Intelligent assembly component whole-process management system

Abstract

The invention relates to the technical field of intelligent monitoring management, particularly discloses an intelligent assembly component whole-process management system, and aims to optimize production efficiency and product quality through real-time data acquisition and analysis. The system comprises a data acquisition module which monitors and records the switching time of each process and the quality inspection qualified rate of each component in real time through a sensor and automation equipment on an integrated production line, a production plan evaluation module which calculates a switching time abnormal coefficient, identifies a bottleneck or low-efficiency operation in the process switching time, and evaluates the quality inspection qualified rate of each component according to the bottleneck or low-efficiency operation. And the process switching time is dynamically adjusted based on the specific numerical values of the influence interference factors and the positive and negative characteristics of the influence interference factors, the production quality evaluation module calculates a quality abnormal coefficient according to the quality inspection qualified rate and judges the quality fluctuation condition of each component, and the influence analysis and adjustment module predicts the quality qualified rate by using a gradient enhanced regression model. The process switching time is dynamically adjusted to eliminate potential quality problems.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent monitoring management, and particularly relates to an intelligent assembly component whole-process management system. BACKGROUND

[0002] In modern manufacturing industry, the production process of assembly components involves multiple complex processes and strict quality control standards. In order to ensure production efficiency and product quality, enterprises widely adopt automated equipment, sensor technology and data analysis tools. However, traditional production management systems rely on static planning and post-analysis, and it is difficult to monitor and adjust key parameters in real time, such as process switching time and quality inspection pass rate. These systems lack the ability to quickly respond to dynamic changes in the production process, resulting in low production efficiency and frequent quality problems.

[0003] The prior art has the following disadvantages: The prior art has the following disadvantages: The traditional production management system usually relies on a static schedule to arrange process switching, and lacks the ability to monitor and dynamically adjust the process switching time in real time. This leads to bottlenecks or redundant time in some processes, but due to the lack of real-time data support, it is difficult to discover and optimize in a timely manner, thereby increasing unnecessary downtime and reducing overall production efficiency. Secondly, in terms of quality control, the traditional method mainly relies on periodic sampling and post-analysis, and cannot achieve real-time monitoring and immediate feedback. Quality problems can only be discovered after the product is completed and quality inspection is performed, resulting in defective products flowing into subsequent processes or even the market, affecting customer satisfaction. In addition, the quality fluctuates greatly between different batches, making it difficult to ensure the high quality consistency of each batch of products. The existing system can only provide single-dimensional data analysis, and lacks the ability to comprehensively evaluate and predict the relationship between production efficiency and quality, making it difficult for management personnel to obtain comprehensive data support and make scientific decisions to optimize the production process. Due to the lack of forward-looking prediction models, enterprises can only take remedial measures after problems occur, and cannot prevent potential problems from occurring. These problems together result in low production efficiency and unstable product quality, restricting the competitiveness and development potential of enterprises. Therefore, an intelligent management system that can monitor in real time, dynamically adjust and comprehensively analyze is urgently needed to solve these deficiencies. SUMMARY

[0004] The purpose of the present application is to provide an intelligent assembly component whole-process management system to solve the problems in the above background.

[0005] The purpose of the present application can be achieved by the following technical solutions: An intelligent assembly component whole-process management system, comprising: A data acquisition module, which collects process switching time and quality inspection pass rate in real time during the component production monitoring cycle; A production plan evaluation module, which is used to analyze the process switching time in the production process within the monitoring period and evaluate the production efficiency of the component production process based on the process switching time; A production quality assessment module, which is used to analyze the quality inspection pass rate of the production process within the monitoring period and evaluate the quality of the component production process based on the degree of fluctuation of the quality pass rate of each component; A comprehensive analysis module, which is used to analyze the production efficiency and quality of the component production process and, based on the analysis results, evaluate the quality consistency of each batch of assembled components during the production process; An impact analysis and adjustment module analyzes the degree of influence of production efficiency on the quality of component production during the production process based on the quality consistency of each batch of assembled components during the production process, and dynamically adjusts the process switching time during the production process based on the degree of influence.

[0006] As a further solution of the present invention: the production efficiency of the evaluation component production process specifically includes: During the component production monitoring cycle, the process switching time in the production process is collected in real time. According to the fluctuation degree of the switching time of different processes, the switching time abnormality coefficient of the overall production process is calculated to determine whether the switching abnormality coefficient of the overall production process is greater than or equal to the preset threshold. If so, it means that the switching time of the production process is normal, and the production efficiency is normal. If not, it means that the switching time of the production process is abnormal, and the production efficiency is abnormal.

[0007] As a further solution of the present invention: the process of obtaining the switching time abnormality coefficient is: Real-time collection of switching time data of each process in the production process, each data represents the switching time from the first process to the nth process; Performing stabilization processing on the collected switching time data, including noise removal and differential operation; Determine the three parameters of the ARIMA model: autoregressive order p, difference number d, and moving average order q, and use historical data to fit the ARIMA model. The expression for calculating the switching time prediction value at each moment is: ; in, Indicates the The switching time data prediction value at the moment, Indicates the acquisition time; according to the actual switching time data and predicted value The difference between , calculate the residual, the calculation expression is: ;in, Indicates the The residual value at the moment; Calculate the residual standard deviation value at all times, and calculate and process the residual value and the residual standard deviation value to obtain the switching time anomaly coefficient. The calculation expression is: ; in, Indicates the total number of acquisition time points, represents the standard deviation of the residuals, Indicates the switching time anomaly coefficient.

[0008] As a further solution of the present invention: within the component production monitoring cycle, the quality inspection pass rate of each component in the production process is collected in real time, and according to the fluctuation degree of the quality pass rate of each component, the quality anomaly coefficient of the corresponding component during quality inspection is calculated, and it is judged whether the quality anomaly coefficient of each component is greater than or equal to a preset threshold. If so, it indicates that there is an abnormal fluctuation in the corresponding quality pass rate, and the production quality of the component is poor. If not, it indicates that there is no abnormal fluctuation in the corresponding quality pass rate, and the production quality of the component is good.

[0009] As a further solution of the present invention: the process of obtaining the mass anomaly coefficient is: During the component production monitoring cycle, the quality inspection pass rate of each component in the production process is collected in real time according to the time series; Get the Quality pass rate at all times , Factors affecting quality pass rate Hedi Quality inspection results at all times , Indicates the collection time; According to the Bayesian theorem, the posterior probability distribution of the quality pass rate is calculated as follows: ; in, represents the posterior probability distribution, represents the likelihood function of the quality detection result, represents the prior probability of quality pass rate, represents the normalization constant; Calculate the expected value of the posterior probability distribution of the quality pass rate obtained by Bayesian inference; The quality anomaly coefficient is obtained by calculating the difference between the quality pass rate of each component and the expected pass rate. The calculation expression is: ; in, Indicates the total number of acquisition time points, It represents the standard deviation of the quality pass rate, represents the quality anomaly coefficient, It represents the expected value of the posterior probability distribution of the quality pass rate obtained by Bayesian inference.

[0010] As a further solution of the present invention: the analysis of the production efficiency and the quality of the component production process specifically includes: Obtain the switching time anomaly coefficient and quality anomaly coefficient during the component production process, perform normalized calculations on the switching time anomaly coefficient and the quality anomaly coefficient, and calculate the quality consistency coefficient to evaluate the quality consistency of each batch of assembled components during the production process; The calculation process of the consistency coefficient is: Collect the switching time abnormality coefficient and quality abnormality coefficient during the production process of each component; Normalize the switching time anomaly coefficient and quality anomaly coefficient; For each component Use Euclidean distance calculation with widgets The distance between them is calculated as: ; in, Representation component and components The Euclidean distance between and Respectively represent components, Representation component The normalized value of the switching time anomaly coefficient, Representation component The normalized value of the switching time anomaly coefficient, Representation component The normalized value of the mass anomaly coefficient, Representation component The standardized value of the mass anomaly coefficient; for each component , select the K neighbors with the closest distance and calculate the distance between these neighbors and the component The average distance between them is used to obtain the quality consistency coefficient, and the calculation expression is: ; in, Representation component The quality consistency coefficient of Indicates the number of neighbors selected.

[0011] As a further solution of the present invention, the evaluation of the quality consistency of each batch of assembly components during the production process specifically includes: Calculate the quality consistency coefficient of each batch of assembly components in the production process respectively, and compare the quality consistency coefficient of each batch of assembly components with the preset threshold value; Determine whether the quality consistency coefficient is greater than or equal to the preset threshold value, if yes, the component quality is consistent, if not, the component quality is inconsistent.

[0012] As a further scheme of the present application: the analysis of the influence degree of production efficiency on the quality of component production process, specifically includes: According to the influence of production efficiency on the quality of component production process, calculate the influence interference factor, and determine whether the influence interference factor in the component production process is greater than or equal to the preset threshold value, if yes, it means that the production efficiency in the component production process has an impact on the quality of the component production process, if not, it means that the production efficiency in the component production process has no impact on the quality of the component production process; The process of obtaining the influence interference factor is: Collect the switching time abnormality coefficient and the quality abnormality coefficient in each component production process; Take the switching time abnormality coefficient and the quality abnormality coefficient as input features, and collect the quality inspection pass rate in the component production process as the target variable; Normalize the switching time abnormality coefficient and the quality abnormality coefficient; Based on the normalized switching time abnormality coefficient and the quality abnormality coefficient and the target variable quality pass rate, a regression model is established by gradient boosting regression algorithm to predict the quality pass rate; Based on the trained gradient boosting regression model, calculate the influence interference factor of the production efficiency of each batch of components on the quality, the calculation expression is: Wherein, The influence interference factor of the production efficiency of the i-th component on the quality, The i-th component, The actual quality pass rate of the i-th component, The predicted quality pass rate based on the normalized switching time abnormality coefficient and the quality abnormality coefficient, The switching time abnormality coefficient of the i-th component, The normalized value of the switching time abnormality coefficient of the i-th component, The normalized value of the quality abnormality coefficient of the i-th component.

[0013] As a further scheme of the present application: the process switching time in the production process is dynamically adjusted according to the influence degree, specifically includes: ​​​​determining whether the influence interference factor is greater than or equal to a preset threshold value, if yes, it is indicated that the process switching time in the corresponding production process has an impact on the quality pass rate of the component, and the process switching time needs to be adjusted; According to the specific value of the influence interference factor, the adjustment direction of the switching time of the affected process is determined, and the adjustment amplitude is calculated: for a positive influence factor, the switching time is reduced; for a negative influence factor, the switching time is increased, and the operation process of the automation equipment is updated, and a new process switching time parameter is set.

[0014] The beneficial effects of the present application are: (1) The present application monitors and records the switching time of each process in real time through the data acquisition module, and calculates the switching time abnormality coefficient using the production plan evaluation module, thereby accurately evaluating the efficiency in the production process. Specifically, the system can identify which process switching time has abnormal fluctuations, and dynamically adjust the process switching time based on these abnormal conditions. For example, when the switching time abnormality coefficient of a certain process is higher than the preset threshold value, it indicates that there may be a bottleneck or inefficient operation in this process. At this time, the system will further analyze the specific value of the influence interference factor and its positive and negative nature to determine whether the switching time needs to be increased or decreased, and automatically update the relevant parameter settings on the production line, such as the operation process of the automation equipment and the worker's job guide. This fine time management not only reduces unnecessary downtime, but also optimizes resource allocation and balances the workload of each process, thereby significantly improving overall production efficiency. In addition, through deep learning and model prediction of historical data, the system can early warning potential production bottlenecks, so that management personnel can take preventive measures before problems occur, further improving the overall operation efficiency and response speed of the production line.

[0015] (2) The application ensures that the quality of each link in the production process can be accurately evaluated and effectively controlled by real-time monitoring and analyzing the quality inspection pass rate of each component. The production quality evaluation module calculates the quality abnormality coefficient based on the quality inspection pass rate, thereby accurately judging the quality fluctuation of each component. Once the quality abnormality coefficient of a certain component exceeds the preset threshold, the system immediately identifies the quality problems existing in the production process of the component and feeds back these key information to the comprehensive analysis module. The comprehensive analysis module calculates the quality consistency coefficient of each batch of assembled components to comprehensively evaluate the quality stability in the entire production process. Based on the evaluation results, the influence analysis and adjustment module can dynamically adjust the process switching time to eliminate potential quality problems. For example, when it is found that the quality consistency coefficient of a batch of components is low, the system will automatically adjust the switching time of the related process to reduce the quality fluctuation caused by improper switching. This intelligent quality control not only improves the quality level of individual components, but also ensures the high consistency between batches of assembled components. In addition, by combining advanced algorithms such as Bayesian theorem and gradient boosting regression model, the system can more accurately predict and prevent the occurrence of quality problems, providing more scientific and reliable quality management solutions for enterprises and helping them achieve the goal of lean production and continuous improvement. BRIEF DESCRIPTION OF DRAWINGS

[0016] The application will be further described below with reference to the accompanying drawings.

[0017] Figure 1 is a flow chart of an intelligent assembled component whole-process management system of the application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the application.

[0019] Please refer to Figure 1 The application is an intelligent assembled component whole-process management system, which comprises: A data acquisition module, which acquires the process switching time and the quality inspection pass rate in the production process in real time within a component production monitoring period; A production plan evaluation module, which is used for analyzing the process switching time in the production process in the monitoring period and evaluating the production efficiency in the component production process according to the process switching time; a production quality evaluation module for analyzing the quality inspection pass rate in the production process within the monitoring period, and evaluating the quality of the component production process according to the fluctuation degree of the quality pass rate of each component; a comprehensive analysis module for analyzing the production efficiency in the component production process and the quality in the component production process, and evaluating the quality consistency of the assembled components in each batch in the production process according to the analysis result; an influence analysis and adjustment module for analyzing the influence degree of the production efficiency in the component production process on the quality in the component production process according to the quality consistency of the assembled components in each batch in the production process, and dynamically adjusting the process switching time in the production process according to the influence degree.

[0020] In the data acquisition module, the data acquisition module acquires the process switching time and the quality inspection pass rate in the production process in real time within the component production monitoring period, specifically including: In the component production monitoring period, the data acquisition module first acquires the switching time of each process in the production process in real time by integrating with various sensors and automatic devices on the production line. These sensors and devices are deployed on key process nodes and can accurately record the time stamp of the start and end of each process. Whenever a process is completed and is ready to enter the next process, the system will automatically capture this moment and transmit these time data to the central database for storage and subsequent analysis. In addition, the system will also combine production plans and work order information to ensure that the collected data accurately correspond to each specific production batch and component; The data acquisition module is also responsible for real-time monitoring of the quality inspection link and recording the quality inspection pass rate of each component. At each quality detection point, the inspection equipment (such as a visual inspection system) will check the component according to the preset standards and automatically generate a quality report. The report contains information on whether the component passes each quality standard, as well as any existing defects or non-conformities. All these quality data are also transmitted to the central database in real time and associated with the corresponding production batch and process switching time data for subsequent analysis. In this way, the system can fully grasp the efficiency and quality status of the production process, providing solid data support for subsequent evaluation and optimization.

[0021] In the production plan evaluation module, the process switching time in the production process within the monitoring period is analyzed, and the production efficiency in the component production process is evaluated according to the process switching time, specifically including: In the component production monitoring period, the process switching time in the production process is collected in real time, the switching time abnormal coefficient of the overall production process is calculated according to the fluctuation degree of the switching time of different processes, whether the switching abnormal coefficient of the overall production process is greater than or equal to the preset threshold is judged, if yes, it is indicated that the switching time of the production process is normal, then the production efficiency is normal, if not, it is indicated that the switching time of the production process is abnormal, then the production efficiency is abnormal; The acquisition process of the switching time abnormal coefficient is as follows: The switching time data of each process in the production process is collected in real time, and each data represents the switching time of the first process to the nth process; The collected switching time data is smoothed, including removing noise and difference operation; Three parameters of the ARIMA model are determined: autoregressive order p, difference number d and moving average order q, and the ARIMA model is fitted using historical data, and the expression of the switching time prediction value at each time is: Wherein, represents the switching time data prediction value at the t th time, represents the switching time data prediction value at the t th time, represents the collection time; According to the difference between the actual switching time data and the prediction value , the residual error is calculated, and the calculation expression is: Wherein, represents the residual error value at the t th time, represents the residual error value at the t th time, The standard deviation value of the residual error at all times is calculated, and the residual error value and the standard deviation value of the residual error are calculated to obtain the switching time abnormal coefficient, and the calculation expression is: Wherein, represents the total number of collection time points, represents the standard deviation value of the residual error, represents the switching time abnormal coefficient; It should be noted that the switching time abnormal coefficient reflects whether the switching time of the corresponding production process is normal, and the greater the value of the switching time abnormal coefficient, the more problems in the switching time of the corresponding production process.

[0022] In the production quality evaluation module, the quality inspection qualified rate in the production process in the monitoring period is analyzed, the quality of the component production process is evaluated according to the fluctuation degree of the quality qualified rate of each component, which specifically includes: ​​​In the component production monitoring period, the quality inspection qualified rate of each component in the production process is collected in real time, the quality anomaly coefficient of the corresponding component when the quality detection is performed is calculated according to the fluctuation degree of the quality qualified rate of each component, and it is judged whether the quality anomaly coefficient of each component is greater than or equal to a preset threshold value, if yes, it is indicated that the corresponding quality qualified rate has abnormal fluctuation, and the production quality of the component is poor, if not, it is indicated that the corresponding quality qualified rate has no abnormal fluctuation, and the production quality of the component is good; The acquisition process of the quality anomaly coefficient is as follows: In the component production monitoring period, the quality inspection qualified rate of each component in the production process is collected in real time according to the time sequence; The quality qualified rate of the first time is acquired , the factors affecting the quality qualified rate and the quality detection result of the first time , indicates the acquisition time; the posterior probability distribution of the quality qualified rate is inferred according to the Bayes theorem, and the calculation expression is as follows: ; Among them, indicates the posterior probability distribution, indicates the likelihood function of the quality detection result, indicates the prior probability of the quality qualified rate, indicates the standardization constant; The expected value of the posterior probability distribution of the quality qualified rate obtained by the Bayes inference is calculated; The quality anomaly coefficient is obtained by calculating the difference between the quality qualified rate of each component and the expected qualified rate, and the calculation expression is as follows: ; Among them, indicates the total number of acquisition time points, indicates the standard deviation of the quality qualified rate, indicates the quality anomaly coefficient, indicates the expected value of the posterior probability distribution of the quality qualified rate obtained by the Bayes inference.

[0023] It should be noted that the quality anomaly coefficient reflects whether the component product quality in the production process is abnormal, and the greater the value of the quality anomaly coefficient, the higher the degree of quality anomaly of the corresponding component.

[0024] In the comprehensive analysis module, the production efficiency in the component production process and the quality in the component production process are analyzed, and according to the analysis result, the quality consistency of each batch of assembled components in the production process is evaluated, specifically including: The switching time abnormality coefficient and the quality abnormality coefficient in the production process of the component are acquired, the switching time abnormality coefficient and the quality abnormality coefficient are normalized and processed, the quality consistency coefficient is calculated, and the quality consistency of each batch of assembled components in the production process is evaluated. The calculation process of the consistency coefficient is as follows: The switching time abnormality coefficient and the quality abnormality coefficient in the production process of each component are acquired. The switching time abnormality coefficient and the quality abnormality coefficient are normalized and processed. The Euclidean distance between the component and the K nearest neighbors is calculated. The calculation expression is as follows: Wherein, represents the Euclidean distance between the component and the component, and the component, and respectively represent the component, For each component, the K nearest neighbors are selected, and the average distance between the neighbors and the component is calculated to obtain the quality consistency coefficient. The calculation expression is as follows: ; Wherein, represents the quality consistency coefficient of the component, represents the number of selected neighbors.The quality consistency of each batch of assembled components in the production process is evaluated, specifically including: The quality consistency coefficients of each batch of assembled components in the production process are calculated respectively, and the quality consistency coefficients of each batch of assembled components are compared with the preset threshold value. If yes, the component quality is consistent, and if no, the component quality is inconsistent.

[0025] It should be noted that the quality consistency coefficient reflects the quality consistency of each batch of assembled components in the production process, and the greater the value of the quality consistency coefficient, the higher the quality consistency of the corresponding assembled component.

[0026] ​​​​​​​​​In the influence analysis and adjustment module, according to the quality consistency of each batch of assembly components in the production process, the influence degree of the production efficiency in the component production process on the quality in the component production process is analyzed, and the switching time of the process in the production process is dynamically adjusted according to the influence degree, which specifically includes: According to the influence of the production efficiency in the component production process on the quality in the component production process, the influence interference factor is calculated, and it is judged whether the influence interference factor is greater than or equal to the preset threshold value, if yes, it means that the production efficiency in the component production process has an impact on the quality in the component production process, if not, it means that the production efficiency in the component production process has no impact on the quality in the component production process; The acquisition process of the influence interference factor is: Collect the switching time abnormality coefficient and the quality abnormality coefficient in each component production process; The switching time abnormality coefficient and the quality abnormality coefficient are used as input features, and the quality inspection pass rate in the component production process is collected as a target variable; The switching time abnormality coefficient and the quality abnormality coefficient are normalized; Based on the normalized switching time abnormality coefficient and the quality abnormality coefficient and the target variable quality pass rate, a regression model is established by gradient boosting regression algorithm to predict the quality pass rate; Based on the trained gradient boosting regression model, the influence interference factor of the production efficiency of each batch of components on the quality is calculated, and the calculation expression is: ; wherein, The production efficiency of the th component has an influence on the quality of the component, The production efficiency of the th component has an influence on the quality of the component, The actual quality pass rate of the th component is The switching time abnormality coefficient of the component is standardized, The quality abnormality coefficient of the component is standardized; it is judged whether the influence interference factor is greater than or equal to the preset threshold value, if yes, it means that the switching time of the process in the corresponding production process has an impact on the quality pass rate of the component, and the switching time of the process needs to be adjusted; according to the specific value of the influence interference factor, the adjustment direction of the switching time of the affected process is determined, and the adjustment amplitude is calculated: for the positive influence factor, reduce the switching time; for the negative influence factor, increase the switching time, and update the operation process of the automatic equipment and set the new process switching time parameter.

[0027] The working principle of the present application: the system includes a data acquisition module, a production plan evaluation module, a production quality evaluation module, a comprehensive analysis module and an influence analysis and adjustment module. The data acquisition module integrates sensors and automated equipment on the production line, real-time acquires and records the switching time of each process and the quality inspection pass rate of each component, and transmits these data to the central database for storage and association. The production plan evaluation module analyzes the process switching time, calculates the switching time abnormality coefficient to evaluate the production efficiency; the production quality evaluation module calculates the quality abnormality coefficient according to the quality inspection pass rate to evaluate the product quality fluctuation. The comprehensive analysis module calculates the quality consistency coefficient by normalizing the switching time abnormality coefficient and the quality abnormality coefficient, and evaluates the quality consistency of each batch of assembled components. The influence analysis and adjustment module calculates the influence interference factor based on the gradient boosting regression model, evaluates the influence degree of production efficiency on quality, and dynamically adjusts the process switching time accordingly. Specifically, when the influence interference factor exceeds the preset threshold, the system will determine the adjustment direction (increase or decrease the switching time) according to its positive and negative nature, and realize parameter adjustment by updating the operation process of the automated equipment, so as to ensure that the production process runs efficiently while maintaining high quality standards. Through fine data management and intelligent adjustment strategy, the system realizes the dual optimization of production efficiency and product quality.

[0028] The above formulas are dimensionless values calculated, and the formulas are obtained by collecting a large amount of data to simulate the most recent real situation. The preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0029] The above-described embodiments can be implemented in part or in whole through software, hardware, firmware or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded into a computer, all or part of the processes described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0030] It should be understood that the term "and / or" herein merely describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent three cases of A alone, A and B together, and B alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents that the associated objects before and after it are in an "or" relationship, but it can also represent an "and / or" relationship, which can be understood according to the context before and after it.

[0031] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of the processes should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0032] The above describes one embodiment of the present application in detail, but the content described is only the preferred embodiment of the present application, and cannot be considered as limiting the scope of the implementation of the present application. Any equivalent changes and improvements made within the scope of the present application are still within the scope of the patent coverage of the present application.

Claims

1. An intelligent assembly component full process management system, characterized in that: include: A data acquisition module, which collects process switching time and quality inspection pass rate in real time during the component production monitoring cycle; A production plan evaluation module, which is used to analyze the process switching time in the production process within the monitoring period and evaluate the production efficiency of the component production process based on the process switching time; A production quality assessment module, which is used to analyze the quality inspection pass rate of the production process within the monitoring period and evaluate the quality of the component production process based on the degree of fluctuation of the quality pass rate of each component; A comprehensive analysis module, which is used to analyze the production efficiency and quality of the component production process and, based on the analysis results, evaluate the quality consistency of each batch of assembled components during the production process; An impact analysis and adjustment module analyzes the degree of influence of production efficiency on the quality of component production during the production process based on the quality consistency of each batch of assembled components during the production process, and dynamically adjusts the process switching time during the production process based on the degree of influence.

2. The intelligent assembly component whole process management system according to claim 1, characterized in that: The production efficiency of the component production process is evaluated, specifically including: During the component production monitoring cycle, the process switching time in the production process is collected in real time. According to the fluctuation degree of the switching time of different processes, the switching time abnormality coefficient of the overall production process is calculated to determine whether the switching abnormality coefficient of the overall production process is greater than or equal to the preset threshold. If so, it means that the switching time of the production process is normal, and the production efficiency is normal. If not, it means that the switching time of the production process is abnormal, and the production efficiency is abnormal.

3. The intelligent assembly component whole process management system according to claim 2, characterized in that: The process of obtaining the switching time anomaly coefficient is as follows: Real-time collection of switching time data of each process in the production process, each data represents the switching time from the first process to the nth process; Performing stabilization processing on the collected switching time data, including noise removal and differential operation; Determine the three parameters of the ARIMA model: autoregressive order p, difference number d, and moving average order q, and use historical data to fit the ARIMA model. The expression for calculating the switching time prediction value at each moment is: ; in, Indicates the The switching time data prediction value at the moment, Indicates the acquisition time; according to the actual switching time data and predicted value The difference between , calculate the residual, the calculation expression is: ;in, Indicates the The residual value at the moment; Calculate the residual standard deviation value at all times, and calculate and process the residual value and the residual standard deviation value to obtain the switching time anomaly coefficient. The calculation expression is: ; in, Indicates the total number of acquisition time points, represents the standard deviation of the residuals, Indicates the switching time anomaly coefficient.

4. The intelligent assembly component whole process management system according to claim 1, characterized in that: The quality assessment during the component production process specifically includes: During the component production monitoring cycle, the quality inspection pass rate of each component in the production process is collected in real time. According to the fluctuation degree of the quality pass rate of each component, the quality abnormality coefficient of the corresponding component during quality inspection is calculated to determine whether the quality abnormality coefficient of each component is greater than or equal to the preset threshold. If so, it means that there is an abnormal fluctuation in the corresponding quality pass rate, and the production quality of the component is poor. If not, it means that there is no abnormal fluctuation in the corresponding quality pass rate, and the production quality of the component is good.

5. The intelligent assembly component whole process management system according to claim 4, characterized in that: The process of obtaining the mass anomaly coefficient is as follows: During the component production monitoring cycle, the quality inspection pass rate of each component in the production process is collected in real time according to the time series; Quality pass rate at all times , Factors affecting quality pass rate Hedi Quality inspection results at all times , Indicates the collection time; According to the Bayesian theorem, the posterior probability distribution of the quality pass rate is calculated as follows: ; in, represents the posterior probability distribution, represents the likelihood function of the quality detection result, represents the prior probability of quality pass rate, represents the normalization constant; Calculate the expected value of the posterior probability distribution of the quality pass rate obtained by Bayesian inference; The quality anomaly coefficient is obtained by calculating the difference between the quality pass rate of each component and the expected pass rate. The calculation expression is: ; in, Indicates the total number of acquisition time points, It represents the standard deviation of the quality pass rate, represents the quality anomaly coefficient, It represents the expected value of the posterior probability distribution of the quality pass rate obtained by Bayesian inference.

6. The intelligent assembly component whole process management system according to claim 1, characterized in that: The analysis of the production efficiency and quality of the component production process specifically includes: Obtain the switching time anomaly coefficient and quality anomaly coefficient during the component production process, perform normalized calculations on the switching time anomaly coefficient and the quality anomaly coefficient, and calculate the quality consistency coefficient to evaluate the quality consistency of each batch of assembled components during the production process; The calculation process of the consistency coefficient is: Collect the switching time abnormality coefficient and quality abnormality coefficient during the production process of each component; Normalize the switching time anomaly coefficient and quality anomaly coefficient; Use Euclidean distance calculation with widgets The distance between them is calculated as: ； in, Representation component and components The Euclidean distance between and Respectively represent components, Representation component The normalized value of the switching time anomaly coefficient, Representation component The normalized value of the switching time anomaly coefficient, Representation component The normalized value of the mass anomaly coefficient, Representation component The standardized value of the mass anomaly coefficient; for each component , select the K neighbors with the closest distance and calculate the distance between these neighbors and the component The average distance between them is used to obtain the quality consistency coefficient, and the calculation expression is: ; in, Representation component The quality consistency coefficient of Indicates the number of neighbors selected.

7. The intelligent assembly component whole process management system according to claim 6, characterized in that: The evaluation of the quality consistency of each batch of assembly components during the production process specifically includes: Calculate the quality consistency coefficient of each batch of assembly components during the production process, and compare the quality consistency coefficient of each batch of assembly components with a preset threshold; Determine whether the quality consistency coefficient is greater than or equal to a preset threshold. If so, the component quality is consistent; if not, the component quality is inconsistent.

8. The intelligent assembly component whole process management system according to claim 1, characterized in that: The analysis of the degree of influence of the production efficiency in the component production process on the quality of the component production process specifically includes: According to the influence of the production efficiency in the component production process on the quality of the component production process, the influencing interference factor is calculated, and it is determined whether the influencing interference factor in the component production process is greater than or equal to a preset threshold. If so, it means that the production efficiency in the component production process has an influence on the quality of the component production process; if not, it means that the production efficiency in the component production process has no influence on the quality of the component production process; The acquisition process of the interference factor is as follows: Collect the switching time abnormality coefficient and quality abnormality coefficient during the production process of each component; The switching time anomaly coefficient and quality anomaly coefficient are used as input features, and the quality inspection pass rate during component production is collected as the target variable; Normalize the switching time anomaly coefficient and quality anomaly coefficient; Based on the normalized switching time anomaly coefficient and quality anomaly coefficient as well as the target variable quality pass rate, a regression model is established by using the gradient enhancement regression algorithm to predict the quality pass rate. Based on the trained gradient enhancement regression model, the interference factor affecting the quality of each batch of components’ production efficiency is calculated. The calculation expression is: ; in, Indicates the The interference factor of the production efficiency of each component on the quality, Represents a component, Indicates the The actual quality pass rate of each component, is the quality qualification rate predicted based on the normalized switching time anomaly coefficient and quality anomaly coefficient, Representation component The normalized value of the switching time anomaly coefficient, Representation component The normalized value of the mass anomaly coefficient.

9. The intelligent assembly component whole process management system according to claim 1, characterized in that: The process switching time in the production process is dynamically adjusted according to the degree of impact, specifically including: Determine whether the interference factor is greater than or equal to a preset threshold. If so, it means that the process switching time in the corresponding production process affects the quality qualification rate of the component, and the process switching time needs to be adjusted; According to the specific values ​​of the interference factors, determine the adjustment direction of the switching time of the affected processes and calculate the adjustment range: for positive influencing factors, reduce the switching time; for negative influencing factors, increase the switching time, update the operating procedures of the automation equipment, and set new process switching time parameters.

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