Intelligent control method and system for metal product production line

Abstract

The invention relates to the technical field of production line production control, and discloses an intelligent control method and system for a metal product production line, and the method comprises the steps: obtaining the function of production equipment, the quality of raw materials, the processing duration of the raw materials, and the content of metal elements in the raw materials; obtaining production index parameters according to production equipment functions and raw material treatment duration; vector similarity is calculated according to the raw material mass and the metal element content; obtaining a first weight parameter according to the raw material mass and the metal element content; obtaining a second weight parameter according to the raw material processing duration and the raw material quality; obtaining a third weight parameter according to the raw material processing duration and the metal element content; obtaining a quality characteristic value according to the vector similarity and each weight parameter; obtaining a production quality parameter according to the production index parameter, the second weight parameter and the quality characteristic value; and obtaining an optimal production control instruction according to matching of the production quality parameters and preset data. According to the method, the production of the metal product can be completed by selecting the optimal production control instruction.

Description

Technical Field

[0001] The present invention relates to the technical field of production line production control, and in particular to an intelligent control method and system for a metal product production line. Background Art

[0002] With the development of artificial intelligence technology, its application in the production process of metal products is becoming more and more extensive. Different types of metal products require different raw materials and production processes.

[0003] In the existing metal product production technology, production efficiency has been improved to a certain extent by using artificial intelligence technology to obtain production line control instructions by presetting production plans, training production prediction models, etc. However, the control instructions are obtained through advance training. When the composition of production raw materials cannot be determined or the production process is uncertain, the use of the control instructions will lead to unstable product quality.

[0004] Therefore, the metal product production control method in the prior art will lead to the problem of unstable quality of the produced products when the raw material composition cannot be determined or the production process is uncertain. Summary of the invention

[0005] The present invention provides an intelligent control method and system for a metal product production line to solve the problem of unstable quality of the products produced when the composition of the raw materials cannot be determined or the production process is uncertain in the metal product production control method in the prior art.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides an intelligent control method for a metal product production line, comprising: Obtain production equipment capabilities, raw material quality, raw material processing time, and metal content in raw materials; Parameter matching is performed according to the function of the production equipment and the processing time of the raw materials to obtain production index parameters; Constructing a vector according to the raw material mass and the metal element content, and calculating the vector similarity; Calculating a correction coefficient according to the raw material quality and the metal element content, comparing the correction coefficient with the production index parameter, and determining a first weight parameter according to the comparison result; Perform reshaping calculation according to the raw material processing time and the raw material quality to obtain a second weight parameter; Constructing a weight matrix according to the raw material processing time and the metal element content, and calculating a third weight parameter according to the weight matrix; Calculate according to the vector similarity, the first weight parameter, the second weight parameter and the third weight parameter to obtain a quality feature value; Calculate characteristic parameters according to the production index parameter, the second weight parameter and the quality characteristic value to obtain production quality parameters; According to the production quality parameters, the optimal production control instructions are obtained by matching with the data in the preset production database to control the production of metal products.

[0007] In an optional implementation, constructing a vector according to the raw material quality and the metal element content, and calculating the vector similarity, includes: Constructing a raw material mass vector and a metal element vector according to the raw material mass and the metal element content respectively; Performing similarity calculation based on the raw material mass vector and the metal element vector to obtain vector similarity; Among them, the raw material mass vector and the metal element vector are respectively: In the formula, is the raw material mass vector, They are No. 1 to No. The quality of the raw materials, is the number of raw material types, is the metal element vector, They are No. 1 to No. The metal content, is the number of metal types; The vector similarity calculation formula is: In the formula, is the vector similarity, is the modulus of the raw material mass vector, is the magnitude of the metal element vector.

[0008] In an optional implementation, comparing the correction coefficient with the production index parameter and determining the first weight parameter according to the comparison result includes: Compare the correction coefficient with the production index parameter: When the correction coefficient is greater than the production index parameter, determining that the value of the first weight parameter is 1; When the correction coefficient is less than the production index parameter, determining that the value of the first weight parameter is 0; Wherein, the calculation formula of the correction coefficient is: In the formula, is the correction factor, For the The quality of the raw materials, For the The metal content, is the quantity of each raw material, is the modulus of the raw material mass vector, is the magnitude of the metal element vector.

[0009] In an optional implementation, the reshaping calculation is performed according to the raw material processing time and the raw material quality to obtain the second weight parameter, including: The second weight parameter is calculated by the following formula: In the formula, is the second weight parameter, For the The processing time of each raw material, For the The quality of the raw materials, is the amount of raw materials, It is the preset maximum raw material processing time.

[0010] In an optional implementation, constructing a weight matrix according to the raw material processing time and the metal element content, and calculating the third weight parameter according to the weight matrix includes: Constructing a raw material duration vector according to the raw material processing duration; Constructing a weight matrix according to the raw material duration vector and the metal element vector; Perform calculation according to the weight matrix to obtain a third weight parameter; The third weight parameter is calculated by the following formula: In the formula, is the third weight parameter, is the weight matrix Line The elements of the column, is the number of rows in the weight matrix, is the number of columns in the weight matrix.

[0011] In an optional implementation, the calculating according to the vector similarity, the first weight parameter, the second weight parameter and the third weight parameter to obtain the quality feature value includes: The quality characteristic value is calculated by the following formula: In the formula, is the quality characteristic value, is the first weight parameter, is the second weight parameter, is the third weight parameter, is the vector similarity.

[0012] In an optional implementation, the calculating characteristic parameters according to the production index parameter, the second weight parameter and the quality characteristic value to obtain the production quality parameter includes: Constructing a production indicator parameter matrix according to the production indicator parameters; Calculate according to the production index parameter matrix, the second weight parameter and the quality characteristic value to obtain a production quality parameter; Wherein, the calculation formula of the production quality parameter is: In the formula, is the production quality parameter, is the quality characteristic value, is the second weight parameter, is the element in the production index parameter matrix, is the number of rows of the production indicator parameter matrix, is the number of columns of the production indicator parameter matrix.

[0013] In an optional implementation, the production indicator parameter matrix includes: Perform truncation operation on each element in the production index parameter matrix: When the value of each element in the production index parameter matrix is ​​less than the preset production index constraint parameter value, the value of each element in the production index parameter matrix remains unchanged; When the value of each element in the production index parameter matrix is ​​greater than or equal to the preset production index constraint parameter value, the value of each element in the production index parameter matrix takes the preset production index constraint parameter value.

[0014] In a second aspect, the present invention provides an intelligent control system for a metal product production line, comprising: Data acquisition module, used to obtain production equipment functions, raw material quality, raw material processing time and metal element content in raw materials; A production index calculation module, used to perform parameter matching according to the production equipment function and the raw material processing time to obtain production index parameters; A similarity calculation module, used to construct a vector according to the raw material quality and the metal element content, and calculate the vector similarity; A first weight calculation module, used to calculate a correction coefficient according to the raw material quality and the metal element content, compare the correction coefficient with the production index parameter, and determine a first weight parameter according to the comparison result; A second weight calculation module, used for performing a reshaping calculation according to the raw material processing time and the raw material quality to obtain a second weight parameter; A third weight calculation module, used to construct a weight matrix according to the raw material processing time and the metal element content, and calculate a third weight parameter according to the weight matrix; an eigenvalue calculation module, configured to calculate according to the vector similarity, the first weight parameter, the second weight parameter and the third weight parameter to obtain a quality eigenvalue; A control parameter calculation module, used to calculate characteristic parameters according to the production index parameter, the second weight parameter and the quality characteristic value to obtain production quality parameters; The production instruction matching module is used to match the instruction data in the preset production instruction database according to the production quality parameters to obtain the optimal production control instruction to control the production of metal products.

[0015] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a control method for an ultrasonic cleaning device as described in any one of the above.

[0016] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a control method for an ultrasonic cleaning device as described in any one of the above.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses an intelligent control method for a metal product production line, comprising obtaining a production equipment function, raw material quality, raw material processing time and the metal element content in the raw material; performing parameter matching according to the production equipment function and the raw material processing time to obtain a production index parameter; constructing a vector according to the raw material quality and the metal element content, and calculating the vector similarity; calculating a correction coefficient according to the raw material quality and the metal element content, comparing the correction coefficient with the production index parameter, and determining a first weight parameter according to the comparison result; performing a reshaping calculation according to the raw material processing time and the raw material quality to obtain a second weight parameter; constructing a weight matrix according to the raw material processing time and the metal element content, and calculating a third weight parameter according to the weight matrix; calculating according to the vector similarity, the first weight parameter, the second weight parameter and the third weight parameter to obtain a quality characteristic value; performing characteristic parameter calculation according to the production index parameter, the second weight parameter and the quality characteristic value to obtain a production quality parameter; matching the production quality parameter with data in a preset production database to obtain an optimal production control instruction to control the production of metal products. The method collects data such as production equipment functions, raw material quality, processing time and metal element content, calculates production index parameters, vector similarity, correction coefficients and multiple weight parameters, and then obtains quality characteristic values ​​and production quality parameters. Finally, the production quality parameters are matched with a preset database to determine and implement the optimal production control instructions, thereby intelligently controlling the production process of metal products. The method can calculate the production quality parameters corresponding to different production equipment and raw material components according to various factors such as different production equipment and raw material components, thereby obtaining the optimal production control instructions to control the production of metal products. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flow chart of an intelligent control method for a metal product production line provided by the first embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of an intelligent control system for a metal product production line provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Reference Figure 1The first embodiment of the present invention provides an intelligent control method for a metal product production line, comprising the following steps: S11, obtaining the production equipment function, raw material quality, raw material processing time and metal element content in the raw materials; S12, performing parameter matching according to the function of the production equipment and the raw material processing time to obtain production index parameters; S13, constructing a vector according to the raw material mass and the metal element content, and calculating the vector similarity; S14, calculating a correction coefficient according to the raw material mass and the metal element content, comparing the correction coefficient with the production index parameter, and determining a first weight parameter according to the comparison result; S15, performing a reshaping calculation according to the raw material processing time and the raw material quality to obtain a second weight parameter; S16, constructing a weight matrix according to the raw material processing time and the metal element content, and calculating a third weight parameter according to the weight matrix; S17, performing calculation according to the vector similarity, the first weight parameter, the second weight parameter and the third weight parameter to obtain a quality feature value; S18, calculating characteristic parameters according to the production index parameter, the second weight parameter and the quality characteristic value to obtain production quality parameters; S19, matching the production quality parameters with the data in a preset production database to obtain optimal production control instructions to control the production of metal products.

[0021] In step S11, the function of the production equipment, the quality of the raw materials, the processing time of the raw materials and the content of metal elements in the raw materials are obtained.

[0022] It should be noted that, for obtaining the functions of production equipment, the detailed functions and performance parameters of the equipment can be obtained through the equipment's manual, technical specifications or direct communication with the equipment manufacturer. For obtaining the quality of raw materials, the impurities, pollutants and component contents in the raw materials can be detected by physical, chemical and other analytical methods. For obtaining the processing time of raw materials, it can be obtained through the production data and equipment status information collection and analysis management system or industrial production equipment data collector. For obtaining the metal element content in the raw materials, it can be obtained through spectral analysis, wet chemical analysis, carbon and sulfur analysis, gas analysis, mass spectrometry, thermal analysis and electrochemical analysis. Different analytical methods are used for different metal elements, and the present invention will not go into details.

[0023] In one implementation, the quality of the raw material is obtained by spectral analysis. Spectral analysis is a physical and chemical analysis method based on the interaction between matter and light, including near-infrared spectroscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, ultraviolet-visible spectroscopy, atomic absorption spectroscopy and X-ray fluorescence spectroscopy. For example, when obtaining the quality of the raw material, near-infrared spectroscopy can be used to identify different raw materials and accurately measure the content of specific components in the raw materials, such as the content of anhydrous sodium pyrophosphate. Near-infrared spectroscopy is a fast and non-destructive method for raw material quality detection, which obtains the quality data of the raw material by analyzing the spectral information of the raw material absorbing near-infrared light. First, the raw material sample is scanned using a near-infrared spectrometer to collect its absorption spectrum in the near-infrared spectral region. A calibration model is established by a standard sample with a known component content, which can convert the spectral data into the specific component content of the raw material. For example, the percentage of pyrophosphate content in disodium hydrogen phosphate can be quantitatively predicted by partial least squares method. For a disodium hydrogen phosphate sample containing anhydrous sodium pyrophosphate, the sample spectrum is collected by a near-infrared spectrometer, and the calibration model established previously is used for quantitative analysis to determine the content range of anhydrous sodium pyrophosphate in the sample. Finally, by analyzing the spectral differences between each raw material, the content of each component in the raw material can be determined.

[0024] In one implementation, the content of metal elements in the raw material is obtained by thermal analysis. Thermal analysis is a technique for measuring the temperature dependence of the physical properties of a substance under program-controlled temperature. First, the metal raw material is cut into thin slices and cut into several samples of equal size. The samples are placed in a thermal analysis instrument. According to the temperature change controlled by the program, the physical quantities such as thermogravimetric and heat flow of the samples are measured. In differential scanning calorimetry, the heat flow rate occurring when the sample is heated, cooled or kept constant is measured, and the temperature change and thermal effect of the sample during heating or cooling, such as endothermic peaks or exothermic peaks, are recorded. These peaks can reflect the physical and chemical changes of metal elements, such as melting, phase change, redox reaction, etc. Finally, the characteristic peaks on the thermal analysis curve can be used in combination with known standard sample data to calculate the content of metal elements in the sample. For example, the purity can be determined by the van't Hoff equation, and the content of metal elements can be calculated.

[0025] In step S12, parameter matching is performed according to the function of the production equipment and the raw material processing time to obtain production index parameters.

[0026] It should be noted that in the above steps, the obtained production equipment functions and raw material processing times are matched with parameters, and each production equipment function corresponds to a different raw material processing time, so that different raw material processing times corresponding to different production equipment functions can be obtained. The matched information is combined into a one-dimensional array, and each element in the array is a production indicator parameter.

[0027] In one implementation, there are three production devices, and the functional parameters of each device are as follows: Equipment A: can handle temperatures ranging from 100°C to 200°C and is suitable for rapid processing.

[0028] Equipment B: can handle temperatures ranging from 150°C to 250°C and is suitable for standard processing.

[0029] Equipment C: can handle temperatures ranging from 200°C to 300°C and is suitable for slow and fine processing.

[0030] At the same time, there are three kinds of raw materials, and the processing time of each raw material is as follows: Raw material 1: needs to be processed at 150°C for 45 minutes.

[0031] Raw material 2: needs to be processed at 200°C for 60 minutes.

[0032] Raw material 3: needs to be processed at 250°C for 75 minutes.

[0033] For raw material 1, you need to find a device that can handle 150°C. Both device A and device B can handle this temperature, but device A is suitable for fast processing, so device A is selected and matched with the processing time of 45 minutes for raw material 1. For raw material 2, you need to find a device that can handle 200°C. Both device B and device C can handle this temperature, but device B is suitable for standard processing, so device B is selected and matched with the processing time of 60 minutes for raw material 2. For raw material 3, you need to find a device that can handle 250°C. Only device C can handle this temperature, so device C is selected and matched with the processing time of 75 minutes for raw material 3. Finally, the matched information is combined into a one-dimensional array, and each element of the array is a production indicator parameter. For example, production indicator parameter array = [A_45, B_60 minutes, C_75].

[0034] In step S13, a vector is constructed according to the raw material mass and the metal element content, and the vector similarity is calculated.

[0035] Constructing a raw material mass vector and a metal element vector according to the raw material mass and the metal element content respectively; Performing similarity calculation based on the raw material mass vector and the metal element vector to obtain vector similarity; Among them, the raw material mass vector and the metal element vector are respectively: In the formula, is the raw material mass vector, They are No. 1 to No. The quality of the raw materials, is the number of raw material types, is the metal element vector, They are No. 1 to No. The metal content, is the number of metal types; The vector similarity calculation formula is: In the formula, is the vector similarity, is the modulus of the raw material mass vector, is the magnitude of the metal element vector.

[0036] It should be noted that the raw material quality vector and metal elements vector They contain information about the quality of the raw materials and the content of metal elements respectively. Vectors are points in high-dimensional space that can represent the multi-dimensional characteristics of raw materials. These vectors are constructed to convert multi-dimensional data into a form that can be mathematically operated, which is convenient for the subsequent calculation of vector similarity. The vector similarity calculation formula determines the similarity between two vectors by calculating the dot product of the two vectors divided by the product of their moduli. This calculation formula can quantify the degree of similarity between two vectors in direction, and calculates the vector similarity regardless of their size. The formula for calculating the modulus length of the raw material quality vector is: , the calculation formula of the metal element vector modulus is ,The modulus calculation formula is a generalization of the Pythagorean theorem in Euclidean space. It is used to calculate the length of a vector in multi-dimensional space. It standardizes the vector so that vectors of different lengths can be compared.

[0037] For example, the vector similarity ranges from -1 to 1, where 1 indicates that the vectors are exactly the same, -1 indicates that they are completely opposite, and 0 indicates that the vectors are orthogonal, that is, have no correlation. When the raw material mass vector and metal elements vector The similarity is high; when the calculation When the raw material mass vector and metal elements vector The similarity is low.

[0038] In step S14, a correction coefficient is calculated based on the raw material quality and the metal element content, the correction coefficient is compared with the production index parameter, and a first weight parameter is determined based on the comparison result.

[0039] Compare the correction coefficient with the production index parameter: When the correction coefficient is greater than the production index parameter, determining that the value of the first weight parameter is 1; When the correction coefficient is less than the production index parameter, determining that the value of the first weight parameter is 0; Wherein, the calculation formula of the correction coefficient is: In the formula, is the correction factor, For the The quality of the raw materials, For the The metal content, is the quantity of each raw material, is the modulus of the raw material mass vector, is the magnitude of the metal element vector.

[0040] It should be noted that the calculation formula of the correction coefficient measures the matching degree between the raw material and the metal element by multiplying the raw material quality and the metal element content. The formula compares the weighted product of the raw material and the metal element with the product of their moduli to obtain a correction coefficient that comprehensively considers the raw material quality and the metal element content. The calculated correction coefficient is compared with the above-obtained production index parameter to obtain the value of the first weight parameter.

[0041] For example, when the calculation is When the production index parameter of the first type of equipment obtained by the above matching is 7.1, the correction coefficient is greater than the production index parameter, and the value of the first weight parameter is determined to be 1; when the production index parameter of the second type of equipment obtained by the above matching is 12.1, the correction coefficient is less than the production index parameter, and the value of the first weight parameter is determined to be 0.

[0042] In step S15, a reshaping calculation is performed according to the raw material processing time and the raw material quality to obtain a second weight parameter.

[0043] The second weight parameter is calculated by the following formula: In the formula, is the second weight parameter, For the The processing time of each raw material, For the The quality of the raw materials, is the amount of raw materials, It is the preset maximum raw material processing time.

[0044] It should be noted that the calculation formula of the second weight parameter takes into account that the raw materials not only need a certain amount of time to process during the production process, but also that this time is proportional to the quality of the raw materials. The total weighted processing time is obtained by summing up the weighted processing time of all raw materials. This sum reflects the total time required for the entire production process under the current raw material quality and processing time. Finally, the total weighted processing time is added to the preset maximum raw material processing time. The comparison is performed to obtain a standardized weight parameter, which can reflect the relative relationship between the actual production time and the maximum allowed time.

[0045] For example, when there are three raw materials in a production, their processing time and quality for: min kg, min kg, min kg, the second weight parameter can be calculated at this time .

[0046] In step S16, a weight matrix is ​​constructed according to the raw material processing time and the metal element content, and a third weight parameter is obtained by calculation according to the weight matrix.

[0047] Constructing a raw material duration vector according to the raw material processing duration; Constructing a weight matrix according to the raw material duration vector and the metal element vector; Perform calculation according to the weight matrix to obtain a third weight parameter; The third weight parameter is calculated by the following formula: In the formula, is the third weight parameter, is the weight matrix Line The elements of the column, is the number of rows in the weight matrix, is the number of columns in the weight matrix.

[0048] It should be noted that a weight matrix is ​​constructed according to the raw material duration vector and the metal element vector, and the third weight parameter is calculated by the third weight parameter calculation formula. This formula provides an overall measure for evaluating the cumulative impact of all raw materials and metal elements in the production process.

[0049] Wherein, the weight matrix is: In the formula, is the raw material duration vector, is the transpose of the metal element vector, is the weight matrix Line Elements of a column.

[0050] For example, when At this time, through the formula Calculated .

[0051] In step S17, a quality feature value is obtained by performing calculation according to the vector similarity, the first weight parameter, the second weight parameter and the third weight parameter.

[0052] The quality characteristic value is calculated by the following formula: In the formula, is the quality characteristic value, is the first weight parameter, is the second weight parameter, is the third weight parameter, is the vector similarity.

[0053] It should be noted that through the above analysis and calculation, the quality characteristic value can be obtained by substituting various parameters When calculating, the formula comprehensively considers the interaction of weight parameters, vector similarity and multi-dimensional evaluation. , and They represent weight parameters of different aspects, which can be adjusted according to different needs and goals in the production process, making the formula very flexible and adaptable. For example, if a production stage pays more attention to the length of raw material processing, it can be appropriately increased. The values ​​of . They are similar to the vector The combination of these two factors can fully reflect the quality of raw materials and key factors in the production process. This combination takes into account the interaction between raw material characteristics, production conditions and metal element content. In addition, incorporating similarity into the calculation of quality characteristic values ​​can ensure a more appropriate match between the quality of raw materials and the metal element content, thus achieving a multi-dimensional assessment of raw material quality.

[0054] For example, when When the raw material mass vector and metal elements vector They are orthogonal, and the quality characteristic value can be calculated .

[0055] In step S18, characteristic parameter calculation is performed according to the production index parameter, the second weight parameter and the quality characteristic value to obtain production quality parameters.

[0056] Constructing a production indicator parameter matrix according to the production indicator parameters; Calculate according to the production index parameter matrix, the second weight parameter and the quality characteristic value to obtain a production quality parameter; Wherein, the calculation formula of the production quality parameter is: In the formula, is the production quality parameter, is the quality characteristic value, is the second weight parameter, is the element in the production index parameter matrix, is the number of rows of the production indicator parameter matrix, is the number of columns of the production indicator parameter matrix.

[0057] It should be noted that through the above analysis and calculation, the production quality parameters can be obtained by substituting various parameters In the calculation, the formula takes into account the quality characteristic values , the second weight parameter and the elements in the production index parameter matrix . Quality characteristic values It represents a comprehensive evaluation of the raw material quality and metal element content, and incorporates it into the calculation of production quality parameters to ensure that the raw material characteristics are reflected in the production quality assessment. The introduction of is to weight the production index parameters, which can reflect the different effects of different production stages or different raw materials on the quality of the final product. The importance of the elements in the production indicator parameter matrix can be highlighted or reduced. The double summation ensures that all production indicators are taken into account, thus providing a comprehensive production quality assessment.

[0058] Perform truncation operation on each element in the production index parameter matrix: When the value of each element in the production index parameter matrix is ​​less than the preset production index constraint parameter value, the value of each element in the production index parameter matrix remains unchanged; When the value of each element in the production index parameter matrix is ​​greater than or equal to the preset production index constraint parameter value, the value of each element in the production index parameter matrix takes the preset production index constraint parameter value.

[0059] It should be noted that the production index parameter matrix , Each element in the production indicator parameter matrix represents a different production indicator parameter. When the value of an element in the production indicator parameter matrix is ​​less than the preset production indicator constraint parameter value, the value of an element in the production indicator parameter matrix remains unchanged; when the value of an element in the production indicator parameter matrix is ​​greater than or equal to the preset production indicator constraint parameter value, the value of an element in the production indicator parameter matrix takes the preset production indicator constraint parameter value. In the metal production process, some indicator parameters need to be limited to a specific range to ensure product quality. Through the truncation operation, it can be ensured that the elements in the production indicator parameter matrix will not exceed the preset constraint parameter value, and the truncation operation can be used to standardize the data, making the distribution of the data set more uniform and reducing the impact of extreme values ​​on the analysis results.

[0060] For example, when the production index parameter matrix , the preset production index constraint parameter value is 6, at this time, each element in the production index parameter matrix is ​​truncated to obtain a new production index parameter matrix , so that the production quality parameters can be calculated .

[0061] In step S19, the production quality parameters are matched with the data in the preset production database to obtain the optimal production control instructions to control the production of metal products.

[0062] It should be noted that the production database has a preset production data range, which sets the production quality parameters By matching with different production data range segments, the production control instructions for the metal type and the production equipment can be obtained, and the instructions are the optimal production control instructions to control the production of metal products.

[0063] Exemplarily, the preset production data ranges are the first production data segment, the second production data segment, the third production data segment, the fourth production data segment and the fifth production data segment, and the corresponding value ranges are: 0-1500, 1500-3000, 3000-4500, 4500-6000, 6000-7500.

[0064] In one implementation, the calculated production quality parameter Belonging to the fourth production data segment, the production control instruction for the metal type and the production equipment can be obtained as the fourth production instruction, which is the optimal production control instruction for the metal type and the production equipment, so the production control instruction is used to control the production of metal products.

[0065] In summary, the present invention discloses an intelligent control method for a metal product production line, including obtaining a production equipment function, raw material quality, raw material processing time and the metal element content in the raw material; performing parameter matching according to the production equipment function and the raw material processing time to obtain a production index parameter; constructing a vector according to the raw material quality and the metal element content, and calculating the vector similarity; calculating a correction coefficient according to the raw material quality and the metal element content, comparing the correction coefficient with the production index parameter, and determining a first weight parameter according to the comparison result; reshaping calculation according to the raw material processing time and the raw material quality to obtain a second weight parameter; constructing a weight matrix according to the raw material processing time and the metal element content, and calculating a third weight parameter according to the weight matrix; calculating according to the vector similarity, the first weight parameter, the second weight parameter and the third weight parameter to obtain a quality characteristic value; calculating characteristic parameters according to the production index parameter, the second weight parameter and the quality characteristic value to obtain a production quality parameter; matching the production quality parameter with the data in a preset production database to obtain an optimal production control instruction to control the production of metal products. The method collects data such as production equipment functions, raw material quality, processing time and metal element content, calculates production index parameters, vector similarity, correction coefficients and multiple weight parameters, and then obtains quality characteristic values ​​and production quality parameters. Finally, the production quality parameters are matched with a preset database to determine and implement the optimal production control instructions, thereby intelligently controlling the production process of metal products. The method can calculate the production quality parameters corresponding to different production equipment and raw material components according to various factors such as different production equipment and raw material components, thereby obtaining the optimal production control instructions to control the production of metal products.

[0066] Reference Figure 2 The second embodiment of the present invention provides an intelligent control system for a metal product production line, comprising: Data acquisition module, used to obtain production equipment functions, raw material quality, raw material processing time and metal element content in raw materials; A production index calculation module, used to perform parameter matching according to the production equipment function and the raw material processing time to obtain production index parameters; A similarity calculation module, used to construct a vector according to the raw material quality and the metal element content, and calculate the vector similarity; A first weight calculation module, used to calculate a correction coefficient according to the raw material quality and the metal element content, compare the correction coefficient with the production index parameter, and determine a first weight parameter according to the comparison result; A second weight calculation module, used for performing a reshaping calculation according to the raw material processing time and the raw material quality to obtain a second weight parameter; A third weight calculation module, used to construct a weight matrix according to the raw material processing time and the metal element content, and calculate a third weight parameter according to the weight matrix; an eigenvalue calculation module, configured to calculate according to the vector similarity, the first weight parameter, the second weight parameter and the third weight parameter to obtain a quality eigenvalue; A control parameter calculation module, used to calculate characteristic parameters according to the production index parameter, the second weight parameter and the quality characteristic value to obtain production quality parameters; The production instruction matching module is used to match the instruction data in the preset production instruction database according to the production quality parameters to obtain the optimal production control instruction to control the production of metal products.

[0067] It should be noted that the intelligent control device for a metal product production line provided in an embodiment of the present invention is used to execute all process steps of the intelligent control method for a metal product production line in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be elaborated upon.

[0068] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an intelligent control program for a metal product production line. When the processor executes the computer program, the steps in the above-mentioned intelligent control method for a metal product production line are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0069] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0070] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0071] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0072] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0073] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0074] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0075] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent control method for a metal product production line, characterized in that: include: Obtain production equipment capabilities, raw material quality, raw material processing time, and metal content in raw materials; Parameter matching is performed according to the function of the production equipment and the processing time of the raw materials to obtain production index parameters; Constructing a vector according to the raw material mass and the metal element content, and calculating the vector similarity; Calculating a correction coefficient according to the raw material quality and the metal element content, comparing the correction coefficient with the production index parameter, and determining a first weight parameter according to the comparison result; Perform reshaping calculation according to the raw material processing time and the raw material quality to obtain a second weight parameter; Constructing a weight matrix according to the raw material processing time and the metal element content, and calculating a third weight parameter according to the weight matrix; Calculate according to the vector similarity, the first weight parameter, the second weight parameter and the third weight parameter to obtain a quality feature value; Calculate characteristic parameters according to the production index parameter, the second weight parameter and the quality characteristic value to obtain production quality parameters; According to the production quality parameters, the optimal production control instructions are obtained by matching with the data in the preset production database to control the production of metal products.

2. The intelligent control method for a metal product production line according to claim 1, characterized in that: The constructing of a vector according to the raw material quality and the metal element content, and calculating the vector similarity, includes: Constructing a raw material mass vector and a metal element vector according to the raw material mass and the metal element content respectively; Performing similarity calculation based on the raw material mass vector and the metal element vector to obtain vector similarity; Among them, the raw material mass vector and the metal element vector are respectively: In the formula, is the raw material mass vector, The first to the The quality of the raw materials, is the number of raw material types, is the metal element vector, The first to the The metal content, is the number of metal types; The vector similarity calculation formula is: In the formula, is the vector similarity, is the modulus of the raw material mass vector, is the magnitude of the metal element vector.

3. The intelligent control method for a metal product production line according to claim 2 is characterized in that: The step of comparing the correction coefficient with the production index parameter and determining a first weight parameter according to the comparison result includes: Compare the correction coefficient with the production index parameter: When the correction coefficient is greater than the production index parameter, determining that the value of the first weight parameter is 1; When the correction coefficient is less than the production index parameter, determining that the value of the first weight parameter is 0; Wherein, the calculation formula of the correction coefficient is: In the formula, is the correction factor, For the The quality of the raw materials, For the The metal content, is the quantity of each raw material, is the modulus of the raw material mass vector, is the magnitude of the metal element vector.

4. The intelligent control method for a metal product production line according to claim 1, characterized in that: The reshaping calculation is performed according to the raw material processing time and the raw material quality to obtain the second weight parameter, including: The second weight parameter is calculated by the following formula: In the formula, is the second weight parameter, For the The processing time of each raw material, For the The quality of the raw materials, is the amount of raw materials, It is the preset maximum raw material processing time.

5. The intelligent control method for a metal product production line according to claim 2, characterized in that: The step of constructing a weight matrix according to the raw material processing time and the metal element content, and calculating a third weight parameter according to the weight matrix includes: Constructing a raw material duration vector according to the raw material processing duration; Constructing a weight matrix according to the raw material duration vector and the metal element vector; Perform calculation according to the weight matrix to obtain a third weight parameter; The third weight parameter is calculated by the following formula: In the formula, is the third weight parameter, is the weight matrix Line The elements of the column, is the number of rows in the weight matrix, is the number of columns in the weight matrix.

6. The intelligent control method for a metal product production line according to claim 1, characterized in that: The calculating according to the vector similarity, the first weight parameter, the second weight parameter and the third weight parameter to obtain the quality feature value includes: The quality characteristic value is calculated by the following formula: In the formula, is the quality characteristic value, is the first weight parameter, is the second weight parameter, is the third weight parameter, is the vector similarity.

7. The intelligent control method for a metal product production line according to claim 1, characterized in that: The calculating of characteristic parameters according to the production index parameter, the second weight parameter and the quality characteristic value to obtain the production quality parameter comprises: Constructing a production indicator parameter matrix according to the production indicator parameters; Calculate according to the production index parameter matrix, the second weight parameter and the quality characteristic value to obtain a production quality parameter; Wherein, the calculation formula of the production quality parameter is: In the formula, is the production quality parameter, is the quality characteristic value, is the second weight parameter, is the element in the production index parameter matrix, is the number of rows of the production indicator parameter matrix, is the number of columns of the production indicator parameter matrix.

8. The intelligent control method for a metal product production line according to claim 7, characterized in that: The production indicator parameter matrix includes: Perform truncation operation on each element in the production index parameter matrix: When the value of each element in the production index parameter matrix is ​​less than the preset production index constraint parameter value, the value of each element in the production index parameter matrix remains unchanged; When the value of each element in the production index parameter matrix is ​​greater than or equal to the preset production index constraint parameter value, the value of each element in the production index parameter matrix takes the preset production index constraint parameter value.

9. An intelligent control system for a metal product production line, characterized in that: include: Data acquisition module, used to obtain production equipment functions, raw material quality, raw material processing time and metal element content in raw materials; A production index calculation module, used to perform parameter matching according to the production equipment function and the raw material processing time to obtain production index parameters; A similarity calculation module, used to construct a vector according to the raw material quality and the metal element content, and calculate the vector similarity; A first weight calculation module, used to calculate a correction coefficient according to the raw material quality and the metal element content, compare the correction coefficient with the production index parameter, and determine a first weight parameter according to the comparison result; A second weight calculation module, used for performing a reshaping calculation according to the raw material processing time and the raw material quality to obtain a second weight parameter; A third weight calculation module, used to construct a weight matrix according to the raw material processing time and the metal element content, and calculate a third weight parameter according to the weight matrix; an eigenvalue calculation module, configured to calculate according to the vector similarity, the first weight parameter, the second weight parameter and the third weight parameter to obtain a quality eigenvalue; A control parameter calculation module, used to calculate characteristic parameters according to the production index parameter, the second weight parameter and the quality characteristic value to obtain production quality parameters; The production instruction matching module is used to match the instruction data in the preset production instruction database according to the production quality parameters to obtain the optimal production control instruction to control the production of metal products.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the intelligent control method for a metal product production line according to any one of claims 1 to 8.

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