Intelligent control method and system based on industrial production

Abstract

The invention is suitable for the technical field of industrial manufacturing, and provides an intelligent control method and system based on industrial production, and the method comprises the following steps: identifying RFID tags through readers to determine a material batch and a product batch, each reader corresponding to a production process, each production process corresponding to a parameter item, each parameter item corresponds to a parameter range and an ideal value; determining a material quality value and an environment comprehensive index of the production process according to the material batch and the parameter item; determining a worker proficiency degree I of a production process and a worker proficiency degree II of an associated process according to the product batch; determining an associated inspection item of the production process according to the parameter item, calling a corresponding inspection value, and determining a deviation index; and determining parameter values of the parameter items according to the parameter range, the ideal value, the material mass value, the environment comprehensive index, the worker proficiency level I, the worker proficiency level II and the deviation index. The influence of various factors on the production process is comprehensively considered, and the stability and consistency of the product quality are guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial manufacturing, in particular to an intelligent control method and system based on industrial production. BACKGROUND

[0002] In traditional industrial production, production control mainly relies on manual experience and fixed parameter setting, which has the following problems: production parameters are usually static values preset based on historical experience, which are difficult to adapt to dynamic changes in material quality, environmental conditions and manual operation differences, resulting in fluctuations in product quality. The influence of various factors on production stability is not fully considered, and precise compensation is difficult to achieve. And in the production and manufacturing process, the multi-dimensional data in the workshop is not fully utilized. Therefore, it is necessary to provide an intelligent control method and system based on industrial production, which aims to solve the above problems. SUMMARY

[0003] In view of the defects in the prior art, the present application aims to provide an intelligent control method and system based on industrial production to solve the problems in the background art.

[0004] The present application is implemented as follows: an intelligent control method based on industrial production, the method comprising the following steps:

[0005] The RFID tag on the product is identified by the reader to determine the product code, and the material batch and product batch are determined according to the product code. Each reader corresponds to a production process, each production process corresponds to a parameter item, and each parameter item corresponds to a parameter range and an ideal value;

[0006] The material quality value and the environmental comprehensive index of the production process are determined according to the material batch and the parameter item;

[0007] The worker proficiency one of the production process and the worker proficiency two of the associated process are determined according to the product batch;

[0008] The associated inspection item of the production process is determined according to the parameter item, the corresponding inspection value is retrieved, and the deviation index is determined;

[0009] The parameter value of the parameter item is determined according to the parameter range, the ideal value, the material quality value, the environmental comprehensive index, the worker proficiency one, the worker proficiency two and the deviation index, and the production process is controlled according to the parameter value.

[0010] As a further scheme of the present application: the step of determining the material quality value and the environmental comprehensive index of the production process according to the material batch and the parameter item, specifically comprising:

[0011] The material evaluation formula of the production process and the environment evaluation formula are called according to the parameter item, the material evaluation formula contains the associated material item, and the environment evaluation formula contains the associated environment item;

[0012] The material specific value of the associated material item is determined according to the material batch, and the material specific value is input into the material evaluation formula to obtain a material quality value;

[0013] The environment specific value of the associated environment item is collected, and the environment specific value is input into the environment evaluation formula to obtain an environment comprehensive index.

[0014] As a further scheme of the present application, the step of determining the worker proficiency one of the production process and the worker proficiency two of the associated process according to the product batch specifically comprises:

[0015] The worker proficiency one of the production process is determined according to the worker roster;

[0016] The associated process is determined according to the production process, and the worker proficiency two of the associated process is determined according to the worker roster.

[0017] As a further scheme of the present application, the step of determining the parameter value of the parameter item according to the parameter range, the ideal value, the material quality value, the environment comprehensive index, the worker proficiency one and the worker proficiency two and the deviation index specifically comprises:

[0018] The material factor α, the environment factor β, the proficiency one factor γ, the proficiency two factor δ and the deviation factor ∈ are obtained according to the material quality value MQ, the environment comprehensive index EI, the worker proficiency one WS1, the worker proficiency two WS2 and the deviation index DI;

[0019] The total adjustment factor Δ is determined, Δ represents the deviation proportion relative to the ideal value, Δ = w1 × α + w2 × β + w3 × γ + w4 × δ + w5 × ∈, w1, w2, w3, w4 and w5 are weight coefficients;

[0020] The parameter value PV of the parameter item is determined according to the total adjustment factor Δ and the ideal value BV, PV = BV + s × (Pmax-Pmin) × Δ, Pmax and Pmin are respectively the upper limit value and the lower limit value of the parameter range, and s represents a sensitivity coefficient.

[0021] As a further scheme of the present application, the method further comprises updating and adjusting the weight coefficients, and the specific steps are:

[0022] The historical batch data are called, each historical batch data includes α, β, γ, δ, ∈ and corresponding process output quality Q, and there are N batches;

[0023] The data set is constructed: each batch is a sample Xi=(αi, βi, γi, δi, ∈i), each Xi corresponds to a label yi=Qi;

[0024] The factor influence Inf is calculated by the weighted correlation coefficient, j represents the factor type index, i represents the batch index, X ij represents the value of the jth factor in the ith batch, represents the average value of the jth factor, represents the average value of the process output quality;

[0025] The weight coefficient of each factor is calculated, τ is a smoothing factor.

[0026] As a further scheme of the application: α=1-MQ, β=1-EI, γ=1-WS1, δ=1-WS2, ∈=DI.

[0027] Another object of the present application is to provide an intelligent control system based on industrial production, which comprises:

[0028] A product code reading module is configured to identify the RFID tag on the product through a reader, determine the product code, determine the material batch and the product batch according to the product code, each reader corresponding to a production process, each production process corresponding to a parameter item, and each parameter item corresponding to a parameter range and an ideal value;

[0029] A material environment index module is configured to determine the material quality value and the environment comprehensive index of the production process according to the material batch and the parameter item;

[0030] A proficiency determination module is configured to determine the worker proficiency one of the production process and the worker proficiency two of the associated process according to the product batch;

[0031] A deviation index determination module is configured to determine the associated inspection item of the production process according to the parameter item, call the corresponding inspection value, and determine the deviation index;

[0032] A parameter value determination module is configured to determine the parameter value of the parameter item according to the parameter range, the ideal value, the material quality value, the environment comprehensive index, the worker proficiency one, the worker proficiency two, and the deviation index, and perform production control on the process according to the parameter value.

[0033] As a further scheme of the application: the material environment index module comprises:

[0034] An evaluation formula calling unit is configured to call the material evaluation formula and the environment evaluation formula of the production process according to the parameter item, the material evaluation formula containing the associated material item, and the environment evaluation formula containing the associated environment item;

[0035] A material quality value unit is configured to determine a material specific value of the material item according to the material batch, input the material specific value into a material evaluation formula, and obtain a material quality value.

[0036] An environment comprehensive index unit is configured to collect environment specific values of the environment items, input the environment specific values into an environment evaluation formula, and obtain an environment comprehensive index.

[0037] As a further scheme of the present application, the proficiency determination module comprises:

[0038] A proficiency one calculation unit is configured to retrieve a worker schedule according to the product batch, and determine a worker proficiency one of the production process.

[0039] A proficiency two calculation unit is configured to determine an associated process according to the production process, and determine a worker proficiency two of the associated process according to the worker schedule.

[0040] As a further scheme of the present application, the parameter value determination module comprises:

[0041] A multi-factor determination unit is configured to obtain a material factor α, an environment factor β, a proficiency one factor γ, a proficiency two factor δ, and a deviation factor ∈ according to the material quality value MQ, the environment comprehensive index EI, the worker proficiency one WS1, the worker proficiency two WS2, and the deviation index DI.

[0042] A total adjustment factor unit is configured to determine a total adjustment factor Δ, wherein Δ represents a deviation proportion relative to the ideal value, Δ = w1 × α + w2 × β + w3 × γ + w4 × δ + w5 × ∈, w1, w2, w3, w4, and w5 are weight coefficients.

[0043] A parameter value determination unit is configured to determine a parameter value PV of the parameter item according to the total adjustment factor Δ and the ideal value BV, PV = BV + s × (Pmax - Pmin) × Δ, Pmax and Pmin are respectively an upper limit value and a lower limit value of a parameter range, and s represents a sensitivity coefficient.

[0044] Compared with the prior art, the present application has the following beneficial effects:

[0045] This invention identifies product batches and product batches by identifying RFID tags on products and obtaining product codes. Based on the characteristics of different batches, and combined with factors such as material quality values, comprehensive environmental indices, worker skill levels, and deviation indices, it accurately determines the parameter values ​​for production processes. This enables personalized and precise control of the production process, effectively improving product quality stability and reducing the defect rate. This invention comprehensively considers the impact of multiple factors on the production process, fully utilizes multidimensional data, and incorporates these factors into the determination of production control parameters. This makes production control more precise and comprehensive, better adaptable to various changes in the production process, and ensures the stability and consistency of product quality. Attached Figure Description

[0046] Figure 1 This is a flowchart of an intelligent control method based on industrial production.

[0047] Figure 2 This is a flowchart for determining material quality values ​​and comprehensive environmental indices in an intelligent control method based on industrial production.

[0048] Figure 3 This is a flowchart for determining worker proficiency in an intelligent control method based on industrial production.

[0049] Figure 4 This is a flowchart for determining parameter values ​​in an intelligent control method based on industrial production.

[0050] Figure 5 This is a flowchart illustrating the updating and adjustment of weight coefficients in an intelligent control method based on industrial production.

[0051] Figure 6 This is a schematic diagram of the structure of an intelligent control system based on industrial production. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0053] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0054] like Figure 1 As shown, this embodiment of the invention provides an intelligent control method based on industrial production, the method comprising the following steps:

[0055] S100 identifies the RFID tag on the product through a reader, determines the product code, and determines the material batch and product batch based on the product code. Each reader corresponds to a production process, each production process corresponds to a parameter item, and each parameter item corresponds to a parameter range and ideal value.

[0056] S200, determine the material quality value and comprehensive environmental index of the production process based on the material batch and parameter items;

[0057] S300, determine the worker proficiency level 1 for the production process and the worker proficiency level 2 for the related process based on the product batch;

[0058] S400, determine the associated inspection items of the production process based on the parameter items, retrieve the corresponding inspection values, and determine the deviation index;

[0059] S500 determines the parameter values ​​of the aforementioned parameter items based on parameter range, ideal value, material quality value, comprehensive environmental index, worker proficiency level 1, worker proficiency level 2, and deviation index.

[0060] It should be noted that most production parameters currently use fixed parameters, which cannot be adjusted in real time according to fluctuations in material quality (such as changes in raw material hardness) or environmental interference (such as sudden changes in workshop temperature and humidity), leading to an increase in the defect rate. Furthermore, product inspection values ​​are usually obtained after production is completed, making it impossible to correct parameters in real time, resulting in batch defects. The embodiments of this invention aim to solve the above problems.

[0061] This invention enables dynamic control of production parameters. When dynamic control is required for a particular production process, a reader is installed before that process. It should be noted that RFID tags are affixed to the products being manufactured, and these tags contain a product code. This product code includes information such as the material batch and the product batch. By identifying the RFID tags on the products using the reader, the corresponding material batch and product batch can be determined. Furthermore, each production process corresponds to specific parameters, each with a parameter range and an ideal value. The ideal value refers to the parameter value used under ideal conditions. Then, based on the material batch and parameter items, the material quality value and environmental comprehensive index of the production process can be determined. Additionally, based on the product batch, the worker proficiency level 1 for the production process and the worker proficiency level 2 for related processes can be determined. The production operation process of related processes will have a certain impact on the production process itself. In addition, during the production process, various inspections are conducted in real time. Parameter items correspond to related inspection items, and the inspection values ​​of these related items have a certain impact on the determination of parameter values ​​for that production process. At this point, the corresponding inspection value is retrieved and compared with the acceptable range and standard value of the related inspection item to determine the deviation index. The deviation index is calculated as: |Inspection value - Standard value| / Length of the acceptable range. Then, the parameter values ​​for the aforementioned items are determined by comprehensively considering the parameter range, ideal value, material quality value, comprehensive environmental index, worker skill level 1, worker skill level 2, and deviation index. Production control is then applied to the process based on these parameter values. The final parameter values ​​can dynamically compensate for multiple factors, resulting in higher parameter accuracy. By comprehensively considering the impact of various factors such as material quality, environmental conditions, worker skill level, and inspection deviation on the production process, and incorporating these factors into the determination of production control parameters, production control becomes more precise and comprehensive, better adapting to various changes during the production process and ensuring the stability and consistency of product quality.

[0062] like Figure 2 As shown, in a preferred embodiment of the present invention, the step of determining the material quality value and environmental comprehensive index of the production process based on the material batch and parameter items specifically includes:

[0063] S201, retrieve the material evaluation formula and environmental evaluation formula of the production process according to the parameter items. The material evaluation formula contains related material items, and the environmental evaluation formula contains related environmental items.

[0064] S202, determine the specific material value of the associated material item based on the material batch, and input the specific material value into the material evaluation formula to obtain the material quality value;

[0065] S203: Collect specific environmental values ​​of related environmental projects and input these values ​​into the environmental assessment formula to obtain the comprehensive environmental index.

[0066] In this embodiment of the invention, it is necessary to construct material evaluation formulas and environmental evaluation formulas for each production process in advance. The material evaluation formula contains one or more related material items, and the environmental evaluation formula contains one or more related environmental items. It is easy to understand that the corresponding related material items and environmental items are different for different production processes. Then, the inspection values ​​of the material items for each batch need to be retrieved, thus determining the specific material values ​​of the related material items. These specific material values ​​are then input into the material evaluation formula to obtain the material quality value. Simultaneously, various sensors collect the specific environmental values ​​of the related environmental items, and inputting these environmental values ​​into the environmental evaluation formula yields the comprehensive environmental index.

[0067] like Figure 3 As shown, in a preferred embodiment of the present invention, the step of determining the worker skill level one of the production process and the worker skill level two of the related process based on the product batch specifically includes:

[0068] S301, retrieve the worker schedule based on the product batch and determine the worker proficiency level for the production process.

[0069] S302, determine the related processes based on the production process, and determine the worker proficiency level of the related processes based on the worker schedule.

[0070] In this embodiment of the invention, a worker shift schedule is established for each product batch. Workers do not change shifts during the production of the same product batch. The worker shift schedule includes the operators and their skill levels for each production process. Thus, the worker skill level 1 for each production process can be directly determined. Each production process corresponds to a related process; after determining the related processes, the worker skill level 2 can be determined based on the worker shift schedule.

[0071] like Figure 4 As shown, in a preferred embodiment of the present invention, the step of determining the parameter values ​​of the parameter items based on parameter range, ideal value, material quality value, comprehensive environmental index, worker proficiency level 1, worker proficiency level 2, and deviation index specifically includes:

[0072] S501, based on the material quality value MQ, the comprehensive environmental index EI, the worker skill level 1 WS1, the worker skill level 2 WS2, and the deviation index DI, we obtain the material factor α, the environmental factor β, the skill level 1 factor γ, the skill level 2 factor δ, and the deviation factor ∈.

[0073] S502, determine the total adjustment factor Δ, where Δ represents the offset ratio relative to the ideal value, Δ=w1×α+w2×β+w3×γ+w4×δ+w5×∈;

[0074] S503, determine the parameter value PV of the parameter item based on the total adjustment factor Δ and the ideal value BV, PV=BV+s×(Pmax-Pmin)×Δ.

[0075] In this embodiment of the invention, the material quality value MQ, the environmental comprehensive index EI, the worker skill level 1 WS1, the worker skill level 2 WS2, and the deviation index DI are all within the range of 0 to 1. The closer the material quality value is to 1, the higher the material quality; the closer the environmental comprehensive index is to 1, the higher the environmental quality; the closer the worker skill level is to 1, the higher the skill level; and the closer the deviation index is to 1, the greater the deviation. Wherein, α = 1 - MQ, β = 1 - EI, γ = 1 - WS1, δ = 1 - WS2, and ∈ = DI. Negative factors (such as low MQ, low EI, low WS1, low WS2, and high DI) generally require conservative adjustments (moving away from the ideal value to compensate for risk); positive factors (such as high MQ, high EI, high WS1, high WS2, and low DI) allow for aggressive adjustments (approaching the ideal value). Next, the total adjustment factor Δ is determined, representing the offset ratio relative to the ideal value. Δ = w1×α + w2×β + w3×γ + w4×δ + w5×∈, where w1, w2, w3, w4, and w5 are weighting coefficients. Finally, the parameter value PV of the aforementioned parameter item needs to be determined based on the total adjustment factor Δ and the ideal value BV. PV = BV + s × (Pmax - Pmin) × Δ, where Pmax and Pmin are the upper and lower limits of the parameter range, respectively, and s represents the sensitivity coefficient, a predefined constant (e.g., +0.2) to ensure the adjustment is within a reasonable range. The sign of s indicates upward or downward adjustment; for example, if the parameter item is a speed-related parameter, slower speed means lower risk and more controllable quality, then s is negative. Furthermore, the final parameter value PV should be within the corresponding parameter range. When PV is less than the lower limit of the parameter range, PV is taken as the lower limit; when PV is greater than the upper limit of the parameter range, PV is taken as the upper limit.

[0076] like Figure 5 As shown, in a preferred embodiment of the present invention, the method further includes updating and adjusting the weighting coefficients, specifically the following steps:

[0077] S601, retrieve historical batch data, each historical batch data includes α, β, γ, δ, ∈ and the corresponding process output quality Q;

[0078] S602, Construct the dataset: Each batch consists of samples Xi = (αi, βi, γi, δi, ∈i), and each Xi corresponds to a label yi = Qi;

[0079] S603 calculates the factor influence Inf using weighted correlation coefficients.

[0080] S604, calculate the weight coefficients of each factor.

[0081] In this embodiment of the invention, the weighting coefficients are also dynamically updated to ensure their accuracy. Specifically, historical batch data of the most recent product of the same model are retrieved. Each historical batch includes α, β, γ, δ, ∈ and the corresponding process output quality Q, for a total of N batches, where N can be 100. Then, a dataset is constructed based on these 100 historical batches: each batch is a sample Xi = (αi, βi, γi, δi, ∈i), labeled yi = Qi. Next, the factor influence Inf is calculated using the weighted correlation coefficient. j represents the factor type index, where j ranges from 1 to 5; i represents the batch index, where i ranges from 1 to 100; X ij This represents the value of the j-th factor in the i-th batch. This represents the average value of the j-th factor. This represents the average output quality of the process. Finally, the weighting coefficients of each factor are calculated. k ranges from 1 to 5, and τ is a smoothing factor, such as τ = 0.01, to prevent the weights from reaching zero. The weight coefficients are updated periodically.

[0082] like Figure 6 As shown, this embodiment of the invention also provides an intelligent control system based on industrial production, the system comprising:

[0083] The product code reading module 100 is used to identify the RFID tag on the product through the reader, determine the product code, and determine the material batch and product batch based on the product code. Each reader corresponds to a production process, each production process corresponds to a parameter item, and each parameter item corresponds to a parameter range and ideal value.

[0084] The material environment index module 200 is used to determine the material quality value and comprehensive environmental index of the production process based on the material batch and parameter items.

[0085] The proficiency determination module 300 is used to determine the worker proficiency level 1 of the production process and the worker proficiency level 2 of the related process based on the product batch.

[0086] The deviation index determination module 400 is used to determine the associated inspection items of the production process based on the parameter items, retrieve the corresponding inspection values, and determine the deviation index.

[0087] The parameter value determination module 500 is used to determine the parameter values ​​of the parameter items based on the parameter range, ideal value, material quality value, comprehensive environmental index, worker proficiency level 1, worker proficiency level 2, and deviation index, and to perform production control on the process based on the parameter values.

[0088] In a preferred embodiment of the present invention, the material environment index module 200 includes:

[0089] The evaluation formula retrieval unit is used to retrieve the material evaluation formula and environmental evaluation formula of the production process according to the parameter items. The material evaluation formula contains related material items, and the environmental evaluation formula contains related environmental items.

[0090] The material quality value unit is used to determine the specific material value of the associated material item based on the material batch. The specific material value is then input into the material evaluation formula to obtain the material quality value.

[0091] The comprehensive environmental index unit is used to collect specific environmental values ​​of related environmental projects and input these values ​​into the environmental assessment formula to obtain the comprehensive environmental index.

[0092] In a preferred embodiment of the present invention, the proficiency determination module 300 includes:

[0093] A proficiency calculation unit is used to retrieve the worker schedule based on the product batch and determine the worker proficiency level of the production process.

[0094] The Proficiency 2 calculation unit is used to determine the related processes based on the production process and to determine the proficiency 2 of the workers in the related processes based on the worker schedule.

[0095] In a preferred embodiment of the present invention, the parameter value determination module 500 includes:

[0096] The multi-factor determination unit is used to obtain material factor α, environmental factor β, skill level 1 factor γ, skill level 2 factor δ, and deviation factor ∈ based on material quality value MQ, environmental comprehensive index EI, worker skill level 1 WS1, worker skill level 2 WS2, and deviation index DI.

[0097] The total adjustment factor unit is used to determine the total adjustment factor Δ, where Δ represents the offset ratio relative to the ideal value. Δ = w1×α + w2×β + w3×γ + w4×δ + w5×∈, where w1, w2, w3, w4, and w5 are weighting coefficients.

[0098] The parameter value determination unit is used to determine the parameter value PV of the parameter item based on the total adjustment factor Δ and the ideal value BV. PV = BV + s × (Pmax - Pmin) × Δ, where Pmax and Pmin are the upper and lower limits of the parameter range, respectively, and s represents the sensitivity coefficient.

[0099] The above description only details the preferred embodiments of the present invention and 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 within the scope of protection of the present invention.

[0100] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0101] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0102] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. An intelligent control method based on industrial production, characterized in that, The method includes the following steps: The product code is determined by identifying the RFID tag on the product using a reader. The material batch and product batch are then determined based on the product code. Each reader corresponds to a production process, each production process has corresponding parameter items, and each parameter item has corresponding parameter range and ideal value. The material quality value and comprehensive environmental index of the production process are determined based on the material batch and parameter items. The worker proficiency level 1 for the production process and the worker proficiency level 2 for the related process are determined based on the product batch. Based on the parameter items, determine the associated inspection items of the production process, retrieve the corresponding inspection values, and determine the deviation index; The parameter values ​​for the aforementioned parameters are determined based on the parameter range, ideal value, material quality value, comprehensive environmental index, worker proficiency level 1, worker proficiency level 2, and deviation index. Production control of the process is then performed based on these parameter values.

2. The intelligent control method based on industrial production according to claim 1, characterized in that, The steps for determining the material quality value and comprehensive environmental index of the production process based on material batches and parameter items specifically include: The material evaluation formula and environmental evaluation formula for the production process are retrieved based on the parameter items. The material evaluation formula contains related material items, and the environmental evaluation formula contains related environmental items. The specific material value of the associated material item is determined based on the material batch, and the specific material value is input into the material evaluation formula to obtain the material quality value; Collect specific environmental values ​​of related environmental projects and input these values ​​into the environmental assessment formula to obtain the comprehensive environmental index.

3. The intelligent control method based on industrial production according to claim 1, characterized in that, The step of determining the worker skill level 1 for the production process and the worker skill level 2 for the related process based on the product batch specifically includes: Based on the product batch, retrieve the worker shift schedule and determine the worker proficiency level for the aforementioned production process. Determine the related processes based on the production process, and determine the worker proficiency level for the related processes based on the worker schedule.

4. The intelligent control method based on industrial production according to claim 1, characterized in that, The steps for determining the parameter values ​​of the aforementioned parameter items based on parameter range, ideal value, material quality value, comprehensive environmental index, worker proficiency level 1, worker proficiency level 2, and deviation index specifically include: Based on the material quality value MQ, the comprehensive environmental index EI, the worker skill level 1 WS1, the worker skill level 2 WS2, and the deviation index DI, we obtain the material factor α, the environmental factor β, the skill level 1 factor γ, the skill level 2 factor δ, and the deviation factor ∈. Determine the total adjustment factor Δ, where Δ represents the offset ratio relative to the ideal value, Δ=w1×α+w2×β+w3×γ+w4×δ+w5×∈, where w1, w2, w3, w4 and w5 are weighting coefficients; The parameter value PV of the parameter item is determined based on the total adjustment factor Δ and the ideal value BV. PV = BV + s × (Pmax - Pmin) × Δ, where Pmax and Pmin are the upper and lower limits of the parameter range, respectively, and s represents the sensitivity coefficient.

5. The intelligent control method based on industrial production according to claim 4, characterized in that, The method also includes updating and adjusting the weighting coefficients, with the following specific steps: Retrieve historical batch data. Each historical batch includes α, β, γ, δ, ∈ and the corresponding process output quality Q, for a total of N batches. Dataset construction: Each batch consists of samples Xi = (αi, βi, γi, δi, ∈ i), and each Xi corresponds to a label yi = Qi; The factor influence Inf is calculated using the weighted correlation coefficient. j represents the factor type index, i represents the batch index, and X... ij This represents the value of the j-th factor in the i-th batch. This represents the average value of the j-th factor. This represents the average quality of the output from the process. Calculate the weight coefficients of each factor. τ is the smoothing factor.

6. The intelligent control method based on industrial production according to claim 4, characterized in that, α=1-MQ, β=1-EI, γ=1-WS1, δ=1-WS2, ∈=DI.

7. An intelligent control system based on industrial production, characterized in that, The system includes: The product code reading module is used to identify the RFID tag on the product through the reader, determine the product code, and determine the material batch and product batch based on the product code. Each reader corresponds to a production process, each production process corresponds to a parameter item, and each parameter item corresponds to a parameter range and ideal value. The material environment index module is used to determine the material quality value and comprehensive environmental index of the production process based on the material batch and parameter items. The proficiency determination module is used to determine the worker proficiency level 1 for the production process and the worker proficiency level 2 for the related process based on the product batch. The deviation index determination module is used to determine the associated inspection items of the production process based on the parameter items, retrieve the corresponding inspection values, and determine the deviation index. The parameter value determination module is used to determine the parameter values ​​of the parameter items based on the parameter range, ideal value, material quality value, comprehensive environmental index, worker proficiency level 1, worker proficiency level 2, and deviation index, and to perform production control on the process based on the parameter values.

8. The intelligent control system based on industrial production according to claim 7, characterized in that, The material environment index module includes: The evaluation formula retrieval unit is used to retrieve the material evaluation formula and environmental evaluation formula of the production process according to the parameter items. The material evaluation formula contains related material items, and the environmental evaluation formula contains related environmental items. The material quality value unit is used to determine the specific material value of the associated material item based on the material batch. The specific material value is then input into the material evaluation formula to obtain the material quality value. The comprehensive environmental index unit is used to collect specific environmental values ​​of related environmental projects and input these values ​​into the environmental assessment formula to obtain the comprehensive environmental index.

9. The intelligent control system based on industrial production according to claim 7, characterized in that, The proficiency determination module includes: A proficiency calculation unit is used to retrieve the worker schedule based on the product batch and determine the worker proficiency level of the production process. The Proficiency 2 calculation unit is used to determine the related processes based on the production process and to determine the proficiency 2 of the workers in the related processes based on the worker schedule.

10. The intelligent control system based on industrial production according to claim 7, characterized in that, The parameter value determination module includes: The multi-factor determination unit is used to obtain material factor α, environmental factor β, skill level 1 factor γ, skill level 2 factor δ, and deviation factor ∈ based on material quality value MQ, environmental comprehensive index EI, worker skill level 1 WS1, worker skill level 2 WS2, and deviation index DI. The total adjustment factor unit is used to determine the total adjustment factor Δ, where Δ represents the offset ratio relative to the ideal value. Δ = w1×α + w2×β + w3×γ + w4×δ + w5×∈, where w1, w2, w3, w4, and w5 are weighting coefficients. The parameter value determination unit is used to determine the parameter value PV of the parameter item based on the total adjustment factor Δ and the ideal value BV. PV = BV + s × (Pmax - Pmin) × Δ, where Pmax and Pmin are the upper and lower limits of the parameter range, respectively, and s represents the sensitivity coefficient.

Images