Edible mushroom cultivation environment uniformity optimization system based on airflow field reconstruction and control method

Abstract

The invention discloses an edible fungus cultivation environment uniformity optimization system based on airflow field reconstruction and a control method, and relates to the field of facility agriculture environment control, and the method comprises the steps: building a high-precision computational fluid mechanics flow field model based on the geometric dimension of a shelter, shelf layout parameters and fungus bag porosity; initializing a carbon dioxide concentration target value, a maximum allowable air age and a wind speed uniformity constraint condition, and configuring a two-parameter optimization weight; inputting the collected real-time environment data into a high-precision computational fluid mechanics flow field model, and outputting a three-dimensional wind speed cloud picture, a carbon dioxide concentration distribution cloud picture and an air age distribution cloud picture; and comparing the three-dimensional wind speed cloud picture, the carbon dioxide concentration distribution cloud picture and the air age distribution cloud picture with the initialized carbon dioxide concentration target value, the maximum allowable air age and the wind speed uniformity constraint condition. The method solves the problem that a traditional single-variable control model is difficult to coordinate multi-parameter conflicts.

Description

Technical Field

[0001] This invention relates to the field of environmental control in facility agriculture, and in particular to a system and control method for optimizing the uniformity of edible fungi cultivation environment based on airflow field reconstruction. Background Technology

[0002] In the field of environmental control for facility agriculture, the optimization of airflow organization in shelf-type edible mushroom cultivation cabins has always been a key focus of technical research. Traditional environmental control systems mainly adopt ventilation designs based on fixed ducts or unidirectional jets, and their structural design is usually based on steady-state simulation results from computational fluid dynamics (CFD). PID control algorithms are used to adjust fan speed, which can maintain basic temperature, humidity, and C... Concentration uniformity. Existing technologies can achieve wind speed control accuracy at the level of 0.3 m / s, and energy utilization can be improved through heat recovery devices.

[0003] However, existing technologies still have room for improvement in achieving multi-objective collaborative optimization of environmental parameters, especially when the system simultaneously addresses C. When there is a dual need to regulate both concentration distribution and air age, traditional univariate control models struggle to effectively reconcile the conflict between the two. For example, increasing ventilation can reduce C... Concentration can shorten air age, while reducing wind speed can prolong air age but may lead to localized C. The data is accumulated, but the differences in gas exchange requirements at different growth stages of edible fungi are not fully considered. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for controlling the uniformity of the edible fungi cultivation environment based on airflow field reconstruction, which solves the problem of C The dynamic balance regulation of concentration and air age at different growth stages of edible fungi.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction, which includes: constructing a high-precision computational fluid dynamics flow field model based on the geometric dimensions of the container, shelf layout parameters and porosity of the mushroom bags; initializing the target value of carbon dioxide concentration, the maximum allowable air age and wind speed uniformity constraints; and configuring dual-parameter optimization weights. The collected real-time environmental data is input into a high-precision computational fluid dynamics flow field model, and outputs three-dimensional wind speed cloud map, carbon dioxide concentration distribution cloud map, and air age distribution cloud map. The three-dimensional wind speed cloud map, the carbon dioxide concentration distribution cloud map and the air age distribution cloud map are compared with the initialized carbon dioxide concentration target value, the maximum allowed air age and the wind speed uniformity constraint condition, a non-dominated sorting genetic algorithm is used for multi-objective optimization decision, and the target air volume of the total heat exchanger, the rotation speed of each micro jet fan and the deflection angle of the guide vane are output. The target air volume of the total heat exchanger, the rotation speed of each micro jet fan and the deflection angle of the guide vane are sent to the total heat exchanger, the micro jet fan array and the piezoelectric ceramic driver, the total heat exchanger operates according to the target air volume, the micro jet fan array adjusts the rotation speed of each fan according to the pressure loss principle, and the piezoelectric ceramic driver drives the guide vane to deflect to the calculated angle. When the deployed temperature and humidity sensor, carbon dioxide concentration sensor and wind speed sensor are abnormal, switch to the open-loop control mode predicted by the high-precision computational fluid dynamics flow field model, monitor the wind speed uniformity standard deviation, and start the high-precision computational fluid dynamics flow field model recalibration process based on the monitoring result.

[0007] As a preferred scheme of the edible mushroom cultivation environment uniformity control method based on airflow field reconstruction, the high-precision computational fluid dynamics flow field model is constructed based on the geometric size, shelf layout parameters and porosity of the shelter, the carbon dioxide concentration target value, the maximum allowed air age and the wind speed uniformity constraint condition are initialized, and the double-parameter optimization weight is configured, including the following steps: By inputting the geometric size, shelf layout parameters and porosity of the cultivation shelter, the cultivation space is discretized by using an unstructured grid, a high-precision computational fluid dynamics flow field model is constructed, a carbon dioxide concentration target value is initialized, a maximum allowed air age is initialized, a wind speed uniformity constraint condition is initialized, a double-parameter optimization weight is configured, and the weight proportion of air age and carbon dioxide concentration deviation is set according to the cultivation stage.

[0008] As a preferred scheme of the edible mushroom cultivation environment uniformity control method based on airflow field reconstruction, the real-time environmental data collected are input into the high-precision computational fluid dynamics flow field model, and three-dimensional wind speed cloud maps, carbon dioxide concentration distribution cloud maps and air age distribution cloud maps are output, including the following steps: The temperature and humidity data collected by the temperature and humidity sensor, the carbon dioxide concentration data collected by the carbon dioxide concentration sensor, the wind speed data collected by the wind speed sensor and the air age data collected by the sulfur hexafluoride tracer gas detection device are input into the high-precision computational fluid dynamics flow field model as dynamic boundary conditions, the k-ε turbulence model is used to solve the Navier-Stokes equation in the high-precision computational fluid dynamics flow field model, and the wind speed vector, carbon dioxide concentration value and air age value of each point in the cultivation space are calculated. A three-dimensional wind speed cloud map is generated based on the wind speed vector, carbon dioxide concentration value and air age value of each point in the cultivation space, a low flow area with a wind speed lower than 0.1 m / s is marked in the three-dimensional wind speed cloud map, a carbon dioxide concentration distribution cloud map is generated, a high carbon dioxide concentration gradient area with a carbon dioxide concentration gradient greater than 200 ppm per meter is marked in the carbon dioxide concentration distribution cloud map, and an air age distribution cloud map is generated, and a high air age area with an air age exceeding 300 seconds is marked in the air age distribution cloud map.

[0009] As a preferred scheme of the edible mushroom cultivation environment uniformity control method based on airflow field reconstruction, the three-dimensional wind speed cloud map, the carbon dioxide concentration distribution cloud map and the air age distribution cloud map are compared with the initialized carbon dioxide concentration target value, the maximum allowed air age and the wind speed uniformity constraint condition, including the following steps: The wind speed data of each region is extracted from the three-dimensional wind speed cloud map, the wind speed uniformity standard deviation is calculated, and the wind speed uniformity constraint condition is compared, the carbon dioxide concentration value of each region is extracted from the carbon dioxide concentration distribution cloud map, and the carbon dioxide concentration target value is compared, and the carbon dioxide concentration deviation is calculated; The air age value of each region is extracted from the air age distribution cloud map, compared with the maximum allowed air age, and the air age deviation is calculated.

[0010] As a preferred scheme of the edible mushroom cultivation environment uniformity control method based on airflow field reconstruction, the three-dimensional wind speed cloud map, the carbon dioxide concentration distribution cloud map and the air age distribution cloud map are compared with the initialized carbon dioxide concentration target value, the maximum allowed air age and the wind speed uniformity constraint condition, including the following steps: The wind speed uniformity standard deviation, the carbon dioxide concentration deviation and the air age deviation are input as target function parameters, a double-objective optimization function containing carbon dioxide concentration deviation and air age is constructed, and a Pareto optimal solution set is obtained by solving the non-dominated sorting genetic algorithm; The target air volume of the total heat exchanger is calculated according to the optimal solution, the rotation speed of each micro-jet fan is calculated according to the optimal solution, and the deflection angle of the guide vane is calculated according to the optimal solution.

[0011] As a preferred scheme of the edible mushroom cultivation environment uniformity control method based on airflow field reconstruction, the three-dimensional wind speed cloud map, the carbon dioxide concentration distribution cloud map and the air age distribution cloud map are compared with the initialized carbon dioxide concentration target value, the maximum allowed air age and the wind speed uniformity constraint condition, including the following steps: The target air volume of the total heat exchanger is sent to the total heat exchanger control unit, and the total heat exchanger operates according to the target air volume. The speed of each micro jet fan is sent to the micro jet fan array control unit, and the micro jet fan array adjusts the speed of each fan according to the principle of equal pressure loss. The deflection angle of the guide vanes is sent to the piezoelectric ceramic driver control unit, and the piezoelectric ceramic driver drives the guide vanes to deflect to the calculated angle.

[0012] As a preferred embodiment of the method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction described in this invention, the method includes the following steps: when the deployed temperature and humidity sensors, carbon dioxide concentration sensors, and wind speed sensors malfunction, the system switches to an open-loop control mode predicted by a high-precision computational fluid dynamics flow field model, monitors the standard deviation of wind speed uniformity, and initiates a high-precision computational fluid dynamics flow field model recalibration process based on the monitoring results. When the monitored data exceeds the normal range, the sensor is determined to be abnormal. The abnormal sensor signal triggers a control mode switching command to stop using the abnormal sensor data. The control mode switching command activates the open-loop control mode predicted by the high-precision computational fluid dynamics flow field model. The high-precision computational fluid dynamics flow field model continuously monitors the standard deviation of wind speed uniformity in the cultivation space under open-loop control mode, based on historical data and current environmental parameters. A wind speed threshold is set based on the optimal wind speed uniformity requirements for the growth of edible fungi in the cultivation space. When the standard deviation of wind speed uniformity continuously exceeds the wind speed threshold, a model calibration command is generated. The model calibration command initiates the high-precision computational fluid dynamics flow field model recalibration process, which updates the model boundary conditions and mesh parameters.

[0013] Secondly, the present invention provides a system for optimizing the uniformity of edible fungi cultivation environment based on airflow field reconstruction, including a data acquisition module that constructs a high-precision computational fluid dynamics flow field model based on the geometric dimensions of the container, shelf layout parameters and porosity of the mushroom bags, initializes the target value of carbon dioxide concentration, the maximum allowable air age and wind speed uniformity constraints, and configures dual-parameter optimization weights. The simulation module inputs the collected real-time environmental data into a high-precision computational fluid dynamics flow field model and outputs three-dimensional wind speed cloud map, carbon dioxide concentration distribution cloud map, and air age distribution cloud map. The planning module compares the 3D wind speed cloud map, carbon dioxide concentration distribution cloud map, and air age distribution cloud map with the initialized carbon dioxide concentration target value, maximum allowable air age, and wind speed uniformity constraints. It uses a non-dominated sorting genetic algorithm to make multi-objective optimization decisions and outputs the target air volume of the total heat exchanger, the rotation speed of each micro jet fan, and the deflection angle of the guide vanes. The calibration module sends the target air volume of the total heat exchanger, the speed of each micro jet fan, and the deflection angle of the guide vanes to the total heat exchanger, the micro jet fan array, and the piezoelectric ceramic driver. The total heat exchanger operates according to the target air volume, the micro jet fan array adjusts the speed of each fan according to the pressure loss principle, and the piezoelectric ceramic driver drives the guide vanes to deflect to the calculated angle. The evaluation module switches to the open-loop control mode predicted by the high-precision computational fluid dynamics flow field model when the deployed temperature and humidity sensors, carbon dioxide concentration sensors, and wind speed sensors malfunction. It monitors the standard deviation of wind speed uniformity and initiates the high-precision computational fluid dynamics flow field model recalibration process based on the monitoring results.

[0014] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By constructing a high-precision computational fluid dynamics flow field model and initializing environmental parameters, environmental data is collected in real time and input into the model to generate three-dimensional wind speed cloud maps, carbon dioxide concentration distribution cloud maps, and air age distribution cloud maps. A non-dominated sorting genetic algorithm is used for multi-objective optimization decision-making, and control commands such as the target air volume of the total heat exchanger, the speed of the micro jet fan, and the deflection angle of the guide vanes are output and executed. When the sensor is abnormal, the model switches to the open-loop control mode predicted by the model and starts the model recalibration process. This realizes the dynamic balance regulation of carbon dioxide concentration and air age in the edible fungus cultivation environment and solves the problem that traditional single-variable control models are difficult to coordinate multi-parameter conflicts. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Fig. 1 This is a flowchart of a method for controlling the uniformity of the edible fungi cultivation environment based on airflow field reconstruction.

[0019] Fig. 2This is a schematic diagram of a system for optimizing the uniformity of the edible fungi cultivation environment based on airflow field reconstruction. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Reference Figs. 1-2 This is one embodiment of the present invention, which provides a method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction, including the following steps: S1. Based on the geometric dimensions of the container, the shelf layout parameters, and the porosity of the mushroom bags, a high-precision computational fluid dynamics flow field model is constructed. The target value of carbon dioxide concentration, the maximum allowable air age, and the uniformity of wind speed are initialized, and dual-parameter optimization weights are configured.

[0024] S1.1 By inputting the geometric dimensions of the cultivation container, shelf layout parameters, and porosity of the mushroom bags, the cultivation space is discretized using an unstructured grid to construct a high-precision computational fluid dynamics flow field model. The target value of carbon dioxide concentration is initialized, the maximum allowable air age is initialized, the wind speed uniformity constraint is initialized, and the dual-parameter optimization weights are configured. The weight ratio of the deviation between air age and carbon dioxide concentration is set according to the cultivation stage.

[0025] Furthermore, the geometric dimensions of the cultivation container, including length, width, and height data, and the shelf layout parameters, including shelf spacing, shelf height, and arrangement, are input. The porosity parameters of the mushroom bags are also input. Unstructured mesh generation technology is used to discretize the cultivation space, with the mesh unit size controlled within a reasonable range. Based on these input parameters, a high-precision computational fluid dynamics (CFD) flow field model is constructed. The initial carbon dioxide concentration target value is set according to the growth requirements of edible fungi, the maximum allowable air age is set according to air freshness requirements, and the wind speed uniformity constraint is set according to airflow distribution uniformity requirements. When configuring the dual-parameter optimization weights, the weight ratio between air age deviation and carbon dioxide concentration deviation is set according to the characteristics of different cultivation stages of edible fungi, thus completing the establishment of the high-precision CFD flow field model and parameter initialization.

[0026] S2. Input the collected real-time environmental data into the high-precision computational fluid dynamics flow field model, and output three-dimensional wind speed cloud map, carbon dioxide concentration distribution cloud map, and air age distribution cloud map.

[0027] S2.1 The temperature and humidity data collected by the temperature and humidity sensor, the carbon dioxide concentration data collected by the carbon dioxide concentration sensor, the wind speed data collected by the wind speed sensor, and the air age data collected by the sulfur hexafluoride tracer gas detection device are used as dynamic boundary conditions and input into the high-precision computational fluid dynamics flow field model. The k-ε turbulence model is used to solve the Navier-Stokes equation in the high-precision computational fluid dynamics flow field model to calculate the wind speed vector, carbon dioxide concentration value and air age value at each point in the cultivation space. Specifically, the expression is: ; in, For variables The density-weighted differential, For time partial derivatives, For variables Convective transport items, For variables gradient, For variables The source item, For variables diffusion flux, For variable indexing.

[0028] Furthermore, temperature and humidity data collected by the temperature and humidity sensors are acquired according to a preset sampling frequency; carbon dioxide concentration data collected by the carbon dioxide concentration sensor is measured using the infrared absorption principle; wind speed data collected by the wind speed sensor is acquired using a hot-wire anemometer or an ultrasonic anemometer; and air age data collected by the sulfur hexafluoride tracer gas detection device is determined using gas chromatography analysis. The aforementioned real-time environmental data are input as dynamic boundary conditions into a high-precision computational fluid dynamics (CFD) flow field model. In this model, the k-ε turbulence model is used to describe the turbulence characteristics, and the general form of the Navier-Stokes equations is solved, where the variables... The physical quantities to be solved include velocity components, turbulent kinetic energy, turbulent dissipation rate, carbon dioxide concentration, and air age. The transient terms in the equations reflect the time rate of change of the physical quantities, the convection terms describe the transport process of the physical quantities with fluid motion, the diffusion terms characterize the diffusion effect of the physical quantities driven by the gradient, and the source terms contain the generation or consumption mechanism of the physical quantities. The wind speed vector distribution, carbon dioxide concentration, and air age values ​​at each point in the cultivation space are obtained through numerical solutions.

[0029] S2.2. Generate a three-dimensional wind speed cloud map based on the wind speed vector, carbon dioxide concentration value and air age value of each point in the cultivation space. Mark low flow velocity areas with wind speeds below 0.1 m / s in the three-dimensional wind speed cloud map, generate a carbon dioxide concentration distribution cloud map, mark high carbon dioxide concentration gradient areas with carbon dioxide concentration gradients greater than 200 ppm per meter in the carbon dioxide concentration distribution cloud map, generate an air age distribution cloud map, and mark high air age areas with air age exceeding 300 seconds in the air age distribution cloud map.

[0030] Furthermore, a three-dimensional visualization technique is used to generate a three-dimensional wind speed cloud map, in which a specific color is used to mark low-velocity areas where the wind speed is below a set threshold; using spatial distribution data of carbon dioxide concentration values, an interpolation algorithm is used to generate a carbon dioxide concentration distribution cloud map, and high-gradient areas where the concentration gradient exceeds a set range are marked on the map; based on the calculation results of air age values, an air age distribution cloud map is constructed, and high-air age areas where the air age exceeds a set threshold are displayed using contour lines or color blocks; thus completing the visualization representation of the three-dimensional flow field distribution characteristics.

[0031] S3, the three-dimensional wind speed cloud map, carbon dioxide concentration distribution cloud map, and air age distribution cloud map are compared with the initialized target value of carbon dioxide concentration, maximum allowable air age, and wind speed uniformity constraints.

[0032] S3.1 Extract wind speed data for each region from the three-dimensional wind speed cloud map, calculate the standard deviation of wind speed uniformity, and compare it with the initial wind speed uniformity constraint. Extract carbon dioxide concentration values ​​for each region from the carbon dioxide concentration distribution cloud map, compare them with the initial carbon dioxide concentration target value, and calculate the carbon dioxide concentration deviation.

[0033] Specifically, the expression is: ; in, This represents the deviation in carbon dioxide concentration. This refers to the real-time monitoring of carbon dioxide concentration. The target carbon dioxide concentration.

[0034] Furthermore, when extracting wind speed data for each region from the 3D wind speed cloud map, a grid partitioning method is used to divide the cultivation space into several calculation units, with the magnitude of the wind speed vector within each unit taken as a representative value. The standard deviation of wind speed uniformity is calculated based on the wind speed values ​​of each unit, and statistical methods are used to assess the dispersion of wind speed distribution. The calculated standard deviation of wind speed uniformity is compared with the initialized wind speed uniformity constraints to determine if it exceeds the allowable range. When extracting carbon dioxide concentration values ​​for each region from the carbon dioxide concentration distribution cloud map, concentration data is obtained according to the same grid partitioning. The regional carbon dioxide concentration values ​​are compared with the initialized target carbon dioxide concentration values, and the carbon dioxide concentration deviation is obtained through difference calculation, reflecting the degree of deviation between the current concentration and the target value. This completes the quantitative assessment of wind speed distribution uniformity and carbon dioxide concentration deviation.

[0035] S3.2 Extract the air age value of each region from the air age distribution cloud map, compare it with the initial maximum allowable air age, and calculate the air age deviation.

[0036] Specifically, the expression is: ; in, This is the air age deviation. To measure the actual air age, For the maximum permissible air age, Furthermore, when extracting air age values ​​for each region from the air age distribution cloud map, the same spatial grid division method as the wind speed and carbon dioxide concentration analysis is maintained. After obtaining the air age values ​​within each grid cell, they are directly compared with the initialized maximum allowable air age. The air age deviation is calculated through subtraction, with positive values ​​indicating that the air age exceeds the allowable range, and negative or zero values ​​indicating that it meets the requirements. The air age deviation is used to quantitatively assess the air retention situation in the cultivation space, providing key input parameters for subsequent multi-objective optimization. This completes the quantitative analysis of the air age distribution.

[0037] S4. A non-dominated sorting genetic algorithm is used for multi-objective optimization decision-making, and the target air volume of the total heat exchanger, the speed of each micro jet fan, and the deflection angle of the guide vanes are output.

[0038] S4.1. Using the standard deviation of wind speed uniformity, the deviation of carbon dioxide concentration, and the deviation of air age as input parameters of the objective function, a bi-objective optimization function containing the deviation of carbon dioxide concentration and air age is constructed, and the Pareto optimal solution set is obtained by solving it through a non-dominated sorting genetic algorithm.

[0039] Furthermore, the standard deviation of wind speed uniformity, the deviation of carbon dioxide concentration, and the deviation of air age are used as input parameters for a multi-objective optimization problem. A bi-objective optimization function incorporating carbon dioxide concentration deviation and air age is constructed, where the carbon dioxide concentration deviation reflects the degree of difference between the current environment and the ideal gas composition, and the air age deviation characterizes the efficiency of airflow organization. A non-dominated sorting genetic algorithm is used to solve the problem. With reasonable population size and genetic operation parameters set, the diversity of solutions is maintained through fast non-dominated sorting and crowding calculation. After multiple generations of evolution, a Pareto optimal solution set is obtained. The optimal solution that satisfies the wind speed uniformity constraint and is adapted to the weight allocation of the current cultivation stage is selected from the solution set to provide a decision-making basis for subsequent equipment control.

[0040] S4.2 Calculate the target air volume of the total heat exchanger based on the optimal solution, calculate the rotational speed of each micro jet fan based on the optimal solution, and calculate the deflection angle of the guide vanes based on the optimal solution.

[0041] Specifically, the expression is: ; in, The target air volume for the total heat exchanger, Basic air volume, This is the gain coefficient for airflow regulation. Weighted by the deviation in carbon dioxide concentration. As the weight of air age deviation, Specifically, the expression is: ; in, For the first The target speed of a miniature jet fan This is the minimum allowable fan speed. The maximum allowable standard deviation, The standard deviation of current wind speed uniformity. The maximum speed allowed by the fan. Specifically, the expression is: ; in, The target deflection angle of the guide vane. Vertical velocity component The differential, Vertical direction Differential operators, Horizontal direction Differential operators, The horizontal velocity component The differential.

[0042] Furthermore, based on the optimal solution obtained from the non-dominated sorting genetic algorithm, when calculating the target airflow of the total heat exchanger, linear adjustment is performed based on the base airflow and airflow adjustment gain coefficient, combined with the weighted sum of the carbon dioxide concentration deviation and air age deviation; when calculating the rotational speed of each micro-jet fan, the relative relationship between the current wind speed uniformity standard deviation and the maximum allowable standard deviation is considered, and the speed is adjusted proportionally within the allowable minimum and maximum rotational speed range of the fan; when calculating the deflection angle of the guide vanes, the optimal deflection angle of the vanes is determined by solving the difference between the partial derivative of the vertical velocity component with respect to the horizontal direction and the partial derivative of the horizontal velocity component with respect to the vertical direction, combined with the proportional adjustment term of the carbon dioxide concentration deviation; the collaborative control parameters of the total heat exchanger, micro-jet fan array, and guide vanes are calculated.

[0043] S5. The target air volume of the total heat exchanger, the speed of each micro jet fan, and the deflection angle of the guide vanes are sent to the total heat exchanger, the micro jet fan array, and the piezoelectric ceramic driver. The total heat exchanger operates according to the target air volume, the micro jet fan array adjusts the speed of each fan according to the pressure loss principle, and the piezoelectric ceramic driver drives the guide vanes to deflect to the calculated angle.

[0044] S5.1 The target air volume of the total heat exchanger is sent to the total heat exchanger control unit. The total heat exchanger operates according to the target air volume. The speed of each micro jet fan is sent to the micro jet fan array control unit. The micro jet fan array adjusts the speed of each fan according to the principle of equal pressure loss. The deflection angle of the guide vanes is sent to the piezoelectric ceramic driver control unit. The piezoelectric ceramic driver drives the guide vanes to deflect to the calculated angle.

[0045] Furthermore, the target airflow of the total heat exchanger is transmitted to the total heat exchanger control unit via a digital communication interface. The built-in variable frequency fan of the total heat exchanger adjusts its operating frequency according to the received target airflow value to ensure that the actual airflow is consistent with the target value. The speed command of the micro jet fan is sent to each micro jet fan array control unit through a distributed control network. Each control unit calculates the speed matching relationship between adjacent fans according to the principle of equal pressure loss and dynamically adjusts the drive signal of each fan motor through a PID algorithm to ensure uniform airflow distribution. The deflection angle command of the guide vane is transmitted to the piezoelectric ceramic actuator control unit in the form of a high-precision digital signal. The piezoelectric ceramic actuator generates nanometer-level displacement based on the inverse piezoelectric effect. The displacement amplification mechanism converts the electrical signal into the mechanical deflection action of the guide vane to achieve accurate positioning of the calculated angle. This completes the execution process of total heat exchanger airflow regulation, micro jet fan array collaborative control, and guide vane precise positioning.

[0046] S6. When the deployed temperature and humidity sensor, carbon dioxide concentration sensor and wind speed sensor malfunction, switch to the open-loop control mode predicted by the high-precision computational fluid dynamics flow field model, monitor the standard deviation of wind speed uniformity, and start the high-precision computational fluid dynamics flow field model recalibration process based on the monitoring results.

[0047] S6.1 When the monitored data exceeds the normal range, the sensor is determined to be abnormal. The abnormal sensor signal triggers the control mode switching command, stops the use of abnormal sensor data, and activates the open-loop control mode predicted by the high-precision computational fluid dynamics flow field model.

[0048] Furthermore, the environmental parameters collected in real time by temperature and humidity sensors, carbon dioxide concentration sensors, and wind speed sensors are compared with preset normal operating ranges. When the detected data continuously exceeds the threshold range, a sensor abnormality flag is generated. The abnormality flag triggers the control mode switching logic, immediately interrupting the data acquisition channel of the abnormal sensor, and at the same time sending a mode switching command to the control core. The control core switches the operating mode from closed-loop control to open-loop control mode predicted by a high-precision computational fluid dynamics flow field model according to the command, ensuring the continuity of environmental regulation.

[0049] S6.2 The high-precision computational fluid dynamics flow field model continuously monitors the standard deviation of wind speed uniformity in the cultivation space under open-loop control mode, based on historical data and current environmental parameters. It sets a wind speed threshold based on the optimal wind speed uniformity requirements for the growth of edible fungi in the cultivation space. When the standard deviation of wind speed uniformity continuously exceeds the wind speed threshold, a model calibration command is generated. The model calibration command initiates the high-precision computational fluid dynamics flow field model recalibration process, which updates the model boundary conditions and mesh parameters.

[0050] Furthermore, by using typical environmental parameters from the historical database as input and combining them with currently available effective sensor data, the missing parameters are supplemented through interpolation algorithms. The predicted wind speed distribution in the cultivation space is continuously calculated and output. Based on the predicted values, the standard deviation of wind speed uniformity is calculated. When the standard deviation continuously exceeds the wind speed uniformity threshold set according to the growth characteristics of edible fungi, a model recalibration trigger signal is generated. The recalibration process first checks the rationality of the current boundary conditions, then dynamically densifies or sparses the local areas of the unstructured mesh, and finally recalibrates the turbulence model parameters based on the latest environmental data, thus completing the adaptive update of the high-precision computational fluid dynamics flow field model.

[0051] This embodiment also provides a system for optimizing the uniformity of edible fungi cultivation environment based on airflow field reconstruction, including: a data acquisition module, which constructs a high-precision computational fluid dynamics flow field model based on the geometric dimensions of the container, shelf layout parameters and porosity of the mushroom bags, initializes the target value of carbon dioxide concentration, the maximum allowable air age and wind speed uniformity constraints, and configures dual-parameter optimization weights; The simulation module inputs the collected real-time environmental data into a high-precision computational fluid dynamics flow field model and outputs three-dimensional wind speed cloud map, carbon dioxide concentration distribution cloud map, and air age distribution cloud map. The planning module compares the 3D wind speed cloud map, carbon dioxide concentration distribution cloud map, and air age distribution cloud map with the initialized carbon dioxide concentration target value, maximum allowable air age, and wind speed uniformity constraints. It uses a non-dominated sorting genetic algorithm to make multi-objective optimization decisions and outputs the target air volume of the total heat exchanger, the rotation speed of each micro jet fan, and the deflection angle of the guide vanes. The calibration module sends the target air volume of the total heat exchanger, the speed of each micro jet fan, and the deflection angle of the guide vanes to the total heat exchanger, the micro jet fan array, and the piezoelectric ceramic driver. The total heat exchanger operates according to the target air volume, the micro jet fan array adjusts the speed of each fan according to the pressure loss principle, and the piezoelectric ceramic driver drives the guide vanes to deflect to the calculated angle. The evaluation module switches to the open-loop control mode predicted by the high-precision computational fluid dynamics flow field model when the deployed temperature and humidity sensors, carbon dioxide concentration sensors, and wind speed sensors malfunction. It monitors the standard deviation of wind speed uniformity and initiates the high-precision computational fluid dynamics flow field model recalibration process based on the monitoring results.

[0052] This embodiment also provides a computer device applicable to the method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction as proposed in the above embodiment.

[0053] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0054] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the method for controlling the uniformity of the edible fungi cultivation environment based on airflow field reconstruction as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0055] In summary, this invention constructs a high-precision computational fluid dynamics flow field model and initializes environmental parameters. It then collects environmental data in real time and inputs it into the model to generate three-dimensional wind speed cloud maps, carbon dioxide concentration distribution cloud maps, and air age distribution cloud maps. A non-dominated sorting genetic algorithm is used for multi-objective optimization decision-making. The invention outputs and executes control commands such as the target air volume of the total heat exchanger, the speed of the micro-jet fan, and the deflection angle of the guide vanes. When the sensor malfunctions, the invention switches to the open-loop control mode predicted by the model and initiates the model recalibration process. This achieves dynamic balance control of carbon dioxide concentration and air age in the edible fungus cultivation environment, solving the problem that traditional single-variable control models have difficulty coordinating multiple parameter conflicts.

[0056] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction, characterized in that: include, Based on the geometric dimensions of the container, the shelf layout parameters, and the porosity of the mushroom bags, a high-precision computational fluid dynamics flow field model was constructed. The target value of carbon dioxide concentration, the maximum allowable air age, and the wind speed uniformity constraints were initialized, and dual-parameter optimization weights were configured. The collected real-time environmental data is input into a high-precision computational fluid dynamics flow field model, and outputs three-dimensional wind speed cloud map, carbon dioxide concentration distribution cloud map, and air age distribution cloud map. The three-dimensional wind speed cloud map, carbon dioxide concentration distribution cloud map, and air age distribution cloud map are compared with the initial carbon dioxide concentration target value, maximum allowable air age, and wind speed uniformity constraints. A non-dominated sorting genetic algorithm is used for multi-objective optimization decision-making, and the target air volume of the total heat exchanger, the rotation speed of each micro jet fan, and the deflection angle of the guide vanes are output. The target air volume of the total heat exchanger, the speed of each micro jet fan, and the deflection angle of the guide vanes are sent to the total heat exchanger, the micro jet fan array, and the piezoelectric ceramic actuator. The total heat exchanger operates according to the target air volume, the micro jet fan array adjusts the speed of each fan according to the pressure loss principle, and the piezoelectric ceramic actuator drives the guide vanes to deflect to the calculated angle. When the deployed temperature and humidity sensors, carbon dioxide concentration sensors, and wind speed sensors malfunction, the system switches to the open-loop control mode predicted by the high-precision computational fluid dynamics flow field model, monitors the standard deviation of wind speed uniformity, and initiates the high-precision computational fluid dynamics flow field model recalibration process based on the monitoring results.

2. The method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction as described in claim 1, characterized in that: Based on the geometric dimensions of the modular container, shelf layout parameters, and porosity of the bacterial bags, a high-precision computational fluid dynamics flow field model was constructed. The target value of carbon dioxide concentration, maximum allowable air age, and wind speed uniformity constraints were initialized, and two-parameter optimization weights were configured. The process included the following steps: By inputting the geometric dimensions of the cultivation container, shelf layout parameters, and porosity of the mushroom bags, the cultivation space is discretized using an unstructured grid to construct a high-precision computational fluid dynamics flow field model. The target value of carbon dioxide concentration is initialized, the maximum allowable air age is initialized, the wind speed uniformity constraint is initialized, and the dual-parameter optimization weights are configured. The weight ratio of the deviation between air age and carbon dioxide concentration is set according to the cultivation stage.

3. The method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction as described in claim 2, characterized in that: The collected real-time environmental data is input into a high-precision computational fluid dynamics flow field model, which outputs three-dimensional wind speed cloud maps, carbon dioxide concentration distribution cloud maps, and air age distribution cloud maps, including the following steps: Temperature and humidity data collected by temperature and humidity sensors, carbon dioxide concentration data collected by carbon dioxide concentration sensors, wind speed data collected by wind speed sensors, and air age data collected by sulfur hexafluoride tracer gas detection devices are used as dynamic boundary conditions and input into a high-precision computational fluid dynamics flow field model. The k-ε turbulence model is used to solve the Navier-Stokes equations in the high-precision computational fluid dynamics flow field model to calculate the wind speed vector, carbon dioxide concentration value, and air age value at each point in the cultivation space. A three-dimensional wind speed cloud map is generated based on the wind speed vector, carbon dioxide concentration value, and air age value at each point in the cultivation space. Low-velocity areas with wind speeds below 0.1 m / s are marked in the three-dimensional wind speed cloud map, and a carbon dioxide concentration distribution cloud map is generated. High carbon dioxide concentration gradient areas with carbon dioxide concentration gradients greater than 200 ppm per meter are marked in the carbon dioxide concentration distribution cloud map, and an air age distribution cloud map is generated. High-air age areas with air ages exceeding 300 seconds are marked in the air age distribution cloud map.

4. The method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction as described in claim 3, characterized in that: The three-dimensional wind speed cloud map, carbon dioxide concentration distribution cloud map, and air age distribution cloud map are compared with the initialized target value of carbon dioxide concentration, maximum allowable air age, and wind speed uniformity constraints, including the following steps: Wind speed data for each region is extracted from the 3D wind speed cloud map, the standard deviation of wind speed uniformity is calculated, and it is compared with the initial wind speed uniformity constraint. Carbon dioxide concentration values ​​for each region are extracted from the carbon dioxide concentration distribution cloud map, and they are compared with the initial carbon dioxide concentration target value to calculate the carbon dioxide concentration deviation. The air age values ​​for each region are extracted from the air age distribution cloud map and compared with the initial maximum allowable air age to calculate the air age deviation.

5. The method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction as described in claim 4, characterized in that: A non-dominated sorting genetic algorithm is used for multi-objective optimization decision-making, outputting the target air volume of the total heat exchanger, the rotational speed of each micro-jet fan, and the deflection angle of the guide vanes. This includes the following steps: By using the standard deviation of wind speed uniformity, the deviation of carbon dioxide concentration, and the deviation of air age as input parameters of the objective function, a bi-objective optimization function containing the deviation of carbon dioxide concentration and air age is constructed, and the Pareto optimal solution set is obtained by solving it through a non-dominated sorting genetic algorithm. The target air volume of the total heat exchanger is calculated based on the optimal solution. The rotational speed of each micro jet fan is calculated based on the optimal solution. The deflection angle of the guide vanes is calculated based on the optimal solution.

6. The method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction as described in claim 5, characterized in that: The target airflow of the total heat exchanger, the speed of each micro-jet fan, and the deflection angle of the guide vanes are sent to the total heat exchanger, the micro-jet fan array, and the piezoelectric ceramic actuator. The total heat exchanger operates according to the target airflow, the micro-jet fan array adjusts the speed of each fan according to the pressure loss principle, and the piezoelectric ceramic actuator drives the guide vanes to deflect to the calculated angle, including the following steps: The target air volume of the total heat exchanger is sent to the total heat exchanger control unit, and the total heat exchanger operates according to the target air volume. The speed of each micro jet fan is sent to the micro jet fan array control unit, and the micro jet fan array adjusts the speed of each fan according to the principle of equal pressure loss. The deflection angle of the guide vanes is sent to the piezoelectric ceramic driver control unit, and the piezoelectric ceramic driver drives the guide vanes to deflect to the calculated angle.

7. The method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction as described in claim 6, characterized in that: When the deployed temperature and humidity sensors, carbon dioxide concentration sensors, and wind speed sensors malfunction, the system switches to the open-loop control mode predicted by the high-precision computational fluid dynamics flow field model. It monitors the standard deviation of wind speed uniformity and, based on the monitoring results, initiates the high-precision computational fluid dynamics flow field model recalibration process, including the following steps: When the monitored data exceeds the normal range, the sensor is determined to be abnormal. The abnormal sensor signal triggers a control mode switching command to stop using the abnormal sensor data. The control mode switching command activates the open-loop control mode predicted by the high-precision computational fluid dynamics flow field model. The high-precision computational fluid dynamics flow field model continuously monitors the standard deviation of wind speed uniformity in the cultivation space under open-loop control mode, based on historical data and current environmental parameters. A wind speed threshold is set based on the optimal wind speed uniformity requirements for the growth of edible fungi in the cultivation space. When the standard deviation of wind speed uniformity continuously exceeds the wind speed threshold, a model calibration command is generated. The model calibration command initiates the high-precision computational fluid dynamics flow field model recalibration process, which updates the model boundary conditions and mesh parameters.

8. A system for optimizing the uniformity of edible mushroom cultivation environment based on airflow field reconstruction, based on the method for controlling the uniformity of edible mushroom cultivation environment based on airflow field reconstruction as described in any one of claims 1 to 7, characterized in that: This includes a data acquisition module that, based on the geometric dimensions of the container, shelf layout parameters, and porosity of the mushroom bags, constructs a high-precision computational fluid dynamics flow field model, initializes the target value of carbon dioxide concentration, the maximum allowable air age, and wind speed uniformity constraints, and configures dual-parameter optimization weights. The simulation module inputs the collected real-time environmental data into a high-precision computational fluid dynamics flow field model and outputs three-dimensional wind speed cloud map, carbon dioxide concentration distribution cloud map, and air age distribution cloud map. The planning module compares the 3D wind speed cloud map, carbon dioxide concentration distribution cloud map, and air age distribution cloud map with the initialized carbon dioxide concentration target value, maximum allowable air age, and wind speed uniformity constraints. It uses a non-dominated sorting genetic algorithm to make multi-objective optimization decisions and outputs the target air volume of the total heat exchanger, the rotation speed of each micro jet fan, and the deflection angle of the guide vanes. The calibration module sends the target air volume of the total heat exchanger, the speed of each micro jet fan, and the deflection angle of the guide vanes to the total heat exchanger, the micro jet fan array, and the piezoelectric ceramic driver. The total heat exchanger operates according to the target air volume, the micro jet fan array adjusts the speed of each fan according to the pressure loss principle, and the piezoelectric ceramic driver drives the guide vanes to deflect to the calculated angle. The evaluation module switches to the open-loop control mode predicted by the high-precision computational fluid dynamics flow field model when the deployed temperature and humidity sensors, carbon dioxide concentration sensors, and wind speed sensors malfunction. It monitors the standard deviation of wind speed uniformity and initiates the high-precision computational fluid dynamics flow field model recalibration process based on the monitoring results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for controlling the uniformity of edible fungi cultivation environment based on airflow field reconstruction as described in any one of claims 1 to 7.

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