Industrial automatic control system based on batch production

Abstract

The invention discloses an industrial automatic control system based on batch production, and relates to the technical field of industrial automatic control, a matrix construction module compares the importance of indexes in pairs to obtain a judgment matrix, the judgment matrix is subjected to normalization processing, and then the weight vector of each index is calculated; the strategy output module generates the grey correlation degree of each alternative control strategy according to the difference coefficient, then substitutes the grey correlation degree of each alternative control strategy and the weight vector of the index into a fusion algorithm so as to calculate the comprehensive score of each alternative control strategy, sorts all the alternative control strategies according to the comprehensive score, and finally outputs the sorted alternative control strategies. And selecting the alternative control strategy with the first sequence to be applied to the current production scene of the production line. The control system can carry out multi-target comprehensive evaluation under an uncertain condition, so that the model can adapt to different production scenes, comprehensive optimization of multiple indexes is realized, and an optimal control strategy is selected for use.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automatic control, and particularly to an industrial automatic control system based on mass production. Background Art

[0002] With the rapid development of industrial technology and the continuous increase in manufacturing demands, the traditional production methods are difficult to meet the requirements of high efficiency and precision in modern mass production. In a mass production environment, the process flow is often complex and diverse, involving the coordinated work of multiple links, such as raw material processing, product assembly, quality inspection, packaging and transportation, etc. Manual operation is difficult to achieve efficient control and precise coordination of the production process, which makes the following requirements particularly important. The industrial automatic control system emerges as the times require and becomes the core technical means to support mass production.

[0003] The prior art has the following deficiencies:

[0004] 1. The mass production scenario involves multiple links and complex production conditions. Traditional control methods cannot fully cover all scenario requirements. When there are changes in order quantity, product type, process complexity, etc., it is difficult for traditional systems to flexibly adjust control strategies, and the applicability evaluation of different control strategies is insufficient, resulting in limited optimization effects. The control strategies cannot be adjusted according to the diversity of production tasks, which limits the versatility of the system, and the overall efficiency of the process flow and resource utilization rate are relatively low;

[0005] 2. Traditional optimization schemes are mostly static designs and cannot be dynamically adjusted according to the real-time changes in the production environment (such as load, equipment status, order requirements). The index weights cannot be updated in real time when the production line status changes, resulting in a lagging optimization model. The alternative strategies cannot be dynamically sorted according to the latest production conditions, resulting in the selected scheme may no longer be applicable. The system response speed is slow, and it cannot quickly adapt to the dynamically changing production scenario, and the resource allocation efficiency is low, which may lead to excessive equipment load or idle waste;

[0006] 3. Traditional methods mainly rely on the experience of managers or technical experts for decision-making, and lack scientific quantification means. The allocation of index weights lacks systematic basis, and it is difficult to accurately measure the importance of different indexes. The weight judgments of different experts for the same problem are significantly different, resulting in a lack of consistency and objectivity in the decision-making process. The decision-making results are easily affected by human factors, with low transparency and credibility. In the face of complex or dynamically changing production scenarios, traditional methods cannot make adjustments quickly and reasonably.

[0007] Based on this, the present invention proposes an industrial automatic control system based on mass production, which can perform multi-objective comprehensive evaluation under uncertain conditions, enable the model to adapt to different production scenarios, achieve comprehensive optimization of multiple indexes, and thus select the best control strategy for use. Summary of the Invention

[0008] The object of the present invention is to provide an industrial automatic control system based on mass production to solve the deficiencies in the background art.

[0009] To achieve the above object, the present invention provides the following technical solution: An industrial automatic control system based on mass production, including a matrix construction module, an inspection module, and a strategy output module:

[0010] Matrix construction module: Obtain the current alternative control strategies of the production line, obtain evaluation indicators, make pairwise comparisons of the importance of the indicators to obtain a judgment matrix, and calculate the weight vector of each indicator after normalizing the judgment matrix;

[0011] Inspection module: Inspect the normalized judgment matrix to determine whether it meets the consistency requirement. If not, dynamically update the importance of the indicators. If it meets, select the ideal solution as the reference sequence for each alternative control strategy and calculate the difference coefficient;

[0012] Strategy output module: After generating the grey relational degree of each alternative control strategy based on the difference coefficient, substitute the grey relational degree of the alternative control strategy and the weight vector of the indicators into the fusion algorithm, so as to calculate the comprehensive score of each alternative control strategy. After sorting all alternative control strategies according to the comprehensive score, select the alternative control strategy ranked first and apply it to the current production scenario of the production line.

[0013] In a preferred embodiment, the inspection module selects the ideal solution as the reference sequence. Let be the ideal solution and n be the number of evaluation indicators. Among them, is the ideal value of the i-th evaluation indicator;

[0014] For each alternative control strategy Among them, represents the value of the k-th alternative control strategy in the i-th evaluation indicator. Then the expression of the difference coefficient is: In the formula, represents the difference coefficient of the k-th alternative control strategy in the i-th evaluation indicator.

[0015] In a preferred embodiment, the strategy output module generates the correlation index of each alternative control strategy based on the difference coefficient. The expression is: In the formula, represents the correlation index of the k-th alternative control strategy in the i-th evaluation indicator, represents the difference coefficient of the k-th alternative control strategy in the i-th evaluation indicator, min 1≤j≤n Δ j represents finding the minimum value of the difference value among all alternative control strategies and all evaluation indicators, max 1≤j≤nΔ j represents finding the maximum value of the difference among all alternative control strategies and all evaluation metrics. ρ is the discrimination coefficient with a value of 0.5;

[0016] For each alternative control strategy, calculate the grey relational grade based on the correlation index of all evaluation metrics. The expression is: In the formula, represents the grey relational grade of the k-th alternative control strategy for the i-th evaluation metric, and n is the number of evaluation metrics.

[0017] In a preferred embodiment, after the strategy output module obtains the grey relational grade of each alternative control strategy and the weight vector of the metrics, substitute them into the fusion algorithm to calculate the comprehensive score. The expression is: In the formula, STR is the comprehensive score, n is the number of evaluation metrics, is the weight vector of the i-th evaluation metric, represents the grey relational grade of the k-th alternative control strategy for the i-th evaluation metric. Sort all alternative control strategies in descending order according to the comprehensive score to generate a strategy ranking table, and select the alternative control strategy ranked first in the strategy ranking table for application in the current production scenario of the production line.

[0018] In a preferred embodiment, after the matrix construction module obtains the importance score of each evaluation metric, construct a judgment matrix. The judgment matrix construction rule is:

[0019] Compare the importance scores of evaluation metric i and evaluation metric j. The value of the judgment matrix element is:

[0020] In the formula, a ij is the comparison value of evaluation metric i and evaluation metric j, S i is the importance score of evaluation metric i, S j is the importance score of evaluation metric j, and n is the number of evaluation metrics;

[0021] Perform normalization processing on the constructed judgment matrix to ensure that the sum of each index weight is. Calculate the column vector sum of the judgment matrix. The expression is: column sum Column sum(j) represents the column vector sum. Perform normalization processing on each element in the judgment matrix. The expression is: In the formula, a ij ’ is the normalized comparison value of evaluation metric i and evaluation metric j;

[0022] Based on the normalized judgment matrix, calculate the weight vector of each evaluation metric, and obtain the average value of each row element of the normalized judgment matrix. The expression is:

[0023] In the formula, Wi is the weight vector of the i-th evaluation index, and the weight vector W = [W 1 , W 2 ,...., W n .

[0024] In a preferred embodiment, the calculation logic for the matrix construction module to obtain the importance score of each evaluation index is as follows: obtain the fluctuation amplitude and correlation coefficient of each evaluation index, perform normalization processing on the correlation coefficient and the fluctuation amplitude, map the value ranges of the fluctuation amplitude and the correlation coefficient to between [0, 1], obtain the normalized value of the fluctuation amplitude and the normalized value of the correlation coefficient, and subtract the normalized value of the fluctuation amplitude from the normalized value of the correlation coefficient to obtain the importance score of the evaluation index.

[0025] In a preferred embodiment, the matrix construction module obtains evaluation indexes, and the evaluation indexes include production efficiency, product quality qualification rate, energy consumption, and production cost.

[0026] In a preferred embodiment, the inspection module uses the consistency index to inspect the normalized judgment matrix, and calculates the consistency index of the normalized judgment matrix. The expression is as follows: In the formula, CI is the consistency index, λ max is the maximum eigenvalue of the judgment matrix, and n represents the number of evaluation indexes. Calculate the consistency ratio based on the consistency index. The expression is as follows: In the formula, CR is the consistency ratio, and RI is the random consistency index;

[0027] If the consistency ratio CR < 0.1, it indicates that the judgment matrix meets the consistency requirement. If the consistency ratio CR ≥ 0.1, it indicates that the judgment matrix does not meet the consistency requirement, and the weight vector of the evaluation index needs to be updated.

[0028] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0029] In the present invention, the matrix construction module compares the importance of indicators pairwise to obtain a judgment matrix, calculates the weight vector of each indicator after normalizing the judgment matrix, and the verification module verifies the normalized judgment matrix to determine whether it meets the consistency requirement. If it meets the requirement, the ideal solution is selected as the reference sequence for each alternative control strategy and the difference coefficient is calculated. After the strategy output module generates the grey relational degree of each alternative control strategy based on the difference coefficient, the grey relational degree of the alternative control strategies and the weight vector of the indicators are substituted into the fusion algorithm to calculate the comprehensive score of each alternative control strategy. After sorting all the alternative control strategies according to the comprehensive score, the alternative control strategy ranked first is selected and applied to the current production scenario of the production line. The control system can perform multi-objective comprehensive evaluation under uncertain conditions, enabling the model to adapt to different production scenarios, achieve comprehensive optimization of multiple indicators, and thus select the best control strategy for use. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0031] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0033] Embodiment 1: Please refer to Figure 1 As shown, the industrial automatic control system based on batch production in this embodiment includes a matrix construction module, a verification module, and a strategy output module:

[0034] Matrix construction module: Obtain the current alternative control strategies of the production line, obtain the evaluation indicators, compare the importance of the indicators pairwise to obtain a judgment matrix, calculate the weight vector of each indicator after normalizing the judgment matrix, send the weight vector to the strategy output module, and send the judgment matrix to the strategy output module;

[0035] Inspection module: Inspect the normalized judgment matrix to determine whether it meets the consistency requirement. If not, dynamically update the importance of indicators. If it meets the requirement, select the ideal solution as the reference sequence for each alternative control strategy and calculate the difference coefficient, and send the difference coefficient to the policy output module;

[0036] Policy output module: After generating the grey relational grade of each alternative control strategy based on the difference coefficient, substitute the grey relational grade of the alternative control strategy and the weight vector of the indicators into the fusion algorithm to calculate the comprehensive score of each alternative control strategy. After sorting all alternative control strategies according to the comprehensive score, select the alternative control strategy ranked first and apply it to the current production scenario of the production line.

[0037] In this application, the matrix construction module makes pairwise comparisons of the importance of indicators to obtain a judgment matrix, calculates the weight vector of each indicator after normalizing the judgment matrix. The inspection module inspects the normalized judgment matrix to determine whether it meets the consistency requirement. If it meets the requirement, select the ideal solution as the reference sequence for each alternative control strategy and calculate the difference coefficient. After generating the grey relational grade of each alternative control strategy based on the difference coefficient, substitute the grey relational grade of the alternative control strategy and the weight vector of the indicators into the fusion algorithm to calculate the comprehensive score of each alternative control strategy. After sorting all alternative control strategies according to the comprehensive score, select the alternative control strategy ranked first and apply it to the current production scenario of the production line. The control system can perform multi-objective comprehensive evaluation under uncertain conditions, enable the model to adapt to different production scenarios, realize the comprehensive optimization of multiple indicators, and thus select the best control strategy for use.

[0038] The working process of the control system is as follows:

[0039] The control system obtains the current alternative control strategies of the production line and obtains the evaluation indicators, makes pairwise comparisons of the importance of the indicators to obtain a judgment matrix, calculates the weight vector of each indicator after normalizing the judgment matrix, inspects the normalized judgment matrix to determine whether it meets the consistency requirement. If not, dynamically update the importance of indicators. If it meets the requirement, select the ideal solution as the reference sequence for each alternative control strategy and calculate the difference coefficient. After generating the grey relational grade of each alternative control strategy based on the difference coefficient, substitute the grey relational grade of the alternative control strategy and the weight vector of the indicators into the fusion algorithm to calculate the comprehensive score of each alternative control strategy. After sorting all alternative control strategies according to the comprehensive score, select the alternative control strategy ranked first and apply it to the current production scenario of the production line.

[0040] Examples of multiple application scenarios of the industrial automatic control system based on mass production:

[0041] In the process of automobile manufacturing, there are multiple complex processes including body welding, painting, and final assembly. The operating parameters of each process (such as welding speed, spraying volume, etc.) will vary according to the vehicle model and order requirements. Through the control system, the optimal control strategy can be dynamically selected based on evaluation indicators (such as production efficiency, resource consumption, and process stability) to ensure the best balance of production rhythm in mixed-model production of multiple vehicle models.

[0042] Before leaving the factory, the production line of electronic devices needs to undergo multiple tests, including functional tests, performance tests, and durability tests. The test sequence, test parameters, and scheduling of test equipment will affect the production line efficiency and test accuracy. The control system selects the optimal test process by comprehensively evaluating indicators (such as test success rate, equipment utilization rate, and test cost) to shorten the test time while ensuring test quality.

[0043] In the food processing process, different formulas and processing technologies will affect the taste, quality, and production cost of products. For example, in beverage production, for the quality of raw materials in different batches, the control system can dynamically adjust the ingredient ratio and production parameters (such as temperature, pressure, etc.), and comprehensively consider taste consistency, raw material utilization rate, and equipment operation stability to select the best control strategy.

[0044] In the chemical production line, the operating parameters of the reaction kettle (such as temperature, pressure, reaction time, etc.) need to be dynamically adjusted according to the raw material characteristics and product requirements. Through the control system, combined with evaluation indicators (such as reaction efficiency, energy consumption, and product purity), the optimal process parameter control strategy is selected to ensure stable product quality while reducing energy consumption.

[0045] The textile industry often faces the challenges of personalized orders and small-batch production. Different orders have different requirements for fabric types, dyeing processes, and weaving densities. The control system selects the optimal production mode according to the order requirements and the status of production equipment to ensure that production efficiency and quality are not affected when quickly switching orders.

[0046] In home appliance manufacturing, the production line needs to quickly switch between different product models such as air conditioners, refrigerators, and washing machines. The control system dynamically selects the optimal strategy through comprehensive evaluation of equipment operating status, product process requirements, and resource allocation, optimizes the production switching time, and reduces resource waste.

[0047] The pharmaceutical industry needs to strictly control the quality of drugs in batch production, such as parameters like pressure, coating thickness, and content uniformity in tablet production. The control system combines raw material fluctuations and the status of production equipment to select the best control strategy to ensure that the drug quality meets the standards while improving production efficiency.

[0048] In the production of building materials, such as cement, bricks and tiles, etc., different production processes have different requirements for equipment load and operating status. The control system dynamically selects production processes and equipment scheduling strategies by evaluating equipment utilization rate, energy consumption and product qualification rate, so as to improve production stability.

[0049] The production of aviation parts involves multiple processes, such as casting, heat treatment, precision machining, etc., and each process has different impacts on product quality and production time. The control system dynamically selects the optimal production scheduling plan according to order requirements, production progress and process complexity to ensure the efficient coordination of each process.

[0050] Example 2: The matrix construction module obtains the current alternative control strategies of the production line and obtains evaluation indicators, makes pairwise comparisons of the importance of the indicators to obtain a judgment matrix, and calculates the weight vector of each indicator after normalizing the judgment matrix;

[0051] The matrix construction module obtains evaluation indicators, and the evaluation indicators include production efficiency, product quality qualification rate, energy consumption and production cost;

[0052] Obtain the fluctuation amplitude and correlation coefficient of each evaluation indicator, normalize the correlation coefficient and the fluctuation amplitude, map the value ranges of the fluctuation amplitude and the correlation coefficient to between [0, 1], obtain the normalized value of the fluctuation amplitude and the normalized value of the correlation coefficient, and subtract the normalized value of the fluctuation amplitude from the normalized value of the correlation coefficient to obtain the importance score of the evaluation indicator;

[0053] The calculation expression of the fluctuation amplitude is: In the formula, σ X is the fluctuation amplitude, m is the number of monitoring time points, x i is the evaluation indicator value at the i-th time point, is the mean value of the evaluation indicator. The larger the fluctuation amplitude, the greater the fluctuation amplitude of the evaluation indicator and the lower the stability, that is, the lower the importance score.

[0054] The calculation expression of the correlation coefficient is: In the formula, r XY represents the correlation coefficient between evaluation indicator X and evaluation indicator Y, M is the number of time points, x i is the evaluation indicator value of evaluation indicator X at the i-th time point, y i is the evaluation indicator value of evaluation indicator Y at the i-th time point, is the mean value of evaluation indicator X, is the mean value of evaluation indicator Y. The larger the value of the correlation coefficient, the greater the correlation degree between the current evaluation indicator and other evaluation indicators, and the greater the importance score.

[0055] The normalized value of the amplitude of fluctuations and the normalized value of the correlation coefficient are calculated by the general formula for normalization of the maximum and minimum values, and the expression is: In the formula, x g is the normalized value of the parameter, x is the original parameter, x max is the maximum value of the parameter, x min is the minimum value of the parameter.

[0056] After obtaining the importance scores of all evaluation indicators, a judgment matrix is constructed. The rules for constructing the judgment matrix are as follows:

[0057] Compare the importance scores of evaluation indicator i and evaluation indicator j. The value of the element in the judgment matrix is:

[0058] In the formula, a ij is the comparison value between evaluation indicator i and evaluation indicator j, S i is the importance score of evaluation indicator i, S j is the importance score of evaluation indicator j, and n is the number of evaluation indicators;

[0059] Normalize the constructed judgment matrix to ensure that the sum of the weights of each indicator is, and calculate the column vector sum of the judgment matrix. The expression is: column sum Column sum(j) represents the column vector sum. Normalize each element in the judgment matrix. The expression is: In the formula, a ij ’ is the normalized comparison value between evaluation indicator i and evaluation indicator j, a ij is the comparison value between evaluation indicator i and evaluation indicator j;

[0060] According to the normalized judgment matrix, calculate the weight vector of each evaluation indicator, and obtain the average value of each row element of the normalized judgment matrix. The expression is:

[0061] In the formula, W i is the weight vector of the i-th evaluation indicator, a ij ’ is the normalized comparison value between evaluation indicator i and evaluation indicator j, n is the number of evaluation indicators, and the weight vector W = [W 1 , W 2 ,...., W n ;

[0062] By automatically analyzing data to evaluate the importance of indicators, a judgment matrix is generated, which avoids the interference of human experience and improves the scientificity and objectivity of indicator weight allocation. It can effectively adapt to the dynamically changing production conditions in a mass production environment and provides a reliable basis for subsequent strategy optimization.

[0063] The inspection module inspects the normalized judgment matrix to determine whether the consistency requirement is met. If not, the importance of the indicators is updated dynamically. If so, the ideal solution is selected as the reference sequence for each alternative control strategy and the difference coefficient is calculated;

[0064] The inspection module uses the consistency index to inspect the normalized judgment matrix and calculates the consistency index of the normalized judgment matrix. The expression is: In the formula, CI is the consistency index, and λ max is the maximum eigenvalue of the judgment matrix, and n represents the number of evaluation indicators (i.e., the order of the judgment matrix). The consistency ratio is calculated based on the consistency index. The expression is: In the formula, CR is the consistency ratio, and RI is the random consistency index, which is obtained by looking up the table according to the number of evaluation indicators. For example, when n = 3, RI = 0.58;

[0065] If the consistency ratio CR < 0.1, it is judged that the consistency of the judgment matrix is good and it can continue. If the consistency ratio CR ≥ 0.1, it means that the consistency is not met and the index weights need to be updated;

[0066] When the consistency is not met, the judgment matrix is reconstructed by adjusting the importance of the indicators. The dynamic update can be based on the following methods:

[0067] Re-evaluate the standard deviation (fluctuation amplitude) and correlation of each indicator, and update the weights of the indicators according to the new data. Compare the changes in production goals, adjust the influence of the indicators, ensure that the importance distribution is reasonable. After the update, generate a new judgment matrix with the new weights and recalculate its consistency until the consistency meets the requirements;

[0068] When the judgment matrix meets the consistency requirement, the inspection module selects the ideal solution as the reference sequence. The ideal solution refers to the value that can maximize all evaluation indicators among all alternative control strategies. Usually, it is the maximum value or the best expected value of each indicator. Let be the ideal solution, and n be the number of evaluation indicators. Among them, is the ideal value of the i-th evaluation indicator (in this application, the ideal values of production efficiency and product quality pass rate are both the maximum values, and the ideal values of energy consumption and production cost are both the minimum values). The reference sequence is determined by the distance (difference) between the ideal solution and each alternative control strategy. By comparing the similarity between each strategy and the ideal solution, it is decided which strategy best meets the current production goal;

[0069] For each alternative control strategy Among them, represents the value of the k-th alternative control strategy for the i-th evaluation indicator. Then the expression of the difference coefficient is:

[0070] In the formula, is the ideal value of the i-th evaluation index, represents the difference coefficient of the k-th alternative control strategy in the i-th evaluation index, represents the value of the k-th alternative control strategy in the i-th evaluation index, that is, the larger the difference coefficient, the greater the difference between the evaluation index in the alternative control strategy and the ideal value.

[0071] After the strategy output module generates the grey relational grade of each alternative control strategy based on the difference coefficient, it substitutes the grey relational grade of the alternative control strategy and the weight vector of the index into the fusion algorithm, so as to calculate the comprehensive score of each alternative control strategy. After sorting all alternative control strategies according to the comprehensive score, the alternative control strategy ranked first is selected and applied to the current production scenario of the production line;

[0072] The strategy output module generates the correlation index of each alternative control strategy based on the difference coefficient, and the expression is: In the formula, represents the correlation index of the k-th alternative control strategy in the i-th evaluation index, represents the difference coefficient of the k-th alternative control strategy in the i-th evaluation index, min 1≤j≤n Δ j represents finding the minimum value of the difference value among all alternative control strategies and all evaluation indexes, max 1≤j≤n Δ j represents finding the maximum value of the difference value among all alternative control strategies and all evaluation indexes, and ρ is the resolution coefficient with a value of 0.5;

[0073] For each alternative control strategy, calculate the grey relational grade based on the correlation indexes of all evaluation indexes, and the expression is: In the formula, represents the grey relational grade of the k-th alternative control strategy in the i-th evaluation index, represents the correlation index of the k-th alternative control strategy in the i-th evaluation index, and n is the number of evaluation indexes.

[0074] After the strategy output module obtains the grey relational grade of each alternative control strategy and the weight vector of the index, it substitutes them into the fusion algorithm to calculate the comprehensive score, and the expression is: In the formula, STR is the comprehensive score, n is the number of evaluation indexes, is the weight vector of the i-th evaluation index, represents the grey relational grade of the k-th alternative control strategy in the i-th evaluation index. Sort all alternative control strategies from large to small according to the comprehensive score to generate a strategy ranking table, and select the alternative control strategy ranked first in the strategy ranking table and apply it to the current production scenario of the production line.

[0075] The above formulas are all dimensionless and only take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0076] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Among them, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0077] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0078] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0079] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. Industrial automatic control system based on mass production, characterized by: Including matrix construction module, test module, strategy output module: Matrix construction module: obtain the current alternative control strategy of the production line and the evaluation index, compare the importance of the indexes pairwise to obtain the judgment matrix, normalize the judgment matrix and calculate the weight vector of each index; Verification module: Verify the normalized judgment matrix to determine whether it meets the consistency requirements. If not, dynamically update the index importance. If it is satisfied, select the ideal solution as the reference sequence for each alternative control strategy and calculate the difference coefficient. Strategy output module: After generating the grey correlation degree of each alternative control strategy based on the difference coefficient, the grey correlation degree of the alternative control strategy and the weight vector of the indicator are substituted into the fusion algorithm to calculate the comprehensive score of each alternative control strategy. After sorting all the alternative control strategies according to the comprehensive score, the first-ranked alternative control strategy is selected and applied to the current production scenario of the production line.

2. The industrial automatic control system based on mass production according to claim 1, characterized in that: The test module selects the ideal solution as the reference sequence, is the ideal solution, n is the number of evaluation indicators, where is the ideal value of the i-th evaluation index; For each alternative control strategy in, It means that the kth alternative control strategy is at the ith evaluation index value, and the difference coefficient expression is: In the formula, It represents the difference coefficient of the kth alternative control strategy in the i-th evaluation index.

3. The industrial automatic control system based on batch production according to claim 2, characterized in that: The strategy output module generates a correlation index for each candidate control strategy based on the difference coefficient, and the expression is: In the formula, represents the correlation index of the kth alternative control strategy in the i-th evaluation index, represents the difference coefficient of the kth alternative control strategy in the i-th evaluation index, min 1≤j≤n Δ j Indicates finding the minimum difference value among all alternative control strategies and all evaluation indicators, max 1≤j≤n Δ j It means finding the maximum value of the difference among all the alternative control strategies and all the evaluation indicators, ρ is the resolution coefficient, and its value is 0.5; For each alternative control strategy, the grey correlation degree is calculated according to the correlation index of all evaluation indicators, and the expression is: In the formula, It represents the grey relational degree of the kth alternative control strategy in the ith evaluation index, and n is the number of evaluation indexes.

4. The industrial automatic control system based on batch production according to claim 3 is characterized in that: After the strategy output module obtains the grey correlation degree of each candidate control strategy and the weight vector of the index, it is substituted into the fusion algorithm to calculate the comprehensive score, and the expression is: In the formula, STR is the comprehensive score, n is the number of evaluation indicators, and is the weight vector of the i-th evaluation indicator. It represents the grey correlation degree of the kth alternative control strategy in the i-th evaluation index. All alternative control strategies are sorted from large to small according to the comprehensive score, and a strategy sorting table is generated. The alternative control strategy ranked first in the strategy sorting table is selected and applied to the current production scenario of the production line.

5. The industrial automatic control system based on batch production according to claim 4 is characterized in that: After the matrix construction module obtains the importance score of each evaluation indicator, it constructs a judgment matrix. The judgment matrix construction rule is: Compare the importance scores of evaluation index i and evaluation index j, and judge the value of the matrix element as follows: i,j∈{1,2,…,n}, where a ij is the comparison value between evaluation index i and evaluation index j, S i is the importance score of evaluation index i, S j is the importance score of evaluation index j, and n is the number of evaluation indicators; Normalize the constructed judgment matrix to ensure that the sum of the weights of each indicator is, and calculate the column vector sum of the judgment matrix. The expression is: column sum The column sum (j) represents the column vector sum, and each element in the judgment matrix is ​​normalized. The expression is: In the formula, a ij ' is the normalized comparison value of evaluation index i and evaluation index j; According to the normalized judgment matrix, the weight vector of each evaluation index is calculated, and the average value of each row element of the normalized judgment matrix is ​​obtained. The expression is: i∈{1,2,…,n}, where W i is the weight vector of the i-th evaluation index, and the weight vector W=[W1,W2,....,W n ].

6. The industrial automatic control system based on batch production according to claim 5, characterized in that: The calculation logic of the matrix construction module to obtain the importance score of each evaluation indicator is as follows: obtain the fluctuation amplitude and correlation coefficient of each evaluation indicator, normalize the correlation coefficient and the fluctuation amplitude, map the value range of the fluctuation amplitude and the correlation coefficient to [0,1], obtain the normalized value of the fluctuation amplitude and the normalized value of the correlation coefficient, and subtract the normalized value of the fluctuation amplitude from the normalized value of the correlation coefficient to obtain the importance score of the evaluation indicator.

7. The industrial automatic control system based on batch production according to claim 6, characterized in that: The matrix construction module obtains evaluation indicators, which include production efficiency, product quality qualification rate, energy consumption and production cost.

8. The industrial automatic control system based on batch production according to claim 7, characterized in that: The inspection module uses the consistency index to inspect the normalized judgment matrix and calculates the consistency index of the normalized judgment matrix, which is expressed as: In the formula, CI is the consistency index, λ max is the maximum eigenvalue of the judgment matrix, n represents the number of evaluation indicators, and the consistency ratio is calculated based on the consistency index. The expression is: In the formula, CR is the consistency ratio, RI is the random consistency index; If the consistency ratio CR<0.1, it indicates that the judgment matrix meets the consistency requirements. If the consistency ratio CR≥0.1, it indicates that the judgment matrix does not meet the consistency requirements and the evaluation index weight vector needs to be updated.

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