Intelligent production line and production process for instant dry rice noodles

Abstract

The invention relates to the technical field of intelligent regulation and control of dry rice noodle production lines, and particularly discloses an intelligent production line and a production process of instant dry rice noodles, comprising: a processing module for processing all types of core production processes of all dry rice noodle reference batches of a current dry rice noodle production batch based on all types of production data of all types of core production processes of all dry rice noodle reference batches of the current dry rice noodle production batch; obtaining a production analysis matrix of each dry rice noodle reference batch of the current dry rice noodle production batch; the analysis module is used for obtaining the setting priority of each dry rice noodle reference batch of the current dry rice noodle production batch based on the production analysis matrix of all the dry rice noodle reference batches of the current dry rice noodle production batch; and the control module is used for obtaining the optimal production result of the current dry rice noodle production batch according to the setting priorities of all the dry rice noodle reference batches of the current dry rice noodle production batch. According to the invention, the storage stability, the elasticity and the hardness of the dry rice noodles produced in the current dry rice noodle production batch reach a comprehensive optimal state.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of dried rice noodle production lines, and particularly relates to an instant dried rice noodle intelligent production line and a production process. Background Art

[0002] At present, during the production of dried rice noodles, storage stability, elasticity, and softness / hardness are important indicators for measuring the quality of dried rice noodles. These indicators not only affect the taste and flavor of dried rice noodles but also are directly related to the product shelf life and consumer satisfaction. During the actual production process, storage stability, elasticity, and softness / hardness are mainly determined by all types of core production processes, including the pre-drying process, aging process, and drying process. Therefore, how to precisely control all types of production data of all types of core production processes of the current dried rice noodle production batch, so that the storage stability, elasticity, and softness / hardness of the dried rice noodles produced in the current dried rice noodle production batch reach an optimal comprehensive state has become an urgent problem in current dried rice noodle production.

[0003] However, the existing instant dried rice noodle intelligent production line and production process only achieve the full-automatic production of instant dried rice noodles from raw materials to finished products, greatly improving production efficiency, and do not consider precisely controlling all types of production data of all types of core production processes of the current dried rice noodle production batch, so that the storage stability, elasticity, and softness / hardness of the dried rice noodles produced in the current dried rice noodle production batch reach an optimal comprehensive state. For example, the patent with the publication number "CN101461486A" and the patent name "An Instant Dried Rice Noodle Intelligent Production Line and Its Production Process" has the following method steps: a rice hopper for material transfer, a jet rice milling machine, a specific gravity sand remover, a rice color sorter, a rice conveyor, a rice extractor, a jet rice washer, a water-rice separation tank, a primary pulp grinder, a secondary pulp grinder, a pulp sieve, a pulp storage machine, a stirring type automatic pulp dropping machine, a steam powder machine, a powder skin pre-dryer, a powder skin freezer, a primary powder skin restoration machine, a secondary powder skin restoration machine, a drum type transition conveyor, a wire cutter, a vermicelli conveyor, a quantitative cutting machine, an automatic shaping machine, a dryer, an air cooler, and a packaging conveyor. The production line and process method of the above patent can achieve the full-automatic production of instant dried rice noodles from raw materials to finished products, and the produced instant dried rice noodles have good quality and high production efficiency. However, this patent only realizes the full-automatic production of instant dried rice noodles from raw materials to finished products, greatly improving production efficiency, and does not consider precisely controlling all types of production data of all types of core production processes of the current dried rice noodle production batch, so that the storage stability, elasticity, and softness / hardness of the dried rice noodles produced in the current dried rice noodle production batch reach an optimal comprehensive state.

[0004] Therefore, the present invention proposes an instant dried rice noodle intelligent production line and a production process. Summary of the Invention

[0005] The present invention provides an instant dry rice noodle intelligent production line and production process, which are used to obtain the production analysis matrix of each dry rice noodle reference batch of the current dry rice noodle production batch based on all types of production data and all types of detection score values of all types of core production processes of all dry rice noodle reference batches of the current dry rice noodle production batch, realizing the construction of a matrix for analyzing the influence degree of all types of production data of all types of core production processes of each dry rice noodle reference batch of the current dry rice noodle production batch on the storage stability, elasticity, softness and hardness of the produced dry rice noodles. Furthermore, based on the production analysis matrix of all dry rice noodle reference batches of the current dry rice noodle production batch, the production optimization matrix of the current dry rice noodle production batch is obtained, facilitating the subsequent calculation of the setting priority. According to the production optimization matrix of the current dry rice noodle production batch, the setting priority of each dry rice noodle reference batch of the current dry rice noodle production batch is obtained, realizing the quantification of all types of production data of all types of core production processes of each dry rice noodle reference batch of the current dry rice noodle production batch at the set priority level. Furthermore, based on the setting priority of all dry rice noodle reference batches of the current dry rice noodle production batch, the optimal setting value of all types of production data of all types of core production processes of the current dry rice noodle production batch is obtained. Finally, according to the optimal setting value of all types of production data of all types of core production processes of the current dry rice noodle production batch, the optimal production result of the current dry rice noodle production batch is obtained, realizing the precise control of all types of production data of all types of core production processes of the current dry rice noodle production batch, and making the storage stability, elasticity, softness and hardness of the dry rice noodles produced in the current dry rice noodle production batch reach the comprehensive optimal state.

[0006] The present invention provides an instant dry rice noodle intelligent production line, including:

[0007] An acquisition module, configured to obtain all dry rice noodle reference batches of the current dry rice noodle production batch based on the key production data of all types of non-core production processes of the current dry rice noodle production batch and AI technology;

[0008] A processing module, configured to obtain the production analysis matrix of each dry rice noodle reference batch of the current dry rice noodle production batch based on all types of production data and all types of detection score values of all types of core production processes of all dry rice noodle reference batches of the current dry rice noodle production batch;

[0009] An analysis module, configured to obtain the production optimization matrix of the current dry rice noodle production batch based on the production analysis matrix of all dry rice noodle reference batches of the current dry rice noodle production batch, and obtain the setting priority of each dry rice noodle reference batch of the current dry rice noodle production batch based on the production optimization matrix of the current dry rice noodle production batch;

[0010] The control module is used to obtain the optimal setting values of all types of production data for all types of core production processes in the current batch of dried rice noodles based on the set priorities of all reference batches of dried rice noodles in the current batch of dried rice noodles production, and obtain the optimal production result of the current batch of dried rice noodles production based on the optimal setting values of all types of production data for all types of core production processes in the current batch of dried rice noodles production.

[0011] Preferably, for the instant dried rice noodle intelligent production line, the acquisition module includes:

[0012] The first acquisition sub-module is used to acquire the key production data of all types of non-core production processes in the current batch of dried rice noodles production, where all types of non-core production processes include the pulp grinding process, the pulp adjusting process, and the ripening process;

[0013] The extraction sub-module is used to obtain all reference batches of dried rice noodles in the current batch of dried rice noodles production based on the key production data of all types of non-core production processes in the current batch of dried rice noodles production, the historical database, and AI technology.

[0014] Preferably, for the instant dried rice noodle intelligent production line, the extraction sub-module includes:

[0015] The acquisition unit is used to acquire the key production data of all types of non-core production processes in all historical batches of dried rice noodles production in the historical database based on AI technology;

[0016] The extraction unit is used to regard the historical batches of dried rice noodles production within all historical batches of dried rice noodles production in the historical database, which are the closest to the production time of the current batch of dried rice noodles production and the key production data of all types of non-core production processes are the same as those of the current batch of dried rice noodles production for a preset number of batches, as the reference batches of dried rice noodles in the current batch of dried rice noodles production.

[0017] Preferably, for the instant dried rice noodle intelligent production line, the processing module includes:

[0018] The second acquisition sub-module is used to acquire all types of production data of all types of core production processes for each reference batch of dried rice noodles in the current batch of dried rice noodles production, where all types of core production processes include the pre-drying process, the aging process, and the drying process, and all types of production data include temperature data and time length data;

[0019] The third acquisition sub-module is used to acquire all types of detection score values for each reference batch of dried rice noodles in the current batch of dried rice noodles production, where all types of detection score values include storage stability detection score values, elasticity detection score values, and softness and hardness detection score values;

[0020] The first processing sub-module is used to take the mean value of the numerical values of each type of production data in each type of core production process of all dry rice noodle reference batches of the current dry rice noodle production batch as the standard value of the corresponding type of production data in the corresponding type of core production process of the current dry rice noodle production batch, and take the mean value of the detection score values of all dry rice noodle reference batches of the current dry rice noodle production batch as the standard value of the corresponding type of detection score value of the current dry rice noodle production batch;

[0021] The second processing sub-module is used to obtain the production analysis matrix of each dry rice noodle reference batch of the current dry rice noodle production batch based on the standard values of all types of production data and the standard values of all types of detection score values of all types of core production processes of the current dry rice noodle production batch.

[0022] Preferably, for the instant dry rice noodle intelligent production line, the method by which the second processing sub-module obtains the production analysis matrix of each dry rice noodle reference batch of the current dry rice noodle production batch based on the standard values of all types of production data and the standard values of all types of detection score values of all types of core production processes of the current dry rice noodle production batch includes:

[0023]

[0024] Among them, E is the production analysis matrix of the currently calculated dry rice noodle reference batch of the current dry rice noodle production batch, and A 1 is the numerical value of the temperature data of the pre-drying process of the currently calculated dry rice noodle reference batch of the current dry rice noodle production batch, and A 2 is the numerical value of the temperature data of the aging process of the currently calculated dry rice noodle reference batch of the current dry rice noodle production batch, and A 3 is the numerical value of the temperature data of the drying process of the currently calculated dry rice noodle reference batch of the current dry rice noodle production batch, and a 1 is the standard value of the temperature data of the pre-drying process of the current dry rice noodle production batch, and a 2 is the standard value of the temperature data of the aging process of the current dry rice noodle production batch, and a 3 is the standard value of the temperature data of the drying process of the current dry rice noodle production batch, and B 1 is the numerical value of the time length data of the pre-drying process of the currently calculated dry rice noodle reference batch of the current dry rice noodle production batch, and B 2 is the numerical value of the time length data of the aging process of the currently calculated dry rice noodle reference batch of the current dry rice noodle production batch, and B 3 is the numerical value of the time length data of the drying process of the currently calculated dry rice noodle reference batch of the current dry rice noodle production batch, and b 1 is the standard value of the time length data of the pre-drying process of the current dry rice noodle production batch, and b 2 is the standard value of the time length data of the aging process of the current dry rice noodle production batch, and b3 is the standard value of the time length data of the drying process for the current production batch of dried rice noodles, C 1 is the score assigned for the storage stability test of the currently calculated reference batch of dried rice noodles for the current production batch of dried rice noodles, C 2 is the score assigned for the elasticity test of the currently calculated reference batch of dried rice noodles for the current production batch of dried rice noodles, C 3 is the score assigned for the softness and hardness test of the currently calculated reference batch of dried rice noodles for the current production batch of dried rice noodles, c 1 is the standard value of the score assigned for the storage stability test of the current production batch of dried rice noodles, c 2 is the standard value of the score assigned for the elasticity test of the current production batch of dried rice noodles, c 3 is the standard value of the score assigned for the softness and hardness test of the current production batch of dried rice noodles.

[0025] Preferably, for the instant dried rice noodle intelligent production line, the analysis module includes:

[0026] An analysis sub-module, which is used to regard all the eigenvalue of the production analysis matrix of each reference batch of dried rice noodles in the current production batch of dried rice noodles as the analysis value of the corresponding reference batch of dried rice noodles in the current production batch of dried rice noodles, and obtain all the analysis values of each reference batch of dried rice noodles in the current production batch of dried rice noodles;

[0027] A construction sub-module, which is used to obtain the production optimization matrix of the current production batch of dried rice noodles based on all the analysis values of all the reference batches of dried rice noodles in the current production batch of dried rice noodles;

[0028] A calculation sub-module, which is used to obtain the setting priority of each reference batch of dried rice noodles in the current production batch of dried rice noodles based on the production optimization matrix of the current production batch of dried rice noodles.

[0029] Preferably, for the instant dried rice noodle intelligent production line, the construction sub-module includes:

[0030] An analysis unit, which is used to regard the maximum value among all the analysis values of each reference batch of dried rice noodles in the current production batch of dried rice noodles as the first analysis value of the corresponding reference batch of dried rice noodles in the current production batch of dried rice noodles, regard the minimum value among all the analysis values of each reference batch of dried rice noodles in the current production batch of dried rice noodles as the second analysis value of the corresponding reference batch of dried rice noodles in the current production batch of dried rice noodles, and regard the numerical mean of the first analysis value and the second analysis value of each reference batch of dried rice noodles in the current production batch of dried rice noodles as the third analysis value of the corresponding reference batch of dried rice noodles in the current production batch of dried rice noodles;

[0031] A building unit is used to define the ordinal numbers of all the dried rice noodle reference batches of the current dried rice noodle production batch in ascending order from 1 in the chronological order from the front to the back, obtain the ordinal number definition result of all the dried rice noodle reference batches of the current dried rice noodle production batch, and based on the first analysis value, the second analysis value, the third analysis value and the ordinal number definition result of all the dried rice noodle reference batches of the current dried rice noodle production batch, obtain the production optimization matrix of the current dried rice noodle production batch, that is:

[0032]

[0033] Where Q is the production optimization matrix of the current dried rice noodle production batch, and r 1 is the first analysis value of the dried rice noodle reference batch with the ordinal number 1 of the current dried rice noodle production batch, and r 2 is the first analysis value of the dried rice noodle reference batch with the ordinal number 2 of the current dried rice noodle production batch, and r n is the first analysis value of the dried rice noodle reference batch with the ordinal number n of the current dried rice noodle production batch, and y 1 is the second analysis value of the dried rice noodle reference batch with the ordinal number 1 of the current dried rice noodle production batch, and y 2 is the second analysis value of the dried rice noodle reference batch with the ordinal number 2 of the current dried rice noodle production batch, and y n is the second analysis value of the dried rice noodle reference batch with the ordinal number n of the current dried rice noodle production batch, and g 1 is the third analysis value of the dried rice noodle reference batch with the ordinal number 1 of the current dried rice noodle production batch, and g 2 is the third analysis value of the dried rice noodle reference batch with the ordinal number 2 of the current dried rice noodle production batch, and g n is the third analysis value of the dried rice noodle reference batch with the ordinal number n of the current dried rice noodle production batch, and n is the number of all the dried rice noodle reference batches of the current dried rice noodle production batch.

[0034] Preferably, an instant dried rice noodle intelligent production line, a calculation sub-module, includes:

[0035] A preprocessing unit is used to regard the rank of the production optimization matrix of the current dried rice noodle production batch as the standard rank value of the current dried rice noodle production batch, and regard the spectral radius of the production analysis matrix of each dried rice noodle reference batch of the current dried rice noodle production batch as the standard calculation value of the corresponding dried rice noodle reference batch of the current dried rice noodle production batch;

[0036] A calculation unit is used to obtain the setting priority of each dried rice noodle reference batch of the current dried rice noodle production batch based on the standard rank value of the current dried rice noodle production batch and the standard calculation values of all the dried rice noodle reference batches, that is:

[0037]

[0038] Among them, τ is the set priority of the currently calculated dried rice noodle reference batch for the current production batch of dried rice noodles, δ is the standard calculated value of the currently calculated dried rice noodle reference batch for the current production batch of dried rice noodles, and S δ is the sum value of the standard calculated values of all dried rice noodle reference batches for the current production batch of dried rice noodles, and δ 0 is the mean value of the standard calculated values of all dried rice noodle reference batches for the current production batch of dried rice noodles, ε is the standard rank value of the current production batch of dried rice noodles, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0039] Preferably, for the instant dried rice noodle intelligent production line, the control module includes:

[0040] A numerical determination sub-module, which is used to regard the numerical values of each type of production data of each type of core production process of the dried rice noodle reference batch with the maximum set priority among all dried rice noodle reference batches of the current production batch of dried rice noodles as the optimal set numerical values of the corresponding type of production data of the corresponding type of core production process of the current production batch of dried rice noodles;

[0041] A control sub-module, which is used to adjust the numerical values of each type of production data of each type of core production process of the current production batch of dried rice noodles to be the same as the optimal set numerical values of the corresponding type of production data of the corresponding type of core production process of the current production batch of dried rice noodles, so as to obtain the optimal production result of the current production batch of dried rice noodles.

[0042] The present invention provides an instant dried rice noodle production process, which is applied to any one of the instant dried rice noodle intelligent production lines in Embodiments 1-9, and includes:

[0043] S1: Based on the key production data of all non-core production processes of the current production batch of dried rice noodles and AI technology, obtain all dried rice noodle reference batches of the current production batch of dried rice noodles;

[0044] S2: Based on all types of production data and all types of detection assignment scores of all types of core production processes of all dried rice noodle reference batches of the current production batch of dried rice noodles, obtain the production analysis matrix of each dried rice noodle reference batch of the current production batch of dried rice noodles;

[0045] S3: Based on the production analysis matrix of all dried rice noodle reference batches of the current production batch of dried rice noodles, obtain the production optimization matrix of the current production batch of dried rice noodles, and based on the production optimization matrix of the current production batch of dried rice noodles, obtain the set priority of each dried rice noodle reference batch of the current production batch of dried rice noodles;

[0046] S4: Obtain the optimal setting values of all types of production data for all types of core production processes of the current dried rice noodle production batch based on the set priorities of all dried rice noodle reference batches of the current dried rice noodle production batch, and obtain the optimal production result of the current dried rice noodle production batch based on the optimal setting values of all types of production data for all types of core production processes of the current dried rice noodle production batch.

[0047] The beneficial effects of the present invention compared with the prior art are as follows: According to all types of production data and all types of detection assignment scores of all types of core production processes of all dried rice noodle reference batches of the current dried rice noodle production batch, obtain the production analysis matrix of each dried rice noodle reference batch of the current dried rice noodle production batch, realizing the construction of a matrix for analyzing the influence degree of all types of production data of all types of core production processes of each dried rice noodle reference batch of the current dried rice noodle production batch on the storage stability, elasticity, softness and hardness of the produced dried rice noodles. Furthermore, according to the production analysis matrix of all dried rice noodle reference batches of the current dried rice noodle production batch, obtain the production optimization matrix of the current dried rice noodle production batch, which is convenient for the subsequent calculation of set priorities. According to the production optimization matrix of the current dried rice noodle production batch, obtain the set priorities of each dried rice noodle reference batch of the current dried rice noodle production batch, realizing the quantification of the set priority degree of all types of production data of all types of core production processes of each dried rice noodle reference batch of the current dried rice noodle production batch. Furthermore, according to the set priorities of all dried rice noodle reference batches of the current dried rice noodle production batch, obtain the optimal setting values of all types of production data of all types of core production processes of the current dried rice noodle production batch. Finally, according to the optimal setting values of all types of production data of all types of core production processes of the current dried rice noodle production batch, obtain the optimal production result of the current dried rice noodle production batch, realizing the precise regulation of all types of production data of all types of core production processes of the current dried rice noodle production batch, and making the storage stability, elasticity, softness and hardness of the dried rice noodles produced in the current dried rice noodle production batch reach the comprehensive optimal state.

[0048] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structure specifically pointed out in the written application documents of the present application.

[0049] The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments. Description of the Drawings

[0050] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the accompanying drawings:

[0051] Figure 1 Schematic diagram of an intelligent production line for instant dried rice noodles in an embodiment of the present invention;

[0052] Figure 2 Process flow chart of the production of instant dried rice noodles in an embodiment of the present invention. Detailed implementation manners

[0053] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not intended to limit the present invention.

[0054] Embodiment 1:

[0055] The present invention provides an intelligent production line for instant dried rice noodles. Referring to Figure 1 , including:

[0056] An acquisition module, configured to obtain all reference batches of dried rice noodles for the current production batch of dried rice noodles based on the key production data and AI technology of all non-core production processes of the current production batch of dried rice noodles;

[0057] A processing module, configured to obtain a production analysis matrix for each reference batch of dried rice noodles in the current production batch of dried rice noodles based on all production data and all detection score values of all core production processes of all reference batches of dried rice noodles in the current production batch of dried rice noodles;

[0058] An analysis module, configured to obtain a production optimization matrix for the current production batch of dried rice noodles based on the production analysis matrices of all reference batches of dried rice noodles in the current production batch of dried rice noodles, and obtain the setting priority for each reference batch of dried rice noodles in the current production batch of dried rice noodles based on the production optimization matrix of the current production batch of dried rice noodles;

[0059] A control module, configured to obtain the optimal setting values of all production data of all core production processes of the current production batch of dried rice noodles based on the setting priorities of all reference batches of dried rice noodles in the current production batch of dried rice noodles, and obtain the optimal production result of the current production batch of dried rice noodles based on the optimal setting values of all production data of all core production processes of the current production batch of dried rice noodles.

[0060] In this embodiment, the current production batch of dried rice noodles is the batch of dried rice noodles that is being produced by the intelligent production line for instant dried rice noodles (the grinding process, sizing process, and cooking process have been completed, but the pre-drying process, aging process, and drying process have not been carried out).

[0061] In this embodiment, the non-core production processes are the partial production processes that play a non-critical role in the storage stability, elasticity, and softness and hardness of the produced dried rice noodles during the production of instant dried rice noodles, including the grinding process, sizing process, and cooking process.

[0062] In this embodiment, the key production data is the production data that has the greatest impact on the produced dried rice noodles in the non-core production process. For example, the key production data of the pulp grinding process is the fineness of the rice pulp (the size of the solid particles in the rice pulp, usually represented by the particle size).

[0063] In this embodiment, the AI technology is the existing artificial intelligence technology used to obtain the key production data of all types of non-core production processes of all historical dried rice noodle production batches from the historical database, such as machine learning algorithms.

[0064] In this embodiment, the reference batch of dried rice noodles is a part of the historical dried rice noodle production batches required to obtain the optimal setting values for analyzing all types of production data of all types of core production processes of the current dried rice noodle production batch based on the key production data of all types of non-core production processes of the current dried rice noodle production batch.

[0065] In this embodiment, the core production process is the part of the production process that plays a key role in the storage stability, elasticity, and softness and hardness of the produced dried rice noodles during the production of instant dried rice noodles, including the pre-drying process, the aging process, and the drying process.

[0066] In this embodiment, all types of production data are the set temperature data and time length data during each type of core production process.

[0067] In this embodiment, all types of detection score values are the score values assigned to the storage stability, elasticity, and softness and hardness conditions of the dried rice noodles produced by each reference batch of dried rice noodles in the current dried rice noodle production batch.

[0068] In this embodiment, the production analysis matrix is a matrix used to analyze the degree of influence of all types of production data of all types of core production processes of each reference batch of dried rice noodles in the current dried rice noodle production batch on the storage stability, elasticity, and softness and hardness of the produced dried rice noodles.

[0069] In this embodiment, the production optimization matrix of the current dried rice noodle production batch is a matrix constructed based on the production analysis matrices of all reference batches of dried rice noodles in the current dried rice noodle production batch and used to analyze the setting priority of each reference batch of dried rice noodles in the current dried rice noodle production batch.

[0070] In this embodiment, the setting priority is a value obtained based on the production optimization matrix of the current dried rice noodle production batch and used to characterize the degree of priority setting of all types of production data of all types of core production processes of each reference batch of dried rice noodles in the current dried rice noodle production batch.

[0071] In this embodiment, the optimal setting value is the value to which all types of production data of all types of core production processes in the current dried rice noodle production batch need to be adjusted so that the storage stability, elasticity, softness and hardness of the dried rice noodles produced in the current dried rice noodle production batch reach an overall optimal state.

[0072] In this embodiment, the optimal production result of the current dried rice noodle production batch is the production result obtained by adjusting the values of all types of production data of each type of core production process in the current dried rice noodle production batch to be the same as the optimal setting values of the corresponding types of production data of the corresponding types of core production processes in the current dried rice noodle production batch.

[0073] The beneficial effects of the above technology are as follows: According to all types of production data and all types of detection score values of all types of core production processes of all dried rice noodle reference batches in the current dried rice noodle production batch, a production analysis matrix of each dried rice noodle reference batch in the current dried rice noodle production batch is obtained, realizing the construction of a matrix for analyzing the influence degree of all types of production data of all types of core production processes of each dried rice noodle reference batch in the current dried rice noodle production batch on the storage stability, elasticity, softness and hardness of the produced dried rice noodles. Furthermore, according to the production analysis matrices of all dried rice noodle reference batches in the current dried rice noodle production batch, a production optimization matrix of the current dried rice noodle production batch is obtained, which is convenient for the subsequent calculation of the setting priority. According to the production optimization matrix of the current dried rice noodle production batch, the setting priority of each dried rice noodle reference batch in the current dried rice noodle production batch is obtained, realizing the quantification of the setting priority of all types of production data of all types of core production processes of each dried rice noodle reference batch in the current dried rice noodle production batch. Furthermore, according to the setting priorities of all dried rice noodle reference batches in the current dried rice noodle production batch, the optimal setting values of all types of production data of all types of core production processes in the current dried rice noodle production batch are obtained. Finally, according to the optimal setting values of all types of production data of all types of core production processes in the current dried rice noodle production batch, the optimal production result of the current dried rice noodle production batch is obtained, realizing the precise control of all types of production data of all types of core production processes in the current dried rice noodle production batch, so that the storage stability, elasticity, softness and hardness of the dried rice noodles produced in the current dried rice noodle production batch reach an overall optimal state.

[0074] Embodiment 2:

[0075] Based on Embodiment 1, the instant dried rice noodle intelligent production line, the acquisition module includes:

[0076] The first acquisition sub-module is used to acquire the key production data of all types of non-core production processes in the current dried rice noodle production batch, where all types of non-core production processes include the pulp grinding process, the pulp adjusting process and the ripening process;

[0077] An extraction sub-module, configured to obtain all reference batches of the current batch of dried rice noodles based on the key production data, historical database, and AI technology of all non-core production processes of the current batch of dried rice noodle production.

[0078] In this embodiment, the pulping process is a process of grinding rice using a suitable pulping device (such as a stone mill, a grinding wheel mill, or a colloid mill, etc.).

[0079] In this embodiment, the slurry adjusting process is a process of adding appropriate amounts of starch, edible oil, and edible salt to the ground rice slurry and stirring.

[0080] In this embodiment, the ripening process is a process of using a steam ripening device (such as a steam box or a continuous cooking machine, etc.) to fully gelatinize the starch in the rice slurry to form a gel-like structure.

[0081] In this embodiment, the historical database is a database storing relevant data of all completed batches of dried rice noodle production.

[0082] The beneficial effects of the above technology are as follows: The specific process items of all non-core production processes of the current batch of dried rice noodle production are clarified. Furthermore, based on the key production data and historical database of all non-core production processes of the current batch of dried rice noodle production, all reference batches of the current batch of dried rice noodles are obtained.

[0083] Embodiment 3:

[0084] Based on Embodiment 2, for the intelligent production line of instant dried rice noodles, the extraction sub-module includes:

[0085] An acquisition unit, configured to obtain the key production data of all non-core production processes of all historical batches of dried rice noodle production in the historical database based on AI technology;

[0086] An extraction unit, configured to regard the preset number of historical batches of dried rice noodle production within all historical batches of dried rice noodle production in the historical database, whose production time is closest to the production time of the current batch of dried rice noodle production (the production time is the closest in time series), and the key production data of all non-core production processes are the same as those of the current batch of dried rice noodle production (the key production data of each non-core production process is the same as that of the corresponding non-core production process of the current batch of dried rice noodle production), as the reference batches of the current batch of dried rice noodles.

[0087] In this embodiment, the production time is the production time of the current batch of dried rice noodle production or the production time of each historical batch of dried rice noodle production. The production time of the current batch of dried rice noodle production is set as the current moment, and the production time of each historical batch of dried rice noodle production is based on the time when the dried rice noodles come off the production line.

[0088] In this embodiment, the preset number of batches is the pre-set number of batches, for example, 6.

[0089] The beneficial effects of the above technology are as follows: This embodiment details a specific method for obtaining all the reference batches of dried rice noodles for the current production batch of dried rice noodles based on the key production data and historical database of all non-core production processes of the current production batch of dried rice noodles, which is convenient for subsequent construction of the production optimization matrix for the current production batch of dried rice noodles.

[0090] Embodiment 4:

[0091] Based on Embodiment 1, the instant dried rice noodle intelligent production line, the processing module includes:

[0092] The second acquisition sub-module is used to acquire all types of production data of all types of core production processes of each reference batch of dried rice noodles for the current production batch of dried rice noodles, where all types of core production processes include the pre-drying process, the aging process, and the drying process, and all types of production data include temperature data and time length data;

[0093] The third acquisition sub-module is used to acquire all types of detection score values of each reference batch of dried rice noodles for the current production batch of dried rice noodles, where all types of detection score values include storage stability detection score values, elasticity detection score values, and softness and hardness detection score values;

[0094] The first processing sub-module is used to take the average value of the numerical values of each type of production data of each type of core production process of all the reference batches of dried rice noodles for the current production batch of dried rice noodles as the standard value of the corresponding type of production data of the corresponding type of core production process for the current production batch of dried rice noodles, and take the average value of each type of detection score value of all the reference batches of dried rice noodles for the current production batch of dried rice noodles as the standard value of the corresponding type of detection score value for the current production batch of dried rice noodles;

[0095] The second processing sub-module is used to obtain the production analysis matrix of each reference batch of dried rice noodles for the current production batch of dried rice noodles based on the standard values of all types of production data and all types of detection score values of all types of core production processes for the current production batch of dried rice noodles.

[0096] In this embodiment, the pre-drying process is a process of pre-drying the formed rice noodles by using hot air drying or natural drying to ensure that the moisture content in the rice noodles is moderate.

[0097] In this embodiment, the aging process is a process of aging the pre-dried rice noodles (standing at room temperature, cooling at low temperature, or maintaining for a certain time in an aging room) to enhance their toughness and taste.

[0098] In this embodiment, the drying process is a process of fully drying the shaped rice noodles to remove excess moisture.

[0099] In this embodiment, the temperature data is the temperature data set in each type of core production process of each dry rice noodle reference batch in the current dry rice noodle production batch.

[0100] In this embodiment, the time length data is the time length data of each type of core production process of each dry rice noodle reference batch in the current dry rice noodle production batch.

[0101] In this embodiment, the storage stability detection score is the score assigned to the storage stability of the dry rice noodles produced from each dry rice noodle reference batch in the current dry rice noodle production batch.

[0102] In this embodiment, the elasticity detection score is the score assigned to the elasticity of the dry rice noodles produced from each dry rice noodle reference batch in the current dry rice noodle production batch.

[0103] In this embodiment, the softness and hardness detection score is the score assigned to the softness and hardness of the dry rice noodles produced from each dry rice noodle reference batch in the current dry rice noodle production batch.

[0104] The beneficial effects of the above technology are as follows: According to all types of production data and all types of detection scores of all types of core production processes of all dry rice noodle reference batches in the current dry rice noodle production batch, a production analysis matrix of each dry rice noodle reference batch in the current dry rice noodle production batch is obtained, realizing the construction of a matrix for analyzing the influence degree of all types of production data of all types of core production processes of each dry rice noodle reference batch in the current dry rice noodle production batch on the storage stability, elasticity, and softness and hardness of the produced dry rice noodles, thereby realizing the precise regulation of production data in the follow-up.

[0105] Embodiment 5:

[0106] Based on Embodiment 4, for the instant dry rice noodle intelligent production line, the method by which the second processing sub-module obtains the production analysis matrix of each dry rice noodle reference batch in the current dry rice noodle production batch based on the standard values of all types of production data and the standard values of all types of detection scores of all types of core production processes in the current dry rice noodle production batch includes:

[0107]

[0108] Among them, E is the production analysis matrix of the currently calculated dry rice noodle reference batch in the current dry rice noodle production batch, and A 1 is the numerical value of the temperature data of the pre-drying process of the currently calculated dry rice noodle reference batch in the current dry rice noodle production batch, and A 2 is the numerical value of the temperature data of the aging process of the currently calculated dry rice noodle reference batch in the current dry rice noodle production batch.3 is the numerical value of the temperature data of the drying process of the currently calculated dried rice noodle reference batch for the current dried rice noodle production batch, a 1 is the standard value of the temperature data of the pre-drying process for the current dried rice noodle production batch, a 2 is the standard value of the temperature data of the aging process for the current dried rice noodle production batch, a 3 is the standard value of the temperature data of the drying process for the current dried rice noodle production batch, B 1 is the numerical value of the time length data of the pre-drying process of the currently calculated dried rice noodle reference batch for the current dried rice noodle production batch, B 2 is the numerical value of the time length data of the aging process of the currently calculated dried rice noodle reference batch for the current dried rice noodle production batch, B 3 is the numerical value of the time length data of the drying process of the currently calculated dried rice noodle reference batch for the current dried rice noodle production batch, b 1 is the standard value of the time length data of the pre-drying process for the current dried rice noodle production batch, b 2 is the standard value of the time length data of the aging process for the current dried rice noodle production batch, b 3 is the standard value of the time length data of the drying process for the current dried rice noodle production batch, C 1 is the storage stability detection score value of the currently calculated dried rice noodle reference batch for the current dried rice noodle production batch, C 2 is the elasticity detection score value of the currently calculated dried rice noodle reference batch for the current dried rice noodle production batch, C 3 is the softness and hardness detection score value of the currently calculated dried rice noodle reference batch for the current dried rice noodle production batch, c 1 is the standard value of the storage stability detection score value for the current dried rice noodle production batch, c 2 is the standard value of the elasticity detection score value for the current dried rice noodle production batch, c 3 is the standard value of the softness and hardness detection score value for the current dried rice noodle production batch.

[0109] The beneficial effects of the above technology are as follows: This embodiment details a specific method for obtaining the production analysis matrix of each dried rice noodle reference batch of the current dried rice noodle production batch based on the standard values of all types of production data of all types of core production processes of the current dried rice noodle production batch and the standard values of all types of detection score values.

[0110] Example 6:

[0111] On the basis of Example 1, the instant dried rice noodle intelligent production line, the analysis module, includes:

[0112] An analysis sub-module, which is used to regard all the eigenvalues of the production analysis matrix of each dried rice noodle reference batch of the current dried rice noodle production batch as the analysis values of the corresponding dried rice noodle reference batch of the current dried rice noodle production batch, and obtain all the analysis values of each dried rice noodle reference batch of the current dried rice noodle production batch;

[0113] A construction sub-module, which is used to obtain the production optimization matrix of the current dried rice noodle production batch based on all the analysis values of all the dried rice noodle reference batches of the current dried rice noodle production batch;

[0114] A calculation sub-module, which is used to obtain the setting priority of each dried rice noodle reference batch of the current dried rice noodle production batch based on the production optimization matrix of the current dried rice noodle production batch.

[0115] The beneficial effects of the above technology are as follows: According to the production analysis matrix of all the dried rice noodle reference batches of the current dried rice noodle production batch, the production optimization matrix of the current dried rice noodle production batch is obtained, which is convenient for the subsequent calculation of the setting priority. Furthermore, based on the production optimization matrix of the current dried rice noodle production batch, the setting priority of each dried rice noodle reference batch of the current dried rice noodle production batch is obtained, realizing the quantification of the setting priority of all types of production data of all types of core production processes of each dried rice noodle reference batch of the current dried rice noodle production batch, which is convenient for the subsequent determination of the best setting value.

[0116] Example 7:

[0117] On the basis of Example 6, for the instant dried rice noodle intelligent production line, the construction sub-module includes:

[0118] An analysis unit, which is used to regard the maximum value among all the analysis values of each dried rice noodle reference batch of the current dried rice noodle production batch as the first analysis value of the corresponding dried rice noodle reference batch of the current dried rice noodle production batch, regard the minimum value among all the analysis values of each dried rice noodle reference batch of the current dried rice noodle production batch as the second analysis value of the corresponding dried rice noodle reference batch of the current dried rice noodle production batch, and regard the numerical average of the first analysis value and the second analysis value of each dried rice noodle reference batch of the current dried rice noodle production batch as the third analysis value of the corresponding dried rice noodle reference batch of the current dried rice noodle production batch;

[0119] A construction unit, which is used to define the ordinal numbers of all the dried rice noodle reference batches of the current dried rice noodle production batch in the order from front to back in time sequence starting from 1, obtain the ordinal number definition result of all the dried rice noodle reference batches of the current dried rice noodle production batch, and obtain the production optimization matrix of the current dried rice noodle production batch based on the first analysis value, the second analysis value, the third analysis value and the ordinal number definition result of all the dried rice noodle reference batches of the current dried rice noodle production batch, that is:

[0120]

[0121] Among them, Q is the production optimization matrix of the current batch of dried rice noodles production, and r 1 is the first analysis value of the dried rice noodles reference batch with ordinal number 1 of the current batch of dried rice noodles production, and r 2 is the first analysis value of the dried rice noodles reference batch with ordinal number 2 of the current batch of dried rice noodles production, and r n is the first analysis value of the dried rice noodles reference batch with ordinal number n of the current batch of dried rice noodles production, and y 1 is the second analysis value of the dried rice noodles reference batch with ordinal number 1 of the current batch of dried rice noodles production, and y 2 is the second analysis value of the dried rice noodles reference batch with ordinal number 2 of the current batch of dried rice noodles production, and y n is the second analysis value of the dried rice noodles reference batch with ordinal number n of the current batch of dried rice noodles production, and g 1 is the third analysis value of the dried rice noodles reference batch with ordinal number 1 of the current batch of dried rice noodles production, and g 2 is the third analysis value of the dried rice noodles reference batch with ordinal number 2 of the current batch of dried rice noodles production, and g n is the third analysis value of the dried rice noodles reference batch with ordinal number n of the current batch of dried rice noodles production, and n is the number of all dried rice noodles reference batches of the current batch of dried rice noodles production.

[0122] The beneficial effects of the above technology are as follows: According to all the analysis values of each dried rice noodles reference batch of the current batch of dried rice noodles production, the first analysis value, the second analysis value, and the third analysis value of all the dried rice noodles reference batches of the current batch of dried rice noodles production are obtained, and then the production optimization matrix of the current batch of dried rice noodles production is obtained. This embodiment details a construction method of the production optimization matrix of the current batch of dried rice noodles production.

[0123] Example 8:

[0124] Based on Example 6, the instant dried rice noodles intelligent production line, the calculation sub-module includes:

[0125] A preprocessing unit for taking the rank of the production optimization matrix of the current batch of dried rice noodles production as the standard rank value of the current batch of dried rice noodles production, and taking the spectral radius of the production analysis matrix of each dried rice noodles reference batch of the current batch of dried rice noodles production as the standard calculation value of the corresponding dried rice noodles reference batch of the current batch of dried rice noodles production;

[0126] A calculation unit for obtaining the setting priority of each dried rice noodles reference batch of the current batch of dried rice noodles production based on the standard rank value of the current batch of dried rice noodles production and the standard calculation values of all dried rice noodles reference batches, that is:

[0127]

[0128] where τ is the set priority of the currently calculated dried rice noodle reference batch for the current dried rice noodle production batch, δ is the standard calculated value of the currently calculated dried rice noodle reference batch for the current dried rice noodle production batch, S δ is the sum value of the standard calculated values of all dried rice noodle reference batches for the current dried rice noodle production batch, δ 0 is the mean value of the standard calculated values of all dried rice noodle reference batches for the current dried rice noodle production batch, ε is the standard rank value of the current dried rice noodle production batch, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0129] In this embodiment, the spectral radius of the production analysis matrix of each dried rice noodle reference batch of the current dried rice noodle production batch is the maximum value of the moduli of all eigenvalues of the production analysis matrix of each dried rice noodle reference batch of the current dried rice noodle production batch.

[0130] The beneficial effects of the above technology are as follows: According to the production optimization matrix of the current dried rice noodle production batch and the production analysis matrices of all dried rice noodle reference batches, the standard rank value of the current dried rice noodle production batch and the standard calculated values of all dried rice noodle reference batches are obtained. Furthermore, according to the standard rank value of the current dried rice noodle production batch and the standard calculated values of all dried rice noodle reference batches, the set priority of each dried rice noodle reference batch of the current dried rice noodle production batch is obtained, realizing the quantification of the set priority of all types of production data of all types of core production processes of each dried rice noodle reference batch of the current dried rice noodle production batch, which is convenient for the subsequent determination of the optimal set value.

[0131] Example 9:

[0132] Based on Example 1, the instant dried rice noodle intelligent production line, the control module includes:

[0133] A numerical determination sub-module, which is used to regard the numerical value of each type of production data of each type of core production process of the dried rice noodle reference batch with the maximum set priority among all dried rice noodle reference batches of the current dried rice noodle production batch as the optimal set value of the corresponding type of production data of the corresponding type of core production process of the current dried rice noodle production batch;

[0134] A control sub-module, which is used to adjust the numerical value of each type of production data of each type of core production process of the current dried rice noodle production batch to be the same as the optimal set value of the corresponding type of production data of the corresponding type of core production process of the current dried rice noodle production batch, so as to obtain the optimal production result of the current dried rice noodle production batch.

[0135] The beneficial effects of the above technology are as follows: According to the setting priorities of all dry rice noodle reference batches of the current dry rice noodle production batch, the optimal setting values of all types of production data of all types of core production processes of the current dry rice noodle production batch are obtained. Finally, based on the optimal setting values of all types of production data of all types of core production processes of the current dry rice noodle production batch, the optimal production result of the current dry rice noodle production batch is obtained, realizing the precise regulation of all types of production data of all types of core production processes of the current dry rice noodle production batch, and making the storage stability, elasticity, softness and hardness of the dry rice noodles produced in the current dry rice noodle production batch reach the comprehensive optimal state.

[0136] Example 10:

[0137] The present invention provides an instant dry rice noodle production process, which is applied to any one of the instant dry rice noodle intelligent production lines in Examples 1-9, and includes:

[0138] S1: Based on the key production data of all types of non-core production processes of the current dry rice noodle production batch and AI technology, all dry rice noodle reference batches of the current dry rice noodle production batch are obtained;

[0139] S2: Based on all types of production data and all types of detection assignment scores of all types of core production processes of all dry rice noodle reference batches of the current dry rice noodle production batch, a production analysis matrix of each dry rice noodle reference batch of the current dry rice noodle production batch is obtained;

[0140] S3: Based on the production analysis matrix of all dry rice noodle reference batches of the current dry rice noodle production batch, a production optimization matrix of the current dry rice noodle production batch is obtained, and based on the production optimization matrix of the current dry rice noodle production batch, the setting priority of each dry rice noodle reference batch of the current dry rice noodle production batch is obtained;

[0141] S4: Based on the setting priorities of all dry rice noodle reference batches of the current dry rice noodle production batch, the optimal setting values of all types of production data of all types of core production processes of the current dry rice noodle production batch are obtained, and based on the optimal setting values of all types of production data of all types of core production processes of the current dry rice noodle production batch, the optimal production result of the current dry rice noodle production batch is obtained.

[0142] The beneficial effects of the above technology are as follows: According to all types of production data and all types of detection score values of all types of core production processes of all dry rice noodle reference batches in the current dry rice noodle production batch, a production analysis matrix of each dry rice noodle reference batch in the current dry rice noodle production batch is obtained, realizing the construction of a matrix for analyzing the influence degree of all types of production data of all types of core production processes of each dry rice noodle reference batch in the current dry rice noodle production batch on the storage stability, elasticity, softness and hardness of the produced dry rice noodles. Furthermore, according to the production analysis matrix of all dry rice noodle reference batches in the current dry rice noodle production batch, a production optimization matrix of the current dry rice noodle production batch is obtained, which is convenient for the subsequent calculation of the setting priority. According to the production optimization matrix of the current dry rice noodle production batch, the setting priority of each dry rice noodle reference batch in the current dry rice noodle production batch is obtained, realizing the quantification of all types of production data of all types of core production processes of each dry rice noodle reference batch in the current dry rice noodle production batch at the set priority level. Furthermore, according to the setting priorities of all dry rice noodle reference batches in the current dry rice noodle production batch, the optimal setting values of all types of production data of all types of core production processes of the current dry rice noodle production batch are obtained. Finally, according to the optimal setting values of all types of production data of all types of core production processes of the current dry rice noodle production batch, the best production result of the current dry rice noodle production batch is obtained, realizing the precise regulation of all types of production data of all types of core production processes of the current dry rice noodle production batch, and making the storage stability, elasticity, softness and hardness of the dry rice noodles produced in the current dry rice noodle production batch reach the comprehensive optimal state.

[0143] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention, and the present invention is also intended to include these changes and modifications.

Claims

1. An intelligent production line for instant dry rice noodles, characterized in that: include: An acquisition module is used to obtain all reference batches of dry rice noodles of the current production batch of dry rice noodles based on key production data and AI technology of all non-core production processes of the current production batch of dry rice noodles; A processing module, for obtaining a production analysis matrix of each dry noodle reference batch of the current dry noodle production batch based on all types of production data and all types of detection scores of all types of core production processes of all dry noodle reference batches of the current dry noodle production batch; An analysis module, for obtaining a production optimization matrix of the current dry river noodle production batch based on a production analysis matrix of all dry river noodle reference batches of the current dry river noodle production batch, and obtaining a setting priority of each dry river noodle reference batch of the current dry river noodle production batch based on the production optimization matrix of the current dry river noodle production batch; A control module is used to obtain the optimal setting values ​​of all types of production data of all types of core production processes of the current dry river noodle production batch based on the setting priorities of all dry river noodle reference batches of the current dry river noodle production batch, and to obtain the optimal production result of the current dry river noodle production batch based on the optimal setting values ​​of all types of production data of all types of core production processes of the current dry river noodle production batch.

2. The intelligent production line of instant dry rice noodles according to claim 1 is characterized in that: Get modules, including: The first acquisition submodule is used to obtain key production data of all non-core production processes of the current dry rice noodle production batch, wherein all non-core production processes include pulping process, pulping process and maturation process; The extraction submodule is used to obtain all reference batches of dry rice noodles of the current production batch of dry rice noodles based on the key production data, historical database and AI technology of all non-core production processes of the current production batch of dry rice noodles.

3. The intelligent production line of instant dry rice noodles according to claim 2 is characterized in that: Extract submodules, including: An acquisition unit is used to acquire key production data of all non-core production processes of all historical dry rice noodle production batches in the historical database based on AI technology; An extraction unit is used to use the historical dry rice noodle production batches among all historical dry rice noodle production batches in the historical database, which have the closest production time to the current dry rice noodle production batch and whose key production data of all types of non-core production processes correspond to the same preset batch number as the key production data of all types of non-core production processes of the current dry rice noodle production batch, as the dry rice noodle reference batch for the current dry rice noodle production batch.

4. The intelligent production line of instant dry rice noodles according to claim 1, characterized in that: Processing modules, including: The second acquisition submodule is used to obtain all types of production data of all types of core production processes of each reference batch of dry rice noodles in the current production batch of dry rice noodles, wherein all types of core production processes include pre-drying process, aging process and drying process, and all types of production data include temperature data and time length data; The third acquisition submodule is used to obtain all the test scores of each dry rice noodle reference batch of the current dry rice noodle production batch, wherein all the test scores include storage stability test scores, elasticity test scores, and hardness test scores; The first processing submodule is used to take the mean of the numerical values ​​of each type of production data of each type of core production process of all the dry river noodle reference batches of the current dry river noodle production batch as the standard value of the corresponding type of production data of the corresponding type of core production process of the current dry river noodle production batch, and take the mean of the detection score values ​​of each type of all the dry river noodle reference batches of the current dry river noodle production batch as the standard value of the detection score values ​​of the corresponding type of the current dry river noodle production batch; The second processing submodule is used to obtain the production analysis matrix of each dry river noodle reference batch of the current dry river noodle production batch based on the standard values ​​of all types of production data of all types of core production processes of the current dry river noodle production batch and the standard values ​​of all types of detection scoring values.

5. The intelligent production line of instant dry rice noodles according to claim 4 is characterized in that: The second processing submodule obtains a method of obtaining a production analysis matrix of each dry rice noodle reference batch of the current dry rice noodle production batch based on the standard values ​​of all types of production data of all types of core production processes of the current dry rice noodle production batch and the standard values ​​of all types of detection scoring values, including: Among them, E is the production analysis matrix of the currently calculated dry river noodle reference batch of the current dry river noodle production batch, A1 is the value of the temperature data of the pre-drying process of the current calculated dry river noodle reference batch of the current dry river noodle production batch, A2 is the value of the temperature data of the aging process of the current calculated dry river noodle reference batch of the current dry river noodle production batch, A3 is the value of the temperature data of the drying process of the current calculated dry river noodle reference batch of the current dry river noodle production batch, a1 is the standard value of the temperature data of the pre-drying process of the current dry river noodle production batch, a2 is the standard value of the temperature data of the aging process of the current dry river noodle production batch, a3 is the standard value of the temperature data of the drying process of the current dry river noodle production batch, B1 is the value of the time length data of the pre-drying process of the dry river noodle reference batch of the current calculated dry river noodle production batch, and B2 is the value of the time length data of the aging process of the current calculated dry river noodle reference batch of the current dry river noodle production batch , B3 is the numerical value of the time length data of the drying process of the currently calculated reference batch of dry river noodles for the current production batch of dry river noodles, b1 is the standard value of the time length data of the pre-drying process of the current production batch of dry river noodles, b2 is the standard value of the time length data of the aging process of the current production batch of dry river noodles, b3 is the standard value of the time length data of the drying process of the current production batch of dry river noodles, C1 is the storage stability test score of the currently calculated reference batch of dry river noodles for the current production batch of dry river noodles, C2 is the elasticity test score of the currently calculated reference batch of dry river noodles for the current production batch of dry river noodles, C3 is the softness and hardness test score of the currently calculated reference batch of dry river noodles for the current production batch of dry river noodles, c1 is the standard value of the storage stability test score of the current production batch of dry river noodles, c2 is the standard value of the elasticity test score of the current production batch of dry river noodles, and c3 is the standard value of the softness and hardness test score of the current production batch of dry river noodles.

6. The intelligent production line for instant dry rice noodles according to claim 1, characterized in that: Analysis modules, including: An analysis submodule, for taking all eigenvalues ​​of the production analysis matrix of each dry river noodle reference batch of the current dry river noodle production batch as analysis values ​​of the dry river noodle reference batch corresponding to the current dry river noodle production batch, and obtaining all analysis values ​​of each dry river noodle reference batch of the current dry river noodle production batch; Constructing a submodule for obtaining a production optimization matrix for a current dry noodle production batch based on all analysis values ​​of all dry noodle reference batches for the current dry noodle production batch; The calculation submodule is used to obtain the setting priority of each dry river noodle reference batch of the current dry river noodle production batch based on the production optimization matrix of the current dry river noodle production batch.

7. The intelligent production line for instant dry rice noodles according to claim 6, characterized in that: Build submodules, including: an analysis unit, for taking the maximum value among all analysis values ​​of each dry river noodle reference batch of a current dry river noodle production batch as the first analysis value of the dry river noodle reference batch corresponding to the current dry river noodle production batch, taking the minimum value among all analysis values ​​of each dry river noodle reference batch of the current dry river noodle production batch as the second analysis value of the dry river noodle reference batch corresponding to the current dry river noodle production batch, and taking the numerical mean of the first analysis value and the second analysis value of each dry river noodle reference batch of the current dry river noodle production batch as the third analysis value of the dry river noodle reference batch corresponding to the current dry river noodle production batch; A construction unit is used to define all the dry river noodle reference batches of the current dry river noodle production batch in an ordinal order from front to back in time sequence, starting from 1, to obtain the ordinal definition results of all the dry river noodle reference batches of the current dry river noodle production batch, and based on the first analysis value, the second analysis value, the third analysis value and the ordinal definition results of all the dry river noodle reference batches of the current dry river noodle production batch, to obtain the production optimization matrix of the current dry river noodle production batch, which is: Among them, Q is the production optimization matrix of the current dry rice noodle production batch, r1 is the first analysis value of the dry rice noodle reference batch with the ordinal number 1 of the current dry rice noodle production batch, r2 is the first analysis value of the dry rice noodle reference batch with the ordinal number 2 of the current dry rice noodle production batch, r n is the first analysis value of the dry noodle reference batch with ordinal number n of the current dry noodle production batch, y1 is the second analysis value of the dry noodle reference batch with ordinal number 1 of the current dry noodle production batch, y2 is the second analysis value of the dry noodle reference batch with ordinal number 2 of the current dry noodle production batch, y n is the second analysis value of the dry noodle reference batch with ordinal number n of the current dry noodle production batch, g1 is the third analysis value of the dry noodle reference batch with ordinal number 1 of the current dry noodle production batch, g2 is the third analysis value of the dry noodle reference batch with ordinal number 2 of the current dry noodle production batch, g n is the third analysis value of the dry rice noodle reference batch with ordinal number n of the current dry rice noodle production batch, and n is the number of all dry rice noodle reference batches of the current dry rice noodle production batch.

8. The intelligent production line for instant dry rice noodles according to claim 6, characterized in that: Computing submodule, including: A preprocessing unit is used to regard the rank of the production optimization matrix of the current dry river noodle production batch as the standard rank value of the current dry river noodle production batch, and regard the spectral radius of the production analysis matrix of each dry river noodle reference batch of the current dry river noodle production batch as the standard calculated value of the dry river noodle reference batch corresponding to the current dry river noodle production batch; The calculation unit is used to obtain the setting priority of each dry river noodle reference batch of the current dry river noodle production batch based on the standard rank value of the current dry river noodle production batch and the standard calculation values ​​of all dry river noodle reference batches, that is: Among them, τ is the setting priority of the current calculated dry noodle reference batch of the current dry noodle production batch, δ is the standard calculation value of the current calculated dry noodle reference batch of the current dry noodle production batch, S δ is the sum of the standard calculated values ​​of all the reference batches of dry river noodles of the current production batch of dry river noodles, δ0 is the mean of the standard calculated values ​​of all the reference batches of dry river noodles of the current production batch of dry river noodles, ε is the standard rank value of the current production batch of dry river noodles, ln is the natural logarithm, and the value of the natural constant e is 2.

718.

9. The intelligent production line for instant dry rice noodles according to claim 1, characterized in that: Control module, including: The value determination submodule is used to set the value of each type of production data of each type of core production process of the dry rice noodle reference batch with the highest priority among all the dry rice noodle reference batches of the current dry rice noodle production batch as the optimal setting value of the corresponding type of production data of the corresponding type of core production process of the current dry rice noodle production batch; The control submodule is used to adjust the numerical value of each type of production data of each type of core production process of the current dry river noodle production batch to the same as the optimal setting numerical value of the corresponding type of production data of the corresponding type of core production process of the current dry river noodle production batch, so as to obtain the optimal production result of the current dry river noodle production batch.

10. A production process for instant dry rice noodles, characterized in that: An intelligent production line for instant dry rice noodles as described in any one of claims 1 to 9, comprising: S1: Based on the key production data and AI technology of all non-core production processes of the current dry rice noodle production batch, obtain all the dry rice noodle reference batches of the current dry rice noodle production batch; S2: Based on all types of production data and all types of detection scores of all types of core production processes of all types of dry rice noodle reference batches of the current dry rice noodle production batch, obtain the production analysis matrix of each dry rice noodle reference batch of the current dry rice noodle production batch; S3: based on the production analysis matrix of all dry noodle reference batches of the current dry noodle production batch, obtain the production optimization matrix of the current dry noodle production batch, and based on the production optimization matrix of the current dry noodle production batch, obtain the setting priority of each dry noodle reference batch of the current dry noodle production batch; S4: Based on the setting priorities of all dry rice noodle reference batches of the current dry rice noodle production batch, the optimal setting values ​​of all types of production data of all types of core production processes of the current dry rice noodle production batch are obtained, and based on the optimal setting values ​​of all types of production data of all types of core production processes of the current dry rice noodle production batch, the optimal production result of the current dry rice noodle production batch is obtained.

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