An intelligent management system for industrial intelligent module manufacturing production process

Abstract

The present invention discloses an intelligent management system for industrial intelligent module manufacturing production processes, which belongs to the technical field of industrial production management; it is used to solve the technical problem of poor accuracy and reliability of targeted optimization management of production anomalies in different aspects of different production modules of industrial manufacturing in existing solutions; periodic supervision and processing classification are performed on abnormal production data corresponding to different aspects of different production modules of industrial manufacturing, abnormal production processing data corresponding to different production modules in the supervision period are obtained, different abnormal classification data in the abnormal production processing data corresponding to the production modules are processed and analyzed respectively, production status corresponding to different aspects of the production modules are obtained, and management effectiveness verification is performed on the abnormal production status of different aspects, and targeted production anomaly management solutions are implemented for different abnormal production statuses of the production modules according to the verification results.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial production management, and in particular to an intelligent management system for industrial intelligent module manufacturing production processes. Background Art

[0002] Industrial intelligent module manufacturing production process management refers to the use of intelligent technologies and systems to plan, control and optimize the entire production process during industrial production and manufacturing. Its core goal is to enhance corporate competitiveness by improving production efficiency, reducing production costs, ensuring product quality and enhancing production flexibility.

[0003] When implementing existing industrial intelligent module manufacturing production process management solutions, most of them still stay at the production data monitoring, statistics, processing and analysis of different production modules of industrial manufacturing, and directly manage the production of the corresponding production modules based on the processing and analysis results. They are unable to perform multi-dimensional processing and classification of abnormal data in the production modules, and implement diversified production abnormality management solutions for abnormal data of different dimensions of processing and classification. As a result, the accuracy and reliability of targeted optimization management of production abnormalities in different aspects of different production modules of industrial manufacturing are poor. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent management system for industrial intelligent module manufacturing production processes, which is used to solve the technical problems of poor accuracy and reliability in the targeted optimization management of production anomalies in different aspects of different production modules of industrial manufacturing in existing solutions.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An intelligent management system for industrial intelligent module manufacturing production processes includes an industrial manufacturing production module abnormality supervision module, which is used to obtain abnormal production data corresponding to different industrial manufacturing production modules according to a preset supervision cycle, process and classify the abnormal production data of different production modules within the supervision cycle, and obtain abnormal production processing data corresponding to different production modules; the abnormal production processing data includes first abnormality classification data, second abnormality classification data, and third abnormality classification data;

[0007] The abnormality management module of the industrial manufacturing production module is used to process and analyze the different abnormal classification data in the abnormal production processing data corresponding to the production module, obtain the production status corresponding to different aspects of the production module, and verify the management effectiveness of the abnormal production status in different aspects. According to the verification results, a targeted production abnormality management plan is implemented for different abnormal production statuses of the production module;

[0008] Among them, for abnormal production status in different aspects of the production module, the first management effective verification plan or the second management effective verification plan is dynamically implemented.

[0009] Preferably, when processing and classifying abnormal production data of different production modules within the supervision cycle, the total number of self-production anomalies, the total number of passive production anomalies and the total number of collaborative production anomalies of the production module within the supervision cycle are counted, and the self-production anomaly rate, passive production anomaly rate and collaborative production anomaly rate of the production module within the supervision cycle are calculated based on the obtained total number of self-production anomalies, total number of passive production anomalies and total number of collaborative production anomalies.

[0010] Preferably, the productivity of the production module during the supervision period is obtained, and the productivity of the production module is sorted and combined with its own production abnormality rate, passive production abnormality rate and collaborative production abnormality rate to obtain the first abnormality classification data, second abnormality classification data and third abnormality classification data corresponding to the production module.

[0011] Preferably, different abnormal classification data in the abnormal production processing data corresponding to the production module are obtained, and different abnormal classification data are analyzed by the production state identification function to output the production state identification value ZSk corresponding to the production module;

[0012] Among them, the expression of the production state identification function is: ; In the formula, SS0 is the productivity corresponding to the production module; k is 1, 2, or 3, and SSk is SS1, SS2, and SS3, which are the production exception rate, passive production exception rate, and collaborative production exception rate corresponding to the production module, respectively; SS´0 is the standard production rate corresponding to the production module; SS´k is SS´1, SS´2, and SS´3, which are the production standard exception rate, passive production standard exception rate, and collaborative production standard exception rate corresponding to the production module, respectively.

[0013] Preferably, data analysis is performed on the production status identification value obtained by the production module;

[0014] If the production status identification value is 0, it indicates that the production status of the production module cycle quantity is normal and the production status of different aspects are normal;

[0015] If the production status identification value is 1, it indicates that the production status of the production module cycle quantity is abnormal and the production status of different aspects is normal;

[0016] If the production status identification value is 2, it indicates that the cycle number production status of the production module is abnormal and that there are abnormalities in the production status of different aspects.

[0017] Preferably, when verifying the management effectiveness of different abnormal production states of the production module according to the production status identification value with a value of 1 or 2, if the abnormal production state history of the production module has been processed, the first management effectiveness verification scheme is implemented for the abnormal production state; if the abnormal production state history of the production module has not been processed, the second management effectiveness verification scheme is implemented for the abnormal production state.

[0018] Preferably, when implementing the first management effectiveness verification scheme, the abnormal production status that has been processed in the history is marked as the first abnormal production status, and the processing verification effectiveness value CHi corresponding to the first abnormal production status is calculated by the formula CHi=SSi-SS´´i; wherein, i is 0, 1, 2 or 3; SSi is SS0, SS1, SS2 or SS3; SS´´i is SS´´0, SS´´1, SS´´2 or SS´´3, which is the production rate, self-production abnormality rate, passive production abnormality rate or collaborative production abnormality rate of the production module corresponding to the previous supervision cycle.

[0019] Preferably, performing data analysis on the calculated processing verification effectiveness value;

[0020] If the processing verification validity value is less than 0, the first abnormal production state is associated with the processing verification validity tag, and the existing production process management plan is implemented for the first abnormal production state according to the processing verification validity tag;

[0021] Otherwise, the first abnormal production state is associated with the verification invalid label, the mark of the first abnormal production state is updated to the second abnormal production state, and the unfamiliar production process management solution is implemented for the second abnormal production state.

[0022] Preferably, when the second management validation scheme is implemented, the abnormal production status that has not been processed in the history is marked as the third abnormal production status, and the unfamiliar production process management scheme is implemented for the third abnormal production.

[0023] Preferably, when implementing the unfamiliar production process management solution, the cycle production abnormality rates corresponding to the second abnormal production state and the third abnormal production state of the production module are processed and calculated using the preset N management sub-cycles, and data analysis is performed on the N cycle production abnormality rates corresponding to the second abnormal production state and the third abnormal production state; N is an odd number greater than 1;

[0024] If there are no less than The production abnormality rate of a regular cycle is determined to be effective, and a prompt is given; j is a positive integer;

[0025] Otherwise, the production process management corresponding to the unfamiliar production process management plan is determined to be invalid and a prompt is given.

[0026] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0027] The present invention obtains abnormal production processing data corresponding to different production modules in the supervision period by periodically supervising and classifying abnormal production data in different aspects of different production modules of industrial manufacturing. This can not only realize diversified processing of production anomalies in different production modules, but also provide reliable data support for targeted production optimization analysis and management of subsequent production modules in different aspects.

[0028] The present invention obtains the production status of different aspects of the production module by separately processing and analyzing different abnormal classification data in the abnormal production processing data corresponding to the production module, and verifies the management effectiveness of the abnormal production status in different aspects. According to the verification results, targeted production abnormality management plans are implemented for the different abnormal production statuses of the production module, thereby realizing targeted supervision processing and verification analysis of different abnormal production statuses of different production modules, and then different abnormal production statuses of different production modules can be managed in a diversified manner, thereby improving the accuracy and reliability of targeted optimization management of production abnormalities in different aspects of different production modules in industrial manufacturing. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention will be further described below with reference to the accompanying drawings.

[0030] Figure 1 This is a flowchart of the operation of an intelligent management system for manufacturing production processes of industrial intelligent modules according to the present invention.

[0031] Figure 2 This is a flowchart of the data analysis of the production status identification value obtained by the production module in the present invention.

[0032] Figure 3 This is a module block diagram of an industrial intelligent module manufacturing production process intelligent management system of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] like Figure 1 、 Figure 3 As shown, the present invention is an intelligent management system for industrial intelligent module manufacturing production process, including an industrial manufacturing production module abnormality supervision module and an industrial manufacturing production module abnormality management module;

[0035] The industrial manufacturing production module abnormality supervision module is used to obtain abnormal production data corresponding to different industrial manufacturing production modules according to a preset supervision cycle, process and classify the abnormal production data of different production modules within the supervision cycle, and obtain abnormal production processing data corresponding to different production modules; the abnormal production processing data includes first abnormal classification data, second abnormal classification data and third abnormal classification data; including:

[0036] The preset supervision cycle can be determined based on the production time or the quantity of products produced. The specific value is not limited and can be customized according to the application requirements of the actual application scenario.

[0037] In addition, the division of different production modules in industrial manufacturing can be based on production equipment or production processes. The specific division requirements are not limited and can also be customized according to the application requirements of actual application scenarios. Different production modules can be numbered according to the production sequence.

[0038] When processing and classifying abnormal production data of different production modules within the supervision cycle, the total number of self-production anomalies, the total number of passive production anomalies, and the total number of collaborative production anomalies of the production module within the supervision cycle are counted, and the self-production anomaly rate, passive production anomaly rate, and collaborative production anomaly rate of the production module within the supervision cycle are calculated based on the obtained total number of self-production anomalies, total number of passive production anomalies, and total number of collaborative production anomalies;

[0039] It should be noted that self-production anomaly refers to the abnormality of the product produced by the production module itself;

[0040] Passive production anomaly refers to the production anomaly of the previous production module that causes the production product anomaly of the production module;

[0041] Collaborative production anomalies refer to product anomalies caused by both the production module itself and the previous production module. The classification and labeling of different production anomalies can be reviewed and determined by professional technicians in this field based on their work experience and work requirements.

[0042] The calculation of the production abnormality rate corresponding to different aspects of the production module is obtained by comparing the total number of production abnormalities in different aspects with the total number of products produced by the production module during the supervision period;

[0043] and obtaining the productivity of the production module during the supervision cycle, and combining the productivity of the production module with its own production anomaly rate, passive production anomaly rate, and collaborative production anomaly rate, respectively, to obtain first anomaly classification data, second anomaly classification data, and third anomaly classification data corresponding to the production module;

[0044] In an embodiment of the present invention, by periodically supervising and classifying abnormal production data corresponding to different aspects of different production modules of industrial manufacturing, abnormal production processing data corresponding to different production modules in the supervision period are obtained, which can not only realize diversified processing of production anomalies of different production modules, but also provide reliable data support for targeted production optimization analysis and management of subsequent production modules corresponding to different aspects.

[0045] The industrial manufacturing production module exception management module is used to process and analyze different abnormal classification data in the production module's corresponding abnormal production processing data, obtain the production status of different aspects of the production module, and verify the management effectiveness of different abnormal production statuses. Based on the verification results, targeted production exception management plans are implemented for different abnormal production statuses of the production module; including:

[0046] Obtain different abnormal classification data in the abnormal production processing data corresponding to the production module, namely the first abnormal classification data, the second abnormal classification data and the third abnormal classification data, and perform data analysis on the different abnormal classification data through the production status identification function, and output the production status identification value ZSk corresponding to the production module;

[0047] Among them, the expression of the production state identification function is: ; In the formula, SS0 is the production rate corresponding to the production module; k is 1, 2, 3, SSk is SS1, SS2, SS3, which are the production exception rate, passive production exception rate and collaborative production exception rate corresponding to the production module respectively; SS'0 is the standard production rate corresponding to the production module; SS'k is SS'1, SS'2, SS'3, which are the production standard exception rate, passive production standard exception rate and collaborative production standard exception rate corresponding to the production module respectively. Among them, the specific values ​​of the production standard exception rate, passive production standard exception rate and collaborative production standard exception rate corresponding to the production module are not limited, and can be determined according to the existing production design requirement data corresponding to the production module, or according to the historical production exception data corresponding to different aspects of the production module. For example, the median value of all the production exception rates of the production module in different historical supervision cycles is set as the corresponding production standard exception rate;

[0048] It should be noted that the production status identification value is used to process and match the early regulatory data of different aspects of the production module to digitally represent its production status in different aspects;

[0049] In addition, by analyzing the production status identification values ​​of different aspects of the production module, we can not only obtain the production status of the production module in different aspects, but also provide reliable data support for subsequent production optimization analysis and management of different aspects of the production module;

[0050] like Figure 2 As shown, data analysis is performed on the production status identification value obtained by the production module;

[0051] If the production status identification value is 0, it indicates that the production status of the production module cycle quantity is normal and the production status of different aspects are normal;

[0052] If the production status identification value is 1, it indicates that the production status of the production module cycle quantity is abnormal and the production status of different aspects is normal;

[0053] If the production status identification value is 2, it indicates that the cycle quantity production status of the production module is abnormal and there are abnormalities in different aspects of the production status. There are abnormalities in different aspects of the production status, including the abnormality of the production module's own production status, the abnormality of the passive production status and / or the abnormality of the collaborative production status;

[0054] Furthermore, when verifying the management effectiveness of different abnormal production states of the corresponding production module according to the production state identification value with a value of 1 or 2, if the abnormal production state of the production module has been processed in the past, the first management effectiveness verification scheme is implemented for the corresponding abnormal production state, and the abnormal production state that has been processed in the past is marked as the first abnormal production state, and the processing verification effectiveness value CHi corresponding to the first abnormal production state is obtained by calculating through the formula CHi=SSi-SS´´i; wherein, i is 0, 1, 2 or 3; SSi is SS0, SS1, SS2 or SS3; SS´´i is SS´´0, SS´´1, SS´´2 or SS´´3, which is the production rate, self-production abnormality rate, passive production abnormality rate or collaborative production abnormality rate of the production module corresponding to the previous supervision cycle;

[0055] Conduct data analysis on the calculated processing verification effectiveness values;

[0056] If the processing verification validity value is less than 0, the first abnormal production state is associated with the processing verification validity tag, and the existing production process management plan is implemented for the first abnormal production state according to the processing verification validity tag;

[0057] Otherwise, the first abnormal production state is associated with the verification invalid tag, and the tag of the first abnormal production state is updated to the second abnormal production state, and the unfamiliar production process management plan is implemented for the second abnormal production state;

[0058] The unfamiliar production process management plan may be an alternative production process management plan that has not been implemented in the first abnormal production state. The specific content of the existing production process management plan and the unfamiliar production process management plan implemented in the first abnormal production state is not limited and can be planned and determined by professional and technical personnel in this field according to actual application scenarios.

[0059] It should be noted that, unlike the existing technical solutions, there is no supervision and analysis of whether the abnormal production status history of the production module has been processed, but only a fixed production process management solution is directly implemented for the abnormal production module obtained through analysis;

[0060] In the embodiment of the present invention, by monitoring and analyzing whether the abnormal production status history of the production module has been processed, and dynamically implementing the first management effectiveness verification scheme or the second management effectiveness verification scheme, the accuracy and reliability of the production module's optimized management of abnormal production in different aspects can be effectively improved;

[0061] If the abnormal production status of the production module has not been processed in the past, the second management validation plan will be implemented for the abnormal production status, the abnormal production status that has not been processed in the past will be marked as the third abnormal production status, and the unfamiliar production process management plan will be implemented for the third abnormal production;

[0062] It is understandable that the abnormal production status of the production module has not been processed in the past, so there is no historical processing data for processing and analysis of the abnormal production status, and the first management effectiveness verification plan cannot be implemented. Therefore, the second management effectiveness verification plan is adopted to implement the unfamiliar production process management plan for the marked third abnormal production status and implement periodic supervision analysis of optimized management effects;

[0063] When implementing the unfamiliar production process management plan, the cycle production abnormality rate corresponding to the second abnormal production state and the third abnormal production state of the production module is processed and calculated using the preset N management sub-cycles. The cycle production abnormality rate is obtained by calculating the ratio between the total number of abnormal productions corresponding to the second abnormal production state and the third abnormal production state and the total number of products produced within the management sub-cycle, and performing data analysis on the N cycle production abnormality rates corresponding to the second abnormal production state and the third abnormal production state; N is an odd number greater than 1, and N can be 3;

[0064] If there are no less than If the production abnormality rate of a regular cycle is calculated, the production process management of the corresponding unfamiliar production process management scheme is determined to be effective and a prompt is given; j is a positive integer, the value of j is less than or equal to N, and j can be 1; is the floor function;

[0065] Otherwise, the production process management corresponding to the unfamiliar production process management plan is determined to be invalid and a prompt is issued, notifying the administrator to promptly and proactively perform operation and maintenance management on the implemented unfamiliar production process management plan, including but not limited to adding, deleting, and modifying the content of the implemented unfamiliar production process management plan;

[0066] The steps for obtaining the normal cycle production abnormality rate include:

[0067] Compare and judge the calculated cycle production exception rate with the standard cycle production exception rate processed by the corresponding management sub-cycle; the specific value of the standard cycle production exception rate can be determined according to the production verification design requirements of the corresponding production module, or according to the management design requirements of the implemented unfamiliar production process management plan, and the specific value is not limited;

[0068] If the cycle production abnormality rate is less than the corresponding standard cycle production abnormality rate, the cycle production abnormality rate is marked as the normal cycle production abnormality rate;

[0069] Otherwise, the production abnormality rate of this period will be marked as the special period production abnormality rate.

[0070] It should be noted that the abnormal production status of the production module has not been dealt with in the history. A targeted second management effectiveness verification plan is dynamically implemented, and periodic supervision and analysis of the optimization management effect is carried out, which improves the targetedness and reliability of the production module's optimization management implementation for the second abnormal production status and the third abnormal production status.

[0071] In an embodiment of the present invention, by separately processing and analyzing different abnormal classification data in the abnormal production processing data corresponding to the production module, the production status corresponding to different aspects of the production module is obtained, and the management effectiveness of the abnormal production status in different aspects is verified. According to the verification results, targeted production abnormality management plans are implemented for the different abnormal production statuses of the production module, thereby realizing targeted supervision processing and verification analysis of different abnormal production statuses of different production modules, and then different abnormal production statuses of different production modules can be managed in a diversified manner, thereby improving the accuracy and reliability of targeted optimization management of production abnormalities in different aspects of different production modules in industrial manufacturing.

[0072] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative. For example, the division of modules is only a logical function division, and other division methods may be used in actual implementation.

[0073] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of these modules may be selected to achieve the objectives of this embodiment based on actual needs.

[0074] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or hardware plus software functional modules.

[0075] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, but that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent management system for industrial intelligent module manufacturing production process, characterized in that: It includes an industrial manufacturing production module abnormality supervision module, which is used to obtain abnormal production data corresponding to different industrial manufacturing production modules according to a preset supervision cycle, process and classify the abnormal production data of different production modules within the supervision cycle, and obtain abnormal production processing data corresponding to different production modules; the abnormal production processing data includes first abnormal classification data, second abnormal classification data and third abnormal classification data; wherein, the productivity of the production module within the supervision cycle is obtained, and the productivity of the production module is sorted and combined with its own production abnormality rate, passive production abnormality rate and collaborative production abnormality rate respectively to obtain the first abnormal classification data, second abnormal classification data and third abnormal classification data corresponding to the production module; The abnormality management module of the industrial manufacturing production module is used to process and analyze the different abnormal classification data in the abnormal production processing data corresponding to the production module, obtain the production status corresponding to different aspects of the production module, and verify the management effectiveness of the abnormal production status in different aspects. According to the verification results, a targeted production abnormality management plan is implemented for different abnormal production statuses of the production module; Among them, there are abnormalities in different aspects of production status, including abnormalities in the production module's own production status, abnormalities in passive production status, and / or abnormalities in collaborative production status; Dynamically implement the first management validation plan or the second management validation plan for abnormal production status in different aspects of the production module; If the abnormal production status history of the production module has been processed, the first management validation scheme is implemented for the abnormal production status; if the abnormal production status history of the production module has not been processed, the second management validation scheme is implemented for the abnormal production status; When implementing the second management validation plan, the abnormal production status that has not been handled in the past will be marked as the third abnormal production status, and the unfamiliar production process management plan will be implemented for the third abnormal production; When implementing the unfamiliar production process management plan, the preset N management sub-cycles are used to process and calculate the cycle production abnormality rates corresponding to the second abnormal production state and the third abnormal production state of the production module, and data analysis is performed on the N cycle production abnormality rates corresponding to the second abnormal production state and the third abnormal production state; N is an odd number greater than 1; If there are no less than The production abnormality rate of a regular cycle is determined to be effective, and a prompt is given; j is a positive integer; Otherwise, the production process management corresponding to the unfamiliar production process management solution is determined to be invalid and a prompt is given; The steps for obtaining the normal cycle production abnormality rate include: Compare and judge the calculated cycle production exception rate with the standard cycle production exception rate processed by the corresponding management sub-cycle; If the cycle production abnormality rate is less than the corresponding standard cycle production abnormality rate, the cycle production abnormality rate is marked as the normal cycle production abnormality rate; Otherwise, the production abnormality rate of this period will be marked as the special period production abnormality rate.

2. The intelligent management system for industrial intelligent module manufacturing production process according to claim 1 is characterized in that: When processing and classifying abnormal production data of different production modules within the supervision cycle, the total number of self-production anomalies, the total number of passive production anomalies and the total number of collaborative production anomalies of the production module within the supervision cycle are counted, and based on the obtained total number of self-production anomalies, the total number of passive production anomalies and the total number of collaborative production anomalies, the self-production anomaly rate, passive production anomaly rate and collaborative production anomaly rate of the production module within the supervision cycle are calculated respectively.

3. The intelligent management system for industrial intelligent module manufacturing production process according to claim 2 is characterized in that: Obtain different abnormal classification data from the abnormal production processing data corresponding to the production module, and perform data analysis on the different abnormal classification data through the production status identification function, and output the production status identification value ZSk corresponding to the production module; Among them, the expression of the production state identification function is: ; In the formula, SS0 is the productivity corresponding to the production module; k is 1, 2, or 3, and SSk is SS1, SS2, and SS3, which are the production exception rate, passive production exception rate, and collaborative production exception rate corresponding to the production module, respectively; SS´0 is the standard production rate corresponding to the production module; SS´k is SS´1, SS´2, and SS´3, which are the production standard exception rate, passive production standard exception rate, and collaborative production standard exception rate corresponding to the production module, respectively.

4. The intelligent management system for industrial intelligent module manufacturing production process according to claim 3 is characterized in that: Perform data analysis on the production status identification values ​​obtained by the production module; If the production status identification value is 0, it indicates that the production status of the production module cycle quantity is normal and the production status of different aspects are normal; If the production status identification value is 1, it indicates that the production status of the production module cycle quantity is abnormal and the production status of different aspects is normal; If the production status identification value is 2, it indicates that the cycle number production status of the production module is abnormal and that there are abnormalities in the production status of different aspects.

5. The intelligent management system for industrial intelligent module manufacturing production process according to claim 4 is characterized in that: According to the production status identification value of 1 or 2, the management effectiveness of different abnormal production statuses of the corresponding production module is verified.

6. The intelligent management system for industrial intelligent module manufacturing production process according to claim 5, characterized in that: When implementing the first management effectiveness verification plan, the abnormal production status that has been processed in the history is marked as the first abnormal production status, and the processing verification effectiveness value CHi corresponding to the first abnormal production status is calculated through the formula CHi=SSi-SS´´i; where i is 0, 1, 2 or 3; SSi is SS0, SS1, SS2 or SS3; SS´´i is SS´´0, SS´´1, SS´´2 or SS´´3, which is the production rate, self-production abnormality rate, passive production abnormality rate or collaborative production abnormality rate of the production module corresponding to the previous supervision cycle.

7. The intelligent management system for industrial intelligent module manufacturing production process according to claim 6 is characterized in that: Conduct data analysis on the calculated processing verification effectiveness values; If the processing verification validity value is less than 0, the first abnormal production state is associated with the processing verification validity tag, and the existing production process management plan is implemented for the first abnormal production state according to the processing verification validity tag; Otherwise, the first abnormal production state is associated with the verification invalid label, the mark of the first abnormal production state is updated to the second abnormal production state, and the unfamiliar production process management solution is implemented for the second abnormal production state.

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