An intelligent temperature and humidity control method for Pleurotus ostreatus

Abstract

The present invention relates to the field of agricultural technologies, and particularly relates to an intelligent temperature and humidity regulation method for Pleurotus ostreatus, comprising the following steps: S1: Collect various data of the Pleurotus ostreatus cultivation environment in real time; S2: Conduct preliminary processing on the various data collected in S1; S3: Based on the data preliminarily processed in S2, analyze and extract characteristic data related to the growth of Pleurotus ostreatus; S4: Construct a temperature and humidity regulation prediction model for analyzing and identifying the optimal temperature and humidity requirements at different growth stages of Pleurotus ostreatus; S5: Generate regulation instructions through an optimization algorithm; S6: According to the regulation instructions generated in S5, control environmental equipment to automatically adjust the temperature and humidity of the Pleurotus ostreatus cultivation environment. With the present invention, through a temperature and humidity management method based on real-time data collection, an intelligent prediction model, and optimized regulation instructions, precise regulation of the Pleurotus ostreatus cultivation environment is achieved, significantly improving the cultivation efficiency, while reducing manual intervention and energy consumption.

Description

Technical Field

[0001] The present invention relates to the field of agricultural technologies, and particularly to an intelligent temperature and humidity regulation method for Pleurotus ostreatus. Background Art

[0002] In recent years, with the continuous development of Pleurotus ostreatus cultivation technologies, intelligent temperature and humidity regulation has become a key technology for improving the cultivation efficiency and quality of Pleurotus ostreatus; the growth process of Pleurotus ostreatus is extremely sensitive to environmental factors such as temperature and humidity, and changes in environmental parameters such as temperature, humidity, and light will directly affect its growth rate, yield, and quality; therefore, how to accurately regulate 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 Pleurotus ostreatus; most of the existing Pleurotus ostreatus cultivation environment regulation methods rely on manual experience or simple automated control systems, and these systems usually lack the ability to respond to environmental changes in real time, and the regulation accuracy is limited, resulting in the environmental conditions not being able to adapt to the needs of different growth stages of Pleurotus ostreatus at any time, thus affecting the growth effect and production efficiency of Pleurotus ostreatus.

[0003] Although the existing temperature and humidity regulation technologies have made certain progress in some fields, their application in Pleurotus ostreatus cultivation still faces many challenges; firstly, the existing technologies usually do not consider the specific differences in temperature and humidity requirements of Pleurotus ostreatus at different growth stages, resulting in the inability to accurately adjust environmental parameters, which is likely to cause waste of resources or an unsuitable growth environment; secondly, the existing automated regulation systems lack sufficient intelligence and are difficult to dynamically optimize according to real-time environmental changes. The regulation 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 purposes, the present invention provides an intelligent temperature and humidity regulation method for Pleurotus ostreatus.

[0005] An intelligent temperature and humidity regulation method for Pleurotus ostreatus includes the following steps:

[0006] S1: By deploying temperature and humidity sensors, soil humidity sensors, and light sensors, various data of the Pleurotus ostreatus cultivation environment are collected in real time, including temperature, humidity, light intensity, and soil humidity data;

[0007] S2: The various data collected in S1 are preliminarily processed, including denoising, standardization, and outlier processing;

[0008] S3: Based on the data preliminarily processed in S2, characteristic data related to the growth of Pleurotus ostreatus are analyzed and extracted, including the amplitude and change rate of temperature and humidity fluctuations;

[0009] 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;

[0010] 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;

[0011] 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.

[0012] Optionally, the S1 specifically includes:

[0013] 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;

[0014] 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;

[0015] S13: Installing a photoelectric conversion light sensor in the cultivation environment to collect ambient light intensity data in real time;

[0016] 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.

[0017] Optionally, the S2 specifically includes:

[0018] S21: Using wavelet transform algorithm to denoise the collected temperature, humidity, light intensity and soil moisture data to remove environmental interference and sensor noise;

[0019] 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;

[0020] S23: Use box plot-based statistical methods to detect and remove outliers in the data to ensure the overall consistency of the data set.

[0021] Optionally, the S3 specifically includes:

[0022] 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;

[0023] S32: Calculate the change rate of temperature and humidity by dividing the difference between the temperature and humidity data at two consecutive time points by the time interval.

[0024] S33: Use the Pearson correlation coefficient to calculate the correlation between the amplitude of temperature and humidity fluctuations, the change rate, and the growth parameters of Pleurotus ostreatus, and screen out the characteristic data related to growth.

[0025] Optionally, S33 specifically includes:

[0026] S331: Use the Pearson correlation coefficient formula to calculate the correlation between the amplitude of temperature and humidity fluctuations and the growth parameters of Pleurotus ostreatus. The formula for the Pearson correlation coefficient is:

[0027] , where is the th data point of the amplitude of temperature and humidity fluctuations, is the average value of the amplitude of temperature and humidity fluctuations, is the th data point of the corresponding growth parameter, is the average value of the growth parameter, is the number of data points;

[0028] S332: Also use the Pearson correlation coefficient formula to calculate the correlation between the change rate of temperature and humidity and the growth parameters of Pleurotus ostreatus;

[0029] S333: According to the calculated Pearson correlation coefficient value , screen out the characteristic data that is positively or negatively correlated with the growth parameters of Pleurotus ostreatus. Screen by setting a correlation threshold of 0.7, that is, when the absolute value of is greater than or equal to 0.7, it is considered that the characteristic data is correlated with the growth parameters of Pleurotus ostreatus.

[0030] Optionally, S4 specifically includes:

[0031] S41: Combine the biological characteristics and growth cycle of Pleurotus ostreatus growth, and integrate relevant biological models, including nutrient absorption models, respiratory metabolism models, and growth and development models, as the basic framework of the temperature and humidity regulation prediction model;

[0032] S42: Fuse the characteristic data of the amplitude of temperature and humidity fluctuations and the change rate extracted in S3 with the growth parameters in the biological model to construct a comprehensive temperature and humidity regulation prediction model;

[0033] S43: Use the constructed temperature and humidity regulation prediction model to analyze and identify the specific differences in the temperature and humidity requirements of Pleurotus ostreatus at different growth stages, and clarify the optimal temperature and humidity control parameters for each stage.

[0034] Optionally, S42 specifically includes:

[0035] S421: Identify the growth parameters during the growth process of Pleurotus ostreatus, including the mycelium expansion rate, the fruiting body formation rate, and the biomass accumulation rate;

[0036] S422: Standardize the amplitude of temperature and humidity fluctuations and as well as the rate of temperature and humidity change and extracted in S3 to eliminate the influence between different data dimensions;

[0037] S423: Perform a linear combination of the standardized temperature and humidity characteristic data and the growth parameters to construct a comprehensive data set ;

[0038] S424: Based on the comprehensive data set constructed in S423, adopt the multiple linear regression analysis method to establish a prediction model for temperature and humidity control, and the expression is: , where is the predicted temperature and humidity control parameter, is the comprehensive characteristic data, is the regression coefficient;

[0039] S425: Optimize the prediction model for temperature and humidity control through the cross-validation method, and adjust the regression coefficient and the fusion weight to improve the prediction accuracy and generalization ability of the model.

[0040] Optionally, S43 specifically includes:

[0041] S431: According to the growth cycle of Pleurotus ostreatus, divide the growth process into several stages, including the mycelium growth stage, the fruiting stage, and the maturity stage;

[0042] S432: Input the growth parameters of each growth stage into the temperature and humidity control prediction model constructed in S42 to predict the temperature and humidity requirements of each growth stage, so as to clarify the optimal temperature and optimal humidity of each stage.

[0043] Optionally, S5 specifically includes:

[0044] S51: Obtain the current environmental data in real time, including temperature, humidity, and light intensity;

[0045] 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: ; , where represents the temperature deviation, represents the humidity deviation, is the current ambient temperature, is the current ambient humidity, is the optimal temperature for the th growth stage; is the optimal humidity for the th growth stage;

[0046] S53: Based on the temperature and humidity deviations obtained in S52, calculate the required regulation amount through an optimization algorithm, and generate a regulation instruction according to the deviation amount and the environmental change rate. The calculation formula for the regulation instruction is:

[0047] ; , where is the temperature regulation instruction, is the humidity regulation instruction, and are the temperature and humidity deviations respectively, and are the deviation change rates, is the optimization coefficient.

[0048] Optionally, the said S6 specifically includes:

[0049] S61: Receive the regulation instruction generated by S5 and parse it into control commands for each device. Specifically, convert the instruction into an operation instruction for a heater or a cooling device, convert the instruction into an operation instruction for a humidifier or a dehumidifier, and determine the start, stop or working intensity parameters of the device based on the intensity and duration set in the instruction;

[0050] S62: Send the parsed control commands to each environmental device, including heaters, cooling devices, humidifiers, fans and dehumidifiers, to adjust the temperature and humidity of the cultivation environment.

[0051] Advantages of the present invention:

[0052] In the present invention, by collecting environmental data in real time and analyzing the demand differences of Pleurotus ostreatus for temperature and humidity in combination with a biological model, the environmental parameters can be dynamically adjusted according to the demands of different growth stages, thereby providing the most suitable growth conditions for Pleurotus ostreatus. This method effectively avoids the problem that the traditional regulation method cannot flexibly respond to environmental changes.

[0053] In the present invention, a control instruction is generated through an optimized algorithm, and environmental equipment is automatically adjusted to ensure the real-time performance and accuracy of temperature and humidity adjustment. Compared with the traditional manual control method based on experience, the automatic temperature and humidity control can quickly respond to environmental changes, avoiding resource waste and the emergence of unsuitable growth conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only those of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0055] Figure 1 Schematic diagram of the intelligent temperature and humidity control method for Pleurotus ostreatus in the embodiment of the present invention;

[0056] Figure 2 Schematic diagram of the method for constructing a temperature and humidity control prediction model in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The present invention will be described in detail below in conjunction with the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawings are only for more specifically describing the embodiments and are not intended to specifically limit the present invention.

[0058] It should be pointed out that in the specification, when referring to "an embodiment", "embodiment", "exemplary embodiment", "some embodiments", etc., it indicates that the described embodiment may include specific features, structures or characteristics, but not necessarily every embodiment includes such specific features, structures or characteristics. Additionally, when combining an embodiment to describe a specific feature, structure or characteristic, implementing such feature, structure or characteristic in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0059] Generally, the terms can be understood at least in part from their use in the context. For example, at least in part depending on the context, the term "one or more" used herein can be used to describe any feature, structure or characteristic in a singular sense, or can be used to describe a combination of features, structures or characteristics in a plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but instead, at least in part depending on the context, allowing for the existence of other factors that may not be explicitly described.

[0060] Such as Figure 1 - Figure 2As shown in the figure, an intelligent temperature and humidity control method for Pleurotus ostreatus includes the following steps:

[0061] S1: By deploying temperature and humidity sensors, soil moisture sensors, and light sensors, various data of the Pleurotus ostreatus cultivation environment are collected in real time, including temperature, humidity, light intensity, and soil moisture data;

[0062] S2: The various data collected in S1 are preliminarily processed, including denoising, standardization, and outlier processing, to ensure the accuracy and reliability of the data;

[0063] S3: Based on the data preliminarily processed in S2, characteristic data related to the growth of Pleurotus ostreatus are analyzed and extracted, including the amplitude and change rate of temperature and humidity fluctuations;

[0064] S4: Based on the characteristic 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 at different growth stages of Pleurotus ostreatus;

[0065] S5: According to the optimal temperature and humidity requirements identified in S4, combined with the current environmental data and the preset target values, a control instruction is generated through an optimization algorithm to direct the temperature and humidity control equipment (such as heaters, humidifiers, fans, etc.) to make adjustments;

[0066] S6: According to the control instruction generated in S5, the environmental equipment is controlled to automatically adjust the temperature and humidity of the Pleurotus ostreatus cultivation environment to achieve precise control.

[0067] S1 specifically includes:

[0068] S11: Install multiple temperature and humidity sensors in the Pleurotus ostreatus cultivation area to monitor and collect environmental temperature and relative humidity data in real time;

[0069] S12: Bury capacitive soil moisture sensors in the Pleurotus ostreatus cultivation substrate to measure and transmit the moisture content data in the substrate in real time;

[0070] S13: Install a photoelectric conversion light sensor in the cultivation environment to collect environmental light intensity data in real time;

[0071] S14: Various sensors transmit the collected temperature, humidity, light intensity, and soil moisture data to the data processing device in real time through wired connections or using LoRa or Zigbee wireless communication technologies; through the deployment of various types of sensors in the above steps, the key parameters in the Pleurotus ostreatus cultivation environment can be comprehensively and accurately monitored, thus providing an accurate basis for subsequent temperature and humidity control.

[0072] S2 specifically includes:

[0073] S21: Use the wavelet transform algorithm to denoise the collected temperature, humidity, light intensity, and soil humidity data to remove environmental interference and sensor noise, ensuring the purity and accuracy of the data;

[0074] S22: Perform Z-score standardization on the denoised data, converting each data item into a standard normal distribution with a mean of 0 and a standard deviation of 1, eliminating the influence between different data dimensions for subsequent analysis;

[0075] S23: Use a statistical method based on box plots to detect and remove outliers in the data to ensure the overall consistency of the data set. Specifically, the outlier detection formula based on box plots is as follows:

[0076] The 25th percentile, The 75th percentile;

[0077] Interquartile range ;

[0078] Upper bound of outliers ;

[0079] Lower bound of outliers ;

[0080] According to the above formula, if the data value is less than the lower bound of outliers or greater than the upper bound of outliers, it is regarded as an outlier and removed.

[0081] S3 specifically includes:

[0082] 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 denoised and standardized temperature and humidity data in S2. Specifically, the formula for calculating the temperature and humidity fluctuation range is as follows: ; , where, and respectively represent the maximum and minimum values of temperature within a certain time range, and respectively represent the maximum and minimum values of humidity;

[0083] S32: Obtain the change rate of temperature and humidity by calculating the difference between the temperature and humidity data at two consecutive time points divided by the time interval, reflecting the speed of environmental change. Specifically, let the change rate of temperature and humidity be and , and the calculation formula is as follows:

[0084] ; , where, and represent the temperature data at the current time point and the previous time point respectively, and represent the humidity data at the current time point and the previous time point respectively, represents the time interval between the two time points;

[0085] S33: Calculate the correlation between the temperature and humidity fluctuation amplitude and change rate and the Pleurotus ostreatus growth parameters (such as growth rate, yield) using the Pearson correlation coefficient, and screen out the characteristic data related to growth; By implementing the above steps, the environmental characteristic data closely related to the Pleurotus ostreatus growth 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, provides a scientific basis for the subsequent temperature and humidity control strategy, and thus improves the quality and yield of Pleurotus ostreatus cultivation.

[0086] S33 specifically includes:

[0087] S331: Adopt the Pearson correlation coefficient formula to calculate the correlation between the temperature and humidity fluctuation amplitude and the Pleurotus ostreatus growth parameters (such as growth rate, yield). The Pearson correlation coefficient has the following calculation formula:

[0088] , where is the th data point of the temperature and humidity fluctuation amplitude, is the average value of the temperature and humidity fluctuation amplitude, is the th data point of the corresponding growth parameter (such as growth rate, yield), is the average value of the growth parameter, is the number of data points. By calculating the Pearson correlation coefficient, the linear correlation between the temperature and humidity fluctuation amplitude and the growth parameter is obtained;

[0089] S332: Also adopt the Pearson correlation coefficient formula to calculate the correlation between the temperature and humidity change rate and the Pleurotus ostreatus growth parameters. The formula is the same as that in step S331;

[0090] S333: According to the calculated Pearson correlation coefficient value , screen out the characteristic data that is positively or negatively correlated with the Pleurotus ostreatus growth parameters. Screen by setting the correlation threshold of 0.7, that is, when the absolute value of is greater than or equal to 0.7, it is considered that the characteristic data is correlated with the Pleurotus ostreatus growth parameters, 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 Pleurotus ostreatus growth parameters can be deeply explored, so as to provide a basis for the refined design of the subsequent temperature and humidity control.

[0091] S4 specifically includes:

[0092] S41: Integrate relevant biological models, including nutrient absorption models, respiratory metabolism models, and growth and development models, as the basic framework of the temperature and humidity control prediction model, in combination with the biological characteristics and growth cycle of Pleurotus ostreatus;

[0093] S42: Perform data fusion on the characteristic data of the temperature and humidity fluctuation amplitude and change rate extracted in S3 with the growth parameters (such as mycelium expansion rate, fruiting body formation rate, etc.) in the biological model to construct a comprehensive temperature and humidity control prediction model;

[0094] S43: Use the constructed temperature and humidity control prediction model to analyze and identify the specific differences in the temperature and humidity requirements of Pleurotus ostreatus at different growth stages, and clarify the optimal temperature and humidity control parameters for each stage to guide the optimization of subsequent temperature and humidity control strategies.

[0095] S42 specifically includes:

[0096] S421: Identify the growth parameters during the growth process of Pleurotus ostreatus, including the mycelium expansion rate , the fruiting body formation rate and the biomass accumulation rate , which reflect the growth status and requirements of Pleurotus ostreatus at different growth stages;

[0097] S422: Standardize the temperature and humidity fluctuation amplitude and as well as the temperature and humidity change rate and extracted in S3. Use the Z - score standardization method to convert each characteristic data into a standard normal distribution with a mean of 0 and a standard deviation of 1 to eliminate the influence between different data dimensions;

[0098] S423: Perform a linear combination of the standardized temperature and humidity characteristic data and the growth parameters to construct a comprehensive data set , specifically, set the fusion weights corresponding to and respectively. Then the comprehensive characteristic data calculation formula is:

[0099] , where, are the standardized temperature and humidity fluctuation amplitude and change rate respectively, is the corresponding growth parameter, to are the fusion weight coefficients, determined through data analysis;

[0100] S424: Based on the comprehensive dataset constructed in S423, use the multiple linear regression analysis method to establish a prediction model for temperature and humidity control, and the expression is: , where is the predicted temperature and humidity control parameter, is the comprehensive feature data, is the regression coefficient, which is determined by the least squares method;

[0101] S425: Optimize the prediction model for temperature and humidity control through the cross-validation method, and adjust the regression coefficient and the fusion weight , to improve the prediction accuracy and generalization ability of the model, and ensure that the model can accurately reflect the differences in temperature and humidity requirements of Pleurotus ostreatus at different growth stages; through the above steps, the temperature and humidity characteristic data can be effectively fused with the key growth parameters of Pleurotus ostreatus growth to construct a comprehensive temperature and humidity control prediction model. The standardized processing of the characteristic data eliminates the influence of different data dimensions, and the linear combination method ensures the comprehensive consideration of each characteristic data. The final multiple 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.

[0102] S43 specifically includes:

[0103] S431: According to the growth cycle of Pleurotus ostreatus, divide the growth process into several stages, including the mycelium growth stage, the fruiting stage, and the mature stage. Each stage has different growth characteristics and environmental requirements;

[0104] S432: Input the growth parameters of each growth stage into the temperature and humidity control prediction model constructed in S42 to predict the temperature and humidity requirements of each growth stage, so as to clarify the optimal temperature and optimal humidity of each stage; through the above steps, the specific requirements of Pleurotus ostreatus for temperature and humidity at different growth stages can be accurately analyzed and identified, and the optimal temperature and humidity control parameters of each stage can be clarified.

[0105] S5 specifically includes:

[0106] S51: Obtain the current environmental data in real time, including temperature, humidity, and light intensity, to ensure obtaining the real-time state of the Pleurotus ostreatus cultivation environment. This data is transmitted to the data processing device in real time through sensors (such as temperature and humidity sensors, soil humidity sensors, etc.);

[0107] 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: ; , where represents the temperature deviation, represents the humidity deviation, is the current ambient temperature, is the current ambient humidity, is the optimal temperature for the is the optimal humidity for the

[0108] S53: Based on the temperature and humidity deviations obtained in S52, calculate the required regulation amount through an optimization algorithm, and generate a regulation instruction according to the deviation amount and the environmental change rate. The calculation formula of the regulation instruction is:

[0109] ; , where is the temperature regulation instruction, is the humidity regulation instruction, and are the temperature and humidity deviations respectively, and are the deviation change rates, is the optimization coefficient, obtained through an optimization algorithm. The optimization algorithm includes particle swarm optimization algorithm, genetic algorithm or 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 values, an accurate regulation instruction can be generated using an optimization algorithm. This method can dynamically adjust the temperature and humidity of the cultivation environment, achieve precise environmental control, ensure that Pleurotus ostreatus obtains the most suitable growth conditions at different growth stages, and improve the growth quality and yield of Pleurotus ostreatus.

[0110] S6 specifically includes:

[0111] S61: Receive the regulation instruction generated by S5 and parse it into control commands for each device. Specifically, convert the instruction into an operation instruction for a heater or a cooling device, and convert the instruction into an operation instruction for a humidifier or a dehumidifier, and determine the start, stop or working intensity parameters of the device based on the intensity and duration set in the instruction;

[0112] S62: Send the parsed control commands 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, according to the regulation instruction generated in step S5, the environmental devices can be accurately controlled to automatically adjust the temperature and humidity of the cultivation environment, ensuring that the temperature and humidity always remain within the optimal range at each stage of Pleurotus ostreatus growth, thereby optimizing the Pleurotus ostreatus cultivation environment and improving the growth quality and yield.

[0113] The present invention encompasses any alternatives, modifications, equivalent methods, and solutions made to the essence and scope of the present invention. For the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, and those skilled in the art can fully understand the present invention without such detailed descriptions. Additionally, well-known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0114] The above description is only a preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope 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; 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: Using the constructed temperature and humidity control prediction model, analyze and identify the specific differences in the temperature and humidity requirements of Pleurotus ostreatus at different growth stages, and clarify the optimal temperature and humidity control parameters at each stage; 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 , to improve the prediction accuracy and generalization ability of the model; 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; 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; 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 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.

7. 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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