Intelligent temperature and humidity regulation and control method for oyster mushrooms

Abstract

The invention relates to the technical field of agriculture, in particular to an intelligent oyster mushroom temperature and humidity regulation and control method which comprises the following steps: S1, collecting various data of an oyster mushroom cultivation environment in real time; s2, performing primary processing on the data acquired in the step S1; s3, analyzing and extracting feature data related to oyster mushroom growth on the basis of the data subjected to primary processing in S2; s4, constructing a temperature and humidity regulation and control prediction model for analyzing and identifying optimal temperature and humidity requirements of the oyster mushrooms in different growth stages; s5, generating a regulation and control instruction through an optimization algorithm; and S6, according to the regulation and control instruction generated in the S5, controlling environment equipment to automatically regulate the temperature and humidity of the oyster mushroom cultivation environment. Through the temperature and humidity management method based on real-time data acquisition, the intelligent prediction model and the optimization regulation and control instruction, precise regulation and control of the oyster mushroom cultivation environment are achieved, the cultivation efficiency is remarkably improved, and meanwhile manual intervention and energy consumption are reduced.

Description

Technical Field

[0001] The present invention relates to the field of agricultural technology, and in particular to an intelligent temperature and humidity control method for oyster mushrooms. Background Art

[0002] In recent years, with the continuous development of oyster mushroom cultivation technology, intelligent temperature and humidity control has become a key technology to improve the efficiency and quality of oyster mushroom cultivation; the growth process of oyster mushroom is extremely sensitive to environmental factors such as temperature and humidity. Changes in environmental parameters such as temperature, humidity, and light will directly affect its growth rate, yield and quality; therefore, how to accurately control the temperature and humidity in the cultivation environment to ensure that it is in the best state at different growth stages has become the core issue in the intelligent cultivation of oyster mushroom; most of the existing oyster mushroom cultivation environment control methods rely on manual experience or simple automated control systems. These systems usually lack the ability to respond to environmental changes in real time, and the control accuracy is limited, resulting in environmental conditions that cannot adapt to the needs of different growth stages of oyster mushrooms at any time, thereby affecting the growth effect and production efficiency of oyster mushrooms.

[0003] Although the existing temperature and humidity control technology has made certain progress in some areas, its application in the cultivation of Oyster mushroom still faces many challenges. First, the existing technology usually does not take into account the specific differences in the temperature and humidity requirements of Oyster mushrooms at different growth stages, resulting in the inability to accurately adjust environmental parameters, which can easily lead to waste of resources or an unsuitable growth environment. Secondly, the existing automated control system lacks sufficient intelligence and is difficult to dynamically optimize according to real-time environmental changes. The control process is usually based on fixed rules or empirical data and lacks the ability to adapt to environmental changes. Summary of the invention

[0004] Based on the above purpose, the present invention provides an intelligent temperature and humidity control method for Pleurotus ostreatus.

[0005] An intelligent temperature and humidity control method for oyster mushrooms, comprising the following steps: S1: By deploying temperature and humidity sensors, soil moisture sensors and light sensors, various data of the mushroom cultivation environment are collected in real time, including temperature, humidity, light intensity and soil moisture data; S2: Perform preliminary processing on the data collected by S1, including denoising, standardization and outlier processing; S3: Based on the data preliminarily processed by S2, analyze and extract characteristic data related to the growth of Pleurotus ostreatus, including the fluctuation amplitude and change rate of temperature and humidity; S4: Based on the feature data extracted in S3 and combined with the biological model of Pleurotus ostreatus growth, a temperature and humidity control prediction model is constructed to analyze and identify the optimal temperature and humidity requirements of Pleurotus ostreatus at different growth stages; S5: Based on the optimal temperature and humidity requirements identified by S4, combined with the current environmental data and the preset target values, a control instruction is generated through an optimization algorithm; S6: According to the control instructions generated by S5, the environmental equipment is controlled to automatically adjust the temperature and humidity of the mushroom cultivation environment to achieve precise control.

[0006] Optionally, the S1 specifically includes: S11: Install multiple temperature and humidity sensors in the oyster mushroom cultivation area to monitor and collect environmental temperature and relative humidity data in real time; S12: embedding a capacitive soil moisture sensor in the oyster mushroom cultivation substrate to measure and transmit moisture content data in the substrate in real time; S13: Installing a photoelectric conversion light sensor in the cultivation environment to collect ambient light intensity data in real time; S14: Various sensors transmit the collected temperature, humidity, light intensity and soil moisture data to the data processing equipment in real time through wired connection or using LoRa or Zigbee wireless communication technology.

[0007] Optionally, the S2 specifically includes: S21: Using wavelet transform algorithm to denoise the collected temperature, humidity, light intensity and soil moisture data to remove environmental interference and sensor noise; S22: Perform Z-score standardization on the denoised data, convert each data into a standard normal distribution with a mean of 0 and a standard deviation of 1, and eliminate the influence of different data dimensions; S23: Use box plot-based statistical methods to detect and remove outliers in the data to ensure the overall consistency of the data set.

[0008] Optionally, the S3 specifically includes: S31: determining the fluctuation range of temperature and humidity in the cultivation environment of Pleurotus ostreatus by calculating the difference between the maximum and minimum values ​​of the temperature and humidity data after denoising and standardization in S2; S32: Calculate the difference between the temperature and humidity data at two consecutive time points and divide it by the time interval to obtain the rate of change of temperature and humidity; S33: The Pearson correlation coefficient was used to calculate the correlation between the fluctuation amplitude and change rate of temperature and humidity and the growth parameters of Pleurotus ostreatus, and the characteristic data related to growth were screened out.

[0009] Optionally, the S33 specifically includes: S331: Pearson correlation coefficient formula is used to calculate the correlation between the fluctuation range of temperature and humidity and the growth parameters of Pleurotus ostreatus. The calculation formula is: ,in, The temperature and humidity fluctuation range data points, is the average value of the temperature and humidity fluctuations, is the corresponding growth parameter data points, is the average value of the growth parameter, is the number of data points; S332: Pearson correlation coefficient formula is also used to calculate the correlation between the temperature and humidity change rate and the growth parameters of Pleurotus ostreatus; S333: Based on the calculated Pearson correlation coefficient value , screen out the characteristic data that are positively or negatively correlated with the growth parameters of Pleurotus ostreatus, and screen by setting the correlation threshold to 0.7, that is, when When the absolute value of is greater than or equal to 0.7, it is considered that the characteristic data are correlated with the growth parameters of Pleurotus ostreatus.

[0010] Optionally, the S4 specifically includes: S41: Combined with the biological characteristics and growth cycle of Pleurotus ostreatus, relevant biological models were integrated, including nutrient absorption model, respiratory metabolism model and growth and development model, as the basic framework of the temperature and humidity control prediction model; S42: The characteristic data of temperature and humidity fluctuation amplitude and change rate extracted in S3 are integrated with the growth parameters in the biological model to construct a comprehensive temperature and humidity control prediction model; S43: The constructed temperature and humidity control prediction model is used to analyze and identify the specific differences in the temperature and humidity requirements of Oyster Mushroom at different growth stages, and to clarify the optimal temperature and humidity control parameters at each stage.

[0011] Optionally, the S42 specifically includes: S421: Identify growth parameters during the growth of Pleurotus ostreatus, including mycelium expansion rate, fruiting body formation rate and biomass accumulation rate; S422: Temperature and humidity fluctuation range extracted in S3 and And the rate of change of temperature and humidity and Perform standardization to eliminate the impact between different data dimensions; S423: Linearly combine the standardized temperature and humidity characteristic data with the growth parameters to construct a comprehensive data set ; S424: Based on the comprehensive data set constructed in S423, a prediction model for temperature and humidity control is established using a multivariate linear regression analysis method, and the expression is: ,in, are the predicted temperature and humidity control parameters, is the comprehensive feature data, is the regression coefficient; S425: Optimize the prediction model of temperature and humidity control through cross-validation method and adjust the regression coefficient and fusion weight , in order to improve the prediction accuracy and generalization ability of the model.

[0012] Optionally, the S43 specifically includes: S431: According to the growth cycle of Pleurotus ostreatus, the growth process is divided into several stages, including mycelial growth stage, fruiting stage and maturity stage; S432: Input the growth parameters of each growth stage into the temperature and humidity control prediction model constructed in S42, and predict the temperature and humidity requirements of each growth stage to clarify the optimal temperature and optimal humidity of each stage.

[0013] Optionally, the S5 specifically includes: S51: Real-time acquisition of current environmental data, including temperature, humidity and light intensity; S52: Compare the optimal temperature and humidity requirements identified in S4 with the current environmental data to determine the deviation between the current environmental state and the target requirements; the specific calculation formula is: ; ,in, Indicates the temperature deviation, Indicates humidity deviation, is the current ambient temperature, is the current ambient humidity, For the Optimal temperature for each growth stage; For the Optimal humidity for each growth stage; S53: Based on the temperature and humidity deviation obtained in S52, the required control amount is calculated through the optimization algorithm, and a control instruction is generated according to the deviation amount and the environmental change rate. The control instruction calculation formula is: ; ,in, For temperature control instructions, For humidity control instructions, and are the temperature and humidity deviations, and is the deviation change rate, is the optimization coefficient.

[0014] Optionally, the S6 specifically includes: S61: Receive the control instruction generated by S5 and parse it into control commands for each device. Specifically, The instructions are converted into operating instructions for the heater or cooling equipment. The command is converted into an operation command of the humidifier or dehumidifier, and the start, stop or working intensity parameters of the device are determined based on the intensity and duration set in the command; S62: Send the parsed control command to each environmental device, including a heater, a cooling device, a humidifier, a fan and a dehumidifier, to adjust the temperature and humidity of the cultivation environment.

[0015] Beneficial effects of the present invention: The present invention collects environmental data in real time and analyzes the differences in the temperature and humidity requirements of Pleurotus ostreatus in combination with biological models. It can dynamically adjust environmental parameters according to the requirements of different growth stages, thereby providing the most suitable growth conditions for Pleurotus ostreatus. This method effectively avoids the problem of traditional control methods that cannot flexibly respond to environmental changes.

[0016] The present invention generates control instructions through an optimization algorithm and automatically adjusts environmental equipment to ensure the real-time and accuracy of temperature and humidity adjustment. Compared with the traditional experience-based manual control method, automatic temperature and humidity control can quickly respond to environmental changes and avoid waste of resources and the occurrence of unsuitable growth conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 Schematic diagram of the intelligent temperature and humidity control method for oyster mushrooms according to an embodiment of the present invention; Figure 2 Schematic diagram of a method for constructing a temperature and humidity control prediction model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0020] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0021] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figure 1-Figure 2 As shown, a method for intelligent temperature and humidity control of oyster mushrooms comprises the following steps: S1: By deploying temperature and humidity sensors, soil moisture sensors and light sensors, various data of the mushroom cultivation environment are collected in real time, including temperature, humidity, light intensity and soil moisture data; S2: Perform preliminary processing on the data collected by S1, including denoising, standardization and outlier processing, to ensure the accuracy and reliability of the data; S3: Based on the data preliminarily processed by S2, analyze and extract characteristic data related to the growth of Pleurotus ostreatus, including the fluctuation amplitude and change rate of temperature and humidity; S4: Based on the feature data extracted in S3 and combined with the biological model of Pleurotus ostreatus growth, a temperature and humidity control prediction model is constructed to analyze and identify the optimal temperature and humidity requirements of Pleurotus ostreatus at different growth stages; S5: Based on the optimal temperature and humidity requirements identified by S4, combined with the current environmental data and the preset target value, a control instruction is generated through an optimization algorithm to instruct the temperature and humidity control equipment (such as heaters, humidifiers, fans, etc.) to adjust; S6: According to the control instructions generated by S5, the environmental equipment is controlled to automatically adjust the temperature and humidity of the mushroom cultivation environment to achieve precise control.

[0023] S1 specifically includes: S11: Install multiple temperature and humidity sensors in the oyster mushroom cultivation area to monitor and collect environmental temperature and relative humidity data in real time; S12: embedding a capacitive soil moisture sensor in the oyster mushroom cultivation substrate to measure and transmit moisture content data in the substrate in real time; S13: Installing a photoelectric conversion light sensor in the cultivation environment to collect ambient light intensity data in real time; S14: Various sensors transmit the collected temperature, humidity, light intensity and soil moisture data to the data processing equipment in real time through wired connection or LoRa or Zigbee wireless communication technology; the above steps can comprehensively and accurately monitor the key parameters in the cultivation environment of Oyster Mushroom by deploying various types of sensors, thereby providing an accurate basis for subsequent temperature and humidity control.

[0024] S2 specifically includes: S21: Use wavelet transform algorithm to denoise the collected temperature, humidity, light intensity and soil moisture data to remove environmental interference and sensor noise to ensure the purity and accuracy of the data; S22: Perform Z-score standardization on the denoised data, converting each data into a standard normal distribution with a mean of 0 and a standard deviation of 1, eliminating the influence of different data dimensions and facilitating subsequent analysis; S23: Detect and remove outliers in the data using a statistical method based on a box plot to ensure the overall consistency of the data set. Specifically, the outlier detection formula based on a box plot is as follows: 25th percentile, 75th percentile; Interquartile range ; Outlier upper bound ; Outlier lower bound ; According to the above formula, if the data value is less than the lower limit of the outlier or greater than the upper limit of the outlier, it is considered an outlier and is removed.

[0025] S3 specifically includes: S31: Determine the fluctuation range of temperature and humidity in the Pleurotus ostreatus cultivation environment by calculating the difference between the maximum and minimum values ​​of the temperature and humidity data after denoising and standardization in S2; specifically, the temperature and humidity fluctuation range calculation formula is as follows: ; ,in, and Respectively represent the maximum and minimum temperature within a certain time range, and Respectively represent the maximum and minimum values ​​of humidity; S32: By calculating the difference between the temperature and humidity data at two consecutive time points and dividing it by the time interval, the change rate of temperature and humidity is obtained to reflect the speed of environmental changes; specifically, the change rate of temperature and humidity is and , the calculation formula is as follows: ; ,in, and Respectively represent the temperature data at the current time point and the previous time point, and Respectively represent the humidity data at the current time point and the previous time point, Represents the time interval between two time points; S33: The Pearson correlation coefficient is used to calculate the correlation between the temperature and humidity fluctuation amplitude and change rate and the growth parameters of Pleurotus ostreatus (such as growth rate and yield), and the characteristic data related to growth are screened out; through the implementation of the above steps, the environmental characteristic data closely related to the growth of Pleurotus ostreatus can be accurately extracted. The extraction of the temperature and humidity fluctuation amplitude and change rate helps to understand the response characteristics of Pleurotus ostreatus to environmental changes, and provide a scientific basis for subsequent temperature and humidity control strategies, thereby improving the quality and yield of Pleurotus ostreatus cultivation.

[0026] S33 specifically includes: S331: Pearson correlation coefficient formula is used to calculate the correlation between the fluctuation range of temperature and humidity and the growth parameters of Pleurotus ostreatus (such as growth rate and yield). The calculation formula is: ,in, The temperature and humidity fluctuation range data points, is the average value of the temperature and humidity fluctuations, is the first value of the corresponding growth parameter (such as growth rate, yield). data points, is the average value of the growth parameter, is the number of data points, and the linear correlation between the fluctuation amplitude of temperature and humidity and the growth parameters was obtained by calculating the Pearson correlation coefficient; S332: Pearson correlation coefficient formula is also used to calculate the correlation between the temperature and humidity change rate and the growth parameters of Pleurotus ostreatus, and the formula is the same as step S331; S333: Based on the calculated Pearson correlation coefficient value , screen out the characteristic data that are positively or negatively correlated with the growth parameters of Pleurotus ostreatus, and screen by setting the correlation threshold to 0.7, that is, when When the absolute value of is greater than or equal to 0.7, the characteristic data is considered to be correlated with the growth parameters of Pleurotus ostreatus, and thus it is screened as the characteristic data most closely related to growth; through the above steps, the relationship between the temperature and humidity fluctuation amplitude and change rate and the growth parameters of Pleurotus ostreatus can be deeply explored, thereby providing a basis for the subsequent refined design of temperature and humidity control.

[0027] S4 specifically includes: S41: Combined with the biological characteristics and growth cycle of Pleurotus ostreatus, relevant biological models were integrated, including nutrient absorption model, respiratory metabolism model and growth and development model, as the basic framework of the temperature and humidity control prediction model; S42: The characteristic data of temperature and humidity fluctuation amplitude and change rate extracted in S3 are integrated with the growth parameters in the biological model (such as mycelium expansion rate, fruiting body formation rate, etc.) to construct a comprehensive temperature and humidity control prediction model; S43: Using the constructed temperature and humidity control prediction model, we analyzed and identified the specific differences in the temperature and humidity requirements of Pleurotus ostreatus at different growth stages, and clarified the optimal temperature and humidity control parameters at each stage to guide the subsequent optimization of temperature and humidity control strategies.

[0028] S42 specifically includes: S421: Identify growth parameters during the growth of Pleurotus ostreatus, including mycelium expansion rate , fruiting body formation rate and biomass accumulation rate ,These parameters reflect the growth status and requirements of Pleurotus ostreatus at different growth stages; S422: Temperature and humidity fluctuation range extracted in S3 and And the rate of change of temperature and humidity and Standardization was performed using the Z-score standardization method to convert each feature data into a standard normal distribution with a mean of 0 and a standard deviation of 1 to eliminate the influence of different data dimensions. S423: Linearly combine the standardized temperature and humidity characteristic data with the growth parameters to construct a comprehensive data set Specifically, set the fusion weight Corresponding to and , then the comprehensive feature data The calculation formula is: ,in, are the standardized temperature and humidity fluctuation amplitude and change rate, is the corresponding growth parameter, to is the fusion weight coefficient, which is determined through data analysis; S424: Based on the comprehensive data set constructed in S423, a prediction model for temperature and humidity control is established using a multivariate linear regression analysis method, and the expression is: ,in, are the predicted temperature and humidity control parameters, is the comprehensive feature data, is the regression coefficient, determined by the least squares method; S425: Optimize the prediction model of temperature and humidity control through cross-validation method and adjust the regression coefficient and fusion weight , in order to improve the prediction accuracy and generalization ability of the model, and ensure that the model can accurately reflect the differences in the temperature and humidity requirements of Pleurotus ostreatus at different growth stages; through the above steps, the temperature and humidity characteristic data can be effectively integrated with the key growth parameters of Pleurotus ostreatus growth to construct a comprehensive temperature and humidity control prediction model, in which the standardized processing of characteristic data eliminates the influence of different data dimensions, and the linear combination method ensures the comprehensive consideration of each characteristic data. The final multivariate linear regression model can accurately predict the differences in temperature and humidity requirements of Pleurotus ostreatus at different growth stages. This comprehensive prediction model improves the scientificity and accuracy of the temperature and humidity control strategy.

[0029] S43 specifically includes: S431: According to the growth cycle of Pleurotus ostreatus, the growth process is divided into several stages, including mycelial growth stage, fruiting stage and maturity stage, each stage has different growth characteristics and environmental requirements; S432: Input the growth parameters of each growth stage into the temperature and humidity control prediction model constructed in S42, and predict the temperature and humidity requirements of each growth stage to clarify the optimal temperature and humidity of each stage; through the above steps, the specific requirements of Oyster Mushroom for temperature and humidity at different growth stages can be accurately analyzed and identified, and the optimal temperature and humidity control parameters for each stage can be clarified.

[0030] S5 specifically includes: S51: Real-time acquisition of current environmental data, including temperature, humidity and light intensity, to ensure the real-time status of the cultivation environment of Oyster mushrooms. The data is transmitted to a data processing device in real time through sensors (such as temperature and humidity sensors, soil moisture sensors, etc.); S52: Compare the optimal temperature and humidity requirements identified in S4 with the current environmental data to determine the deviation between the current environmental state and the target requirements; the specific calculation formula is: ; ,in, Indicates the temperature deviation, Indicates humidity deviation, is the current ambient temperature, is the current ambient humidity, For the Optimal temperature for each growth stage; For the Optimal humidity for each growth stage; S53: Based on the temperature and humidity deviation obtained in S52, the required control amount is calculated through the optimization algorithm, and a control instruction is generated according to the deviation amount and the environmental change rate. The control instruction calculation formula is: ; ,in, For temperature control instructions, For humidity control instructions, and are the temperature and humidity deviations, and is the deviation change rate, The optimization coefficient is obtained through an optimization algorithm, and the optimization algorithm includes a particle swarm optimization algorithm, a genetic algorithm or a model predictive control algorithm. Through the above steps, according to the optimal temperature and humidity requirements identified in step S4, combined with the current environmental data and the preset target value, an optimization algorithm is used to generate precise control instructions. This method can dynamically adjust the temperature and humidity of the cultivation environment, achieve precise environmental control, ensure that the oyster mushroom obtains the most suitable growth conditions at different growth stages, and improve the growth quality and yield of the oyster mushroom.

[0031] S6 specifically includes: S61: Receive the control instruction generated by S5 and parse it into control commands for each device. Specifically, The instructions are converted into operating instructions for the heater or cooling equipment. The command is converted into an operation command of the humidifier or dehumidifier, and the start, stop or working intensity parameters of the device are determined based on the intensity and duration set in the command; S62: Send the parsed control command to each environmental device, including heaters, cooling devices, humidifiers, fans and dehumidifiers, to adjust the temperature and humidity of the cultivation environment; through the implementation of the above steps, the environmental devices can be accurately controlled to automatically adjust the temperature and humidity of the cultivation environment according to the control instructions generated in step S5, ensuring that the temperature and humidity are always maintained within the optimal range at all stages of the growth of Pleurotus ostreatus, thereby optimizing the cultivation environment of Pleurotus ostreatus and improving the growth quality and yield.

[0032] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0033] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent temperature and humidity control method for Pleurotus ostreatus, characterized in that: The following steps are involved: S1: By deploying temperature and humidity sensors, soil moisture sensors and light sensors, various data of the mushroom cultivation environment are collected in real time, including temperature, humidity, light intensity and soil moisture data; S2: Perform preliminary processing on the data collected by S1, including denoising, standardization and outlier processing; S3: Based on the data preliminarily processed by S2, analyze and extract characteristic data related to the growth of Pleurotus ostreatus, including the fluctuation amplitude and change rate of temperature and humidity; S4: Based on the feature data extracted in S3 and combined with the biological model of Pleurotus ostreatus growth, a temperature and humidity control prediction model is constructed to analyze and identify the optimal temperature and humidity requirements of Pleurotus ostreatus at different growth stages; S5: Based on the optimal temperature and humidity requirements identified by S4, combined with the current environmental data and the preset target values, a control instruction is generated through an optimization algorithm; S6: According to the control instructions generated by S5, the environmental equipment is controlled to automatically adjust the temperature and humidity of the mushroom cultivation environment to achieve precise control.

2. The intelligent temperature and humidity control method for Pleurotus ostreatus according to claim 1, characterized in that: The S1 specifically includes: S11: Install multiple temperature and humidity sensors in the oyster mushroom cultivation area to monitor and collect environmental temperature and relative humidity data in real time; S12: embedding a capacitive soil moisture sensor in the oyster mushroom cultivation substrate to measure and transmit moisture content data in the substrate in real time; S13: Installing a photoelectric conversion light sensor in the cultivation environment to collect ambient light intensity data in real time; S14: Various sensors transmit the collected temperature, humidity, light intensity and soil moisture data to the data processing equipment in real time through wired connection or using LoRa or Zigbee wireless communication technology.

3. The intelligent temperature and humidity control method for Pleurotus ostreatus according to claim 1, characterized in that: The S2 specifically includes: S21: Using wavelet transform algorithm to denoise the collected temperature, humidity, light intensity and soil moisture data to remove environmental interference and sensor noise; S22: Perform Z-score standardization on the denoised data, convert each data into a standard normal distribution with a mean of 0 and a standard deviation of 1, and eliminate the influence of different data dimensions; S23: Use box plot-based statistical methods to detect and remove outliers in the data to ensure the overall consistency of the data set.

4. The intelligent temperature and humidity control method for Pleurotus ostreatus according to claim 1, characterized in that: The S3 specifically includes: S31: determining the fluctuation range of temperature and humidity in the cultivation environment of Pleurotus ostreatus by calculating the difference between the maximum and minimum values ​​of the temperature and humidity data after denoising and standardization in S2; S32: Calculate the difference between the temperature and humidity data at two consecutive time points and divide it by the time interval to obtain the rate of change of temperature and humidity; S33: The Pearson correlation coefficient was used to calculate the correlation between the fluctuation amplitude and change rate of temperature and humidity and the growth parameters of Pleurotus ostreatus, and the characteristic data related to growth were screened out.

5. The intelligent temperature and humidity control method for Pleurotus ostreatus according to claim 4, characterized in that: The S33 specifically includes: S331: Pearson correlation coefficient formula is used to calculate the correlation between the fluctuation range of temperature and humidity and the growth parameters of Pleurotus ostreatus. The calculation formula is: ,in, The temperature and humidity fluctuation range data points, is the average value of the temperature and humidity fluctuations, is the corresponding growth parameter data points, is the average value of the growth parameter, is the number of data points; S332: Pearson correlation coefficient formula is also used to calculate the correlation between the temperature and humidity change rate and the growth parameters of Pleurotus ostreatus; S333: Based on the calculated Pearson correlation coefficient value , screen out the characteristic data that are positively or negatively correlated with the growth parameters of Pleurotus ostreatus, and screen by setting the correlation threshold to 0.7, that is, when When the absolute value of is greater than or equal to 0.7, it is considered that the characteristic data are correlated with the growth parameters of Pleurotus ostreatus.

6. The intelligent temperature and humidity control method for Pleurotus ostreatus according to claim 1, characterized in that: The S4 specifically includes: S41: Combined with the biological characteristics and growth cycle of Pleurotus ostreatus, relevant biological models were integrated, including nutrient absorption model, respiratory metabolism model and growth and development model, as the basic framework of the temperature and humidity control prediction model; S42: The characteristic data of temperature and humidity fluctuation amplitude and change rate extracted in S3 are integrated with the growth parameters in the biological model to construct a comprehensive temperature and humidity control prediction model; S43: The constructed temperature and humidity control prediction model is used to analyze and identify the specific differences in the temperature and humidity requirements of Oyster Mushroom at different growth stages, and to clarify the optimal temperature and humidity control parameters at each stage.

7. The intelligent temperature and humidity control method for Pleurotus ostreatus according to claim 6, characterized in that: The S42 specifically includes: S421: Identify growth parameters during the growth of Pleurotus ostreatus, including mycelium expansion rate, fruiting body formation rate and biomass accumulation rate; S422: Temperature and humidity fluctuation range extracted from S3 and And the rate of change of temperature and humidity and Perform standardization to eliminate the impact between different data dimensions; S423: Linearly combine the standardized temperature and humidity characteristic data with the growth parameters to construct a comprehensive data set ; S424: Based on the comprehensive data set constructed in S423, a prediction model for temperature and humidity control is established using a multivariate linear regression analysis method, and the expression is: ,in, are the predicted temperature and humidity control parameters, is the comprehensive feature data, is the regression coefficient; S425: Optimize the prediction model of temperature and humidity control through cross-validation method and adjust the regression coefficient and fusion weight , in order to improve the prediction accuracy and generalization ability of the model.

8. The intelligent temperature and humidity control method for Pleurotus ostreatus according to claim 1, characterized in that: The S43 specifically includes: S431: According to the growth cycle of Pleurotus ostreatus, the growth process is divided into several stages, including mycelial growth stage, fruiting stage and maturity stage; S432: Input the growth parameters of each growth stage into the temperature and humidity control prediction model constructed in S42, and predict the temperature and humidity requirements of each growth stage to clarify the optimal temperature and optimal humidity of each stage.

9. The method for intelligent temperature and humidity control of Pleurotus ostreatus according to claim 1, characterized in that: The S5 specifically includes: S51: Real-time acquisition of current environmental data, including temperature, humidity and light intensity; S52: Compare the optimal temperature and humidity requirements identified in S4 with the current environmental data to determine the deviation between the current environmental state and the target requirements; the specific calculation formula is: ; ,in, Indicates the temperature deviation, Indicates humidity deviation, is the current ambient temperature, is the current ambient humidity, For the Optimal temperature for each growth stage; For the Optimal humidity for each growth stage; S53: Based on the temperature and humidity deviation obtained in S52, the required control amount is calculated through the optimization algorithm, and a control instruction is generated according to the deviation amount and the environmental change rate. The control instruction calculation formula is: ; ,in, For temperature control instructions, For humidity control instructions, and are the temperature and humidity deviations, and is the deviation change rate, is the optimization coefficient.

10. The intelligent temperature and humidity control method for Pleurotus ostreatus according to claim 1, characterized in that: The S6 specifically includes: S61: Receive the control instruction generated by S5 and parse it into control commands for each device. Specifically, The instructions are converted into operating instructions for the heater or cooling equipment. The command is converted into an operation command of the humidifier or dehumidifier, and the start, stop or working intensity parameters of the device are determined based on the intensity and duration set in the command; S62: Send the parsed control command to each environmental device, including a heater, a cooling device, a humidifier, a fan and a dehumidifier, to adjust the temperature and humidity of the cultivation environment.

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