An industrial production automation control system

Abstract

The present invention discloses an industrial production automation control system, belonging to the technical field of industrial production supervision; it is used to solve the technical problems of poor autonomous supervision effect of product testing and calibration and poor autonomous optimization control effect in the existing solutions; monitor and process and analyze the production and after-sales of the target from different dimensions to obtain corresponding test calibration monitoring analysis data and after-sales monitoring analysis data, further integrate and calculate the monitoring analysis data of different dimensions to obtain corresponding overall detection influence degree and detection effective state, and optimize and update the existing test calibration implementation standards of the target's automated production according to the detection effective state; perform digital processing and analysis on the detection coverage status of the screened and analyzed test calibration after-sales data, and dynamically expand and update the existing test calibration implementation standards according to the analysis results, realizing diverse expansion and utilization of the existing after-sales processing data.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial production supervision, and particularly relates to an industrial production automation control system. Background Art

[0002] Industrial production automation control refers to the use of automation technology in the industrial production process to improve efficiency, reduce errors, and lower production costs. It usually involves using various devices and technologies to achieve automatic monitoring and operation of the production process, thereby reducing the need for manual intervention.

[0003] When implementing existing industrial production automation control solutions, for the testing and calibration of production products, data analysis on the reliability and optimization necessity of testing and calibration is not carried out from different dimensions, and the testing and calibration of production products cannot be autonomously optimized and controlled according to the analysis results, resulting in poor autonomous supervision effects and poor autonomous optimization control effects for the testing and calibration of industrial production products. Summary of the Invention

[0004] The purpose of the present invention is to provide an industrial production automation control system to solve the technical problems of poor autonomous supervision effects and poor autonomous optimization control effects for the testing and calibration of industrial production products in existing solutions.

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

[0006] An industrial production automation control system includes:

[0007] A test and calibration implementation after-sales supervision module for monitoring and statistically analyzing the implementation data of test and calibration of the target production process to obtain test and calibration implementation data, and monitoring, statistically analyzing, processing, and screening the after-sales data of the target within the monitoring period to obtain test and calibration after-sales data;

[0008] A test and calibration implementation after-sales processing and analysis module for performing multi-dimensional state digital processing and analysis on the test and calibration implementation data and the test and calibration after-sales data, and dynamically optimizing and expanding and updating the existing test and calibration implementation standards according to the analysis results of different dimensions;

[0009] Among them, performing multi-dimensional state digital processing and analysis includes performing digital processing and analysis on the detection effective state of the test and calibration implementation data and the test and calibration after-sales data, and performing digital processing and analysis on the detection coverage state of the test and calibration implementation data and the test and calibration after-sales data.

[0010] Preferably, within the monitoring period, statistically analyze the total number of target processes corresponding to the implementation of test and calibration for the target automated production; obtain all abnormal types that occur during test and calibration and their corresponding total numbers of abnormal types;

[0011] Sort and combine the total number of target processes to be processed, different exception types and their corresponding total numbers of exception types to obtain test calibration implementation data;

[0012] When monitoring and counting the after-sales data of the target within the monitoring period, count all after-sales exception types that occur in the target and their corresponding total numbers of after-sales exceptions;

[0013] When processing and classifying the after-sales data of the target, traverse and match all after-sales exception types with the test calibration exception table corresponding to the target, mark the after-sales exception types that are the same as the sample exception types in the test calibration exception table as selected after-sales exception types, and mark the after-sales exception types that are all different from the sample exception types in the test calibration exception table as target after-sales exception types.

[0014] Preferably, perform validity screening on the total numbers of after-sales exceptions corresponding to all selected after-sales exception types and target after-sales exception types respectively, count the total numbers corresponding to the selected after-sales exception types and target after-sales exception types that meet the exception conditions and mark them as the total number of selected valid after-sales exceptions and the total number of target valid after-sales exceptions respectively;

[0015] Sort and combine all the selected after-sales exception types after screening and their corresponding total numbers of selected valid after-sales exceptions to obtain the first after-sales exception screening data; and sort and combine all the target after-sales exception types after screening and their corresponding total numbers of target valid after-sales exceptions to obtain the second after-sales exception screening data;

[0016] The first after-sales exception screening data and the second after-sales exception screening data constitute the test calibration after-sales data.

[0017] Preferably, when performing digital processing and analysis on the effective status of the test calibration implementation data and the test calibration after-sales data, the total number of target processes, different exception types and their corresponding total numbers of exception types in the test calibration implementation data are passed through the formula Calculate the first detection impact factor YJ corresponding to the target; in the formula, LYi is the exception impact weight corresponding to different exception types of previous test calibration; i is different exception types of previous test calibration; i = 1, 2, 3,..., n; n is a positive integer; ni is the total number of exception types corresponding to different exception types of previous test calibration; N is the total number of all exception types that occurred in previous test calibration; A is the standard exception detection value.

[0018] Preferably, pass the first after-sales exception screening data in the test calibration after-sales data through the formula Calculate the second detection impact factor EJ corresponding to the target; in the formula, LYj is the abnormal impact weight corresponding to different abnormal types statistically monitored after-sales; j is different abnormal types statistically monitored after-sales; j = 1, 2, 3, ……, m; m is a positive integer; mj is the total number of abnormal types corresponding to different abnormal types statistically monitored after-sales; M is the total number of all abnormal types that occur in the after-sales statistics.

[0019] Preferably, when determining the overall detection impact corresponding to the target automated production process test calibration, the first detection impact factor and the second detection impact factor obtained by calculation are used to calculate the overall detection impact degree ZJ corresponding to the target through the overall detection impact recognition piecewise function;

[0020] Among them, the expression of the overall detection impact recognition piecewise function is In the formula, α and β are different weight coefficients; α + β = 1; B is the standard detection evaluation value;

[0021] The overall detection impact degree contains a value of 0 or 1, indicating that the detection effective state of the corresponding target automated production process test calibration is normal or abnormal respectively.

[0022] Preferably, when determining the overall detection impact corresponding to the target automated production process test calibration according to the overall detection impact degree, analyze the overall detection impact degree;

[0023] If the overall detection impact degree is 0, generate a detection effective state normal instruction and prompt;

[0024] On the contrary, generate a detection effective state abnormal instruction and prompt, and optimize and update the existing test calibration implementation standard according to the detection effective state instruction.

[0025] Preferably, when implementing digital processing and analysis of the detection coverage status of the test calibration implementation data and the test calibration after-sales data, obtain the second after-sales abnormal screening data in the test calibration after-sales data, and obtain all target after-sales abnormal types and their corresponding total target effective after-sales abnormalities in the second after-sales abnormal screening data, and calculate the local after-sales abnormal impact factors corresponding to different target after-sales abnormal types;

[0026] Sort and combine all the calculated local after-sales abnormal impact factors to obtain a local after-sales abnormal impact sequence;

[0027] Preferably, when implementing corresponding test calibration implementation optimization control according to the local after-sales abnormal impact sequence, traverse and analyze the local after-sales abnormal impact sequence. If the element value in the local after-sales abnormal impact sequence is greater than or equal to 1, mark the target after-sales abnormal type corresponding to the element as the selected after-sales abnormal type;

[0028] Sort and combine several selected after-sales exception types obtained through analysis to obtain a sequence of selected after-sales exception types, and expand and update the existing test calibration implementation standards according to the sequence of selected after-sales exception types.

[0029] Preferably, the calculation formula for the local after-sales exception impact factor JYi′ corresponding to different target after-sales exception types is In the formula, LYi′ is the exception impact weight corresponding to the target after-sales exception type; i′ = 1, 2, 3, ……, n′; n′ is a positive integer; n′i′ is the total number of exception types corresponding to different target after-sales exception types; exp() is an exponential function with the natural constant e as the base; C is the standard impact degree value.

[0030] Compared with the existing solutions, the beneficial effects achieved by the present invention are:

[0031] The present invention monitors and processes and analyzes the production and after-sales of the target from different dimensions to obtain corresponding test calibration monitoring and analysis data and after-sales monitoring and analysis data, and further integrates and calculates the monitoring and analysis data of different dimensions to obtain the corresponding overall detection impact degree and detection effective status, and optimizes and updates the existing test calibration implementation standards for the target's automated production according to the detection effective status, improving the autonomous supervision effect and autonomous optimization control effect of industrial production automated test calibration.

[0032] The present invention digitally processes and analyzes the detection coverage status of the screened and analyzed test calibration after-sales data, and dynamically expands and updates the existing test calibration implementation standards according to the analysis results, realizing the diversified expansion and utilization of the existing after-sales processing data, and further improving the autonomous supervision effect and autonomous optimization control effect of industrial production automated test calibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The following further describes the present invention with reference to the drawings.

[0034] Figure 1 It is a flow block diagram of the operation of an industrial production automated control system of the present invention.

[0035] Figure 2 It is a flow block diagram for processing and classifying the after-sales data of the target in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0036] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0037] As Figures 1 to 2 shown, the present invention is an industrial production automation control system, including a test calibration implementation after-sales supervision module and a test calibration implementation after-sales processing and analysis module;

[0038] The test calibration implementation after-sales supervision module is used to monitor and statistically analyze the implementation data of test calibration of the target production process to obtain test calibration implementation data, and monitor, statistically analyze, process and screen the after-sales data of the target within the monitoring period to obtain test calibration after-sales data; including:

[0039] During the monitoring period, count the total number of target processes corresponding to the implementation of test calibration for the target automation production; obtain all abnormal types that occur in the test calibration and their corresponding total number of abnormal types;

[0040] It should be noted that the target is a product for industrial automation production, specifically, it can be a valve positioner; the monitoring period is used to conduct periodic supervision and analysis of industrial automation production; specifically, the monitoring period can be determined by the preset number of days, or can be determined according to the preset total number of target production;

[0041] In addition, the abnormal types are determined according to the abnormal type design data corresponding to the specific product. Specifically, it includes, but is not limited to, positioning abnormal types, valve abnormal types, signal abnormal types, etc.;

[0042] Each different abnormal type is associated with a corresponding abnormal weight, and the abnormal weight is used to numerically and differentially represent the abnormal impact corresponding to the abnormal type; the specific value of the abnormal weight can be determined by professionals in the field, or can be determined according to the total number of times the corresponding abnormal type has appeared historically;

[0043] Sort and combine the total number of target processes, different abnormal types and their corresponding total number of abnormal types to obtain test calibration implementation data;

[0044] In the embodiments of the present invention, by monitoring and statistically analyzing various data of the previous test calibration, reliable pre-test supervision data support can be provided for the subsequent evaluation of the test calibration status of the target;

[0045] When monitoring and counting the after-sales data of the target within the monitoring period, count all after-sales exception types that occur for the target and their corresponding total numbers of after-sales exceptions; this can be obtained based on the background statistical data of the target.

[0046] When processing and classifying the after-sales data of the target, traverse and match all after-sales exception types with the test calibration exception table corresponding to the target, mark the after-sales exception types that are the same as the sample exception types in the test calibration exception table as the selected after-sales exception types, and mark the after-sales exception types that are different from all the sample exception types in the test calibration exception table as the target after-sales exception types.

[0047] It should be explained that several different sample exception types are pre-stored in the test calibration exception table; the several sample exception types are determined according to the existing test calibration implementation standards; the test calibration exception table plays a role of matching and screening; by processing and classifying the after-sales data, it can provide reliable screened after-sales data support for the subsequent dynamic optimization of the test calibration implementation standards and the implementation of expansion and update.

[0048] Respectively perform validity screening on the total numbers of after-sales exceptions corresponding to all selected after-sales exception types and target after-sales exception types, count the total numbers corresponding to the selected after-sales exception types and target after-sales exception types that meet the exception conditions and mark them as the total numbers of selected valid after-sales exceptions and target valid after-sales exceptions respectively.

[0049] Among them, the condition for meeting the exception is whether the difference between the production time point of the target corresponding to the after-sales and the after-sales time point is greater than the standard time difference. If it is not greater than the standard time difference, the target corresponding to the after-sales meets the exception condition; otherwise, it is determined not to meet.

[0050] It can be understood that performing validity screening on the total numbers of after-sales exceptions can improve the accuracy of the quality monitoring and analysis of after-sales targets.

[0051] Sort and combine all the selected after-sales exception types and their corresponding total numbers of selected valid after-sales exceptions after screening to obtain the first after-sales exception screening data; and sort and combine all the target after-sales exception types and their corresponding total numbers of target valid after-sales exceptions after screening to obtain the second after-sales exception screening data.

[0052] The first after-sales exception screening data and the second after-sales exception screening data constitute the test calibration after-sales data.

[0053] In the embodiments of the present invention, by periodically monitoring, counting, and processing the test calibration data and after-sales data of the target from different dimensions, it can provide reliable monitoring data support for the subsequent analysis of the target status and expansion management in different dimensions.

[0054] The test calibration implementation after-sales processing analysis module is used to perform multi-dimensional state digital processing and analysis on test calibration implementation data and test calibration after-sales data, and dynamically optimize and expand and update the existing test calibration implementation standards according to the analysis results of different dimensions; including:

[0055] Among them, performing multi-dimensional state digital processing and analysis includes performing digital processing and analysis on the detection effective state of test calibration implementation data and test calibration after-sales data, and performing digital processing and analysis on the detection coverage state of test calibration implementation data and test calibration after-sales data;

[0056] Specifically, when performing digital processing and analysis on the detection effective state of test calibration implementation data and test calibration after-sales data, the total number of target processing in the test calibration implementation data, different abnormal types and their corresponding total number of abnormal types are calculated through the formula to calculate the first detection impact factor YJ corresponding to the target; in the formula, LYi is the abnormal impact weight corresponding to different abnormal types of previous test calibration; i is different abnormal types of previous test calibration; i = 1, 2, 3,..., n; n is a positive integer; ni is the total number of abnormal types corresponding to different abnormal types of previous test calibration; N is the total number of all abnormal types that occurred in previous test calibration; A is the standard abnormal detection value, which is determined according to the design requirement data corresponding to the existing test calibration implementation standard of the target;

[0057] It should be explained that the first detection impact factor is used to perform integrated calculation on different abnormal data of the target's previous test calibration to digitally represent its previous test abnormal local state;

[0058] Through the processing and calculation of the first detection impact factor, it is possible to not only digitally represent the abnormal local state corresponding to different abnormal data of the previous test calibration, but also provide reliable previous test analysis data support for the subsequent monitoring cycle's abnormal overall state processing and analysis of the target;

[0059] And, the first after-sales abnormal screening data in the test calibration after-sales data is calculated through the formula to calculate the second detection impact factor EJ corresponding to the target; in the formula, LYj is the abnormal impact weight corresponding to different abnormal types of after-sales monitoring statistics; j is different abnormal types of after-sales monitoring statistics; j = 1, 2, 3,..., m; m is a positive integer; mj is the total number of abnormal types corresponding to different abnormal types of after-sales monitoring statistics; M is the total number of all abnormal types that occurred in after-sales monitoring statistics;

[0060] It should be noted that the second detection impact factor is used to perform integrated calculation on different abnormal data of the target's later after-sales monitoring statistics to digitally represent its after-sales monitoring abnormal local state;

[0061] Through the processing calculation of the second detection influence factor, it is possible to digitally represent the abnormal local states corresponding to different abnormal data in the later after-sales monitoring, and it can also provide reliable after-sales analysis data support for the processing and analysis of the abnormal overall state corresponding to the subsequent monitoring cycle target;

[0062] When determining the overall detection influence corresponding to the target automated production process test calibration, the first detection influence factor and the second detection influence factor obtained by calculation are used to calculate the overall detection influence degree ZJ corresponding to the target through the overall detection influence recognition piecewise function;

[0063] Among them, the expression of the overall detection influence recognition piecewise function is In the formula, α and β are different weight coefficients; α + β = 1; B is the standard detection evaluation value, which can be determined according to the design requirement data corresponding to the existing test calibration implementation standard of the target, or the existing production quality detection requirement data of the target;

[0064] The overall detection influence degree includes values of 0 or 1, indicating that the detection effective state corresponding to the target automated production process test calibration is normal or abnormal respectively;

[0065] When determining the overall detection influence corresponding to the target automated production process test calibration according to the overall detection influence degree, analyze the overall detection influence degree;

[0066] If the overall detection influence degree is 0, generate a detection effective state normal instruction and prompt;

[0067] Otherwise, generate a detection effective state abnormal instruction and prompt, and optimize and update the existing test calibration implementation standard according to the detection effective state instruction;

[0068] In the embodiments of the present invention, by monitoring, processing and analyzing the production and after-sales of the target from different dimensions, the corresponding test calibration monitoring analysis data and after-sales monitoring analysis data are obtained, and further, the monitoring analysis data of different dimensions are integrated and calculated to obtain the corresponding overall detection influence degree and detection effective state, and the existing test calibration implementation standard of the target automated production is optimized and updated according to the detection effective state, which improves the autonomous supervision effect and autonomous optimization control effect of industrial production automated test calibration;

[0069] When digitally processing and analyzing the detection coverage status of the test calibration implementation data and the test calibration after-sales data, obtain the second after-sales abnormal screening data in the test calibration after-sales data, and obtain all the target after-sales abnormal types and their corresponding target effective after-sales abnormal total numbers in the second after-sales abnormal screening data, and through the formula Calculate the local after-sales exception impact factor JYi′ corresponding to different target after-sales exception types; where LYi′ is the exception impact weight corresponding to the target after-sales exception type; i′ = 1, 2, 3, ……, n′; n′ is a positive integer; n′i′ is the total number of exception types corresponding to different target after-sales exception types; exp() is the exponential function with the natural constant e as the base; C is the standard impact degree value, which can be determined according to the median of all local after-sales exception impact factors that have occurred in history;

[0070] It should be noted that the local after-sales exception impact factor is used to integrate and calculate the exception data corresponding to different target after-sales exception types that occur to digitally represent its local after-sales exception impact;

[0071] Sort and combine all the calculated local after-sales exception impact factors to obtain a local after-sales exception impact sequence;

[0072] When implementing corresponding test calibration implementation optimization control according to the local after-sales exception impact sequence, traverse and analyze the local after-sales exception impact sequence. If the element value in the local after-sales exception impact sequence is greater than or equal to 1, mark the target after-sales exception type corresponding to this element as the selected after-sales exception type;

[0073] Sort and combine several selected after-sales exception types obtained through analysis to obtain a selected after-sales exception type sequence, and expand and update the existing test calibration implementation standard according to the selected after-sales exception type sequence; achieving the effect of processing and analyzing after-sales screening data and adaptively expanding and updating the existing test calibration implementation standard.

[0074] In the embodiments of the present invention, through digital processing and analysis of the detection coverage status of the screened and analyzed test calibration after-sales data, and dynamically expanding and updating the existing test calibration implementation standard according to the analysis results, the diverse expansion and utilization of the existing after-sales processing data are realized, further improving the independent supervision effect and independent optimization control effect of industrial production automation test calibration.

[0075] In addition, the formulas involved above are all calculated by removing the dimension and taking their numerical values, and are obtained by collecting a large amount of data and simulating through software to get a formula that is closest to the real situation.

[0076] In several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described invention embodiments are merely illustrative. For example, the division of modules is only a logical function division, and there can be other division methods in actual implementation.

[0077] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, in each embodiment of the present invention, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware, or in the form of a combination of hardware and software functional modules.

[0079] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An industrial production automation control system, characterized in that: include: The test and calibration implementation after-sales supervision module is used to monitor and count the test and calibration implementation data of the target production process to obtain the test and calibration implementation data, and monitor and count the after-sales data of the target within the monitoring period and process and filter them to obtain the test and calibration after-sales data; The test and calibration implementation and after-sales processing and analysis module is used to implement multi-dimensional status digital processing and analysis of the test and calibration implementation data and the test and calibration after-sales data, and dynamically optimize, expand and update the existing test and calibration implementation standards based on the analysis results of different dimensions; Among them, the implementation of multi-dimensional status digital processing and analysis includes the implementation of digital processing and analysis of the effective status of the test and calibration implementation data and the test and calibration after-sales data, and the implementation of digital processing and analysis of the detection coverage status of the test and calibration implementation data and the test and calibration after-sales data; When the digital processing and analysis of the detection coverage status of the test calibration implementation data and the test calibration after-sales data is performed, the second after-sales abnormality screening data in the test calibration after-sales data is obtained, and all target after-sales abnormality types and their corresponding target effective after-sales abnormality totals in the second after-sales abnormality screening data are obtained, and the local after-sales abnormality impact factors corresponding to different target after-sales abnormality types are calculated; the calculation formula of the local after-sales abnormality impact factor JYi´ is: ; In the formula, LYi´ is the abnormal impact weight corresponding to the target after-sales abnormality type; i´=1, 2, 3, ..., n´; n´ is a positive integer; n´i´ is the total number of abnormality types corresponding to different target after-sales abnormality types; exp() is an exponential function with the natural constant e as the base; C is the standard impact value; All calculated local after-sales abnormality impact factors are sorted and combined to obtain a local after-sales abnormality impact sequence; When implementing the optimization control according to the corresponding test calibration of the local after-sales abnormality impact sequence, the local after-sales abnormality impact sequence is traversed and analyzed. If the value of the element in the local after-sales abnormality impact sequence is greater than or equal to 1, the target after-sales abnormality type corresponding to the element is marked as the selected after-sales abnormality type; The selected after-sales abnormality types obtained through analysis are sorted and combined to obtain a selected after-sales abnormality type sequence, and the existing test and calibration implementation standards are expanded and updated according to the selected after-sales abnormality type sequence.

2. An industrial production automation control system according to claim 1, characterized in that: During the monitoring period, the total number of target processing corresponding to the test and calibration of the target automated production is counted; all abnormal types that occur during the test and calibration and the total number of their corresponding abnormal types are obtained; The total number of target processing, different abnormality types and the total number of corresponding abnormality types are sorted and combined to obtain test calibration implementation data; When monitoring and counting the after-sales data of the target within the monitoring period, all after-sales abnormality types and the corresponding total number of after-sales abnormalities that occur in the target are counted; When processing and classifying the after-sales data of the target, all after-sales exception types are traversed and matched with the test calibration exception table corresponding to the target, and the after-sales exception types that are the same as the sample exception types in the test calibration exception table are marked as selected after-sales exception types, and the after-sales exception types that are different from the sample exception types in the test calibration exception table are marked as target after-sales exception types.

3. An industrial production automation control system according to claim 2, characterized in that: The total number of after-sales anomalies corresponding to all selected after-sales anomaly types and target after-sales anomaly types are respectively screened for effectiveness, and the total number of selected after-sales anomaly types and target after-sales anomaly types that meet the anomaly conditions are counted and marked as the total number of selected valid after-sales anomalies and the total number of target valid after-sales anomalies respectively; Sort and combine all the selected after-sales abnormality types after screening and their corresponding total number of selected valid after-sales abnormalities to obtain first after-sales abnormality screening data; and sort and combine all the target after-sales abnormality types after screening and their corresponding total number of target valid after-sales abnormalities to obtain second after-sales abnormality screening data; The first after-sales abnormality screening data and the second after-sales abnormality screening data constitute the test calibration after-sales data.

4. An industrial production automation control system according to claim 3, characterized in that: When the digital processing and analysis of the effective status of the test and calibration implementation data and the test and calibration after-sales data is implemented, the total number of target processing, different abnormal types and their corresponding total number of abnormal types in the test and calibration implementation data are calculated by the formula Calculate the first detection impact factor YJ corresponding to the target; Where LYi is the anomaly impact weight corresponding to different anomaly types calibrated in the previous test; i is the different anomaly types calibrated in the previous test; i=1, 2, 3, ..., n; n is a positive integer; ni is the total number of abnormality types corresponding to different abnormality types in the previous test calibration; N is the total number of all abnormality types that appeared in the previous test calibration; A is the standard abnormality detection value.

5. An industrial production automation control system according to claim 4, characterized in that: The first post-sales abnormal screening data in the test calibration post-sales data is passed through the formula Calculate the second detection impact factor EJ corresponding to the target; where LYj is the abnormal impact weight corresponding to different abnormal types of after-sales monitoring statistics; j is the different abnormal types of after-sales monitoring statistics; j=1, 2, 3, ..., m; m is a positive integer; mj is the total number of abnormal types corresponding to different abnormal types in after-sales monitoring statistics; M is the total number of all abnormal types that appear in after-sales monitoring statistics.

6. An industrial production automation control system according to claim 5, characterized in that: When determining the overall detection influence corresponding to the target automated production process test calibration, the first detection influence factor and the second detection influence factor obtained by calculation are used to obtain the overall detection influence degree ZJ corresponding to the target through the overall detection influence identification piecewise function calculation; Among them, the expression of the overall detection influences the recognition piecewise function is: ; In the formula, α and β are different weight coefficients; α+β=1; B is the standard test evaluation value; The overall detection impact includes a value of 0 or 1, which respectively indicates that the detection effectiveness status of the corresponding target automated production process test calibration is normal or abnormal.

7. An industrial production automation control system according to claim 6, characterized in that: When determining the overall detection impact corresponding to the target automated production process test calibration based on the overall detection impact, the overall detection impact is analyzed; If the overall detection impact is 0, a normal detection effective state instruction is generated and prompted; Otherwise, an abnormal detection effective state instruction is generated and prompted, and the existing test calibration implementation standard is optimized and updated according to the detection effective state instruction.

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