes:

[0077] S641: Obtaining an abnormality distribution ratio of the first biased domain to the second biased domain according to the first abnormality distribution result;

[0078] S642: Obtaining abnormal receiving nodes in the first bias domain and the second bias domain according to the abnormal distribution ratio;

[0079] S643: The abnormality receiving node includes a first abnormality receiving node and a second abnormality receiving node, wherein the first abnormality receiving node corresponds to the first bias domain, the second abnormality receiving node corresponds to the second bias domain, and there is a receiving relationship between the first abnormality receiving node and the second abnormality receiving node;

[0080] S644: Locate the management target according to the taking-over relationship between the first abnormal taking-over node and the second abnormal taking-over node.

[0081] Specifically, according to the first abnormal distribution result, the abnormal distribution ratio of the first bias domain and the second bias domain is obtained. The abnormal distribution ratio is used to detect whether there is a mark overlap when there are at least two abnormal nodes. Generally, the abnormality of the previous step may cause the abnormality of the next step, and only the abnormality of the previous step is marked as abnormal; according to the abnormal distribution ratio, the abnormal succession nodes in the first bias domain and the second bias domain are obtained, and the abnormality of the abnormal succession nodes needs to be specifically determined through the partial correlation test; the abnormal succession nodes include a first abnormal succession node and a second abnormal succession node, wherein the first abnormal succession node corresponds to the first bias domain, the second abnormal succession node corresponds to the second bias domain, and there is a succession relationship between the first abnormal succession node and the second abnormal succession node. If there is a mark overlap, it means that the operation of the previous step may cause the abnormality of the next step, so it is necessary to give priority to the management of the first process connected by the steps; the management target is located according to the succession relationship between the first abnormal succession node and the second abnormal succession node, and the succession relationship is determined from the production and processing flow of the operation step.

[0082] In summary, the intelligent production management method and system for equipment parts provided by this application have the following technical effects:

[0083] 1. Due to the adoption of this application, a production intelligent management method and system for equipment parts is provided. According to the production process information of the first equipment part, a first configuration instruction is obtained; according to the first configuration instruction, multiple image acquisition nodes are configured; based on the image acquisition fitting device, data is collected on multiple image acquisition nodes to obtain production process detection data; the production process detection data is input into the data sampler, and data sampling is performed according to the data sampler to output the first sample data; by performing parameter distribution bias analysis on the first sample data, a first bias distribution result is output; according to the first bias distribution result, production abnormal point evaluation is performed to obtain the first abnormal distribution result; according to the first abnormal distribution result, the production process management of the first equipment part is realized. The technical problem that the production abnormal results of equipment parts and production process data have low adaptability and the abnormal points of the production process steps are difficult to reasonably evaluate is solved. Through the production process data of the production process of equipment parts and based on the parameter distribution bias analysis, the abnormal points of the production process steps are intelligently evaluated, the abnormal distribution results are determined, and the technical effect of realizing automated management of the production process is achieved.

[0084] 2. By analyzing the production process information of the first equipment parts at the process level, the execution process level of the production and processing of the first equipment parts is determined; the number of data sampling levels is determined according to the number of levels of the execution process level; the execution complexity of each level in the execution process level is analyzed, and the weight of the sampled data at each level is allocated according to the complexity index to determine the data sampling weight; the data sampling model is functionally trained according to the number of data sampling levels and the data sampling weight to generate the data sampling model; the data sampling logic function of the data sampler is executed based on the data sampling model. The data sampling model is obtained to provide a model basis for data sampling, which ensures the reliability of the data sampling step plan.

[0085] 3. Due to the adoption of obtaining the first production environment information of the first equipment parts processing; obtaining the first biased distribution result, wherein the first biased distribution result includes the first biased domain and the second biased domain, the first biased is the automated production process domain, and the second biased is the real-time manual production process domain; using the first production environment information and the two biased information in the first biased distribution result as three groups of variables to perform partial correlation analysis, obtaining the first partial correlation coefficient; based on the first partial correlation coefficient, the production abnormal point evaluation is performed to obtain the first abnormal distribution result. The objectivity of the first abnormal distribution result is guaranteed, and the reliability of the first abnormal distribution result is provided.

[0086] 4. Since a partial correlation test based on the first partial correlation coefficient is adopted, the first partial correlation test result is obtained; if the first partial correlation test result is a successful test, it is determined whether the first partial correlation coefficient is greater than the preset partial correlation coefficient; if the first partial correlation coefficient is greater than the preset partial correlation coefficient, the process flow of the first equipment parts is marked and the receiving process node is determined; if the receiving process node is included in the production abnormal point evaluation, the second abnormal distribution result is obtained. It is convenient to locate the production process steps that need to be optimized abnormally, which is conducive to improving the execution effect of the production process management plan.

[0087] Embodiment 2

[0088] Based on the same inventive concept as the production intelligent management method of equipment parts in the above-mentioned embodiment, Figure 5 As shown, the present application provides a production intelligent management system for equipment parts, wherein the system includes:

[0089] A first obtaining unit 11, wherein the first obtaining unit 11 is used to obtain a first configuration instruction according to production process information of a first device component;

[0090] A first execution unit 12, the first execution unit 12 is used to configure multiple image acquisition nodes according to the first configuration instruction;

[0091] A second obtaining unit 13, the second obtaining unit 13 is used to perform data acquisition on the multiple image acquisition nodes based on the image acquisition and fitting device to obtain production process detection data, wherein the production process detection data includes multiple groups of detection distribution data, wherein the multiple groups of detection distribution data correspond one-to-one to the multiple image acquisition nodes;

[0092] A second execution unit 14, the second execution unit 14 is used to input the production process detection data into a data sampler, perform data sampling according to the data sampler, and output first sample data, wherein a data sampling model is embedded in the data sampler;

[0093] A first output unit 15, the first output unit 15 is used to output a first biased distribution result by performing parameter distribution bias analysis on the first sample data;

[0094] A third obtaining unit 16, the third obtaining unit 16 is used to evaluate the production abnormal point according to the first bias distribution result to obtain a first abnormal distribution result;

[0095] The third execution unit 17 is used to implement production process management of the first equipment component according to the first abnormal distribution result.

[0096] Furthermore, the system comprises:

[0097] A fourth obtaining unit, the fourth obtaining unit is used to determine the execution process level of the production and processing of the first equipment component by performing process level analysis on the production process information of the first equipment component;

[0098] A first determining unit, the first determining unit being used to determine the number of data sampling layers according to the number of levels of the execution process level;

[0099] A second determining unit, the second determining unit is used to determine the data sampling weight by analyzing the execution complexity of each level in the execution process level, and performing weight allocation of sampled data of each level according to the complexity index;

[0100] A first generating unit, the first generating unit is used to perform function training on the data sampling model according to the number of data sampling layers and the data sampling weights to generate the data sampling model;

[0101] A fourth execution unit is used to execute the data sampling logic function of the data sampler based on the data sampling model.

[0102] Furthermore, the system comprises:

[0103] A fifth obtaining unit, the fifth obtaining unit is used to obtain multiple groups of complexity indicators of the execution process level;

[0104] A sixth obtaining unit, the sixth obtaining unit is used to determine whether the multiple groups of complexity indicators are within a preset complexity indicator threshold, and obtain a first judgment result;

[0105] a second output unit, the second output unit being used to classify the multiple groups of complexity indicators according to the first judgment result, and output a first classification indicator and a second classification indicator, wherein the first classification indicator is an indicator set within the preset complexity indicator threshold, and the second classification indicator is an indicator set not within the preset complexity indicator threshold;

[0106] A fifth execution unit, the fifth execution unit is used to perform the first classification index with a first preset weight;

[0107] The third determining unit is used to determine the data sampling weight by allocating weights by ratio to the second classification index and allocating weights by fixed value to the second classification index.

[0108] Furthermore, the system comprises:

[0109] A seventh obtaining unit, the seventh obtaining unit is used to obtain first production environment information of the first equipment component processing;

[0110] an eighth obtaining unit, the eighth obtaining unit being used to obtain the first biased distribution result, wherein the first biased distribution result includes a first biased domain and a second biased domain, the first biased domain is an automated production process domain, and the second biased domain is a real-time manual production process domain;

[0111] a ninth obtaining unit, the ninth obtaining unit being used to perform partial correlation analysis using the first production environment information and two biased information in the first biased distribution result as three groups of variables to obtain a first partial correlation coefficient;

[0112] A tenth obtaining unit is used to perform production anomaly point evaluation based on the first partial correlation coefficient to obtain the first abnormal distribution result.

[0113] Furthermore, the system comprises:

[0114] an eleventh obtaining unit, the eleventh obtaining unit being configured to perform a partial correlation test based on the first partial correlation coefficient to obtain a first partial correlation test result;

[0115] A first judgment unit, the first judgment unit is used to judge whether the first partial correlation coefficient is greater than a preset partial correlation coefficient if the first partial correlation test result is a test success;

[0116] a second judgment unit, the second judgment unit being configured to identify the process flow of the first equipment component and determine a process node to be undertaken if the first partial correlation coefficient is greater than the preset partial correlation coefficient;

[0117] The twelfth obtaining unit is used to obtain a second abnormal distribution result if the production abnormal point evaluation includes the taking-over process node.

[0118] Furthermore, the system comprises:

[0119] A first construction unit, the first construction unit is used to build a first conversion function module, wherein the first conversion function module includes a first recognition unit and a first conversion unit, the first recognition unit is used to perform screen recognition on the multiple image acquisition nodes, and the first conversion unit is used to convert between image acquisition and device interface acquisition according to the screen recognition result;

[0120] A sixth execution unit, the sixth execution unit is used to perform logical configuration on the fitting image acquisition device according to the first conversion function module.

[0121] Furthermore, the system comprises:

[0122] a thirteenth obtaining unit, configured to obtain an abnormal distribution ratio of the first biased domain to the second biased domain according to the first abnormal distribution result;

[0123] a fourteenth obtaining unit, the fourteenth obtaining unit being configured to obtain abnormal receiving nodes in the first biased domain and the second biased domain according to the abnormal distribution ratio;

[0124] A fourth determining unit, wherein the fourth determining unit is used for the abnormal receiving node to include a first abnormal receiving node and a second abnormal receiving node, wherein the first abnormal receiving node corresponds to the first bias domain, the second abnormal receiving node corresponds to the second bias domain, and there is a receiving relationship between the first abnormal receiving node and the second abnormal receiving node;

[0125] The seventh execution unit is used to locate the management target according to the taking-over relationship between the first abnormal taking-over node and the second abnormal taking-over node.

[0126] Exemplary Electronic Devices

[0127] Reference below Figure 6To describe the electronic device of this application,

[0128] Based on the same inventive concept as the production intelligent management method of equipment parts in the aforementioned embodiment, the present application also provides a production intelligent management system for equipment parts, including: a processor, the processor is coupled to a memory, the memory is used to store a program, when the program is executed by the processor, the device executes the steps of any method described in the first aspect.

[0129] The electronic device 300 includes: a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. The communication interface 303, the processor 302, and the memory 301 may be interconnected via the bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0130] The processor 302 may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the present application.

[0131] The communication interface 303 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), wired access networks, etc.

[0132] The memory 301 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, 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 an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor via a bus architecture 304. The memory may also be integrated with the processor.

[0133] The memory 301 is used to store computer-executable instructions for executing the solution of the present application, and the execution is controlled by the processor 302. The processor 302 is used to execute the computer-executable instructions stored in the memory 301, thereby realizing a production intelligent management method and system for equipment parts provided in the above embodiment of the present application.

[0134] Optionally, the computer-executable instructions in the present application may also be referred to as application code, which is not specifically limited in the present application.

[0135] The present application provides a production intelligent management method for equipment parts, wherein the method is applied to a production intelligent management system for equipment parts, the system is communicatively connected with an image acquisition and fitting device, and the method comprises: obtaining a first configuration instruction according to production process information of a first equipment part; configuring a plurality of image acquisition nodes according to the first configuration instruction; performing data acquisition on the plurality of image acquisition nodes based on the image acquisition and fitting device to obtain production process detection data, wherein the production process detection data comprises a plurality of groups of detection distribution data, wherein the plurality of groups of detection distribution data correspond one-to-one to the plurality of image acquisition nodes; inputting the production process detection data into a data sampler, performing data sampling according to the data sampler, and outputting first sample data, wherein a data sampling model is embedded in the data sampler; performing parameter distribution bias analysis on the first sample data, and outputting a first bias distribution result; performing production abnormal point evaluation according to the first bias distribution result, and obtaining a first abnormal distribution result; and realizing production process management of the first equipment part according to the first abnormal distribution result.

[0136] Those skilled in the art will appreciate that the various digital numbers such as the first and second involved in this application are only for the convenience of description, and are not used to limit the scope of this application, nor do they indicate a sequence of precedence. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated with each other are in an "or" relationship. "At least one" refers to one or more. At least two refers to two or more. "At least one", "any one" or similar expressions refer to any combination of these items, including any combination of single items (individuals) or plural items (individuals). For example, at least one item (individual, kind) of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0137] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0138] The various illustrative logic units and circuits described in this application can be implemented or operated by a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of the above. The general-purpose processor can be a microprocessor, and optionally, the general-purpose processor can also be any conventional processor, controller, microcontroller or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.

[0139] The steps of the method or algorithm described in this application can be directly embedded in hardware, software units executed by a processor, or a combination of the two. The software unit can be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM or any other form of storage medium in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC, and the ASIC can be arranged in a terminal. Optionally, the processor and the storage medium can also be arranged in different components in the terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in a box or multiple boxes. Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic creative concepts. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0141] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include these modifications and variations.

Claims

1. A production intelligent management method for equipment parts, It is characterized in that The method is applied to a production intelligent management system for equipment parts, the system is communicatively connected with an image acquisition and fitting device, and the method comprises: Obtaining a first configuration instruction according to the production process information of the first equipment component; According to the first configuration instruction, configure multiple image acquisition nodes; Based on the image acquisition and fitting device, data is collected on the multiple image acquisition nodes to obtain production process detection data, wherein the production process detection data includes multiple groups of detection distribution data, wherein the multiple groups of detection distribution data correspond to the multiple image acquisition nodes one by one; Inputting the production process detection data into a data sampler, performing data sampling according to the data sampler, and outputting first sample data, wherein a data sampling model is embedded in the data sampler; Outputting a first biased distribution result by performing parameter distribution bias analysis on the first sample data; Perform production abnormal point evaluation according to the first biased distribution result to obtain a first abnormal distribution result; Implementing production process management of the first equipment parts according to the first abnormal distribution result; Determine the execution process level of production and processing of the first equipment component by performing process level analysis on the production process information of the first equipment component; Determining the number of data sampling levels according to the number of levels of the execution process level; By analyzing the execution complexity of each level in the execution process hierarchy, weight allocation of sampled data at each level is performed according to the complexity index to determine the data sampling weight; Performing functional training on the data sampling model according to the number of data sampling layers and the data sampling weights to generate the data sampling model; Execute the data sampling logic function of the data sampler based on the data sampling model; The method further comprises: analyzing the execution complexity of each level in the execution process level, allocating the weight of the sampled data of each level according to the complexity index, and determining the data sampling weight. Obtaining multiple sets of complexity indicators at the execution process level; Determine whether the plurality of groups of complexity indicators are within a preset complexity indicator threshold, and obtain a first determination result; Classifying the multiple groups of complexity indicators according to the first judgment result, and outputting a first classification indicator and a second classification indicator, wherein the first classification indicator is an indicator set within the preset complexity indicator threshold, and the second classification indicator is an indicator set not within the preset complexity indicator threshold; The first classification indicator and the second classification indicator are assigned weights with a first preset weight to determine the data sampling weight.

2. The method according to claim 1, It is characterized in that The method further comprises: performing production abnormal point evaluation according to the first biased distribution result to obtain a first abnormal distribution result. Obtaining first production environment information for processing parts of the first equipment; Obtaining the first biased distribution result, wherein the first biased distribution result includes a first biased domain and a second biased domain, the first biased domain is an automated production process domain, and the second biased domain is a real-time manual production process domain; Performing partial correlation analysis using the first production environment information and two biased information in the first biased distribution result as three groups of variables to obtain a first partial correlation coefficient; Production anomaly points are evaluated based on the first partial correlation coefficient to obtain the first anomaly distribution result.

3. The method according to claim 2, It is characterized in that The method further comprises: Perform a partial correlation test based on the first partial correlation coefficient to obtain a first partial correlation test result; If the first partial correlation test result is a successful test, determining whether the first partial correlation coefficient is greater than a preset partial correlation coefficient; If the first partial correlation coefficient is greater than the preset partial correlation coefficient, the process flow of the first equipment component is marked to determine the receiving process node; If the taking-over process node is included in the production anomaly point assessment, a second anomaly distribution result is obtained.

4. The method according to claim 1, It is characterized in that The method further comprises: Building a first conversion function module, wherein the first conversion function module includes a first recognition unit and a first conversion unit, the first recognition unit is used to perform screen recognition on the multiple image acquisition nodes, and the first conversion unit is used to convert between image acquisition and device interface acquisition according to the screen recognition result; The image acquisition and fitting device is logically configured according to the first conversion function module.

5. The method according to claim 2, It is characterized in that The method of implementing production process management of the first equipment component according to the first abnormal distribution result further includes: Obtaining an abnormal distribution ratio of the first biased domain to the second biased domain according to the first abnormal distribution result; Obtaining abnormal receiving nodes in the first biased domain and the second biased domain according to the abnormal distribution ratio; The abnormality undertaking node includes a first abnormality undertaking node and a second abnormality undertaking node, wherein the first abnormality undertaking node corresponds to the first bias domain, the second abnormality undertaking node corresponds to the second bias domain, and there is an undertaking relationship between the first abnormality undertaking node and the second abnormality undertaking node; The management target is located according to the taking-over relationship between the first abnormal taking-over node and the second abnormal taking-over node.

6. An intelligent production management system for equipment parts, It is characterized in that The system is connected in communication with the image acquisition and fitting device, and is used to execute the production intelligent management method of equipment parts according to any one of claims 1 to 5, and the system includes: A first obtaining unit, the first obtaining unit being used to obtain a first configuration instruction according to production process information of a first equipment component; A first execution unit, the first execution unit is used to configure multiple image acquisition nodes according to the first configuration instruction; a second obtaining unit, the second obtaining unit being used to perform data acquisition on the plurality of image acquisition nodes based on the image acquisition and fitting device to obtain production process detection data, wherein the production process detection data includes a plurality of groups of detection distribution data, wherein the plurality of groups of detection distribution data correspond one-to-one to the plurality of image acquisition nodes; a second execution unit, the second execution unit being used to input the production process detection data into a data sampler, perform data sampling according to the data sampler, and output first sample data, wherein a data sampling model is embedded in the data sampler; A first output unit, the first output unit is used to output a first biased distribution result by performing parameter distribution bias analysis on the first sample data; a third obtaining unit, the third obtaining unit being used to evaluate production abnormal points according to the first biased distribution result to obtain a first abnormal distribution result; A third execution unit is used to implement production process management of the first equipment component according to the first abnormal distribution result.

7. An intelligent production management system for equipment parts, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer program product comprising a computer program, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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