Bean curd production environment control method, device and equipment and storage medium

Abstract

The invention relates to a bean curd production environment control method, device and equipment and a storage medium, and relates to the field of industrial production environment control, and the method comprises the steps: carrying out the air conditioning control optimization according to the predicted influence humidity and influence microorganism concentration of a production area and the air control optimization scale to obtain the optimal production area air conditioning parameters, performing air conditioning control on the production area; and according to the predicted influence humidity of the raw material area and the air control optimization scale, air conditioning control optimization of the raw material area is carried out, optimal raw material area air conditioning parameters are obtained, and air conditioning control is carried out on the raw material area. The technical problems that in an existing method, due to the fact that air conditioning parameters of a raw material area and a production area cannot be flexibly adjusted according to bean curd processing scenes, the air environment control precision is low, and the bean curd production quality is affected can be solved; it can be guaranteed that an air conditioning strategy is highly matched with a processing scene, the precision of air environment control is effectively improved, and the bean curd production quality is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of industrial production environment control, and particularly to a method, device, equipment and storage medium for controlling the production environment of tofu. Background Art

[0002] As a traditional food, the production process of tofu has very high requirements for the environment, especially in terms of humidity, temperature and microbial concentration. In order to ensure the quality of tofu, different air conditioning measures are usually adopted in different areas (such as the raw material area and the production area) of the tofu production workshop to meet different process requirements.

[0003] However, the existing tofu production methods usually rely on preset and static air conditioning parameters and cannot be flexibly adjusted according to the actual processing scenarios, resulting in insufficient accuracy of air environment control and significant technical defects. Summary of the Invention

[0004] Aiming at the technical problem in the existing tofu production methods that the air conditioning parameters of the raw material area and the production area cannot be flexibly adjusted according to the tofu processing scenarios, resulting in low accuracy of air environment control and affecting the production quality of tofu, the present invention provides a method, device, equipment and storage medium for controlling the production environment of tofu to solve this problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In the first aspect, the present invention provides a method for controlling the production environment of tofu, which is applied to a tofu production workshop. The tofu production workshop includes a raw material area and a production area. The raw material area and the production area are connected during material taking and not connected at other times. The method includes: during the production process of the tofu production workshop, when taking materials in the raw material area, obtaining the cumulative material taking time, predicting the humidity impact in the raw material area to obtain the predicted humidity impact in the raw material area, and predicting the humidity impact and microbial impact in the production area to obtain the predicted humidity impact in the production area and the predicted microbial concentration impact in the production area; configuring the air control optimization scale according to the cumulative material taking time; according to the predicted humidity impact in the production area and the predicted microbial concentration impact in the production area, and in accordance with the air control optimization scale, performing air conditioning control optimization in the production area to obtain the optimal air conditioning parameters in the production area, and performing air conditioning control on the production area, wherein optimization is performed using an optimization mechanism for the superior and a mechanism for the far inferior; according to the predicted humidity impact in the raw material area and the air control optimization scale, and in accordance with the air control optimization scale, performing air conditioning control optimization in the raw material area to obtain the optimal air conditioning parameters in the raw material area, and performing air conditioning control on the raw material area.

[0007] Second aspect, the present invention provides a device for controlling the tofu production environment. The device is used to implement a method for controlling the tofu production environment as described in the first aspect. The method is applied to a tofu production workshop, which includes a raw material area and a production area. The raw material area and the production area are connected during material taking and not connected at other times. It includes: an environmental impact prediction module, which is used to obtain the cumulative material taking time during the production process in the tofu production workshop when taking materials in the raw material area, conduct humidity impact prediction for the raw material area to obtain the predicted humidity affecting the raw material area, and conduct humidity impact prediction and microbial impact prediction for the production area to obtain the predicted humidity affecting the production area and the predicted microbial concentration affecting the production area; an air control optimization scale configuration module, which is used to configure the air control optimization scale according to the cumulative material taking time; a production area air conditioning control optimization module, which is used to optimize the air conditioning control of the production area according to the predicted humidity affecting the production area and the predicted microbial concentration affecting the production area according to the air control optimization scale to obtain the optimal air conditioning parameters for the production area and conduct air conditioning control for the production area, where optimization is carried out using an optimization mechanism for the better and a mechanism for the far worse; a raw material area air conditioning control optimization module, which is used to optimize the air conditioning control of the raw material area according to the predicted humidity affecting the raw material area and the air control optimization scale according to the air control optimization scale to obtain the optimal air conditioning parameters for the raw material area and conduct air conditioning control for the raw material area.

[0008] Third aspect, the present invention further provides an electronic device, including:

[0009] At least one processor; a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of the method described in any one of the above first aspects.

[0010] Fourth aspect, a computer-readable storage medium stores a computer program, and the computer program implements the steps of the method described in any one of the above first aspects when executed.

[0011] The beneficial effects of the present invention are as follows: During the production process in the tofu production workshop, when taking materials in the raw material area, the cumulative material-taking time is obtained, the humidity impact prediction of the raw material area is carried out to obtain the predicted humidity affecting the raw material area, and the humidity impact prediction and microorganism impact prediction of the production area are carried out to obtain the predicted humidity affecting the production area and the predicted microorganism concentration affecting the production area; then, according to the cumulative material-taking time, the scale of air control optimization is configured; then, according to the predicted humidity affecting the production area and the predicted microorganism concentration affecting the production area, and in accordance with the scale of air control optimization, the air conditioning control optimization of the production area is carried out to obtain the optimal air conditioning parameters for the production area, and the air conditioning control of the production area is carried out, wherein the optimization is carried out by using the mechanism of approaching the optimal and the mechanism of far inferior; on the other hand, according to the predicted humidity affecting the raw material area and the scale of air control optimization, and in accordance with the scale of air control optimization, the air conditioning control optimization of the raw material area is carried out to obtain the optimal air conditioning parameters for the raw material area, and the air conditioning control of the raw material area is carried out; through the above method, the air conditioning parameters of the raw material area and the production area can be dynamically adjusted according to the tofu processing scenario, ensuring that the air conditioning strategy is highly adapted to the processing scenario, thereby effectively improving the accuracy of air environment control, ensuring the smooth progress of tofu production, and significantly improving the quality of tofu production. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic flowchart of a method for controlling the production environment of tofu provided by the present invention;

[0013] Figure 2 It is a schematic structural diagram of a device for controlling the production environment of tofu provided by the present invention;

[0014] Figure 3 It is a schematic structural diagram of an electronic device provided by the present invention;

[0015] Figure 4 It is a schematic structural diagram of a computer-readable storage medium provided by the present invention.

[0016] In the drawings, the descriptions of the components represented by the respective reference numerals are as follows:

[0017] Environmental impact prediction module 01, air control optimization scale configuration module 02, production area air conditioning control optimization module 03, raw material area air conditioning control optimization module 04, electronic device 500, memory 510, processor 520, first computer program 511, computer-readable storage medium 600, second computer program 611. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0020] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. In order for any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid unnecessary details from obscuring the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present invention.

[0021] Embodiment 1, as Figure 1 shown, the embodiment of the present invention provides a method for controlling the tofu production environment. The method is applied to a tofu production workshop, and the tofu production workshop includes a raw material area and a production area. The raw material area and the production area are connected when taking materials and not connected at other times. The method includes the following steps:

[0022] Specifically, the tofu production environment control method provided by the embodiments of the present invention is applied to a tofu production workshop, and is used to accurately adjust the air environment during the tofu production process according to the tofu processing scenario. The tofu production workshop includes a raw material area and a production area; in the traditional tofu production process, air conditioning plays a crucial role in ensuring product quality. The production environment control of tofu mainly focuses on the raw material area and the production area. Among them, the main task of the raw material area is to store soybeans, and soybeans need to be stored under low humidity conditions to prevent mildew and maintain their nutritional components. Excessive humidity will not only cause mildew of soybeans, but also affect the subsequent tofu production process; the production area is used for tofu production, and a higher humidity is required to maintain the moisture of the tofu and ensure its taste and quality. At the same time, the production area must maintain a low microbial concentration to avoid tofu spoilage. The reproduction of microorganisms not only affects the hygienic quality of tofu, but may also affect the taste and shelf life of tofu. Among them, the raw material area and the production area are usually regarded as two independent areas, which are only connected when taking materials and are not connected at other times. When taking materials, the air in these two areas will exchange, causing fluctuations in air humidity and microbial concentration.

[0023] S100: During the production process of the tofu production workshop, when taking materials in the raw material area, obtain the cumulative material taking time, conduct humidity impact prediction on the raw material area to obtain the predicted humidity affecting the raw material area, and conduct humidity impact prediction and microbial impact prediction on the production area to obtain the predicted humidity affecting the production area and the predicted microbial concentration affecting the production area.

[0024] Further, step S100 of the present invention further includes:

[0025] S110: Pre-build an environmental impact analysis channel, where the environmental impact analysis channel includes a raw material area impact analysis branch and a production area impact analysis branch.

[0026] Further, step S110 of the present invention further includes:

[0027] S111: According to the tofu production monitoring data within the historical time, collect the set of sample cumulative material taking times, and collect the humidity of the raw material area, the humidity of the production area, and the microbial concentration after different cumulative material taking times to obtain the set of sample humidity affecting the raw material area, the set of sample humidity affecting the production area, and the set of sample microbial concentration affecting the production area; S112: Use the set of sample cumulative material taking times as input training data, and use the set of sample humidity affecting the raw material area, the set of sample humidity affecting the production area, and the set of sample microbial concentration affecting the production area as output supervision data to train the raw material area impact analysis branch and the production area impact analysis branch respectively until the training converges; S113: Combine the verified raw material area impact analysis branch and the production area impact analysis branch to obtain the environmental impact analysis channel.

[0028] Specifically, first, query the tofu processing logs in the tofu production workshop to obtain the tofu production monitoring data within a historical time period (such as within the most recent month); then, according to the tofu production monitoring data, collect the sample cumulative material taking time in different scenarios (i.e., the total time elapsed from the start of production to the end of material taking) to obtain a set of sample cumulative material taking times; then collect the humidity in the raw material area, the humidity in the production area, and the microbial concentration after different cumulative material taking times to obtain a set of sample raw material area influencing humidities, a set of sample production area influencing humidities, and a set of sample production area influencing microbial concentrations.

[0029] Next, construct a raw material area influence analysis branch and a production area influence analysis branch based on the BP neural network. The raw material area influence analysis branch is used to analyze the influence of air humidity in the raw material area according to the cumulative material taking time, and the production area influence analysis branch is used to analyze the influence of air humidity and microbial concentration in the production area according to the cumulative material taking time; among them, the raw material area influence analysis branch includes an input layer, multiple hidden layers, and an output layer. The input data of its input layer is the cumulative material taking time, and the output data of the output layer is the raw material area influencing humidity; the production area influence analysis branch includes an input layer, multiple hidden layers, and an output layer. The input data of its input layer is the cumulative material taking time, and the output data of the output layer is the production area influencing humidity and the production area influencing microbial concentration.

[0030] Further, use the sample cumulative material taking time as the input, the sample raw material area influencing humidity as the output, and the set of sample cumulative material taking times and the set of sample raw material area influencing humidities as the training data to perform supervised training on the raw material area influence analysis branch. First, the input data (cumulative material taking time) is transmitted through the input layer and calculated by the hidden layer, and finally the prediction result (predicted humidity value) of the output layer is generated; then, by comparing the difference between the predicted humidity value output by the network and the actual humidity value, calculate the loss function (such as the mean square error, MSE). The loss function measures the error between the predicted value and the actual value; then, use the gradient descent algorithm to calculate the gradient of the loss function with respect to each weight through backpropagation, and adjust the network weights according to these gradients to minimize the loss function. Adjust the weights and biases in the neural network through the gradient values obtained by backpropagation; repeat the iterative training, update the weights each time to reduce the error until the loss function converges to a smaller value, then determine that the raw material area influence analysis branch converges; then collect a sample validation set to perform validation training on the raw material area influence analysis branch until the predetermined validation index is met, and obtain a validated raw material area influence analysis branch.

[0031] On the other hand, taking the cumulative sample material taking time as the input, the humidity affected in the sample production area and the microbial concentration affected in the sample production area as the outputs, and using the set of the cumulative sample material taking time, the set of the humidity affected in the sample production area, and the set of the microbial concentration affected in the sample production area as the training data, the production area impact analysis branch is supervised and trained. Among them, the training method of the production area impact analysis branch is the same as that of the above-mentioned raw material area impact analysis branch. Those skilled in the art can refer to the above training method and will not elaborate here. The production area impact analysis branch that passes the verification is obtained.

[0032] Finally, the verified raw material area impact analysis branch and the production area impact analysis branch are combined, that is, the raw material area impact analysis branch and the production area impact analysis branch are combined into a unified environmental impact analysis channel. By constructing the environmental impact analysis channel based on the BP neural network, the intelligence and scientificity of the cumulative material taking time impact analysis can be improved, providing a basis for the precise control of the air environment in the raw material area and the production area in the future.

[0033] S120: During the production process in the tofu production workshop, when taking materials in the raw material area, monitor the start time of taking materials and the end time of taking materials, and calculate the cumulative material taking time; S130: Input the cumulative material taking time into the raw material area impact analysis branch and the production area impact analysis branch respectively, conduct the humidity impact prediction in the raw material area, as well as the humidity impact prediction and microbial impact prediction in the production area, and obtain the predicted humidity affected in the raw material area, the predicted humidity affected in the production area, and the predicted microbial concentration affected in the production area.

[0034] Specifically, during the production process in the tofu production workshop, first monitor the start time of taking materials through sensors or manual records. This time point is usually the moment when the staff starts to take out soybeans or other raw materials from the raw material area. When the staff finishes taking materials and stops the material taking operation, record the end time of taking materials; then subtract the start time of taking materials from the end time of taking materials, and take the difference between the two as the cumulative material taking time. The cumulative material taking time represents the duration of the air exchange between the raw material area and the production area.

[0035] Next, input the cumulative material taking time into the raw material area impact analysis branch and the production area impact analysis branch of the environmental impact analysis channel respectively, conduct the humidity impact prediction in the raw material area, as well as the humidity impact prediction and microbial impact prediction in the production area, and output the predicted humidity affected in the raw material area, the predicted humidity affected in the production area, and the predicted microbial concentration affected in the production area. By obtaining the predicted humidity affected in the raw material area, the predicted humidity affected in the production area, and the predicted microbial concentration affected in the production area, it provides data support for the subsequent air environment control in the raw material area and the production area.

[0036] S200: Configure the air control optimization scale according to the cumulative material taking time.

[0037] Further, step S200 of the present invention further includes:

[0038] S210: Obtain the average material taking time of the production workshop; S220: Calculate the ratio of the cumulative material taking time to the average material taking time to obtain the material taking duration coefficient; S230: Obtain the preset air control optimization scale, where the preset air control optimization scale includes the preset number of the initial population in the optimization; S240: Multiply the material taking duration coefficient by the preset air control optimization scale and round it to obtain the air control optimization scale for configuration.

[0039] Specifically, first, count the time lengths of multiple material taking operations in the production workshop within a period of time (such as within the past month), and calculate the average value of the multiple time lengths to obtain the average material taking time. Then, calculate the ratio of the cumulative material taking time to the average material taking time, and set the ratio as the material taking duration coefficient. The material taking duration coefficient reflects the difference between the current material taking time and the historical average material taking time.

[0040] Obtain the preset air control optimization scale, where the preset air control optimization scale includes the preset number of the initial population in the optimization, that is, the number of the initial solutions generated in the first round of optimization, which can be set according to the actual optimization accuracy requirements. Further, multiply the material taking duration coefficient by the preset air control optimization scale and round it to obtain the air control optimization scale (i.e., the adjusted number of the initial solutions). Among them, the larger the material taking duration coefficient, the greater the influence of the air environment, and at this time, the optimization scale is larger, which means that more initial populations will be generated, thereby increasing the accuracy and global nature of the optimization algorithm; the smaller the material taking duration coefficient, the smaller the influence of the air environment, then the optimization scale is smaller, and fewer initial populations are generated, thereby reducing the calculation consumption in the optimization process.

[0041] By specifically adjusting the preset air control optimization scale according to the material taking duration coefficient, the optimization process can always match the requirements of the current production environment, reduce the consumption of computing resources while ensuring the optimization accuracy, and thus improve the accuracy, flexibility and efficiency of the air environment control in the tofu production workshop.

[0042] S300: According to the predicted humidity impact in the production area and the predicted microbial concentration in the production area, perform air conditioning control optimization in the production area according to the air control optimization scale to obtain the optimal air conditioning parameters in the production area, and perform air conditioning control on the production area, where the optimization is carried out by using the mechanism of approaching the optimal and the mechanism of far inferior.

[0043] Further, step S300 of the present invention further includes:

[0044] S310: Obtain the production area air conditioning parameter space of the production area, where the production area air conditioning parameter space includes a humidity adjustment parameter range, a disinfection adjustment parameter range, and an adjustment time parameter range; S320: Generate a plurality of first production area air conditioning parameters within the production area air conditioning parameter space according to the air control optimization scale.

[0045] Specifically, obtain the production area air conditioning parameter space of the production area, where the production area air conditioning parameter space includes a humidity adjustment parameter range, a disinfection adjustment parameter range, and an adjustment time parameter range. Among them, the humidity adjustment parameter range refers to the humidity adjustment range of the production area, such as a relative humidity of 60% to 90%; disinfection is an important link to prevent the growth of microorganisms and ensure that the tofu is not contaminated. The disinfection adjustment parameter range refers to the adjustment range of the disinfection intensity, which can be set according to the working range of the disinfection equipment and the disinfection standard; the adjustment time parameter range refers to the effective time length of the adjustment parameters (such as humidity and disinfection), such as 10 minutes to 60 minutes.

[0046] Next, randomly select any one humidity adjustment parameter (such as a relative humidity of 70%), a disinfection adjustment parameter (a disinfection intensity of 50%), and an adjustment time parameter (such as 20 minutes) within the humidity adjustment parameter range, the disinfection adjustment parameter range, and the adjustment time parameter range to combine and obtain the first production area air conditioning parameter, and use the same method to randomly select a preset number of first production area air conditioning parameters from the production area air conditioning parameter space, where the preset number is the number of the initial population in the air control optimization scale.

[0047] S330: Respectively, according to the plurality of first production area air conditioning parameters, combine the predicted production area influence humidity and the predicted production area influence microorganism concentration to perform air conditioning control prediction, and calculate and obtain a plurality of first air fitnesses.

[0048] Furthermore, step S330 of the present invention further includes:

[0049] S331: In the air conditioning data log of the production area, collect the sample production area air conditioning parameter set, the sample original production area humidity set, and the sample original production area microbial concentration set as the production area input training data, and the adjusted sample adjusted production area humidity set and the sample adjusted production area microbial concentration set as the production area output supervision data; S332: Use the production area input training data and the production area output supervision data to train the production area air conditioning prediction channel; S333: Respectively combine the multiple first production area air conditioning parameters with the predicted production area humidity impact and the predicted production area microbial concentration impact, and input them into the production area air conditioning prediction channel to predict and obtain multiple first adjusted production area humidities and multiple first adjusted production area microbial concentrations; S334: According to the multiple first adjusted production area humidities and the multiple first adjusted production area microbial concentrations, calculate the multiple first air fitnesses of the multiple first production area air conditioning parameters respectively, as shown in the following formula: where HTF is the air fitness, w s is the humidity weight, w x is the microbial weight, w s and w x sum to 1, S t is the adjusted production area humidity, S y is the standard production area humidity, X t is the adjusted production area microbial concentration, X y is the standard production area microbial concentration.

[0050] Specifically, first, in the air conditioning data log of the production area, collect the sample production area air conditioning parameter set, the sample original production area humidity set, and the sample original production area microbial concentration set as the production area input training data; and collect the adjusted sample adjusted production area humidity set and the sample adjusted production area microbial concentration set as the production area output supervision data, where the sample production area air conditioning parameters, the sample original production area humidity, the sample original production area microbial concentration, and the sample adjusted production area humidity, the sample adjusted production area microbial concentration have a corresponding relationship.

[0051] Next, a production area air conditioning prediction channel is constructed based on a BP neural network. The production area air conditioning prediction channel is a BP neural network model in machine learning that can be iteratively optimized, including an input layer, a layer, multiple hidden layers, and an output layer. The input data of the input layer are the production area air conditioning parameters, the production area humidity, and the production area microorganism concentration, and the output data of the output layer are the adjusted production area humidity and the adjusted production area microorganism concentration. Then, the production area input training data and the production area output supervision data are used to perform supervised training on the production area air conditioning prediction channel. First, the input data is propagated forward through each layer of the neural network to generate predicted values, that is, the adjusted humidity and microorganism concentration. Then, the mean square error is used to calculate the error between the network predicted value and the true value, and the gradient of the loss function with respect to each parameter (weight and bias) in the neural network is calculated to adjust the weights and biases. The goal of backpropagation is to minimize the loss function. Iterative training is repeated until the loss function of the network converges or reaches a predetermined number of training epochs, and then the training is stopped to obtain the trained production area air conditioning prediction channel.

[0052] Then, the multiple first production area air conditioning parameters are respectively combined with the predicted production area humidity impact and the predicted production area microorganism concentration impact to obtain multiple input data, and the multiple input data are input into the production area air conditioning prediction channel, and the predicted output obtains multiple first adjusted production area humidities and multiple first adjusted production area microorganism concentrations. Further, an air fitness calculation function is constructed: In the air fitness calculation function, HTF is the air fitness. The greater the air fitness, the better the air parameter control effect, that is, the higher the control accuracy and the better the tofu production quality; w s is the humidity weight, w x is the microorganism weight, where the sum of w s and w x is 1. The humidity weight and the microorganism weight can be set according to the influence degree of the index on the tofu production quality. The greater the influence degree of the index, the greater the corresponding weight; S t is the adjusted production area humidity, S y is the standard production area humidity (set according to the production standard), X t is the adjusted production area microorganism concentration, X y is the standard production area microorganism concentration (set according to the production standard). Then, using the air fitness calculation function, according to the multiple first adjusted production area humidities and the multiple first adjusted production area microorganism concentrations, the multiple first air fitnesses of the multiple first production area air conditioning parameters are respectively calculated.

[0053] S340: Screen the air conditioning parameters of the first production areas corresponding to the minimum and maximum first air fitness levels among the multiple first air fitness levels, and use them as the local worst solution and the local optimal solution; S350: Using the parameter adjustment step sizes, adjust the air conditioning parameters of the multiple first production areas in the directions away from the local worst solution and towards the local optimal solution respectively, to obtain multiple air conditioning parameters of the second production areas; S360: Continue the air conditioning control prediction for the multiple air conditioning parameters of the second production areas, calculate to obtain multiple second air fitness levels, and update the local worst solution and the local optimal solution; S370: Continue the iterative optimization until convergence, output the air conditioning parameters of the production area with the maximum air fitness level, obtain the optimal air conditioning parameters of the production area, and perform air conditioning control on the production area.

[0054] Specifically, perform screening according to the multiple first air fitness levels, set the air conditioning parameter of the first production area corresponding to the minimum first air fitness level as the local worst solution, and set the air conditioning parameter of the first production area corresponding to the maximum first air fitness level as the local optimal solution. Then obtain the parameter adjustment step sizes, which include the humidity adjustment step size, the disinfection intensity adjustment step size, and the time adjustment step size, and can be set according to the optimization accuracy requirements. Next, use the humidity adjustment step size, the disinfection intensity adjustment step size, and the time adjustment step size to adjust the air conditioning parameters of the multiple first production areas in the directions away from the local worst solution and towards the local optimal solution respectively, that is, starting from the local worst solution, adjust the parameters in the direction away from the inappropriate configuration, for example, the humidity, disinfection intensity, or time parameters can be adjusted in the increasing or decreasing direction to avoid making the air environment worse; starting from the local optimal solution, adjust the parameters towards the optimal configuration to obtain multiple air conditioning parameters of the second production areas.

[0055] Then, using the air conditioning prediction channel of the production area, continue the air conditioning control prediction for the multiple air conditioning parameters of the second production areas, calculate multiple second air fitness levels through the air fitness calculation function, and then set the air conditioning parameter of the second production area corresponding to the maximum second air fitness level among the multiple second air fitness levels as the updated local optimal solution, and set the air conditioning parameter of the second production area corresponding to the minimum second air fitness level as the updated local worst solution. Further, continue the iterative optimization using the same method until the predetermined number of optimization iterations (such as 1000 times) is reached, then the optimization converges. At this time, output the air conditioning parameter of the production area with the maximum air fitness level as the optimal air conditioning parameter of the production area, and perform air conditioning control on the production area according to the optimal air conditioning parameter of the production area.

[0056] Through the screening of local optimal solutions and local worst solutions, the calculation of air fitness, and iterative optimization, the optimal air conditioning parameters for the production area are finally obtained through optimization to control the air conditioning of the production area. The refined control of the air conditioning parameters in the production area can be realized, ensuring that the accuracy of the air environment control in the production area reaches the best level, thereby significantly improving the quality of tofu production.

[0057] S400: According to the predicted humidity and air control optimization scale in the raw material area, and in accordance with the air control optimization scale, perform air conditioning control optimization for the raw material area to obtain the optimal air conditioning parameters for the raw material area, and perform air conditioning control for the raw material area.

[0058] Further, step S400 of the present invention further includes:

[0059] S410: Obtain the air conditioning parameter space of the raw material area, wherein the air conditioning parameter space of the raw material area includes a humidity adjustment parameter range and an adjustment time parameter range; S420: Generate a plurality of first air conditioning parameters for the raw material area within the air conditioning parameter space of the raw material area in accordance with the air control optimization scale; S430: Respectively, according to the plurality of first air conditioning parameters for the raw material area, combine the predicted humidity in the raw material area, perform air conditioning control prediction, obtain a plurality of first adjusted raw material area humidities, and calculate the deviation between the plurality of first adjusted raw material area humidities and the standard raw material area humidity to obtain a plurality of first raw material area humidity deviations; S440: According to the plurality of first raw material area humidity deviations, screen the first air conditioning parameters corresponding to the largest and smallest first raw material area humidity deviations as the local worst solution and the local optimal solution; S450: Use the parameter adjustment step size to adjust the plurality of first air conditioning parameters for the raw material area in the directions away from the local worst solution and towards the local optimal solution respectively to obtain a plurality of second air conditioning parameters for the raw material area; S460: Continue to perform air conditioning control prediction for the plurality of second air conditioning parameters for the raw material area, obtain a plurality of second raw material area humidity deviations, and update the local worst solution and the local optimal solution; S470: Continue to perform iterative optimization until convergence, output the air conditioning parameters for the raw material area with the smallest raw material area humidity deviation, obtain the optimal air conditioning parameters for the raw material area, and perform air conditioning control for the raw material area.

[0060] Specifically, first, obtain the air conditioning parameter space of the raw material area. Among them, the air conditioning parameter space of the raw material area includes a humidity adjustment parameter interval and an adjustment time parameter interval. The humidity adjustment parameter interval is set according to the preservation requirements of the raw material area. For example, the humidity is between 40% and 60%. Then, randomly select any one parameter from the humidity adjustment parameter interval and the adjustment time parameter interval for combination to obtain the first air conditioning parameter of the raw material area. Use the same method for selection until the first air conditioning parameters of the raw material area with the number of the initial population in the air control optimization scale are obtained, and multiple first air conditioning parameters of the raw material area are obtained.

[0061] Further, construct a raw material area air conditioning prediction channel based on the BP neural network. The raw material area air conditioning prediction channel includes an input layer, multiple hidden layers, and an output layer. The input data of the input layer is the air conditioning parameter of the raw material area and the humidity affecting the raw material area, and the output data of the output layer is the adjusted humidity of the raw material area (i.e., the predicted humidity of the raw material area adjusted by the air conditioning parameter of the raw material area). Further collect sample training data to supervise and train the raw material area air conditioning prediction channel to obtain a raw material area air conditioning prediction channel that meets the expected convergence constraint. The training method of the raw material area air conditioning prediction channel is the same as that of the above production area air conditioning prediction channel and will not be elaborated here; further use the raw material area air conditioning prediction channel, respectively according to the multiple first air conditioning parameters of the raw material area, combined with the predicted humidity affecting the raw material area, to perform air conditioning control prediction and obtain multiple first adjusted humidity values of the raw material area. Then, calculate the deviation between the multiple first adjusted humidity values of the raw material area and the standard humidity of the raw material area (set according to the raw material storage standard) to obtain multiple humidity deviations of the raw material area. Among them, the humidity deviation of the raw material area is the absolute value of the difference between the first adjusted humidity of the raw material area and the standard humidity of the raw material area.

[0062] Then, screen according to the multiple humidity deviations of the raw material area, set the first air conditioning parameter of the raw material area corresponding to the largest humidity deviation of the raw material area as the local worst solution, and set the first air conditioning parameter of the raw material area corresponding to the smallest humidity deviation of the raw material area as the local optimal solution; further adopt parameter adjustment step sizes (including humidity adjustment step size and time adjustment step size), and adjust the multiple first air conditioning parameters of the raw material area in the directions of moving away from the local worst solution and approaching the local optimal solution respectively to obtain multiple second air conditioning parameters of the raw material area.

[0063] Further utilize the air conditioning prediction channel of the raw material area to perform air conditioning control prediction on the multiple second raw material area air conditioning parameters, obtain multiple second raw material area humidity deviations, set the second raw material area air conditioning parameter corresponding to the largest second raw material area humidity deviation as the updated local worst solution, and set the second raw material area air conditioning parameter corresponding to the smallest second raw material area humidity deviation as the updated local optimal solution. Continue to perform iterative optimization using the same method until the predetermined number of optimization iterations (such as 1000 optimization iterations) is reached, at which point the optimization converges. At this time, output the raw material area air conditioning parameter with the smallest raw material area humidity deviation as the optimal raw material area air conditioning parameter; finally, perform air conditioning control on the raw material area according to the optimal raw material area air conditioning parameter.

[0064] The tofu production environment control method provided by the embodiment of the present invention has at least the following technical effects:

[0065] During the production process in the tofu production workshop, when taking materials in the raw material area, obtain the cumulative material taking time, perform humidity impact prediction on the raw material area to obtain the predicted raw material area impact humidity, and perform humidity impact prediction and microorganism impact prediction on the production area to obtain the predicted production area impact humidity and the predicted production area impact microorganism concentration; then, configure the air control optimization scale according to the cumulative material taking time; then, according to the predicted production area impact humidity and the predicted production area impact microorganism concentration, perform air conditioning control optimization on the production area according to the air control optimization scale to obtain the optimal production area air conditioning parameter, and perform air conditioning control on the production area, where an optimization mechanism of approaching the optimal and a mechanism of far from the worst are used for optimization; on the other hand, according to the predicted raw material area impact humidity and the air control optimization scale, perform air conditioning control optimization on the raw material area according to the air control optimization scale to obtain the optimal raw material area air conditioning parameter, and perform air conditioning control on the raw material area; through the above method, the air conditioning parameters of the raw material area and the production area can be dynamically adjusted according to the tofu processing scenario, ensuring that the air conditioning strategy is highly adapted to the processing scenario, thereby effectively improving the accuracy of air environment control, ensuring the smooth progress of tofu production, and significantly improving the quality of tofu production.

[0066] Embodiment 2, as Figure 2As shown, based on the same inventive concept as the tofu production environment control method provided in Embodiment 1, an embodiment of the present invention further provides a tofu production environment control device. The device is used to implement a tofu production environment control method as described in the first aspect. The method is applied to a tofu production workshop, which includes a raw material area and a production area. The raw material area and the production area are connected during material taking and not connected at other times. It includes: an environmental impact prediction module 01, which is used to obtain the cumulative material taking time during the production process of the tofu production workshop when taking materials in the raw material area, conduct humidity impact prediction on the raw material area to obtain the predicted humidity impact on the raw material area, and conduct humidity impact prediction and microbial impact prediction on the production area to obtain the predicted humidity impact on the production area and the predicted microbial concentration in the production area; an air control optimization scale configuration module 02, which is used to configure the air control optimization scale according to the cumulative material taking time; a production area air conditioning control optimization module 03, which is used to conduct air conditioning control optimization on the production area according to the predicted humidity impact on the production area and the predicted microbial concentration in the production area, in accordance with the air control optimization scale, to obtain the optimal air conditioning parameters for the production area, and conduct air conditioning control on the production area, where optimization is carried out using an optimization mechanism and a far-inferior mechanism; a raw material area air conditioning control optimization module 04, which is used to conduct air conditioning control optimization on the raw material area according to the predicted humidity impact on the raw material area and the air control optimization scale, in accordance with the air control optimization scale, to obtain the optimal air conditioning parameters for the raw material area, and conduct air conditioning control on the raw material area.

[0067] Furthermore, the tofu production environment control device further includes: a pre-constructed environmental impact analysis channel. Among them, the environmental impact analysis channel includes a raw material area impact analysis branch and a production area impact analysis branch; during the production process of the tofu production workshop, when taking materials in the raw material area, the start time and end time of material taking are monitored, and the cumulative material taking time is calculated; the cumulative material taking time is respectively input into the raw material area impact analysis branch and the production area impact analysis branch to conduct humidity impact prediction on the raw material area, as well as humidity impact prediction and microbial impact prediction on the production area, to obtain the predicted humidity impact on the raw material area, the predicted humidity impact on the production area, and the predicted microbial concentration in the production area.

[0068] Further, the tofu production environment control device further includes: collecting a set of sample cumulative material taking times based on the tofu production monitoring data within a historical time, and collecting the humidity in the raw material area, the humidity in the production area, and the microbial concentration after different cumulative material taking times, to obtain a set of sample raw material area influencing humidities, a set of sample production area influencing humidities, and a set of sample production area influencing microbial concentrations; using the set of sample cumulative material taking times as input training data, and using the set of sample raw material area influencing humidities, the set of sample production area influencing humidities, and the set of sample production area influencing microbial concentrations as output supervision data, respectively training the raw material area influence analysis branch and the production area influence analysis branch until the training converges; combining the verified raw material area influence analysis branch and the production area influence analysis branch to obtain an environmental influence analysis channel.

[0069] Further, the tofu production environment control device further includes: obtaining the average material taking time of the production workshop; calculating the ratio of the cumulative material taking time to the average material taking time to obtain a material taking duration coefficient; obtaining a preset air control optimization scale, where the preset air control optimization scale includes a preset number of the initial population in the optimization; multiplying the material taking duration coefficient by the preset air control optimization scale and rounding up to obtain an air control optimization scale for configuration.

[0070] Further, the tofu production environment control device further includes: obtaining a production area air conditioning parameter space of the production area, where the production area air conditioning parameter space includes a humidity adjustment parameter range, a disinfection adjustment parameter range, and an adjustment time parameter range; generating a plurality of first production area air conditioning parameters within the production area air conditioning parameter space according to the air control optimization scale; respectively performing air conditioning control prediction according to the plurality of first production area air conditioning parameters, in combination with the predicted production area influencing humidity and the predicted production area influencing microbial concentration, and calculating to obtain a plurality of first air fitnesses; screening the first production area air conditioning parameters corresponding to the minimum and maximum first air fitnesses as the local worst solution and the local optimal solution according to the plurality of first air fitnesses; using a parameter adjustment step size, respectively adjusting the plurality of first production area air conditioning parameters in the directions away from the local worst solution and towards the local optimal solution to obtain a plurality of second production area air conditioning parameters; continuing to perform air conditioning control prediction on the plurality of second production area air conditioning parameters, calculating to obtain a plurality of second air fitnesses, and updating the local worst solution and the local optimal solution; continuing to perform iterative optimization until convergence, outputting the production area air conditioning parameter with the maximum air fitness to obtain the optimal production area air conditioning parameter, and performing air conditioning control on the production area.

[0071] Further, the tofu production environment control device further includes: in the air conditioning data log of the production area, collecting a set of air conditioning parameters for the sample production area, a set of original humidity in the sample production area, and a set of original microbial concentrations in the sample production area as the input training data for the production area, and a set of adjusted humidity in the sample adjusted production area and a set of microbial concentrations in the sample adjusted production area as the output supervision data for the production area; using the input training data for the production area and the output supervision data for the production area to train the air conditioning prediction channel for the production area; respectively combining the multiple first air conditioning parameters in the production area with the predicted humidity in the production area and the predicted microbial concentration in the production area, and inputting them into the air conditioning prediction channel for the production area to predict and obtain multiple first adjusted humidity in the production area and multiple first microbial concentrations in the production area; according to the multiple first adjusted humidity in the production area and the multiple first microbial concentrations in the production area, respectively calculating multiple first air fitness degrees of the multiple first air conditioning parameters in the production area, as shown in the following formula: where HTF is the air fitness degree, w s is the humidity weight, w x is the microbial weight, w s and w x sum to 1, S t is the adjusted humidity in the production area, S y is the standard humidity in the production area, X t is the microbial concentration in the adjusted production area, X y is the standard microbial concentration in the production area.

[0072] Further, the tofu production environment control device further includes: obtaining the raw material area air conditioning parameter space of the raw material area, where the raw material area air conditioning parameter space includes a humidity adjustment parameter range and an adjustment time parameter range; generating a plurality of first raw material area air conditioning parameters within the raw material area air conditioning parameter space according to the air control optimization scale; respectively, according to the plurality of first raw material area air conditioning parameters, combining the predicted humidity impact on the raw material area, performing air conditioning control prediction, obtaining a plurality of first adjusted raw material area humidities, calculating the deviation between the plurality of first adjusted raw material area humidities and the standard raw material area humidity, and obtaining a plurality of first raw material area humidity deviations; according to the plurality of first raw material area humidity deviations, screening the first raw material area air conditioning parameters corresponding to the largest and smallest first raw material area humidity deviations as the local worst solution and the local optimal solution; using a parameter adjustment step size, respectively adjusting the plurality of first raw material area air conditioning parameters in the direction of moving away from the local worst solution and approaching the local optimal solution to obtain a plurality of second raw material area air conditioning parameters; continuing to perform air conditioning control prediction on the plurality of second raw material area air conditioning parameters to obtain a plurality of second raw material area humidity deviations, and updating the local worst solution and the local optimal solution; continuing to perform iterative optimization until convergence, outputting the raw material area air conditioning parameter with the smallest raw material area humidity deviation, obtaining the optimal raw material area air conditioning parameter, and performing air conditioning control on the raw material area.

[0073] Embodiment 3, please refer to Figure 3 , Figure 3 which is a schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. As Figure 3 shown, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented: during the production process in the tofu production workshop, when taking materials in the raw material area, obtaining the cumulative material taking time, performing humidity impact prediction on the raw material area to obtain the predicted humidity impact on the raw material area, and performing humidity impact prediction and microorganism impact prediction on the production area to obtain the predicted humidity impact on the production area and the predicted microorganism concentration in the production area; configuring the air control optimization scale according to the cumulative material taking time; according to the predicted humidity impact on the production area and the predicted microorganism concentration in the production area, performing air conditioning control optimization on the production area according to the air control optimization scale to obtain the optimal production area air conditioning parameter, and performing air conditioning control on the production area, where an optimization mechanism of approaching the optimal and a mechanism of moving away from the worst are used for optimization; according to the predicted humidity impact on the raw material area and the air control optimization scale, performing air conditioning control optimization on the raw material area according to the air control optimization scale to obtain the optimal raw material area air conditioning parameter, and performing air conditioning control on the raw material area.

[0074] Example 4. Please refer to Figure 4 , Figure 4 which is a schematic diagram of an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. As Figure 4 shown, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by a processor, the following steps are implemented: during the production process in the tofu production workshop, when taking materials in the raw material area, obtain the cumulative material-taking time, perform humidity impact prediction on the raw material area to obtain the predicted humidity impact on the raw material area, and perform humidity impact prediction and microorganism impact prediction on the production area to obtain the predicted humidity impact on the production area and the predicted microorganism concentration in the production area; configure the air control optimization scale according to the cumulative material-taking time; according to the predicted humidity impact on the production area and the predicted microorganism concentration in the production area, and in accordance with the air control optimization scale, perform air conditioning control optimization on the production area to obtain the optimal air conditioning parameters for the production area, and perform air conditioning control on the production area, where optimization is carried out using an optimization mechanism for the better and a mechanism for the far worse; according to the predicted humidity impact on the raw material area and the air control optimization scale, and in accordance with the air control optimization scale, perform air conditioning control optimization on the raw material area to obtain the optimal air conditioning parameters for the raw material area, and perform air conditioning control on the raw material area.

[0075] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0076] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a device, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0077] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (apparatuses), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded computers, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate for implementing the processes in Figure 1one or more processes and / or blocks Figure 1 means for the functions specified in one or more blocks

[0078] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions in the process Figure 1 one or more processes and / or blocks Figure 1 specified in one or more blocks

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the process Figure 1 one or more processes and / or blocks Figure 1 specified in one or more blocks

[0080] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept

[0081] 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. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these modifications and variations

Claims

1. A method for controlling the tofu production environment, characterized in that: The method is applied to a tofu production workshop, which includes a raw material area and a production area, wherein the raw material area and the production area are connected when taking materials and are not connected at other times. The method includes: During the production process of the tofu production workshop, when taking materials from the raw material area, the accumulated material taking time is obtained, the humidity impact prediction of the raw material area is performed to obtain the predicted humidity impact of the raw material area, and the humidity impact prediction and microbial impact prediction of the production area are performed to obtain the predicted humidity impact of the production area and the predicted microbial concentration of the production area; According to the accumulated material taking time, the air control optimization scale is configured; According to the predicted humidity and the predicted microorganism concentration in the production area, and in accordance with the air control optimization scale, the air conditioning control of the production area is optimized to obtain the optimal air conditioning parameters of the production area, and the air conditioning control of the production area is performed, wherein the optimization is performed using a trending mechanism and a far-from-inferior mechanism; Based on the predicted impact of the raw material area on humidity and air control optimization scale, the air conditioning control of the raw material area is optimized according to the air control optimization scale to obtain the optimal raw material area air conditioning parameters and perform air conditioning control on the raw material area.

2. A tofu production environment control method according to claim 1, characterized in that: During the production process of the tofu production workshop, when taking materials in the raw material area, the accumulated material taking time is obtained, the humidity impact prediction of the raw material area is performed to obtain the predicted humidity impact of the raw material area, and the humidity impact prediction and microbial impact prediction of the production area are performed to obtain the predicted humidity impact of the production area and the predicted microbial concentration of the production area, including: Pre-constructing an environmental impact analysis channel, wherein the environmental impact analysis channel includes a raw material area impact analysis branch and a production area impact analysis branch; During the production process of the tofu production workshop, when taking materials from the raw material area, the time when the material taking starts and the time when the material taking ends are monitored, and the cumulative material taking time is calculated; The accumulated material collection time is respectively input into the raw material area impact analysis branch and the production area impact analysis branch, and the humidity impact prediction of the raw material area, the humidity impact prediction of the production area and the microbial impact prediction are performed to obtain the predicted humidity of the raw material area, the predicted humidity of the production area and the predicted microbial concentration of the production area.

3. A tofu production environment control method according to claim 2, characterized in that: Pre-built Environmental Impact Analysis pipeline, including: According to the tofu production monitoring data in the historical time, the cumulative material collection time set of the samples was collected, and the humidity of the raw material area, the humidity of the production area and the microbial concentration after different cumulative material collection times were collected to obtain the sample raw material area affecting humidity set, the sample production area affecting humidity set and the sample production area affecting microbial concentration set; Using the sample cumulative material collection time set as input training data, using the sample raw material area influence humidity set, the sample production area influence humidity set and the sample production area influence microorganism concentration set as output supervision data, respectively training the raw material area influence analysis branch and the production area influence analysis branch until the training converges; The raw material area impact analysis branch and the production area impact analysis branch that have passed the combination verification are combined to obtain the environmental impact analysis channel.

4. A tofu production environment control method according to claim 1, characterized in that: According to the cumulative material collection time, the air control optimization scale is configured, including: Obtaining the average material collection time of the production workshop; Calculate the ratio of the cumulative material collection time to the average material collection time to obtain the material collection time coefficient; Obtaining a preset air control optimization scale, wherein the preset air control optimization scale includes a preset number of initial populations in the optimization; The air control optimization scale is obtained by multiplying the material fetching time coefficient by the preset air control optimization scale and rounding the result to the integer, and then configuring the result.

5. A tofu production environment control method according to claim 4, characterized in that: According to the predicted humidity and the predicted microorganism concentration in the production area, the air conditioning control optimization of the production area is performed according to the air control optimization scale to obtain the optimal air conditioning parameters of the production area, and the air conditioning control of the production area is performed, including: Acquire the production area air conditioning parameter space of the production area, wherein the production area air conditioning parameter space includes a humidity adjustment parameter interval, a disinfection adjustment parameter interval and an adjustment time parameter interval; Generating a plurality of first production area air conditioning parameters in the production area air conditioning parameter space according to the air control optimization scale; According to the plurality of first production area air conditioning parameters respectively, combined with the predicted production area impact humidity and the predicted production area impact microorganism concentration, air conditioning control prediction is performed, and a plurality of first air fitness is calculated; According to the multiple first air fitnesses, the first production area air conditioning parameters corresponding to the smallest and largest first air fitnesses are selected as the local worst solution and the local best solution; Adopting a parameter adjustment step, adjusting the plurality of air conditioning parameters of the first production area in directions away from the local worst solution and close to the local optimal solution, respectively, to obtain a plurality of air conditioning parameters of the second production area; Continue to perform air conditioning control prediction of the air conditioning parameters of the plurality of second production areas, calculate and obtain a plurality of second air fitnesses, and update the local worst solution and the local best solution; Continue to perform iterative optimization until convergence, output the air conditioning parameters of the production area with the maximum air fitness, obtain the optimal air conditioning parameters of the production area, and perform air conditioning control on the production area.

6. A tofu production environment control method according to claim 5, characterized in that: According to the plurality of first production area air conditioning parameters respectively, combined with the predicted production area impact humidity and the predicted production area impact microbial concentration, air conditioning control prediction is performed, and a plurality of first air fitness is calculated, including: In the air conditioning data log of the production area, the sample production area air conditioning parameter set, the sample original production area humidity set and the sample original production area microbial concentration set are collected as the production area input training data, and the adjusted sample adjusted production area humidity set and the sample adjusted production area microbial concentration set are collected as the production area output supervision data; Using the production area input training data and the production area output supervision data, training the production area air conditioning prediction channel; The plurality of first production area air conditioning parameters are respectively combined with the predicted production area affecting humidity and the predicted production area affecting microorganism concentration, and input into the production area air conditioning prediction channel, and the prediction output obtains a plurality of first adjusted production area humidity and a plurality of first adjusted production area microorganism concentrations; According to the plurality of first regulated production zone humidities and the plurality of first regulated production zone microbial concentrations, a plurality of first air fitness of the plurality of first production zone air conditioning parameters are respectively calculated and obtained, as follows: Among them, HTF is air fitness, w s is the humidity weight, w x is the microbial weight, w s and w x The sum of is 1, S t To adjust the humidity in the production area, S y is the standard production area humidity, X t To adjust the concentration of microorganisms in the production area, X y is the microbial concentration in the standard production area.

7. A tofu production environment control method according to claim 4, characterized in that: According to the predicted raw material area humidity and air control optimization scale, the air conditioning control optimization of the raw material area is performed according to the air control optimization scale to obtain the optimal raw material area air conditioning parameters, and the air conditioning control of the raw material area is performed, including: Acquire a raw material area air conditioning parameter space of the raw material area, wherein the raw material area air conditioning parameter space includes a humidity adjustment parameter interval and an adjustment time parameter interval; Generating a plurality of first raw material area air conditioning parameters in the raw material area air conditioning parameter space according to the air control optimization scale; According to the plurality of first raw material area air conditioning parameters respectively and in combination with the predicted raw material area affected humidity, air conditioning control prediction is performed to obtain a plurality of first regulated raw material area humidities, and deviations between the plurality of first regulated raw material area humidities and the standard raw material area humidity are calculated to obtain a plurality of first raw material area humidity deviations; According to the plurality of first raw material area humidity deviations, selecting first raw material area air conditioning parameters corresponding to the largest and smallest first raw material area humidity deviations as a local worst solution and a local optimal solution; Using a parameter adjustment step, adjusting the plurality of air conditioning parameters of the first raw material area in directions away from the local worst solution and close to the local optimal solution, respectively, to obtain a plurality of air conditioning parameters of the second raw material area; Continue to perform air conditioning control prediction of the air conditioning parameters of the plurality of second raw material areas, obtain humidity deviations of the plurality of second raw material areas, and update the local worst solution and the local best solution; Continue to perform iterative optimization until convergence, output the raw material area air conditioning parameters with the smallest humidity deviation in the raw material area, obtain the optimal raw material area air conditioning parameters, and perform air conditioning control on the raw material area.

8. A tofu production environment control device, characterized in that: The method for executing the method of controlling the tofu production environment according to any one of claims 1 to 7 is applied to a tofu production workshop, wherein the tofu production workshop includes a raw material area and a production area, wherein the raw material area and the production area are connected when taking materials and are not connected at other times, including: The environmental impact prediction module is used to obtain the cumulative material taking time when taking materials in the raw material area during the production process of the tofu production workshop, and to predict the humidity impact of the raw material area to obtain the predicted humidity impact of the raw material area, and to predict the humidity impact and microbial impact of the production area to obtain the predicted humidity impact of the production area and the predicted microbial concentration of the production area; An air control optimization scale configuration module is used to configure the air control optimization scale according to the accumulated material taking time; The production area air conditioning control optimization module is used to optimize the air conditioning control of the production area according to the predicted humidity and the predicted microbial concentration in the production area and the air control optimization scale, obtain the optimal air conditioning parameters of the production area, and perform air conditioning control on the production area, wherein the optimization is performed using a trending mechanism and a far-from-inferior mechanism; The raw material area air conditioning control optimization module is used to optimize the air conditioning control of the raw material area according to the predicted raw material area humidity and air control optimization scale, obtain the optimal raw material area air conditioning parameters, and perform air conditioning control on the raw material area.

9. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the steps of a tofu production environment control method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores a computer software program, and when the computer software program is executed by the processor, the steps of the tofu production environment control method as described in any one of claims 1 to 7 are implemented.

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