Industrial control strategy generation method and system

Abstract

The invention is suitable for the technical field of industrial control, and provides an industrial control strategy generation method and system, and the method comprises the following steps: determining industrial parameter information according to a product model, the industrial parameter information comprises a plurality of parameter items, and each parameter item corresponds to a parameter range, an optimal parameter value and an impact factor; acquiring corresponding influence information according to influence factors in the industrial parameter information, wherein the influence information comprises operation proficiency, material information, environment information and associated parameters; and determining a parameter value of each parameter item according to the influence information, and generating a production control strategy according to all the parameter values. By using the method and the system, the industrial equipment can be controlled to use the optimal parameters to carry out production operation, and the parameter value of each parameter item can be dynamically adjusted, so that a production control strategy which is more suitable for actual conditions is generated, and the industrial control effect is excellent.

Description

Technical Field

[0001] The present invention relates to the field of industrial control technology, and in particular to an industrial control strategy generation method and system. Background Art

[0002] In the process of modern industrial production, the formulation of industrial control strategies is directly related to production efficiency and product quality. In the current field of industrial control, the control of production equipment parameters often only considers the product model, but the factors that affect the formulation of industrial control strategies are far more than the product model itself. Operation proficiency, material information, environmental information, and related parameters will all have an impact on the final control strategy. The changes of these factors are dynamic, and traditional static control strategies are difficult to adapt to such changes, resulting in unsatisfactory control effects. Therefore, it is necessary to provide an industrial control strategy generation method and system to solve the above problems. Summary of the invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an industrial control strategy generation method and system to solve the problems existing in the above-mentioned background technology.

[0004] The present invention is implemented as follows: an industrial control strategy generation method, the method comprising the following steps:

[0005] Determine industrial parameter information according to the product model, wherein the industrial parameter information includes a plurality of parameter items, each parameter item corresponding to a parameter range, an optimal parameter value and an influencing factor;

[0006] Collect corresponding influencing information according to the influencing factors in the industrial parameter information, wherein the influencing information includes operation proficiency, material information, environmental information and associated parameters;

[0007] Determine the parameter value of each parameter item based on the impact information, and generate a production control strategy based on all parameter values;

[0008] Retrieving detection abnormality information, wherein the detection abnormality information includes abnormal items and abnormal data, and determining potential cause devices according to the detection abnormality information;

[0009] Generate a detection control strategy based on the detection anomaly information and potential cause devices to determine the real cause device.

[0010] As a further solution of the present invention: the step of determining industrial parameter information according to the product model specifically includes:

[0011] Input the product model into an industrial manufacturing parameter library, wherein the industrial manufacturing parameter library contains all product models, and each product model corresponds to a number of industrial devices;

[0012] Output a number of matching industrial equipment, each industrial equipment corresponds to a number of parameter items, each parameter item corresponds to a parameter range, an optimal parameter value and an influencing factor, and obtain industrial parameter information.

[0013] As a further solution of the present invention: the step of determining the parameter value of each parameter item according to the impact information specifically includes:

[0014] Sort all parameter items according to the associated parameters to obtain an item sequence;

[0015] The influence information of each parameter item in the item sequence is processed in turn to obtain a proficiency offset value, a material offset value, an environmental offset value and an associated offset value;

[0016] The parameter value Z of the corresponding parameter item is calculated based on the proficiency offset value ΔFs, the material offset value ΔFm, the environment offset value ΔFe, the association offset value ΔFH and their respective weights, Z=Z 0 +λ 1 ×ΔFs+λ 2 ×ΔFm+λ 3 ×ΔFe+λ 4 ×ΔFH,Z 0 represents the optimal parameter value, λ 1 , 2 , 3 and λ 4 They are proficiency weight, material weight, environment weight and association weight respectively.

[0017] As a further solution of the present invention: the step of sorting all parameter items according to the associated parameters to obtain the item sequence specifically includes:

[0018] Filter out the parameter items that do not have the associated parameter in the impact information as the first-level items;

[0019] Filter out the parameter items whose associated parameters only belong to level N items and items before level N as level N+1 items, and repeat this step until all parameter items are filtered out, where N is a positive integer;

[0020] Arrange all parameter items in ascending order according to the level, and calculate the total number of associated parameters of each parameter item in the arrangement in turn. The total number of associated parameters of the parameter item = the cumulative value of the total number of associated parameters of all corresponding associated parameters;

[0021] All parameter items are sorted in ascending order according to the total number of associated parameters to obtain an item sequence.

[0022] As a further solution of the present invention: the step of processing the influence information of the parameter items to obtain the proficiency offset value, the material offset value, the environment offset value and the association offset value specifically includes:

[0023] The proficiency offset value ΔFs is calculated based on the operation proficiency Fs, ΔFs = (Fs-Wfs) × Ks, Wfs is the baseline value of the employee's proficiency, and Ks is the proficiency impact coefficient;

[0024] The material quality score Fm is obtained according to the material information, and the material offset value ΔFm is calculated, ΔFm = (Fm-Wfm) × Km, Wfm is the reference value of the material quality, and Km is the material influence coefficient;

[0025] The environmental quality score Fe is calculated based on the environmental information, and the environmental offset value ΔFe is calculated, ΔFe = (Fe-Wfe) × Ke, Wfe is the baseline value of the environmental factor, Ke is the environmental impact coefficient;

[0026] Determine the influence degree H of each associated parameter on the parameter item, and obtain the associated influence degree FH, The parameter item has n associated parameters in total, α represents the influence weight of the associated parameter on the parameter item, and the associated offset value ΔFH is calculated, ΔFH = (FH-Wfh) × Kh, Wfh is the reference value of the associated influence, and Kh is the associated influence coefficient.

[0027] As a further solution of the present invention: the step of generating a detection control strategy based on the detection abnormality information and the potential cause device specifically includes:

[0028] Determine the inspection order of potential cause equipment according to the production process;

[0029] Determine the detection type according to the abnormal item, wherein the detection type is human eye detection, mobile device detection or fixed device detection;

[0030] The number of continuous inspections is determined based on the abnormal percentage in the abnormal data, and the inspection control strategy is summarized and generated, and the inspection control strategy is sent to the process inspection terminal.

[0031] Another object of the present invention is to provide an industrial control strategy generation system, the system comprising:

[0032] An industrial parameter determination module, used to determine industrial parameter information according to a product model, wherein the industrial parameter information includes a plurality of parameter items, each of which corresponds to a parameter range, an optimal parameter value and an influencing factor;

[0033] An impact information collection module is used to collect corresponding impact information according to the impact factors in the industrial parameter information, wherein the impact information includes operation proficiency, material information, environmental information and associated parameters;

[0034] The production control strategy module is used to determine the parameter value of each parameter item according to the influencing information and generate the production control strategy according to all the parameter values;

[0035] A potential cause device module is used to retrieve detection anomaly information, wherein the detection anomaly information includes an abnormal item and abnormal data, and determine a potential cause device according to the detection anomaly information;

[0036] The detection control strategy module is used to generate a detection control strategy based on the detection anomaly information and potential cause devices to determine the real cause device.

[0037] As a further solution of the present invention: the production control strategy module includes:

[0038] A parameter item sorting unit is used to sort all parameter items according to associated parameters to obtain an item sequence;

[0039] An impact information processing unit, used to sequentially process the impact information of each parameter item in the item sequence to obtain a proficiency offset value, a material offset value, an environment offset value and an associated offset value;

[0040] The parameter value calculation unit is used to calculate the parameter value Z of the corresponding parameter item according to the proficiency offset value ΔFs, the material offset value ΔFm, the environment offset value ΔFe, the association offset value ΔFH and their respective weights, where Z=Z 0 +λ 1 ×ΔFs+λ 2 ×ΔFm+λ 3 ×ΔFe+λ 4 ×ΔFH,Z 0 represents the optimal parameter value, λ 1 , 2 , 3 and λ 4 They are proficiency weight, material weight, environment weight and association weight respectively.

[0041] As a further solution of the present invention: the parameter item sorting unit includes:

[0042] The first-level screening subunit is used to screen out parameter items that do not have an associated parameter in the impact information as first-level items;

[0043] The multi-level screening subunit is used to screen out parameter items whose associated parameters only belong to level N items and items before level N as level N+1 items, and repeat this step until all parameter items are screened, where N is a positive integer;

[0044] The total number quantum unit is used to arrange all parameter items in ascending order according to the series, and calculate the total number of associated parameters of each parameter item in the arrangement in turn, and the total number of associated parameters of the parameter item = the cumulative value of the total number of associated parameters of all corresponding associated parameters;

[0045] The item sequence subunit is used to arrange all parameter items in ascending order according to the total number of associated parameters to obtain an item sequence.

[0046] As a further solution of the present invention: the impact information processing unit includes:

[0047] The proficiency offset value subunit is used to calculate the proficiency offset value ΔFs according to the operation proficiency Fs, ΔFs = (Fs-Wfs) × Ks, Wfs is the baseline value of the employee's proficiency, and Ks is the proficiency influence coefficient;

[0048] The material offset value subunit is used to obtain the material quality score Fm according to the material information, and calculate the material offset value ΔFm, ΔFm = (Fm-Wfm) × Km, Wfm is the reference value of the material quality, and Km is the material influence coefficient;

[0049] The environmental offset value subunit is used to calculate the environmental quality score Fe according to the environmental information, and calculate the environmental offset value ΔFe, ΔFe = (Fe-Wfe) × Ke, Wfe is the reference value of the environmental factor, Ke is the environmental impact coefficient;

[0050] The associated offset value subunit is used to determine the influence degree H of each associated parameter on the parameter item, and obtain the associated influence degree FH. The parameter item has n associated parameters in total, α represents the influence weight of the associated parameter on the parameter item, and the associated offset value ΔFH is calculated, ΔFH = (FH-Wfh) × Kh, Wfh is the reference value of the associated influence, and Kh is the associated influence coefficient.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The present invention will collect corresponding influencing information according to the influencing factors in the industrial parameter information, and the influencing information includes operation proficiency, material information, environmental information and related parameters, and then determine the parameter value of each parameter item according to the influencing information, and generate a production control strategy according to all parameter values, so that the industrial equipment can be controlled to use the best parameters for production operations, and the parameter value of each parameter item can be dynamically adjusted, so as to generate a production control strategy that is more suitable for the actual situation. In addition, the present invention will directly determine the potential cause equipment according to the detection abnormality information, and generate a detection control strategy according to the detection abnormality information and the potential cause equipment, so as to quickly determine the real cause equipment and improve the efficiency of abnormality detection and processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Flowchart of a method for generating industrial control strategies.

[0054] Figure 2 The invention discloses a flow chart for determining industrial parameter information in a method for generating an industrial control strategy.

[0055] Figure 3 The invention discloses a flow chart for determining parameter values ​​of parameter items in a method for generating an industrial control strategy.

[0056] Figure 4 A flowchart of a project sequence obtained in an industrial control strategy generation method.

[0057] Figure 5 The present invention is a flow chart for processing the influence information of parameter items in an industrial control strategy generation method.

[0058] Figure 6 The invention discloses a flow chart for generating a detection control strategy in an industrial control strategy generation method.

[0059] Figure 7 Generate a block diagram of the system for an industrial control strategy. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0062] like Figure 1 As shown, an embodiment of the present invention provides an industrial control strategy generation method, the method comprising the following steps:

[0063] S100, determining industrial parameter information according to the product model, the industrial parameter information including a plurality of parameter items, each parameter item corresponding to a parameter range, an optimal parameter value and an influencing factor;

[0064] S200, collecting corresponding influencing information according to the influencing factors in the industrial parameter information, where the influencing information includes operation proficiency, material information, environmental information, and associated parameters;

[0065] S300, determining the parameter value of each parameter item according to the impact information, and generating a production control strategy according to all the parameter values;

[0066] S400, retrieving detection abnormality information, wherein the detection abnormality information includes abnormal items and abnormal data, and determining a potential cause device according to the detection abnormality information;

[0067] S500, generating a detection control strategy based on the detection abnormality information and the potential cause device to determine the real cause device.

[0068] In the embodiment of the present invention, firstly, the industrial parameter information is determined according to the product model, and the industrial parameter information includes several parameter items, each parameter item corresponds to a parameter range, an optimal parameter value and an influencing factor, and the influencing factor is one or more of the employee operation, material, environment and associated parameters, and then the influencing information corresponding to each parameter item is collected according to the influencing factor, and the influencing information includes operation proficiency, material information, environmental information and associated parameters. It is easy to understand that each parameter item corresponds to industrial equipment, and the operation proficiency can be obtained by calling the current operator of the industrial equipment. The material information can be obtained through the product model and supply chain data, and the environmental information is collected through temperature sensors, humidity sensors, etc., and the associated parameters refer to the associated parameter items and values. After obtaining the influencing information, the parameter value of each parameter item will be determined according to the influencing information, and the production control strategy will be generated according to all the parameter values, so that the industrial equipment can be controlled to use the best parameters for production operations, and the parameter value of each parameter item can be dynamically adjusted, thereby generating a production control strategy that is more suitable for the actual situation. In addition, the embodiment of the present invention will also retrieve detection anomaly information, which includes abnormal items and abnormal data. Here, it is necessary to build an abnormal item library in advance. The abnormal item library contains all abnormal items, and each abnormal item corresponds to a potential cause device. In this way, the potential cause device can be directly determined based on the detection anomaly information. Then, a detection control strategy will be generated based on the detection anomaly information and the potential cause device to quickly determine the real cause device and improve the efficiency of abnormality detection and processing.

[0069] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of determining industrial parameter information according to the product model specifically includes:

[0070] S101, inputting the product model into an industrial manufacturing parameter library, wherein the industrial manufacturing parameter library contains all product models, and each product model corresponds to a number of industrial devices;

[0071] S102, outputting a plurality of matching industrial equipment, each industrial equipment corresponding to a plurality of parameter items, each parameter item corresponding to a parameter range, an optimal parameter value and an influencing factor, and obtaining industrial parameter information.

[0072] In an embodiment of the present invention, an industrial manufacturing parameter library is constructed in advance, and the industrial manufacturing parameter library contains all product models. Each product model corresponds to a number of industrial equipment, each industrial equipment corresponds to a number of parameter items, and each parameter item corresponds to a parameter range, an optimal parameter value and an influencing factor. When the product model is input into the industrial manufacturing parameter library, matching content will be output, and the industrial parameter information will be obtained.

[0073] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of determining the parameter value of each parameter item according to the impact information specifically includes:

[0074] S301, sorting all parameter items according to associated parameters to obtain an item sequence;

[0075] S302, sequentially processing the impact information of each parameter item in the item sequence to obtain a proficiency offset value, a material offset value, an environment offset value, and a correlation offset value;

[0076] S303, calculating the parameter value Z of the corresponding parameter item according to the proficiency offset value ΔFs, the material offset value ΔFm, the environment offset value ΔFe, the association offset value ΔFH and their respective weights.

[0077] In the embodiment of the present invention, in order to determine the parameter value of each parameter item, it is first necessary to sort all the parameter items according to the associated parameters to obtain a project sequence, in which the bottom parameter items are arranged first. For example, if the associated parameters of the third parameter item are the fifth parameter item and the twelfth parameter item, the fifth parameter item and the twelfth parameter item are arranged before the third parameter item. Then, the influence information of each parameter item in the project sequence is processed in turn to obtain the proficiency offset value, material offset value, environmental offset value and associated offset value, and the parameter value Z of the corresponding parameter item is calculated according to the proficiency offset value ΔFs, the material offset value ΔFm, the environmental offset value ΔFe, the associated offset value ΔFH and their respective weights, where Z=Z 0 +λ 1 ×ΔFs+λ 2 ×ΔFm+λ 3 ×ΔFe+λ4 ×ΔFH,Z 0 represents the optimal parameter value, λ 1 , 2 , 3 and λ 4 are proficiency weight, material weight, environment weight and association weight respectively, λ 1 , 2 , 3 and λ 4 It is a preset value, which is easy to understand. For different parameter items, λ 1 , 2 , 3 and λ 4 Not the same.

[0078] like Figure 4 As shown, as a preferred embodiment of the present invention, the step of sorting all parameter items according to the associated parameters to obtain the item sequence specifically includes:

[0079] S3011, filter out parameter items that do not have an associated parameter in the impact information as first-level items;

[0080] S3012, filter out parameter items whose associated parameters only belong to level N items and items before level N as level N+1 items, repeat this step until all parameter items are filtered out, and N is a positive integer;

[0081] S3013, arranging all parameter items in ascending order according to the level, and calculating the total number of associated parameters of each parameter item in the arrangement in turn, the total number of associated parameters of the parameter item = the accumulated value of the total number of associated parameters of all corresponding associated parameters;

[0082] S3014, arranging all parameter items in ascending order according to the total number of associated parameters to obtain an item sequence.

[0083] In the embodiment of the present invention, in order to determine the project sequence, firstly, the parameter items whose associated parameters do not exist in the impact information are screened out as the first-level items, then the parameter items whose associated parameters only belong to the first-level items are screened out as the second-level items, and then the parameter items whose associated parameters only belong to the second-level items and the items before the second level (first-level items) are screened out as the third-level items, and so on, until all the parameter items are screened out. Then, all the parameter items are arranged in ascending order according to the level, and the total number of associated parameters of each parameter item in the arrangement is calculated in turn, and the total number of associated parameters of the parameter item = the cumulative value of the total number of associated parameters of all corresponding associated parameters, for example, the associated parameters of the third parameter item are the fifth parameter item and the twelfth parameter item, then the total number of associated parameters of the third parameter item = the total number of associated parameters of the fifth parameter item + the total number of associated parameters of the twelfth parameter item, and finally all the parameter items are arranged in ascending order according to the total number of associated parameters to obtain the project sequence.

[0084] like Figure 5 As shown, as a preferred embodiment of the present invention, the step of processing the influence information of the parameter items to obtain the proficiency offset value, the material offset value, the environment offset value and the association offset value specifically includes:

[0085] S3021, calculating a proficiency offset value ΔFs according to the operation proficiency Fs;

[0086] S3022, obtaining a material quality score Fm according to the material information;

[0087] S3023, calculate the environmental quality score Fe based on the environmental information;

[0088] S3024, determining the influence degree H of each associated parameter on the parameter item, obtaining the associated influence degree FH, and calculating the associated offset value ΔFH.

[0089] In the embodiment of the present invention, the proficiency offset value ΔFs is first calculated based on the operation proficiency Fs, ΔFs = (Fs-Wfs) × Ks, where Wfs is the baseline value of employee proficiency, and Ks is the proficiency impact coefficient, both of which are fixed values. Then, the material quality score Fm is obtained based on the material information. The material quality score requires a comprehensive analysis of the supplier, material material, and material stability to calculate the material offset value ΔFm, ΔFm = (Fm-Wfm) × Km, Wfm is the baseline value of material quality, and Km is the material impact coefficient. Then, the environmental quality score Fe is calculated based on the environmental information, and the environmental offset value ΔFe is calculated, ΔFe = (Fe-Wfe) × Ke, Wfe is the baseline value of the environmental factor, and Ke is the environmental impact coefficient. Finally, it is also necessary to determine the degree of influence H of each associated parameter on the parameter item to obtain the associated influence degree FH, There are n associated parameters in a parameter item, α represents the influence weight of the associated parameter on the parameter item, which is a fixed value; the influence degree H is retrieved from the influence relationship table, each parameter item also corresponds to the influence relationship table, the influence relationship table contains the associated parameters corresponding to the parameter item, each associated parameter corresponds to a number of parameter value ranges, and each parameter value range corresponds to the influence degree H. Then the associated offset value ΔFH can be calculated, ΔFH = (FH-Wfh) × Kh, Wfh is the reference value of the associated influence degree, Kh is the associated influence coefficient, which is a fixed value.

[0090] like Figure 6 As shown, as a preferred embodiment of the present invention, the step of generating a detection control strategy according to the detection abnormality information and the potential cause device specifically includes:

[0091] S501, determine the inspection sequence of potential cause equipment according to the production process;

[0092] S502, determining a detection type according to the abnormal item, wherein the detection type is human eye detection, mobile device detection or fixed device detection;

[0093] S503, determining the number of continuous inspections according to the abnormal percentage in the abnormal data, summarizing and generating an inspection control strategy, and sending the inspection control strategy to the process inspection terminal.

[0094] In an embodiment of the present invention, the detection control strategy includes the detection sequence, detection type, number of continuous detections and abnormal items of industrial equipment. Here, the detection sequence of the potential cause equipment is determined according to the production process, and then the detection type is determined according to the abnormal items. The embodiment of the present invention has a project detection library constructed in advance, and the project detection library contains all abnormal items. Each abnormal item corresponds to a detection type, and the detection type is human eye detection, mobile equipment detection or fixed equipment detection. Human eye detection does not require the aid of equipment. Mobile equipment detection refers to patrol personnel carrying detection equipment to industrial equipment to detect processed products. Fixed equipment detection refers to patrol personnel carrying products processed by industrial equipment to fixed equipment for detection, and it is necessary to determine the number of continuous detections based on the abnormal percentage in the abnormal data to avoid accidental factors and have higher reliability.

[0095] like Figure 7 As shown, an embodiment of the present invention further provides an industrial control strategy generation system, the system comprising:

[0096] The industrial parameter determination module 100 is used to determine industrial parameter information according to the product model, wherein the industrial parameter information includes a plurality of parameter items, each of which corresponds to a parameter range, an optimal parameter value and an influencing factor;

[0097] The impact information collection module 200 is used to collect corresponding impact information according to the impact factors in the industrial parameter information, and the impact information includes operation proficiency, material information, environmental information and related parameters;

[0098] The production control strategy module 300 is used to determine the parameter value of each parameter item according to the impact information and generate the production control strategy according to all the parameter values;

[0099] Potential cause device module 400, used to retrieve detection abnormality information, the detection abnormality information includes abnormal items and abnormal data, and determine the potential cause device according to the detection abnormality information;

[0100] The detection control strategy module 500 is used to generate a detection control strategy based on the detection abnormality information and the potential cause device to determine the real cause device.

[0101] As a preferred embodiment of the present invention, the production control strategy module 300 includes:

[0102] A parameter item sorting unit is used to sort all parameter items according to associated parameters to obtain an item sequence;

[0103] An impact information processing unit, used to sequentially process the impact information of each parameter item in the item sequence to obtain a proficiency offset value, a material offset value, an environment offset value and an associated offset value;

[0104] The parameter value calculation unit is used to calculate the parameter value Z of the corresponding parameter item according to the proficiency offset value ΔFs, the material offset value ΔFm, the environment offset value ΔFe, the association offset value ΔFH and their respective weights, where Z=Z 0 +λ 1 ×ΔFs+λ 2 ×ΔFm+λ 3 ×ΔFe+λ 4 ×ΔFH,Z 0 represents the optimal parameter value, λ 1 , 2 , 3 and λ 4 They are proficiency weight, material weight, environment weight and association weight respectively.

[0105] As a preferred embodiment of the present invention, the parameter item sorting unit includes:

[0106] The first-level screening subunit is used to screen out parameter items that do not have an associated parameter in the impact information as first-level items;

[0107] The multi-level screening subunit is used to screen out parameter items whose associated parameters only belong to level N items and items before level N as level N+1 items, and repeat this step until all parameter items are screened, where N is a positive integer;

[0108] The total number quantum unit is used to arrange all parameter items in ascending order according to the series, and calculate the total number of associated parameters of each parameter item in the arrangement in turn, and the total number of associated parameters of the parameter item = the cumulative value of the total number of associated parameters of all corresponding associated parameters;

[0109] The item sequence subunit is used to arrange all parameter items in ascending order according to the total number of associated parameters to obtain an item sequence.

[0110] As a preferred embodiment of the present invention, the impact information processing unit includes:

[0111] The proficiency offset value subunit is used to calculate the proficiency offset value ΔFs according to the operation proficiency Fs, ΔFs = (Fs-Wfs) × Ks, Wfs is the baseline value of the employee's proficiency, and Ks is the proficiency influence coefficient;

[0112] The material offset value subunit is used to obtain the material quality score Fm according to the material information, and calculate the material offset value ΔFm, ΔFm = (Fm-Wfm) × Km, Wfm is the reference value of the material quality, and Km is the material influence coefficient;

[0113] The environmental offset value subunit is used to calculate the environmental quality score Fe according to the environmental information, and calculate the environmental offset value ΔFe, ΔFe = (Fe-Wfe) × Ke, Wfe is the reference value of the environmental factor, Ke is the environmental impact coefficient;

[0114] The associated offset value subunit is used to determine the influence degree H of each associated parameter on the parameter item, and obtain the associated influence degree FH. The parameter item has n associated parameters in total, α represents the influence weight of the associated parameter on the parameter item, and the associated offset value ΔFH is calculated, ΔFH = (FH-Wfh) × Kh, Wfh is the reference value of the associated influence, and Kh is the associated influence coefficient.

[0115] The above only describes in detail the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0116] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0117] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0118] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A method for generating an industrial control strategy, characterized in that: The method comprises the following steps: Determine industrial parameter information according to the product model, wherein the industrial parameter information includes a plurality of parameter items, each parameter item corresponding to a parameter range, an optimal parameter value and an influencing factor; Collect corresponding influencing information according to the influencing factors in the industrial parameter information, wherein the influencing information includes operation proficiency, material information, environmental information and associated parameters; Determine the parameter value of each parameter item based on the impact information, and generate a production control strategy based on all parameter values; Retrieving detection abnormality information, wherein the detection abnormality information includes abnormal items and abnormal data, and determining potential cause devices according to the detection abnormality information; Generate a detection control strategy based on the detection anomaly information and potential cause devices to determine the real cause device.

2. The industrial control strategy generation method according to claim 1, characterized in that: The step of determining industrial parameter information according to the product model specifically includes: Input the product model into an industrial manufacturing parameter library, wherein the industrial manufacturing parameter library contains all product models, and each product model corresponds to a number of industrial devices; Output a number of matching industrial equipment, each industrial equipment corresponds to a number of parameter items, each parameter item corresponds to a parameter range, an optimal parameter value and an influencing factor, and obtain industrial parameter information.

3. The industrial control strategy generation method according to claim 1, characterized in that: The step of determining the parameter value of each parameter item according to the impact information specifically includes: Sort all parameter items according to the associated parameters to obtain an item sequence; The influence information of each parameter item in the item sequence is processed in turn to obtain a proficiency offset value, a material offset value, an environmental offset value and an associated offset value; The parameter value Z of the corresponding parameter item is calculated according to the proficiency offset value ΔFs, material offset value ΔFm, environment offset value ΔFe, association offset value ΔFH and their respective weights, Z=Z0+λ1×ΔFs+λ2×ΔFm+λ3×ΔFe+λ4×ΔFH, where Z0 represents the optimal parameter value, λ1, λ2, λ3 and λ4 are proficiency weight, material weight, environment weight and association weight respectively.

4. The industrial control strategy generation method according to claim 3, characterized in that: The step of sorting all parameter items according to the associated parameters to obtain an item sequence specifically includes: Filter out the parameter items that do not have the associated parameter in the impact information as the first-level items; Filter out the parameter items whose associated parameters only belong to level N items and items before level N as level N+1 items, and repeat this step until all parameter items are filtered out, where N is a positive integer; Arrange all parameter items in ascending order according to the level, and calculate the total number of associated parameters of each parameter item in the arrangement in turn. The total number of associated parameters of the parameter item = the cumulative value of the total number of associated parameters of all corresponding associated parameters; All parameter items are arranged in ascending order according to the total number of associated parameters to obtain an item sequence.

5. The industrial control strategy generation method according to claim 3, characterized in that: The step of processing the impact information of the parameter items to obtain the proficiency offset value, the material offset value, the environment offset value and the associated offset value specifically includes: The proficiency offset value ΔFs is calculated based on the operation proficiency Fs, ΔFs = (Fs-Wfs) × Ks, Wfs is the baseline value of the employee's proficiency, and Ks is the proficiency impact coefficient; The material quality score Fm is obtained according to the material information, and the material offset value ΔFm is calculated, ΔFm = (Fm-Wfm) × Km, Wfm is the reference value of the material quality, and Km is the material influence coefficient; The environmental quality score Fe is calculated based on the environmental information, and the environmental offset value ΔFe is calculated, ΔFe = (Fe-Wfe) × Ke, Wfe is the baseline value of the environmental factor, Ke is the environmental impact coefficient; Determine the influence degree H of each associated parameter on the parameter item, and obtain the associated influence degree FH, FH = The parameter item has n associated parameters in total, α represents the influence weight of the associated parameter on the parameter item, and the associated offset value ΔFH is calculated, ΔFH = (FH-Wfh) × Kh, Wfh is the reference value of the associated influence, and Kh is the associated influence coefficient.

6. The industrial control strategy generation method according to claim 1, characterized in that: The step of generating a detection control strategy based on the detection abnormality information and potential cause equipment specifically includes: Determine the inspection order of potential cause equipment according to the production process; Determine the detection type according to the abnormal item, wherein the detection type is human eye detection, mobile device detection or fixed device detection; The number of continuous inspections is determined based on the abnormal percentage in the abnormal data, and the inspection control strategy is summarized and generated, and the inspection control strategy is sent to the process inspection terminal.

7. An industrial control strategy generation system, characterized in that: The system comprises: An industrial parameter determination module, used to determine industrial parameter information according to a product model, wherein the industrial parameter information includes a plurality of parameter items, each of which corresponds to a parameter range, an optimal parameter value and an influencing factor; An impact information collection module is used to collect corresponding impact information according to the impact factors in the industrial parameter information, wherein the impact information includes operation proficiency, material information, environmental information and associated parameters; The production control strategy module is used to determine the parameter value of each parameter item according to the influencing information and generate the production control strategy according to all the parameter values; A potential cause device module is used to retrieve detection anomaly information, wherein the detection anomaly information includes an abnormal item and abnormal data, and determine a potential cause device according to the detection anomaly information; The detection control strategy module is used to generate a detection control strategy based on the detection anomaly information and potential cause devices to determine the real cause device.

8. The industrial control strategy generation system according to claim 7, characterized in that: The production control strategy module includes: A parameter item sorting unit is used to sort all parameter items according to associated parameters to obtain an item sequence; An impact information processing unit, used to sequentially process the impact information of each parameter item in the item sequence to obtain a proficiency offset value, a material offset value, an environment offset value and an associated offset value; The parameter value calculation unit is used to calculate the parameter value Z of the corresponding parameter item according to the proficiency offset value ΔFs, the material offset value ΔFm, the environment offset value ΔFe, the association offset value ΔFH and their respective weights, Z=Z0+λ1×ΔFs+λ2×ΔFm+λ3×ΔFe+λ4×ΔFH, Z0 represents the optimal parameter value, λ1, λ2, λ3 and λ4 are the proficiency weight, material weight, environment weight and association weight respectively.

9. The industrial control strategy generation system according to claim 8, characterized in that: The parameter item sorting unit comprises: The first-level screening subunit is used to screen out parameter items that do not have an associated parameter in the impact information as first-level items; The multi-level screening subunit is used to screen out parameter items whose associated parameters only belong to level N items and items before level N as level N+1 items, and repeat this step until all parameter items are screened, where N is a positive integer; The total number quantum unit is used to arrange all parameter items in ascending order according to the series, and calculate the total number of associated parameters of each parameter item in the arrangement in turn, and the total number of associated parameters of the parameter item = the cumulative value of the total number of associated parameters of all corresponding associated parameters; The item sequence subunit is used to arrange all parameter items in ascending order according to the total number of associated parameters to obtain an item sequence.

10. The industrial control strategy generation system according to claim 8, characterized in that: The impact information processing unit includes: The proficiency offset value subunit is used to calculate the proficiency offset value ΔFs according to the operation proficiency Fs, ΔFs = (Fs-Wfs) × Ks, Wfs is the baseline value of the employee's proficiency, and Ks is the proficiency influence coefficient; The material offset value subunit is used to obtain the material quality score Fm according to the material information, and calculate the material offset value ΔFm, ΔFm = (Fm-Wfm) × Km, Wfm is the reference value of the material quality, and Km is the material influence coefficient; The environmental offset value subunit is used to calculate the environmental quality score Fe according to the environmental information, and calculate the environmental offset value ΔFe, ΔFe = (Fe-Wfe) × Ke, Wfe is the reference value of the environmental factor, Ke is the environmental impact coefficient; The associated offset value subunit is used to determine the influence degree H of each associated parameter on the parameter item, and obtain the associated influence degree FH. The parameter item has n associated parameters in total, α represents the influence weight of the associated parameter on the parameter item, and the associated offset value ΔFH is calculated, ΔFH = (FH-Wfh) × Kh, Wfh is the reference value of the associated influence, and Kh is the associated influence coefficient.

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