A method for dynamic control of carbon cycle timing in edible fungi and hydroponic vegetable planting warehouses

Abstract

The present invention relates to a method for dynamic control of the timing of carbon cycles in edible fungi and hydroponic vegetable planting warehouses. Compared with the prior art, it solves the defects of low carbon resource utilization, extensive control, and lack of systematic solutions in the composite planting system of edible fungi and hydroponic vegetables. The present invention includes the following steps: determination of the control cycle of edible fungi and hydroponic vegetables; collection and preprocessing of carbon dioxide timing data of edible fungi and hydroponic vegetable populations; construction of a timing carbon assimilation rate model of hydroponic vegetables based on an improved raccoon optimization algorithm; construction of a segmented timing respiration rate model of edible fungi; construction of a timing dynamic control model of carbon cycles in edible fungi and hydroponic vegetable planting warehouses; dynamic control of the timing of carbon cycles in planting warehouses. By integrating the growth characteristics of edible fungi respiration and hydroponic vegetable photosynthesis, the present invention innovatively proposes a method and system for dynamic control of the timing of carbon cycles in populations, thereby achieving precise supply and demand matching in the fungus-vegetable symbiotic system.

Description

Technical Field

[0001] The present invention relates to the technical field of planting warehouses, and in particular to a method for dynamically controlling the timing of carbon cycles in planting warehouses for edible fungi and hydroponic vegetables. Background Art

[0002] Edible fungi and hydroponic vegetables are usually grown separately. In this mode, the It cannot be effectively utilized and is directly discharged into the atmosphere, which not only causes a waste of resources, but also exacerbates the climate crisis due to the accumulation of greenhouse gases. It will also affect the growth and development of edible fungi themselves, seriously affecting yield and quality.

[0003] As a common green plant, the growth process of hydroponic vegetables is highly dependent on photosynthesis. During photosynthesis, they need to absorb carbon dioxide to synthesize organic matter to achieve growth and development. Supply and hydroponic vegetables The demand for carbon dioxide has formed a potential supply and demand relationship, which is expected to achieve near-zero carbon. Through a series of effective carbon emission reduction measures and carbon absorption methods, it can be achieved within a certain period of time. The balance between emissions and absorption is essentially achieved, offering new insights into addressing current resource utilization and environmental challenges in agriculture. While the concept of symbiotic cultivation of vegetables and edible fungi has been explored, these studies have largely remained at the conceptual level, simply elaborating on the possibilities and potential advantages of mushroom-vegetable symbiosis. In actual symbiotic systems, the optimal planting ratio between edible mushroom sticks and hydroponic vegetables, as well as dynamic carbon cycle adjustments, have not been thoroughly explored.

[0004] Photosynthetic characteristics of hydroponic vegetables vary with light intensity , growth stage and other multi-variables show nonlinear dynamic changes. Existing traditional methods mostly use static parameter fitting, which cannot capture the instantaneous photosynthetic response and time-series carbon demand fluctuations under different light cycles. However, accurately adapting to the changes in photosynthetic characteristics of vegetables throughout the entire cycle and providing efficient and stable algorithm support for the intelligent environmental regulation of vegetable hydroponics are important contents of the dynamic regulation of carbon cycles in planting warehouses. At the same time, it has been found in actual applications that there is still a lack of accurate measurement and analysis of key data such as the amount of carbon dioxide produced by mushrooms at different growth stages and the amount of carbon dioxide required by the whole vegetable plant. It is even more impossible to provide a scientific basis for determining a reasonable cultivation ratio, which makes it difficult to achieve accurate cultivation management of symbiotic fungi and vegetables in actual applications.

[0005] Therefore, how to develop a time-series dynamic allocation method for low-carbon mushroom-vegetable symbiotic cultivation has become a technical problem that needs to be solved urgently. Summary of the Invention

[0006] The purpose of the present invention is to solve the defects of low carbon resource utilization, extensive regulation and lack of systematic solutions in the existing technology of edible fungi and hydroponic vegetable composite planting systems, and to provide a method for dynamic regulation of carbon cycle timing in edible fungi and hydroponic vegetable planting warehouses to solve the above problems.

[0007] In order to achieve the above object, the technical solution of the present invention is as follows:

[0008] A method for dynamically controlling the carbon cycle timing of an edible fungus and hydroponic vegetable planting warehouse comprises the following steps:

[0009] Determination of the regulation cycle of edible fungi and hydroponic vegetables: Coupling and calibrating the time axis of edible fungi fruiting body period and hydroponic vegetable growth sequence to determine the complete regulation cycle of edible fungi and hydroponic vegetable planting warehouses ;

[0010] Collection and preprocessing of carbon dioxide time series data of edible fungi and hydroponic vegetable populations;

[0011] Construct a temporal carbon assimilation rate model for hydroponic vegetables based on the improved raccoon optimization algorithm;

[0012] Construct a segmented time series respiratory rate model for edible fungi;

[0013] Construct a dynamic control model of carbon cycle in edible fungi and hydroponic vegetable cultivation warehouses;

[0014] Dynamic regulation of carbon cycle timing in planting warehouses: Integrate the dynamic regulation model of carbon cycle timing in edible fungi and hydroponic vegetable planting warehouses into the planting warehouse system to achieve dynamic regulation of carbon cycle timing.

[0015] Determination of the regulation cycle of the edible fungi and hydroponic vegetables comprises the following steps:

[0016] The growth cycles of edible fungi and hydroponic vegetables were measured respectively. The growth cycle of a single crop of edible fungi was Days, long under suitable conditions The growth cycle of edible fungi The growth cycle of hydroponic vegetables is sky;

[0017] Coupled calculation of the complete control cycle of edible fungi and hydroponic vegetable cultivation warehouses ,

[0018] The total cycle of co-cultivation of edible fungi and hydroponic vegetables, To calculate the least common multiple,

[0019] The number of mushroom stick batches planted during the entire growth cycle is , number of batches of hydroponic vegetables planted , make sure to harvest together.

[0020] The carbon dioxide time series data collection and preprocessing of edible fungi and hydroponic vegetable groups are as follows: Time-series release dataset and carbon assimilation of hydroponic vegetables under different light intensities throughout their growth period Time series demand data set, for abnormal data The method performs denoising; it includes the following steps:

[0021] Using the edible fungus group carbon flux monitoring device, under the conditions of suitable and stable microenvironment, high precision The sensor monitors the respiration of a single edible mushroom stick in the fruiting body growth stage in real time, and obtains Time series release dataset ;

[0022] Using plant group carbon assimilation rate measurement equipment, the group carbon assimilation of the whole hydroponic vegetable under different light intensities during the entire growth cycle was measured. Time series requirement dataset ;

[0023] use Law Time series release dataset and group carbon assimilation Time series requirement dataset To handle the outliers in

[0024] set up Time series release dataset Include value, recorded as , ,…, ,in Indicates the number of individual data;

[0025] Assume that group carbon assimilation Time series requirement dataset Include value, recorded as , ,…, ,in Indicates the number of data, calculate separately Time series release dataset and group carbon assimilation Time series requirement dataset The mean and standard deviation ,

[0026] ,

[0027] ,

[0028] in, 、 Respectively represent Time series release dataset The mean and standard deviation of 、 Represents group carbon assimilation Time series requirement dataset The mean and standard deviation of 、 To sum the index, express Time series release dataset The ath data in; represents group carbon assimilation Time series requirement dataset The bth data in

[0029] Then calculate the Z value of each data point:

[0030] ,in, represent Time series release dataset The Z value of the a-th data point in Representative group carbon assimilation Time series requirement dataset The Z value of the b-th data point in

[0031] Setting thresholds >3 and >3, outliers exceeding the threshold are eliminated;

[0032] Timing Rate of change calculation:

[0033] Will Time series release dataset , group carbon assimilation Time series requirement dataset Press = Calculate the concentration change rate at 60s intervals:

[0034] ,

[0035] ,

[0036] in: and The unit is ppm / min. is the time in seconds, represent The carbon flux monitoring device of edible fungi population was used to measure the concentration, represent The carbon flux monitoring device of edible fungi population was used to measure the concentration, represent The carbon assimilation rate of plant populations was measured by the device concentration, represent The carbon assimilation rate of plant populations was measured by the device concentration, Edible mushrooms per minute Release rate, Hydroponic vegetables per minute Absorption rate;

[0037] By calculating the concentration change rate of edible fungi and hydroponic vegetables in multiple time periods every day during the planting cycle, the concentration of edible fungi per minute per day during the planting cycle is calculated. Average release rate and hydroponic vegetables under different light intensities Average absorption rate , respectively expressed as:

[0038] ,

[0039] ,

[0040] Where N is the number of measurements in multiple valid time periods in a day, i is the summation index, The number of days for growing edible fungi, Number of days for hydroponic vegetable cultivation, For the Edible fungi per minute Release rate, For different light intensities hydroponic vegetables per minute Absorption rate, The growth cycle of edible fungi. For the growth cycle of hydroponic vegetables;

[0041] Combined with the effective volume of the edible fungus group carbon flux monitoring device Calculate the time in days Release rate, generating edible fungus respiration Release rate time series dataset , the specific calculation method is:

[0042] ,

[0043] in, Representative Respiration of edible fungi Release rate, in m 3 / sky, The unit is m 3 ;

[0044] Assume that the daytime length of the photoperiod for hydroponic vegetable cultivation is hours, and nights are hours, combined with the effective volume of the plant community carbon assimilation rate measurement equipment Convert to days Absorption rate, generating a time series dataset of carbon assimilation rate of hydroponic vegetables , The calculation method is:

[0045] ,

[0046] in, Representative Carbon assimilation rate of hydroponic vegetables per day, unit: m 3 / sky, The unit is m 3 .

[0047] The construction of a temporal carbon assimilation rate model for hydroponic vegetables based on the improved raccoon optimization algorithm comprises the following steps:

[0048] Hydroponic vegetable carbon assimilation rate time series dataset Divide into training set With the test set , where the number of days of hydroponic vegetable cultivation and light intensity are the characteristics, and the carbon assimilation rate is the label;

[0049] use Method to standardize the data:

[0050] ,

[0051] ,

[0052] ,

[0053] in, 、 、 They are the number of days for hydroponic vegetable cultivation, light intensity, and carbon assimilation rate, 、 、 are the standardized hydroponic vegetable planting days, the standardized light intensity, and the standardized carbon assimilation rate, 、 They are The mean and standard deviation of 、 They are The mean and standard deviation of 、 for The mean and standard deviation of The unit is ;

[0054] Set the relevance vector machine model,

[0055] The kernel function of the RVM model is selected as the Gaussian kernel function ,

[0056] in, 、 The kernel width parameter γ and noise precision parameter α of the RVM model are optimized by using the improved Raccoon optimization algorithm.

[0057] The range of kernel width parameter γ of the RVM model is set to γ∈[10 -3 ,1], the range of noise accuracy parameter α is α∈[10 -3 ,1], set the population size N of the improved raccoon optimization algorithm to 20, and the maximum number of iterations The number of times is set to 50, and the fitness function F is defined as the mean square error of the RVM model test set, and the calculation formula is:

[0058] ,

[0059] in, is the number of test set samples, m represents the mth sample in the test set, is the actual carbon assimilation rate value of the mth sample in the test set, is the model's predicted value for the mth sample;

[0060] Initialize the population and encode the position of each individual in the following way:

[0061] ,

[0062] in, 、 Representing the population The encoding values of the kernel width parameter and noise precision parameter of the correlation vector machine model corresponding to each individual, 、 are the lower and upper bounds of the kernel width parameter γ, 、 are the lower and upper bounds of the noise accuracy parameter α, is a random number uniformly distributed in the interval [0,1]. is the index of the individual in the population, N is the population size;

[0063] The position vectors of N=20 raccoon individuals are generated randomly. , , and use the fitness function F to calculate the fitness value of each individual as the initial value;

[0064] Design a dynamic population size adjustment module: calculate the average Euclidean distance of each individual in the solution space, and use the standardized distance variance as the population diversity evaluation indicator; when the population is initialized or updated, the current population diversity is evaluated by calculating the average Euclidean distance between the position of an individual in the population and the average of all individual positions. When the population diversity is lower than the threshold of 0.3 and the population size is lower than the maximum population size of 100, the population is updated: new sample individuals are randomly generated to expand the population size; when the population diversity is higher than the threshold of 0.3 and the population size is higher than the minimum population size of 30, the population is updated: the fitness function F is used to calculate the fitness value of each individual, the worst individuals are eliminated, and the population size is reduced;

[0065] On the hydroponic vegetable carbon assimilation rate time series dataset, the encoded γ and α were used in the training set. Train the RVM model and use the trained RVM model in the test set The prediction is made on the , and the fitness value is calculated by the difference between the predicted result and the true value. The fitness values of all individuals are traversed, and the individual with the smallest fitness is selected as the current optimal solution. The γ and α parameter combinations corresponding to the optimal solution are recorded as and , and its position vector is recorded as ;

[0066] In the search space, select Each individual updates its position, and the update formula is as follows:

[0067] ,

[0068] Among them, N is the population size, p is the current number of iterations, represents the component of the i-th individual in the j-th dimension at the p-th iteration, represents the component of the i-th individual in the j-th dimension at the p+1-th iteration, represents the component of the optimal individual in the jth dimension at the pth iteration, is a random number uniformly distributed in the interval [0,1], I represents a random integer from the integer set {1, 2}, and i represents the number of the individual selected for location update;

[0069] The fitness function F is used to calculate the fitness value of each individual, and a greedy selection is performed. If the fitness of the individual after the update is better than the fitness value before the update, the updated position is retained, otherwise the position is not updated. The greedy selection strategy formula is as follows:

[0070] ,

[0071] in, is the position of the i-th individual at the p+1-th iteration, is the position of the i-th individual at the p-th iteration, and are the fitness values corresponding to the positions of the i-th individual at the p+1-th iteration and the p-th iteration respectively;

[0072] Design a learning rate adjustment strategy for position adaptive updates:

[0073] Construct a composite function based on the iteration attenuation factor and the diversity feedback coefficient. The learning rate update formula is:

[0074] ,

[0075] in, is the current iteration number, is the attenuation factor, is the maximum number of iterations, is the number of diversity solution sets, i is the summation variable, is the diversity feedback coefficient, For population diversity, represents the learning rate at the p-th iteration, represents the learning rate at the p+1th iteration, is the exponential operation of natural constants;

[0076] A dual control mechanism is used to achieve the coordinated optimization of global exploration and local development: the global search mode is adopted in the early stage of iteration, and the local development mode is activated as the number of iterations increases to accelerate the speed of locating the global optimal neighborhood;

[0077] Select another Individuals update their positions in the search space,

[0078] First, randomly generate a temporary position vector in the search space , and its calculation formula is:

[0079] ,

[0080] The location is updated as follows:

[0081] ,

[0082] ;

[0083] Among them, i represents the number of the individual selected for location update, N is the population size, and Represent the lower and upper bounds of the j-th dimension in the search space, respectively. is a random number uniformly distributed in the interval [0,1], I represents a random integer from the integer set {1, 2}, represents the component of the i-th individual in the j-th dimension at the p-th iteration, represents the component of the i-th individual in the j-th dimension at the p+1-th iteration, Represents the temporary position vector The fitness value of the corresponding solution, is the fitness value corresponding to the position of the i-th individual at the p-th iteration;

[0084] In the search space, the random perturbation mechanism of the simulated individual when facing dynamic perturbations is simulated.

[0085] When the random perturbation mechanism is triggered, a random position is generated near the current position of each individual. The formula is as follows:

[0086] ,

[0087] ,

[0088] ,

[0089] Among them, i represents the number of the individual selected for location update, represents the number of iterations, and are the upper and lower bounds of the j-th dimension variable updated with the number of iterations, is the maximum number of iterations;

[0090] Calculate the fitness value of each individual and execute the greedy selection strategy again;

[0091] If the fitness of the individual after the update is better than the fitness value before the update, the updated position is accepted, otherwise the position is not updated;

[0092] Continue iterating until the preset maximum number of iterations is reached =50, after the iteration, the optimal solution is output and the corresponding optimal value The optimal solution is the optimal combination of the kernel width parameter γ and the noise accuracy parameter α of the correlation vector machine model. and Substitute into the relevant vector machine model and input the training set , the correlation vector machine model fits the nonlinear relationship between input features and carbon assimilation rate, and outputs a time series carbon assimilation rate model ;

[0093] The time series carbon assimilation rate model was trained multiple times, and the model with the smallest mean root mean square error and the largest coefficient of determination was selected as the final time series carbon assimilation rate model using 5-fold cross validation. .

[0094] The construction of the edible fungus segmented time series respiratory rate model comprises the following steps:

[0095] Based on the respiration of edible fungi Release rate time series dataset According to the time range corresponding to the differentiation stage and development stage of edible fungi fruiting body, the data of the corresponding stage of the time range is extracted, and the number of days of edible fungi cultivation is calculated. As input, the respiration of edible fungi during the fruiting body differentiation and development stages Release rate For the output, the polynomial regression algorithm is used for segmented modeling of these two stages:

[0096] ,

[0097] ,

[0098] in, It is a temporal respiration rate model for the fruiting body differentiation stage of edible fungi. It is a temporal respiration rate model of the fruiting body development stage of edible fungi. It is the fruiting body differentiation stage of edible fungi. It is the growth cycle of edible fungi. is the number of days for growing edible fungi, n and m are the orders of the polynomials, , ,…, 、 , ,…, is the coefficient to be determined;

[0099] The coefficients to be determined are fitted by the least squares method to form a segmented time series respiratory rate model of edible fungi. , the formula is as follows:

[0100] .

[0101] The construction of a dynamic control model for the carbon cycle of edible fungi and hydroponic vegetable cultivation warehouses includes the following steps:

[0102] Obtaining the optimal planting ratio of edible fungi and hydroponic vegetables: Discretize the complete control cycle T of the edible fungi and hydroponic vegetable planting warehouse into k time nodes to form a time series , where k is the number of time nodes, represents the kth time node in the time series, , , is the time interval between adjacent time nodes, represents the c+1th time node in the time series, represents the cth time node in the time series,

[0103] Use light intensity , the number of hydroponic vegetables planted , Edible Fungi Segmented Time Series Respiration Rate Model and a temporal carbon assimilation rate model for hydroponic vegetables As the input of the carbon cycle dynamic control model of edible fungi and hydroponic vegetable planting warehouse, on the basis of meeting the carbon metabolism balance, the optimization objective function is constructed to determine the optimal edible fungi planting quantity. , the optimization objective function is:

[0104] ,

[0105] in, and are the planting quantities of edible fungi and hydroponic vegetables respectively, The number of edible mushroom sticks planted. The number of batches of hydroponic vegetable planting, is the time node, is the respiration of edible fungi at the cth time point Release rate, is the carbon assimilation rate of hydroponic vegetables at time node c, Expressed as the number of edible fungi planted Perform rounding operation upwards. is the regularization coefficient, It's about The penalty function, Control the intensity of punishment,

[0106] Obtain the optimal After that, the optimal planting ratio of edible fungi and hydroponic vegetables is obtained.

[0107] The optimal planting ratio of edible fungi and hydroponic vegetables in the planting warehouse In the planting mode, the carbon cycle time series dynamic control model of edible fungi and hydroponic vegetable planting warehouse is used to dynamically control the environment. The formula of the carbon cycle time series dynamic control model of edible fungi and hydroponic vegetable planting warehouse is as follows:

[0108] ,

[0109] in, To dynamically control the target value of carbon cycle timing in the planting warehouse, For planting warehouse The adaptive loop control algorithm controls the concentration, Using a grow barn for a grow barn Storage and release dynamic control mechanism Concentration gain, is the real time;

[0110] The adaptive cycle control algorithm uses the PID algorithm to control the air exchange circulation fan power between the edible fungus cultivation chamber and the hydroponic vegetable cultivation chamber, that is, the realization of the PWM duty cycle. According to the time-series carbon cycle balance relationship between edible fungi and hydroponic vegetables, the edible fungus segmented time-series respiration rate model and the hydroponic vegetable time-series carbon assimilation rate model are integrated to calculate the change factor of the time-series Kp proportional coefficient, and then establish an adaptive PID control algorithm. The formula is as follows:

[0111] ,

[0112] in, The target PWM duty cycle of the air exchange circulation fan in the growing room is: is the current system time, The daytime hours of the photoperiod, The number of days that edible fungi and hydroponic vegetables are grown together in the growing chamber. For the Carbon assimilation rate of hydroponic vegetables per day Respiration of edible fungi Release rate The ratio of , is the differential coefficient of the PID control algorithm, Real-time monitoring of edible fungi and hydroponic vegetable planting warehouses concentration difference;

[0113] The constraints of the adaptive loop control algorithm are: ,

[0114] 、 They are real-time data of edible fungi and hydroponic vegetable planting warehouses. concentration, 、 Suitable for edible fungi and hydroponic vegetable planting warehouses Upper concentration limit;

[0115] Concentration gain Through the planting warehouse Dynamic control mechanism of storage and release get, The control logic is as follows:

[0116] ,

[0117] in, For planting warehouse Storage dynamic control mechanism, For planting warehouse Release dynamic control mechanisms;

[0118] Planting Barn The control logic of the release dynamic control mechanism is as follows:

[0119] ,

[0120] in, This is the lower limit of the suitable concentration in the hydroponic vegetable cultivation room. The length of the night;

[0121] Planting Barn The control logic constraints for releasing the dynamic control mechanism are:

[0122] .

[0123] The dynamic regulation of the carbon cycle timing of the planting warehouse includes the following steps:

[0124] Substitute parameter values into In the adaptive loop control algorithm, the parameter values include the current system time , daytime hours of the photoperiod , No. Carbon assimilation rate of hydroponic vegetables Respiration of edible fungi Release rate Ratio And real-time data of edible fungi and hydroponic vegetables in the planting warehouse Concentration difference , and combined with Adaptive cycle control algorithm constraints, real-time calculation of the target PWM duty cycle of the air exchange circulation fan, control of the air exchange circulation fan power, to achieve timing Adaptive regulation makes edible fungi and hydroponic vegetables grow in the warehouse The concentrations were all within the suitable concentration range for their growth;

[0125] Planting Barn Application of dynamic control mechanism of storage and release:

[0126] Planting Barn Storage dynamic control mechanism: real-time monitoring of the edible fungus cultivation warehouse concentration ,when > When the planting chamber is triggered Storage dynamic control mechanism , the system starts the compressor to perform air compression operation; when < , the compressor stops working; among them, Suitable for edible fungus cultivation warehouse Upper concentration limit, Suitable for edible fungus cultivation warehouse Lower concentration limit;

[0127] Planting Barn Unleashing dynamic control mechanisms: Monitoring the hydroponic vegetable growing chamber concentration , Current system time as well as The value of

[0128] when And when the constraints are met, the planting warehouse is triggered Release dynamic control mechanism , the system starts the compressor and performs compressed air release operation; when it detects , the compressor stops working; among them, Suitable for hydroponic vegetable planting warehouse Upper concentration limit, It is the lower limit of suitable concentration in hydroponic vegetable cultivation room. The number of days that edible fungi and hydroponic vegetables are grown together in the growing chamber. For the Carbon assimilation rate of hydroponic vegetables per day Respiration of edible fungi Release rate The ratio of .

[0129] The planting warehouse includes the following: Data acquisition subsystem: Set up sensors to monitor the growth environment parameters of edible fungi and hydroponic vegetables in real time and Concentration key data, and transmit the collected data to the data fusion subsystem, the sensors include temperature and humidity sensors, sensor, Sensors, light intensity sensors, solutions Value and Value measurement sensor;

[0130] Data fusion subsystem: By coupling and calibrating the time axis of the edible fungus fruiting body period and the hydroponic vegetable growth sequence, the total control cycle of the planting warehouse is obtained and the number of edible mushroom stick batches grown during the entire growth cycle and the number of hydroponic vegetable planting batches The optimal planting ratio of edible fungi and hydroponic vegetables is automatically generated by matching the embedded edible fungi segmented time series respiration rate model and the hydroponic vegetable time series carbon assimilation rate model. and Carbon assimilation rate of hydroponic vegetables Respiration of edible fungi Release rate Ratio , thereby obtaining a dynamic control method for the carbon cycle timing of the planting warehouse for the current planting category; the data fusion subsystem combines the real-time environmental parameter values transmitted by the data acquisition subsystem to send control instructions to the dynamic feedback control subsystem;

[0131] Dynamic feedback control subsystem: Receives control instructions transmitted by the data fusion subsystem, drives the actuators to cooperate and regulate the environmental parameters in the planting chamber;

[0132] A cultivation box, an air compression storage device and a control device, wherein an airtight partition is slidingly provided in the cultivation box, the airtight partition divides the cultivation box into an edible fungus cultivation bin and a hydroponic vegetable cultivation bin, the cultivation box is provided with vents in the edible fungus cultivation bin and the hydroponic vegetable cultivation bin, respectively, an air exchange circulation fan is provided on the upper part of the airtight partition, the air compression storage device includes a compressor and an air inlet pipe and an air outlet pipe provided on both sides of the compressor, the air inlet pipe is connected to the edible fungus cultivation bin, and the air outlet pipe is connected to the hydroponic vegetable cultivation bin, the vents, the air inlet pipe and the air outlet pipe are all controlled to open and close by an electromagnetic valve, the air exchange circulation fan is controlled to open and close and the wind speed is adjusted by a motor, and the control device is used to receive The sensor signal is used to control the operation of the compressor, solenoid valve and air exchange circulation fan according to the dynamic control method of the preset carbon cycle timing of the planting bin.

[0133] A computer-readable storage medium stores a computer program. When executed by a processor, the computer program implements a method for dynamically controlling the timing of carbon cycles in a growing chamber containing edible fungi and hydroponic vegetables. A computer device includes a memory, a processor, and a computer program stored in the memory and running on the processor. When executed by the processor, the computer program implements a method for dynamically controlling the timing of carbon cycles in a growing chamber containing edible fungi and hydroponic vegetables.

[0134] Beneficial effects

[0135] The present invention is a method for dynamically controlling the carbon cycle timing of edible fungi and hydroponic vegetable planting warehouses. Compared with the existing technology, by integrating the growth characteristics of edible fungi respiration and hydroponic vegetable photosynthesis, it innovatively proposes a method and system for dynamically controlling the carbon cycle timing of a group, realizing the goal of the fungus-vegetable symbiotic system. The precise supply and demand matching has significantly improved the efficiency of carbon resource utilization and effectively reduced carbon emissions.

[0136] The present invention adopts a hybrid intelligent optimization algorithm of the correlation vector machine model optimized by the improved raccoon optimization algorithm to establish a hydroponic vegetable time series carbon assimilation rate model, and at the same time combines the edible fungus time series respiration rate model constructed by polynomial regression analysis to construct a multi-model fusion edible fungus-hydroponic vegetable model. The adaptive dynamic control model has strong scalability and can adapt to the combination of different strains of bacteria and hydroponic vegetable categories. By designing a complete model and integrating it into the system solution, The precise matching of cumulative release and absorption throughout the entire cycle fills the technical gap in systematic carbon cycle regulation solutions.

[0137] The present invention adopts a dynamic control system based on embedded multi-model and edible fungus growth Inhibitory concentration and hydroponic vegetables The appropriate concentration constraint condition is used to control the air exchange circulation fan speed and the air valve opening to ensure the system The concentration is always within the optimal range. Compared with the traditional static threshold control method, this invention significantly improves the control accuracy and response speed through the fusion of multi-source environmental data and a closed-loop feedback mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0138] Figure 1 This is a method sequence diagram of the present invention;

[0139] Figure 2 This is a time-series carbon assimilation rate model diagram of a single hydroponic lettuce plant within one growth cycle according to an embodiment of the present invention;

[0140] Figure 3 This is a time series respiration rate model diagram of a single Pleurotus ostreatus mushroom stick during the fruiting body period according to an embodiment of the present invention;

[0141] Figure 4 The total planting cycle of the planting warehouse system according to the embodiment of the present invention is Internal determination of light intensity for hydroponic lettuce cultivation 150 Curve diagram of the coupled model of temporal absorption and release. DETAILED DESCRIPTION

[0142] In order to provide a further understanding and appreciation of the structural features and effects achieved by the present invention, a detailed description is provided with reference to preferred embodiments and accompanying drawings as follows:

[0143] like Figure 1 As shown, the method for dynamically controlling the carbon cycle timing of an edible fungus and hydroponic vegetable cultivation warehouse according to the present invention comprises the following steps:

[0144] The first step is to determine the regulation cycle of edible fungi and hydroponic vegetables: couple and calibrate the time axis of edible fungi fruiting body period and hydroponic vegetable growth sequence to determine the complete regulation cycle of edible fungi and hydroponic vegetable planting warehouse In this way, the carbon cycle supply and demand mismatch problem caused by the asynchronous growth cycle of mushrooms and vegetables in the traditional planting mode is solved. The precise coordination of the growth stages of mushrooms and vegetables in the whole cycle is achieved through time phase calibration, which significantly improves the efficiency of the system. dynamic balance ability.

[0145] (1) Determine the growth cycle of edible fungi and hydroponic vegetables respectively. Assume that the growth cycle of a single crop of edible fungi is Days, long under suitable conditions The growth cycle of edible fungi The growth cycle of hydroponic vegetables is sky.

[0146] (2) Coupling calculation of the complete control cycle of edible fungi and hydroponic vegetable cultivation warehouses ,

[0147] The total cycle of co-cultivation of edible fungi and hydroponic vegetables, To calculate the least common multiple,

[0148] The number of mushroom stick batches planted during the entire growth cycle is , number of batches of hydroponic vegetables planted , make sure to harvest together.

[0149] The second step involved collecting and preprocessing time-series data on CO2 emissions from edible fungi and hydroponic vegetable populations. The Z-score method was used to denoise the collected time-series data. This eliminated outliers often caused by environmental interference or equipment errors during traditional data collection, providing a high-quality data foundation for subsequent model construction.

[0150] Obtaining the respiration of edible fungi during the growth stage Time-series release dataset and carbon assimilation of hydroponic vegetables under different light intensities throughout their growth period Time series demand data set, for abnormal data The method performs denoising; it includes the following steps:

[0151] (1) Using the edible fungus group carbon flux monitoring device, under the conditions of suitable and stable microenvironment, high-precision The sensor monitors the respiration of a single edible mushroom stick in the fruiting body growth stage in real time, and obtains Time series release dataset .

[0152] (2) Using plant group carbon assimilation rate measurement equipment, measure the group carbon assimilation of the whole hydroponic vegetable under different light intensities during the entire growth cycle Time series requirement dataset .

[0153] (3) Adoption Law Time series release dataset and group carbon assimilation Time series requirement dataset To handle the outliers in

[0154] set up Time series release dataset Include value, recorded as , ,…, ,in Indicates the number of individual data;

[0155] Assume that group carbon assimilation Time series requirement dataset Include value, recorded as , ,…, ,in Indicates the number of data, calculate separately Time series release dataset and group carbon assimilation Time series requirement dataset The mean and standard deviation ,

[0156] ,

[0157] ,

[0158] in, 、 Respectively represent Time series release dataset The mean and standard deviation of 、 Represents group carbon assimilation Time series requirement dataset The mean and standard deviation of 、 To sum the index, express Time series release dataset The ath data in; represents group carbon assimilation Time series requirement dataset The bth data in

[0159] Then calculate the Z value of each data point:

[0160] ,in, represent Time series release dataset The Z value of the a-th data point in Representative group carbon assimilation Time series requirement dataset The Z value of the b-th data point in

[0161] Setting thresholds >3 and >3, remove outliers that exceed the threshold.

[0162] (4) Timing Rate of change calculation:

[0163] A1) Time series release dataset , group carbon assimilation Time series requirement dataset Press = Calculate the concentration change rate at 60s intervals:

[0164] ,

[0165] ,

[0166] in: and The unit is ppm / min. is the time in seconds, represent The carbon flux monitoring device of edible fungi population was used to measure the concentration, represent The carbon flux monitoring device of edible fungi population was used to measure the concentration, represent The carbon assimilation rate of plant populations was measured by the device concentration, represent The carbon assimilation rate of plant populations was measured by the device concentration, Edible mushrooms per minute Release rate, Hydroponic vegetables per minute Absorption rate;

[0167] By calculating the concentration change rate of edible fungi and hydroponic vegetables in multiple time periods every day during the planting cycle, the concentration of edible fungi per minute per day during the planting cycle is calculated. Average release rate and hydroponic vegetables under different light intensities Average absorption rate , respectively expressed as:

[0168] ,

[0169] ,

[0170] Where N is the number of measurements in multiple valid time periods in a day, i is the summation index, The number of days for growing edible fungi, Number of days for hydroponic vegetable cultivation, For the Edible fungi per minute Release rate, For different light intensities hydroponic vegetables per minute Absorption rate, The growth cycle of edible fungi. For the growth cycle of hydroponic vegetables;

[0171] A2) Combined with the effective volume of the edible fungus colony carbon flux monitoring device Calculate the time in days Release rate, generating edible fungus respiration Release rate time series dataset , the specific calculation method is:

[0172] ,

[0173] in, Representative Respiration of edible fungi Release rate, in m 3 / sky, The unit is m 3 ;

[0174] A3) Assume that the daytime length of the photoperiod for hydroponic vegetable cultivation is hours, and nights are hours, combined with the effective volume of the plant community carbon assimilation rate measurement equipment Convert to days Absorption rate, generating a time series dataset of carbon assimilation rate of hydroponic vegetables , The calculation method is:

[0175] ,

[0176] in, Representative Carbon assimilation rate of hydroponic vegetables per day, unit: m 3 / sky, The unit is m 3 .

[0177] The third step is to construct a time-series carbon assimilation rate model for hydroponic vegetables based on the improved raccoon optimization algorithm.

[0178] Modeling the carbon assimilation rate of hydroponic vegetables faces significant technical challenges. First, existing research on vegetables is mostly limited to single-leaf measurements, lacking accurate determination and analysis of key data such as the amount of carbon dioxide required by the entire vegetable plant. Second, the temporal carbon assimilation rate of hydroponic vegetables is affected by multivariate coupling and exhibits significant nonlinear dynamic changes. Traditional fixed-parameter models have difficulty capturing the changing laws of this multi-factor dynamic coupling. When faced with sudden environmental changes, the model prediction error is large and cannot meet the needs of precise regulation of the carbon cycle. Therefore, the present invention proposes a method for modeling the temporal carbon assimilation rate of hydroponic vegetables based on an improved raccoon optimization algorithm.

[0179] The StandardScaler method was used for standardization to ensure that the contribution weights of different features to the RVM model were consistent, improving parameter optimization efficiency and model convergence speed. To further optimize the kernel width parameter γ and noise accuracy parameter α of the RVM model, an improved Raccoon optimization algorithm was used to optimize the hyperparameters. Compared to the traditional Raccoon Optimization algorithm, the algorithm 1) designs a dynamic population size adjustment module: It calculates the standardized variance of the average Euclidean distance of individuals to assess population diversity. When the diversity falls below a threshold of 0.3 and the population size is less than 100, new individuals are randomly generated to expand the population to enhance global exploration. When the diversity exceeds a threshold of 0.3 and the population size exceeds 30, poor solutions are eliminated and the population is reduced to improve optimization efficiency. This dynamically balances population size and diversity, avoiding premature convergence while balancing search accuracy and computational efficiency. 2) For individual position updates, a learning rate adjustment strategy is designed for position-adaptive updates. A composite function based on an iteration number decay factor and a diversity feedback coefficient is constructed. Initially, the algorithm uses a large step size for global exploration to rapidly cover different regions of the solution space and avoid exploration blind spots caused by the random distribution of initial parameters. As iterations progress, the learning rate is gradually reduced, shifting to localized, refined exploration, focusing on the neighborhood of the current optimal solution and improving parameter fine-tuning accuracy. Based on this, a related vector machine model is constructed and the final model is selected through 5-fold cross-validation. The algorithm balances global exploration and local development by dynamically adjusting the population size to avoid premature convergence; it adopts a dual position update strategy to improve the convergence speed and parameter optimization accuracy; the kernel width parameters and noise precision parameters of the optimized correlation vector machine model are better, which enhances the nonlinear fitting ability of time series data, improves sparsity and generalization ability, reduces the risk of overfitting, is insensitive to initial parameters, adapts to the characteristics of different data sets, and provides reliable dynamic model support for carbon cycle regulation.

[0180] (1) The hydroponic vegetable carbon assimilation rate time series dataset Divide into training set With the test set , where the number of days of hydroponic vegetable cultivation and light intensity are the characteristics, and the carbon assimilation rate is the label.

[0181] (2) Adoption Method to standardize the data:

[0182] ,

[0183] ,

[0184] ,

[0185] in, 、 、 They are the number of days for hydroponic vegetable cultivation, light intensity, and carbon assimilation rate, 、 、 are the standardized hydroponic vegetable planting days, the standardized light intensity, and the standardized carbon assimilation rate, 、 They are The mean and standard deviation of 、 They are The mean and standard deviation of 、 for The mean and standard deviation of The unit is .

[0186] (3) Set the RVM model and select the kernel function of the RVM model as the Gaussian kernel function ,

[0187] in, 、 The kernel width parameter γ and noise precision parameter α of the RVM model are optimized by using the improved Raccoon optimization algorithm.

[0188] The range of kernel width parameter γ of the RVM model is set to γ∈[10 -3 ,1], the range of noise accuracy parameter α is α∈[10 -3 ,1], set the population size N of the improved raccoon optimization algorithm to 20, and the maximum number of iterations The number of times is set to 50, and the fitness function F is defined as the mean square error of the RVM model test set, and the calculation formula is:

[0189] ,

[0190] in, is the number of test set samples, m represents the mth sample in the test set, is the actual carbon assimilation rate value of the mth sample in the test set, is the model's predicted value for the mth sample.

[0191] (4) Initialize the population and encode the position of each individual. The encoding method is as follows:

[0192] ,

[0193] in, 、 Representing the population The encoding values of the kernel width parameter and noise precision parameter of the correlation vector machine model corresponding to each individual, 、 are the lower and upper bounds of the kernel width parameter γ, 、 are the lower and upper bounds of the noise accuracy parameter α, is a random number uniformly distributed in the interval [0,1]. is the index of the individual in the population, N is the population size;

[0194] The position vectors of N=20 raccoon individuals are generated randomly. , , and use the fitness function F to calculate the fitness value of each individual as the initial value.

[0195] (5) Design a dynamic population size adjustment module: calculate the average Euclidean distance of each individual in the solution space, and use the standardized distance variance as the population diversity evaluation indicator; when the population is initialized or updated each time, evaluate the diversity of the current population by calculating the average Euclidean distance between the individual position in the population and the average of all individual positions. When the population diversity is lower than the threshold of 0.3 and the population size is lower than the maximum population size of 100, update the population: randomly generate new sample individuals and expand the population size; when the population diversity is higher than the threshold of 0.3 and the population size is higher than the minimum population size of 30, update the population: use the fitness function F to calculate the fitness value of each individual, eliminate the worst individual, and reduce the population size.

[0196] (6) Using the encoded γ and α in the training set on the hydroponic vegetable carbon assimilation rate time series dataset, Train the RVM model and use the trained RVM model in the test set The prediction is made on the , and the fitness value is calculated by the difference between the predicted result and the true value. The fitness values of all individuals are traversed, and the individual with the smallest fitness is selected as the current optimal solution. The γ and α parameter combinations corresponding to the optimal solution are recorded as and , and its position vector is recorded as .

[0197] (7) In the search space, select Each individual updates its position, and the update formula is:

[0198] ,

[0199] Among them, N is the population size, p is the current number of iterations, represents the component of the i-th individual in the j-th dimension at the p-th iteration, represents the component of the i-th individual in the j-th dimension at the p+1-th iteration, represents the component of the optimal individual in the jth dimension at the pth iteration, is a random number uniformly distributed in the interval [0,1], I represents a random integer from the integer set {1, 2}, and i represents the number of the individual selected for location update.

[0200] (8) The fitness function F is used to calculate the fitness value of each individual, and a greedy selection is performed. If the fitness of the individual after the update is better than the fitness value before the update, the updated position is retained, otherwise the position is not updated. The greedy selection strategy formula is as follows:

[0201] ,

[0202] in, is the position of the i-th individual at the p+1-th iteration, is the position of the i-th individual at the p-th iteration, and are the fitness values corresponding to the positions of the i-th individual at the p+1-th iteration and the p-th iteration, respectively.

[0203] (9) Design a learning rate adjustment strategy for position adaptive updates:

[0204] Construct a composite function based on the iteration attenuation factor and the diversity feedback coefficient. The learning rate update formula is:

[0205] ,

[0206] in, is the current iteration number, is the attenuation factor, is the maximum number of iterations, is the number of diversity solution sets, i is the summation variable, is the diversity feedback coefficient, For population diversity, represents the learning rate at the p-th iteration, represents the learning rate at the p+1th iteration; a dual control mechanism is used to achieve coordinated optimization of global exploration and local development: the global search mode is adopted at the beginning of the iteration, and as the number of iterations increases, the local development mode is started to speed up the positioning of the global optimal neighborhood.

[0207] (10) Select another Individuals update their positions in the search space,

[0208] First, randomly generate a temporary position vector in the search space , and its calculation formula is:

[0209] ,

[0210] The location is updated as follows:

[0211] ,

[0212] ;

[0213] Among them, i represents the number of the individual selected for location update, N is the population size, and Represent the lower and upper bounds of the j-th dimension in the search space, respectively. is a random number uniformly distributed in the interval [0,1], I represents a random integer from the integer set {1, 2}, represents the component of the i-th individual in the j-th dimension at the p-th iteration, represents the component of the i-th individual in the j-th dimension at the p+1-th iteration, Represents the temporary position vector The fitness value of the corresponding solution, is the fitness value corresponding to the position of the i-th individual at the p-th iteration.

[0214] (11) In the search space, simulate the random perturbation mechanism of individuals when facing dynamic perturbations,

[0215] When the random perturbation mechanism is triggered, a random position is generated near the current position of each individual. The formula is as follows:

[0216] ,

[0217] ,

[0218] ,

[0219] Among them, i represents the number of the individual selected for location update, represents the number of iterations, and are the upper and lower bounds of the j-th dimension variable updated with the number of iterations, is the maximum number of iterations.

[0220] (12) Calculate the fitness value of each individual and execute the greedy selection strategy again;

[0221] If the fitness of the individual after the update is better than the fitness value before the update, the updated position is accepted, otherwise the position is not updated.

[0222] (13) Continue iterating until the preset maximum number of iterations is reached =50, after the iteration, the optimal solution is output and the corresponding optimal value The optimal solution is the optimal combination of the kernel width parameter γ and the noise accuracy parameter α of the correlation vector machine model. and Substitute into the relevant vector machine model and input the training set , the correlation vector machine model fits the nonlinear relationship between input features and carbon assimilation rate, and outputs a time series carbon assimilation rate model .

[0223] (14) Train the time series carbon assimilation rate model multiple times and use 5-fold cross validation to select the model with the smallest mean root mean square error and the largest coefficient of determination as the final time series carbon assimilation rate model. .

[0224] The fourth step is to construct a segmented time series respiratory rate model for edible fungi. The polynomial regression analysis method with time as the independent variable and respiratory rate as the dependent variable can accurately describe the growth stage of edible fungi fruiting bodies. The dynamic characteristics of the model, through the combination of segmented modeling and continuous fitting, significantly improve the model's predictive accuracy and applicability. It can provide a quantitative basis for optimizing the carbon cycle in mushroom-vegetable symbiotic systems. In terms of scalability, the model framework can be adapted to different mushroom species, providing a methodological reference for carbon cycle research in agricultural edible mushroom cultivation.

[0225] (1) Based on the respiration of edible fungi Release rate time series dataset According to the time range corresponding to the differentiation stage and development stage of edible fungi fruiting body, the data of the corresponding stage of the time range is extracted, and the number of days of edible fungi cultivation is calculated. As input, the respiration of edible fungi during the fruiting body differentiation and development stages Release rate For the output, the polynomial regression algorithm is used for segmented modeling of these two stages:

[0226] ,

[0227] ,

[0228] in, It is a temporal respiration rate model for the fruiting body differentiation stage of edible fungi. It is a temporal respiration rate model of the fruiting body development stage of edible fungi. It is the fruiting body differentiation stage of edible fungi. It is the growth cycle of edible fungi. is the number of days for growing edible fungi, n and m are the orders of the polynomial, , ,…, 、 , ,…, is the coefficient to be determined.

[0229] (2) Fit the coefficients to be determined by the least squares method to form a segmented time series respiratory rate model of edible fungi , the formula is as follows:

[0230] .

[0231] The fifth step is to construct a dynamic control model of the carbon cycle in edible fungi and hydroponic vegetable cultivation warehouses.

[0232] (1) Obtaining the optimal planting ratio of edible fungi and hydroponic vegetables: Discretize the complete control cycle T of the edible fungi and hydroponic vegetable planting warehouse into k time nodes to form a time series , where k is the number of time nodes, represents the kth time node in the time series, , , is the time interval between adjacent time nodes, represents the c+1th time node in the time series, represents the cth time node in the time series,

[0233] Light intensity , the number of hydroponic vegetables planted , Edible Fungi Segmented Time Series Respiration Rate Model and a temporal carbon assimilation rate model for hydroponic vegetables As the input of the carbon cycle dynamic control model of edible fungi and hydroponic vegetable planting warehouse, on the basis of meeting the carbon metabolism balance, the optimization objective function is constructed to determine the optimal edible fungi planting quantity. , the optimization objective function is:

[0234] ,

[0235] in, and are the planting quantities of edible fungi and hydroponic vegetables respectively, The number of edible mushroom sticks planted. The number of batches of hydroponic vegetable planting, is the time node, is the respiration of edible fungi at the cth time point Release rate, is the carbon assimilation rate of hydroponic vegetables at time node c, Expressed as the number of edible fungi planted Perform rounding operation upwards. is the regularization coefficient, It's about The penalty function, Control the intensity of punishment,

[0236] Obtain the optimal After that, the optimal planting ratio of edible fungi and hydroponic vegetables is obtained. .

[0237] (2) Optimal planting ratio of edible fungi and hydroponic vegetables in the planting warehouse In the planting mode, the carbon cycle time series dynamic control model of edible fungi and hydroponic vegetable planting warehouse is used to dynamically control the environment. The formula of the carbon cycle time series dynamic control model of edible fungi and hydroponic vegetable planting warehouse is as follows:

[0238] ,

[0239] in, To dynamically control the target value of carbon cycle timing in the planting warehouse, For planting warehouse The adaptive loop control algorithm controls the concentration, Using a grow barn for a grow barn Storage and release dynamic control mechanism Concentration gain, The real time.

[0240] (3) The adaptive cycle control algorithm uses the PID algorithm to control the air exchange circulation fan power between the edible fungus cultivation chamber and the hydroponic vegetable cultivation chamber, that is, the realization of the PWM duty cycle. According to the time-series carbon cycle balance relationship between edible fungi and hydroponic vegetables, the edible fungus segmented time-series respiration rate model and the hydroponic vegetable time-series carbon assimilation rate model are integrated to calculate the change factor of the time-series Kp proportional coefficient, and then establish an adaptive PID control algorithm. The formula is as follows:

[0241] ,

[0242] in, The target PWM duty cycle of the air exchange circulation fan in the growing room is: is the current system time, The daytime hours of the photoperiod, The number of days that edible fungi and hydroponic vegetables are grown together in the growing chamber. For the Carbon assimilation rate of hydroponic vegetables per day Respiration of edible fungi Release rate The ratio of , is the differential coefficient of the PID control algorithm, Real-time monitoring of edible fungi and hydroponic vegetable planting warehouses concentration difference;

[0243] The constraints of the adaptive loop control algorithm are: ,

[0244] 、 They are real-time data of edible fungi and hydroponic vegetable planting warehouses. concentration, 、 Suitable for edible fungi and hydroponic vegetable planting warehouses Upper concentration limit.

[0245] (4) Concentration gain Through the planting warehouse Dynamic control mechanism of storage and release get, The control logic is as follows:

[0246]

[0247] in, For planting warehouse Storage dynamic control mechanism, For planting warehouse Release dynamic control mechanisms;

[0248] Planting Barn The control logic of the release dynamic control mechanism is as follows:

[0249]

[0250] in, This is the lower limit of the suitable concentration in the hydroponic vegetable cultivation room. The length of the night;

[0251] Planting Barn The control logic constraints for releasing the dynamic control mechanism are:

[0252] .

[0253] The sixth step is the dynamic regulation of the carbon cycle timing in the planting warehouse: Integrate the dynamic regulation model of the carbon cycle timing in the edible fungus and hydroponic vegetable planting warehouse into the planting warehouse system to achieve dynamic regulation of the carbon cycle timing.

[0254] (1) Substitute the parameter values into In the adaptive loop control algorithm, the parameter values include the current system time , daytime hours of the photoperiod , No. Carbon assimilation rate of hydroponic vegetables Respiration of edible fungi Release rate Ratio And real-time data of edible fungi and hydroponic vegetables in the planting warehouse Concentration difference , and combined with Adaptive cycle control algorithm constraints, real-time calculation of the target PWM duty cycle of the air exchange circulation fan, control of the air exchange circulation fan power, to achieve timing Adaptive regulation makes edible fungi and hydroponic vegetables grow in the warehouse The concentrations were all within the suitable range for their growth.

[0255] (2) Planting warehouse Application of dynamic control mechanism of storage and release:

[0256] B1) Planting Barn Storage dynamic control mechanism: real-time monitoring of the edible fungus cultivation warehouse concentration ,when > When the planting chamber is triggered Storage dynamic control mechanism , the system starts the compressor to perform air compression operation; when < , the compressor stops working; among them, Suitable for edible fungus cultivation warehouse Upper concentration limit, Suitable for edible fungus cultivation warehouse Lower concentration limit;

[0257] B2) Planting Barn Unleashing dynamic control mechanisms: Monitoring the hydroponic vegetable growing chamber concentration , Current system time as well as The value of

[0258] when And when the constraints are met, the planting warehouse is triggered Release dynamic control mechanism , the system starts the compressor and performs compressed air release operation; when it detects , the compressor stops working; among them, Suitable for hydroponic vegetable planting warehouse Upper concentration limit, The lower limit of suitable concentration in hydroponic vegetable planting warehouse, The number of days that edible fungi and hydroponic vegetables are grown together in the growing chamber. For the Carbon assimilation rate of hydroponic vegetables per day Respiration of edible fungi Release rate The ratio of .

[0259] The following is further explained using oyster mushrooms and hydroponic lettuce as examples.

[0260] Step 1: Determine the regulation cycle of oyster mushroom and hydroponic lettuce. The growth cycle of hydroponic lettuce is Days; Single crop growth cycle of Pleurotus ostreatus Days, long under suitable conditions The growth cycle of Oyster mushroom days; therefore, the complete regulation cycle of the oyster mushroom and hydroponic lettuce cultivation warehouse is obtained by coupling calculation Days, number of mushroom log batches planted during the entire growth cycle Batch, number of batches of hydroponic lettuce planted Batch, so after planting a batch of oyster mushrooms and hydroponic lettuce separately, they can be harvested together.

[0261] Step 2: Collecting Oyster Mushrooms and Hydroponic Lettuce Groups Time series data and preprocessing.

[0262] (1) The average size of the selected oyster mushroom sticks was 10*10*16 cm. When the fruiting body period came, the single oyster mushroom sticks were placed in the edible fungus group carbon flux monitoring device for cultivation. All parameters were set within the range suitable for growth, that is, the ambient temperature was controlled at 22℃, the humidity was maintained at 85%, and the light was normal scattered light. During the entire growth stage, five groups of samples were collected in the morning, afternoon, and night time periods respectively. Concentration data, select the time series within the ventilation interval of two devices The concentration data is valid data. The outliers in the data set are removed by Z-score method and then analyzed by time series. Calculate the rate of change and obtain the respiration of Oyster mushroom Release rate time series dataset.

[0263] (2) The selected hydroponic lettuce variety was glass lettuce, which was cultured using Japanese garden-style universal nutrient solution. The hydroponic lettuce seedlings were placed in a hydroponic tank with a size of 29.6*19.7 cm. Eight lettuces were planted in each hydroponic tank and divided into five groups. Each group of lettuces was cultured under different light intensities, with light intensities set to 50, 100, 150, 200, and 300, respectively. During the planting period, the temperature and relative humidity of the growing environment were maintained at 22 / 18±1℃ and 65 / 60% during the day and night, respectively, and the photoperiod was set to 14 / 10 hours, i.e. Hour, hours, the nutrient solution concentration is about 2.80 dS·m -1 Every five days, the hydroponic lettuce was placed in the plant group carbon flux measurement device to measure the 5-minute time series carbon assimilation data. The outliers in the data set were removed by the Z-score method, and then the time series carbon assimilation data were used to measure the carbon flux of the plant group. Calculate the rate of change and obtain a time series dataset of carbon assimilation rate of hydroponic lettuce.

[0264] Step 3: Based on the hydroponic lettuce carbon assimilation rate time series dataset, the number of hydroponic lettuce planting days was used to calculate the carbon assimilation rate of hydroponic lettuce. , light intensity As a two-dimensional input, the carbon assimilation rate As the output, a temporal carbon assimilation rate model of hydroponic lettuce was constructed: the kernel function of the correlation vector machine model was selected as the Gaussian kernel function, and the improved raccoon optimization algorithm was used to optimize the kernel width parameter γ and the noise precision parameter α of the correlation vector machine model; the range of the kernel width parameter γ of the correlation vector machine model was set to γ∈[10-3,1], the range of the noise precision parameter α was set to α∈[10-3,1], the population size N of the improved raccoon optimization algorithm was set to 20, and the maximum number of iterations was set to 0. Set to 50 times; the optimal parameter combination obtained by the improved raccoon optimization algorithm and Set as the parameters of the relevance vector machine (RVM) model, input the standardized training data, the relevance vector machine model fits the nonlinear relationship between the input features and the carbon assimilation rate, outputs the time series carbon assimilation rate model, trains the time series carbon assimilation rate model multiple times, and uses 5-fold cross validation to select the model with the minimum average root mean square error and the maximum determination coefficient as the final time series carbon assimilation rate model for hydroponic lettuce The time series carbon assimilation rate model of a single hydroponic lettuce plant in a growth cycle is shown in the figure below: Figure 2 shown.

[0265] Step 4: Based on the respiration of oyster mushrooms The release rate time series dataset is extracted from the time range corresponding to the differentiation stage and development stage of the oyster mushroom fruiting body, and the data of the corresponding stage in the time range is calculated based on the number of days of oyster mushroom cultivation. As input, the respiration of Pleurotus ostreatus during the fruiting body differentiation and development stages Release rate As the output, the polynomial regression algorithm is used to segment the model for these two stages; the period of differentiation of the fruiting body of Pleurotus ostreatus is known. Days, growth cycle of oyster mushroom By comparing the root mean square error (RMSE) of polynomials of various orders, we found that the second-order model can balance accuracy and complexity in both stages. The root mean square error (RMSE) of the fruiting body differentiation stage and the development stage is better than that of other orders. Therefore, the order of the two-stage model is determined to be 2nd order. Using the least squares method to fit the coefficients, the formula for the segmented time series respiration rate model of Pleurotus ostreatus is determined as follows:

[0266] ,

[0267] The segmented time series respiration rate model of a single Pleurotus ostreatus during the fruiting period is shown in the figure below: Figure 3 shown.

[0268] Step 5: Construct a dynamic control model of carbon cycle timing in the cultivation warehouse of oyster mushroom and hydroponic lettuce.

[0269] (1) Obtaining the optimal planting ratio of oyster mushrooms and hydroponic lettuce: The complete control cycle of the oyster mushroom and hydroponic lettuce planting warehouse Days are discretized into 720 time nodes to form a time series , , , is the time interval between adjacent time nodes, represents the c+1th time node in the time series, represents the cth time node in the time series,

[0270] The light intensity , the number of hydroponic lettuce planted , Pleurotus ostreatus segmented time series respiratory rate model and a temporal carbon assimilation rate model for hydroponic lettuce. As the input of the carbon cycle dynamic control model of the oyster mushroom and hydroponic lettuce planting warehouse, the optimal oyster mushroom planting quantity is obtained by optimizing the objective function , to obtain the optimal planting ratio of oyster mushroom and hydroponic lettuce ;

[0271] (2) The optimal planting ratio of oyster mushrooms and hydroponic lettuce in the planting warehouse Under the cultivation mode, a dynamic control model of the carbon cycle timing of the oyster mushroom and hydroponic lettuce cultivation warehouse is constructed. Step 6: Integrate the constructed dynamic control model of the carbon cycle timing of the oyster mushroom and hydroponic lettuce cultivation warehouse into the cultivation warehouse system to realize the dynamic control of the carbon cycle timing.

[0272] According to calculation, under the condition of single cultivation, a single oyster mushroom stick can The total release amount is 44.1234×10- 3 m 3 , hydroponic lettuce is grown under light intensity hour The total absorption is 2.8001×10- 3 m 3 , we can get The net emission is 41.3224×10 -3 m 3 When oyster mushrooms and hydroponic lettuce are planted together at a ratio of 1:15, The net emission is 2.1084×10 -3 m 3 Therefore, compared with single cultivation, the method of co-cultivation can reduce 94.90% of Emissions into the atmosphere can achieve near-zero carbon emissions, and the total planting cycle of the planting warehouse system Internal determination of light intensity for hydroponic lettuce cultivation 150 The coupling model curve of time series absorption and release is as follows: Figure 4 As shown, the orange area is the early respiration of Oyster mushroom When the release rate is much greater than the carbon assimilation rate of hydroponic lettuce, the planting chamber is executed Storage dynamic control mechanism, the compressor compresses the total storage The purple area is the area of ​​late stage respiration of Oyster mushrooms When the release rate is much lower than the carbon assimilation rate of hydroponic lettuce, execute the planting chamber Release dynamic control mechanism, the compressor releases the stored total The blue area is the When the adaptive cycle control algorithm is used, the oyster mushroom cultivation chamber is exchanged with the hydroponic lettuce cultivation chamber. In addition, the phenomenon that the carbon assimilation rate decreases with the increase of planting days in the early growth stage of hydroponic lettuce is due, on the one hand, to certain mechanical errors in the measuring equipment, and on the other hand, because the seedlings of hydroponic lettuce are small in the early growth stage and the carbon assimilation rate is relatively weak. The fluctuation in the early growth stage is a normal physiological phenomenon, which is negligible compared with the improvement in photosynthetic capacity brought about by the increase in leaf area index in the middle and late stages.

[0273] At the end of the cultivation cycle, the compressed and stored high concentration The air is completely released. During the entire cultivation cycle, when the compressor extracts air from the oyster mushroom planting area and compresses and stores it, it will inhale a certain volume of air from the outside. When the compressed air is added to the hydroponic lettuce planting area in the later stage, the same volume of air will be discharged to the outside. However, due to the air in the hydroponic lettuce planting area, the air in the hydroponic lettuce planting area is discharged. The concentration is low, close to the average atmospheric concentration, and the additional The emissions are negligible and have almost no impact on the atmosphere. The concentration is affected, thereby ensuring that near-zero carbon emissions can be achieved throughout the entire cultivation cycle, which meets the development needs of green agriculture.

[0274] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for dynamically controlling the carbon cycle timing of edible fungi and hydroponic vegetable planting warehouses, characterized in that: The following steps are involved: 11) Determination of the regulation cycle of edible fungi and hydroponic vegetables: Coupling and calibrating the time axis of the edible fungi fruiting body period and the hydroponic vegetable growth sequence to determine the complete regulation cycle of the edible fungi and hydroponic vegetable planting warehouse ; 12) Collection and preprocessing of carbon dioxide time series data of edible fungi and hydroponic vegetable populations; 13) Construct a temporal carbon assimilation rate model for hydroponic vegetables based on the improved raccoon optimization algorithm; 14) Constructing a segmented time-series respiratory rate model for edible fungi; The construction of the edible fungus segmented time series respiratory rate model comprises the following steps: 141) Based on the respiration of edible fungi Release rate time series dataset According to the time range corresponding to the differentiation stage and development stage of edible fungi fruiting body, the data of the corresponding stage of the time range is extracted, and the number of days of edible fungi cultivation is calculated. As input, the respiration of edible fungi during the fruiting body differentiation and development stages Release rate For the output, the polynomial regression algorithm is used for segmented modeling of these two stages: ， ， in, It is a temporal respiration rate model for the fruiting body differentiation stage of edible fungi. It is a temporal respiration rate model of the fruiting body development stage of edible fungi. It is the fruiting body differentiation stage of edible fungi. It is the growth cycle of edible fungi. is the number of days for growing edible fungi, n and m are the orders of the polynomials, , ,…, 、 , ,…, is the coefficient to be determined; 142) The coefficients to be determined are fitted by the least squares method to form a segmented time series respiratory rate model for edible fungi. , the formula is as follows: ； 15) Construct a dynamic carbon cycle control model for edible fungi and hydroponic vegetable cultivation warehouses; 16) Dynamic regulation of carbon cycle timing in cultivation warehouses: Integrate the dynamic regulation model of carbon cycle timing in edible fungi and hydroponic vegetable cultivation warehouses into the cultivation warehouse system to achieve dynamic regulation of carbon cycle timing.

2. The method for dynamically controlling the carbon cycle timing of an edible fungus and hydroponic vegetable planting warehouse according to claim 1, characterized in that: Determination of the regulation cycle of the edible fungi and hydroponic vegetables comprises the following steps: 21) Determine the growth cycle of edible fungi and hydroponic vegetables respectively. Assume that the growth cycle of a single crop of edible fungi is Days, long under suitable conditions The growth cycle of edible fungi The growth cycle of hydroponic vegetables is sky; 22) Coupled calculation of the complete control cycle of edible fungi and hydroponic vegetable cultivation warehouses , The total cycle of co-cultivation of edible fungi and hydroponic vegetables, To calculate the least common multiple, The number of mushroom stick batches planted during the entire growth cycle is , number of batches of hydroponic vegetables planted , make sure to harvest together.

3. The method for dynamically controlling the carbon cycle timing of an edible fungus and hydroponic vegetable planting warehouse according to claim 1, characterized in that: The carbon dioxide time series data collection and preprocessing of edible fungi and hydroponic vegetable groups are as follows: Time-series release dataset and carbon assimilation of hydroponic vegetables under different light intensities throughout their growth period Time series demand data set, for abnormal data Method for denoising; It includes the following steps: 31) Using the edible fungus group carbon flux monitoring device, under the conditions of suitable and stable microenvironment, high-precision The sensor monitors the respiration of a single edible mushroom stick in the fruiting body growth stage in real time, and obtains Time series release dataset ; 32) Use plant group carbon assimilation rate measurement equipment to measure the group carbon assimilation of hydroponic vegetables under different light intensities throughout the growth cycle. Time series requirement dataset ; 33) Adoption Law Time series release dataset and group carbon assimilation Time series requirement dataset To handle the outliers in set up Time series release dataset Include value, recorded as , ,…, ,in Indicates the number of individual data; Assume that group carbon assimilation Time series requirement dataset Include value, recorded as , ,…, ,in Indicates the number of data, calculate separately Time series release dataset and group carbon assimilation Time series requirement dataset The mean and standard deviation , ， ， in, 、 Respectively represent Time series release dataset The mean and standard deviation of 、 Represents group carbon assimilation Time series requirement dataset The mean and standard deviation of 、 To sum the index, express Time series release dataset The ath data in; represents group carbon assimilation Time series requirement dataset The bth data in Then calculate the Z value of each data point: ,in, represent Time series release dataset The Z value of the a-th data point in Representative group carbon assimilation Time series requirement dataset The Z value of the b-th data point in Setting thresholds >3 and >3, outliers exceeding the threshold are eliminated; 34) Timing Rate of change calculation: 341) will Time series release dataset , group carbon assimilation Time series requirement dataset Press = Calculate the concentration change rate at 60s intervals: ， ， in: and The unit is ppm / min. is the time in seconds, represent The carbon flux monitoring device of edible fungi population was used to measure the concentration, represent The carbon flux monitoring device of edible fungi population was used to measure the concentration, represent The carbon assimilation rate of plant populations was measured by the device concentration, represent The carbon assimilation rate of plant populations was measured by the device concentration, Edible mushrooms per minute Release rate, Hydroponic vegetables per minute Absorption rate; By calculating the concentration change rate of edible fungi and hydroponic vegetables in multiple time periods every day during the planting cycle, the concentration of edible fungi per minute per day during the planting cycle is calculated. Average release rate and hydroponic vegetables under different light intensities Average absorption rate , respectively expressed as: ， ， Where N is the number of measurements in multiple valid time periods in a day, i is the summation index, The number of days for growing edible fungi, Number of days for hydroponic vegetable cultivation, For the Edible fungi per minute Release rate, For different light intensities hydroponic vegetables per minute Absorption rate, The growth cycle of edible fungi. For the growth cycle of hydroponic vegetables; 342) Combined with the effective volume of the edible fungus group carbon flux monitoring device Calculate the time in days Release rate, generating edible fungus respiration Rate time series dataset , the specific calculation method is: ， in, Representative Respiration of edible fungi Release rate, in m 3 / sky, The unit is m 3 ; 343) Assume that the daytime length of the photoperiod for hydroponic vegetable cultivation is hours, and nights are hours, combined with the effective volume of the plant community carbon assimilation rate measurement equipment Convert to days Absorption rate, generating a time series dataset of carbon assimilation rate of hydroponic vegetables , The calculation method is: ， in, Representative Carbon assimilation rate of hydroponic vegetables per day, unit: m 3 / sky, The unit is m 3 .

4. The method for dynamically controlling the carbon cycle timing of an edible fungus and hydroponic vegetable planting warehouse according to claim 1, characterized in that: The construction of a temporal carbon assimilation rate model for hydroponic vegetables based on the improved raccoon optimization algorithm comprises the following steps: 41) Hydroponic vegetable carbon assimilation rate time series dataset Divide into training set With the test set , where the number of days of hydroponic vegetable cultivation and light intensity are the characteristics, and the carbon assimilation rate is the label; 42) Adoption Method to standardize the data: ， ， ， in, 、 、 They are the number of days for hydroponic vegetable cultivation, light intensity, and carbon assimilation rate, 、 、 are the standardized hydroponic vegetable planting days, the standardized light intensity, and the standardized carbon assimilation rate, 、 They are The mean and standard deviation of 、 They are The mean and standard deviation of 、 for The mean and standard deviation of The unit is ; 43) Set the relevance vector machine model, The kernel function of the RVM model is selected as the Gaussian kernel function ， in, 、 The kernel width parameter γ and noise precision parameter α of the RVM model are optimized by using the improved Raccoon optimization algorithm. The range of kernel width parameter γ of the RVM model is set to γ∈[10 -3 ,1], the range of noise accuracy parameter α is α∈[10 -3 ,1], set the population size N of the improved raccoon optimization algorithm to 20, and the maximum number of iterations The number of times is set to 50, and the fitness function F is defined as the mean square error of the RVM model test set, and the calculation formula is: ， in, is the number of test set samples, m represents the mth sample in the test set, is the actual carbon assimilation rate value of the mth sample in the test set, is the model's predicted value for the mth sample; 44) Initialize the population and encode the position of each individual in the following way: ， in, 、 Representing the population The encoding values of the kernel width parameter and noise precision parameter of the correlation vector machine model corresponding to each individual, 、 are the lower and upper bounds of the kernel width parameter γ, 、 are the lower and upper bounds of the noise accuracy parameter α, is a random number uniformly distributed in the interval [0,1]. is the index of the individual in the population, N is the population size; The position vectors of N=20 raccoon individuals are generated randomly. , , and use the fitness function F to calculate the fitness value of each individual as the initial value; 45) Design a dynamic population size adjustment module: Calculate the average Euclidean distance of each individual in the solution space and use the standardized distance variance as a population diversity assessment indicator. When the population is initialized or updated, evaluate the current population diversity by calculating the average Euclidean distance between the position of an individual in the population and the average of all individual positions. When the population diversity is below a threshold of 0.3 and the population size is below the maximum population size of 100, update the population by randomly generating new sample individuals and expanding the population size. When the population diversity is above a threshold of 0.3 and the population size is above the minimum population size of 30, update the population by using the fitness function F to calculate the fitness value of each individual, eliminate the worst individuals, and reduce the population size. 46) In the hydroponic vegetable carbon assimilation rate time series dataset, the coded γ and α are used in the training set. Train the RVM model and use the trained RVM model in the test set The prediction is made on the , and the fitness value is calculated by the difference between the predicted result and the true value. The fitness values of all individuals are traversed, and the individual with the smallest fitness is selected as the current optimal solution. The γ and α parameter combinations corresponding to the optimal solution are recorded as and , and its position vector is recorded as ; 47) In the search space, select the population Each individual updates its position, and the update formula is as follows: ， Among them, N is the population size, p is the current number of iterations, represents the component of the i-th individual in the j-th dimension at the p-th iteration, represents the component of the i-th individual in the j-th dimension at the p+1-th iteration, represents the component of the optimal individual in the jth dimension at the pth iteration, is a random number uniformly distributed in the interval [0,1], I represents a random integer from the integer set {1, 2}, and i represents the number of the individual selected for location update; 48) The fitness function F is used to calculate the fitness value of each individual, and a greedy selection is performed. If the fitness of the individual after the update is better than the fitness value before the update, the updated position is retained, otherwise the position is not updated. The greedy selection strategy formula is as follows: ， in, is the position of the i-th individual at the p+1-th iteration, is the position of the i-th individual at the p-th iteration, and are the fitness values corresponding to the positions of the i-th individual at the p+1-th iteration and the p-th iteration respectively; 49) Design a learning rate adjustment strategy for position adaptive updates: Construct a composite function based on the iteration attenuation factor and the diversity feedback coefficient. The learning rate update formula is: ， in, is the current iteration number, is the attenuation factor, is the maximum number of iterations, is the number of diversity solution sets, i is the summation variable, is the diversity feedback coefficient, For population diversity, represents the learning rate at the p-th iteration, represents the learning rate at the p+1th iteration, is the exponential operation of natural constants; A dual control mechanism is used to achieve the coordinated optimization of global exploration and local development: the global search mode is adopted in the early stage of iteration, and the local development mode is activated as the number of iterations increases to accelerate the speed of locating the global optimal neighborhood; 410) Select another Individuals update their positions in the search space, First, randomly generate a temporary position vector in the search space , and its calculation formula is: ， The location is updated as follows: ， ； Among them, i represents the number of the individual selected for location update, N is the population size, and Represent the lower and upper bounds of the j-th dimension in the search space, respectively. is a random number uniformly distributed in the interval [0,1], I represents a random integer from the integer set {1, 2}, represents the component of the i-th individual in the j-th dimension at the p-th iteration, represents the component of the i-th individual in the j-th dimension at the p+1-th iteration, Represents the temporary position vector The fitness value of the corresponding solution, is the fitness value corresponding to the position of the i-th individual at the p-th iteration; 411) In the search space, simulate the random perturbation mechanism of individuals when facing dynamic perturbations, When the random perturbation mechanism is triggered, a random position is generated near the current position of each individual. The formula is as follows: ， ， ， Among them, i represents the number of the individual selected for location update, represents the number of iterations, and are the upper and lower bounds of the j-th dimension variable updated with the number of iterations, is the maximum number of iterations; 412) Calculate the fitness value of each individual and execute the greedy selection strategy again; If the fitness of the individual after the update is better than the fitness value before the update, the updated position is accepted, otherwise the position is not updated; 413) Continue iterating until the preset maximum number of iterations is reached =50, after the iteration, the optimal solution is output and the corresponding optimal value The optimal solution is the optimal combination of the kernel width parameter γ and the noise accuracy parameter α of the correlation vector machine model. and Substitute into the relevant vector machine model and input the training set , the correlation vector machine model fits the nonlinear relationship between input features and carbon assimilation rate, and outputs a time series carbon assimilation rate model ; 414) The time series carbon assimilation rate model was trained multiple times, and the model with the smallest mean root mean square error and the largest coefficient of determination was selected as the final time series carbon assimilation rate model using 5-fold cross validation. .

5. The method for dynamically controlling the carbon cycle timing of an edible fungus and hydroponic vegetable planting warehouse according to claim 1, characterized in that: The construction of a dynamic control model for the carbon cycle of edible fungi and hydroponic vegetable cultivation warehouses includes the following steps: 51) Obtaining the optimal planting ratio of edible fungi and hydroponic vegetables: Discretize the complete control cycle T of the edible fungi and hydroponic vegetable planting warehouse into k time nodes to form a time series , where k is the number of time nodes, represents the kth time node in the time series, , , is the time interval between adjacent time nodes, represents the c+1th time node in the time series, represents the cth time node in the time series, Use light intensity , the number of hydroponic vegetables planted , Edible Fungi Segmented Time Series Respiration Rate Model and a temporal carbon assimilation rate model for hydroponic vegetables As the input of the carbon cycle dynamic control model of edible fungi and hydroponic vegetable planting warehouse, on the basis of meeting the carbon metabolism balance, the optimization objective function is constructed to determine the optimal edible fungi planting quantity. , the optimization objective function is: ， in, and are the planting quantities of edible fungi and hydroponic vegetables respectively, The number of edible mushroom sticks planted. The number of batches of hydroponic vegetable planting, is the time node, is the respiration of edible fungi at the cth time point Release rate, is the carbon assimilation rate of hydroponic vegetables at time node c, Expressed as the number of edible fungi planted Perform rounding operation upwards. is the regularization coefficient, It's about The penalty function, Control the intensity of punishment, Obtain the optimal After that, the optimal planting ratio of edible fungi and hydroponic vegetables is obtained. ; 52) Optimal planting ratio of edible fungi and hydroponic vegetables in the planting warehouse In the planting mode, the carbon cycle time series dynamic control model of edible fungi and hydroponic vegetable planting warehouse is used to dynamically control the environment. The formula of the carbon cycle time series dynamic control model of edible fungi and hydroponic vegetable planting warehouse is as follows: ， in, To dynamically control the target value of carbon cycle timing in the planting warehouse, For planting warehouse The adaptive loop control algorithm controls the concentration, Using a grow barn for a grow barn Storage and release dynamic control mechanism Concentration gain, is the real time; 53) The adaptive cycle control algorithm uses the PID algorithm to control the air exchange circulation fan power between the edible fungus cultivation chamber and the hydroponic vegetable cultivation chamber, that is, the realization of the PWM duty cycle. According to the time-series carbon cycle balance relationship between edible fungi and hydroponic vegetables, the edible fungus segmented time-series respiration rate model and the hydroponic vegetable time-series carbon assimilation rate model are integrated to calculate the change factor of the time-series Kp proportional coefficient, and then establish an adaptive PID control algorithm. The formula is as follows: ， in, The target PWM duty cycle of the air exchange circulation fan in the growing room is: is the current system time, The daytime hours of the photoperiod, The number of days that edible fungi and hydroponic vegetables are grown together in the growing chamber. For the Carbon assimilation rate of hydroponic vegetables per day Respiration of edible fungi Release rate The ratio of , is the differential coefficient of the PID control algorithm, Real-time monitoring of edible fungi and hydroponic vegetable planting warehouses concentration difference; The constraints of the adaptive loop control algorithm are: , 、 They are real-time data of edible fungi and hydroponic vegetable planting warehouses. concentration, 、 Suitable for edible fungi and hydroponic vegetable planting warehouses Upper concentration limit; 54) Concentration gain Through the planting warehouse Dynamic control mechanism of storage and release get, The control logic is as follows: ， in, For planting warehouse Storage dynamic control mechanism, For planting warehouse Release dynamic control mechanisms; Planting Barn The control logic of the release dynamic control mechanism is as follows: ， in, This is the lower limit of the suitable concentration in the hydroponic vegetable cultivation room. The length of the night; Planting Barn The control logic constraints for releasing the dynamic control mechanism are: 。 6. The method for dynamically controlling the carbon cycle timing of an edible fungus and hydroponic vegetable planting warehouse according to claim 1, characterized in that: The dynamic regulation of the carbon cycle timing of the planting warehouse includes the following steps: 61) Substitute the parameter value into In the adaptive loop control algorithm, the parameter values include the current system time , daytime hours of the photoperiod , No. Carbon assimilation rate of hydroponic vegetables Respiration of edible fungi Release rate Ratio And real-time data of edible fungi and hydroponic vegetables in the planting warehouse Concentration difference , and combined with Adaptive cycle control algorithm constraints, real-time calculation of the target PWM duty cycle of the air exchange circulation fan, control of the air exchange circulation fan power, to achieve timing Adaptive regulation makes edible fungi and hydroponic vegetables grow in the warehouse The concentrations were all within the suitable concentration range for their growth; 62) Planting Barn Application of dynamic control mechanism of storage and release: 621) Planting Barn Storage dynamic control mechanism: real-time monitoring of the edible fungus cultivation warehouse concentration ,when > When the planting chamber is triggered Storage dynamic control mechanism , the system starts the compressor to perform air compression operation; when < , the compressor stops working; among them, Suitable for edible fungus cultivation warehouse Upper concentration limit, Suitable for edible fungus cultivation warehouse Lower concentration limit; 622) Planting Barn Unleashing dynamic control mechanisms: Monitoring the hydroponic vegetable growing chamber concentration , Current system time as well as The value of when And when the constraints are met, the planting warehouse is triggered Release dynamic control mechanism , the system starts the compressor and performs compressed air release operation; when it detects , the compressor stops working; among them, Suitable for hydroponic vegetable planting warehouse Upper concentration limit, It is the lower limit of suitable concentration in hydroponic vegetable cultivation room. The number of days that edible fungi and hydroponic vegetables are grown together in the growing chamber. For the Carbon assimilation rate of hydroponic vegetables per day Respiration of edible fungi Release rate The ratio of .

7. The method for dynamically controlling the carbon cycle timing of an edible fungus and hydroponic vegetable planting warehouse according to claim 6, characterized in that: The grow barn includes the following: Data acquisition subsystem: Set up sensors to monitor the growth environment parameters of edible fungi and hydroponic vegetables in real time and Concentration key data, and transmit the collected data to the data fusion subsystem, the sensors include temperature and humidity sensors, sensor, Sensors, light intensity sensors, solutions Value and Value measurement sensor; Data fusion subsystem: By coupling and calibrating the time axis of the edible fungus fruiting body period and the hydroponic vegetable growth sequence, the total control cycle of the planting warehouse is obtained and the number of edible mushroom stick batches grown during the entire growth cycle and the number of hydroponic vegetable planting batches The optimal planting ratio of edible fungi and hydroponic vegetables is automatically generated by matching the embedded edible fungi segmented time series respiration rate model and the hydroponic vegetable time series carbon assimilation rate model. and Carbon assimilation rate of hydroponic vegetables Respiration of edible fungi Release rate Ratio , thereby obtaining a dynamic control method for the carbon cycle timing of the planting warehouse for the current planting category; The data fusion subsystem combines the real-time environmental parameter values transmitted by the data acquisition subsystem and sends control instructions to the dynamic feedback control subsystem; Dynamic feedback control subsystem: Receives control instructions transmitted by the data fusion subsystem, drives the actuators to cooperate and regulate the environmental parameters in the planting chamber; A cultivation box, an air compression storage device and a control device, wherein an airtight partition is slidingly provided in the cultivation box, the airtight partition divides the cultivation box into an edible fungus cultivation bin and a hydroponic vegetable cultivation bin, the cultivation box is provided with vents in the edible fungus cultivation bin and the hydroponic vegetable cultivation bin, respectively, an air exchange circulation fan is provided on the upper part of the airtight partition, the air compression storage device includes a compressor and an air inlet pipe and an air outlet pipe provided on both sides of the compressor, the air inlet pipe is connected to the edible fungus cultivation bin, and the air outlet pipe is connected to the hydroponic vegetable cultivation bin, the vents, the air inlet pipe and the air outlet pipe are all controlled to open and close by an electromagnetic valve, the air exchange circulation fan is controlled to open and close and the wind speed is adjusted by a motor, and the control device is used to receive The sensor signal is used to control the operation of the compressor, solenoid valve and air exchange circulation fan according to the dynamic control method of the preset carbon cycle timing of the planting bin.

8. A computer-readable storage medium, characterized in that The storage medium stores a computer program. When the computer program is executed by the processor, the method for dynamically controlling the timing of carbon cycles in an edible fungus and hydroponic vegetable cultivation warehouse according to any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and running on the processor. When the processor executes the program, the method for dynamically controlling the timing of the carbon cycle of an edible fungus and hydroponic vegetable planting warehouse as described in any one of claims 1 to 6 is realized.

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