Intelligent black fungus planting environment regulation and control method based on Internet of Things

Abstract

The invention discloses an intelligent black fungus planting environment regulation and control method based on the Internet of Things, and relates to the technical field of black fungus planting. Environmental information such as temperature and humidity is collected by arranging various sensors, and after data is preprocessed, a growth model is built by using historical data; according to the model and real-time data, a regulation and control decision is made through multi-objective optimization, and execution of equipment such as temperature control equipment is controlled; and remote monitoring interaction, storage and analysis of data can be realized to optimize the system, and multi-base cooperation and early warning emergency can also be realized. The system can accurately regulate and control the planting environment in real time, predict and prevent diseases and pests in advance, improve the management efficiency, excavate the data value to assist scientific planting, produce high-quality and traceable products, enhance the market competitiveness and promote the sustainable development of the industry.

Description

Technical Field

[0001] The present invention relates to the technical field of black fungus cultivation, and in particular to a method for regulating and controlling a black fungus intelligent cultivation environment based on the Internet of Things. Background Art

[0002] Black fungus (Black Fungus) is a nutritious, uniquely flavored edible fungus with high economic value and market demand. Traditional cultivation of Black Fungus relies heavily on manual experience, with growers managing the growing environment based on past experience and intuition. For example, they rely on their own senses to adjust the temperature and humidity in the mushroom house, and rely on observing sky conditions to determine the duration of daylight hours. This approach has numerous drawbacks. The lack of precision in environmental control makes it difficult to meet the precise environmental requirements of Black Fungus at different growth stages, resulting in unstable yields and inconsistent quality. Furthermore, manual management is inefficient, requiring growers to devote significant time and energy to daily inspections and operations, increasing labor costs.

[0003] With the advancement of technology, some growers have begun to use simple automated equipment to assist in growing, such as temperature and humidity controllers and timer-controlled ventilation systems. However, these devices have limited functionality and lack systematicity and intelligence. They can only perform simple on / off controls according to preset parameters and are unable to adjust environmental parameters in real time based on the actual growth conditions of black fungus. For example, if the black fungus encounters sudden climate changes or pest threats during its growth, these devices cannot respond in a timely manner, and manual intervention is still required. Furthermore, these devices lack effective communication and interoperability, failing to form an integrated whole and achieving comprehensive and precise control of the growing environment.

[0004] In the information age, emerging technologies such as the Internet of Things, big data, and artificial intelligence have brought new development opportunities to the agricultural sector. However, current technologies for regulating the black fungus cultivation environment remain imperfect, and a complete, efficient, and intelligent solution has yet to be established. Existing systems often fail to effectively integrate data from multiple sensors, resulting in incomplete and inaccurate monitoring of black fungus growth, making it difficult to develop a scientific and rational environmental regulation strategy. Therefore, there is an urgent need for an IoT-based intelligent black fungus cultivation environment regulation method to improve its yield and quality, reduce cultivation costs, and promote the modernization of the black fungus cultivation industry. Summary of the Invention

[0005] The present invention proposes an intelligent black fungus planting environment control method based on the Internet of Things to solve the problems mentioned in the above-mentioned prior art.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for controlling the black fungus intelligent planting environment based on the Internet of Things, comprising:

[0007] Environmental information collection steps: Temperature and humidity sensors, light sensors, carbon dioxide concentration sensors, soil moisture sensors, and soil pH sensors are placed in the black fungus planting area; each sensor sends the collected data to the gateway device via the ZigBee wireless communication module;

[0008] Data preprocessing steps: After the gateway device receives the sensor data, it first performs outlier detection on the data. The 3σ criterion based on statistical analysis is used to determine that data points that exceed the mean ±3 times the standard deviation are outliers. The Lagrange interpolation method is used to repair outliers. The formula is: Where x is the point to be interpolated, x i 、y i For known data points, the data is then normalized using the minimum-maximum normalization method to map the data to the [0,1] interval. The formula is: Where x is the original data, x min 、x max are the minimum and maximum values ​​of the group of data respectively;

[0009] Growth model establishment steps: Using historical planting data, including environmental parameters and black fungus growth status data, the black fungus growth model was established using the support vector regression (SVR) algorithm. The model takes environmental parameters as input and black fungus growth indicators as output. By adjusting the model parameters, the mean square error between the model prediction value and the actual growth data is minimized. The mean square error formula is: where y i is the actual value, is the predicted value, n is the number of samples;

[0010] Environmental control decision-making steps: Based on the currently collected environmental data and the established growth model, predict the growth trend of black fungus in the current environment; if the prediction results show that the growth indicators do not meet expectations, formulate an environmental control strategy through the NSGA-II algorithm; calculate the control target values ​​of each environmental parameter through the optimization algorithm, including the temperature control target value T target , humidity control target value H target ;

[0011] Equipment control execution steps: Send the environmental control target value to the corresponding environmental control equipment, including temperature control equipment, humidity control equipment, light control equipment, carbon dioxide generator and irrigation equipment; the temperature control equipment uses the PID control algorithm to adjust the temperature according to the temperature control target value. The formula is: Where u(t) is the control quantity, e(t) is the error between the current temperature and the target temperature, K p , K i , K dThey are proportional, integral and differential coefficients respectively; humidity control equipment, light control equipment, carbon dioxide generator and irrigation equipment also use the same control algorithm and are controlled according to their respective control target values;

[0012] Remote monitoring and interaction steps: Build a web-based remote monitoring platform. Planters access the platform via the internet to view various parameters of the planting environment, equipment operating status, and black fungus growth in real time. The platform also provides a human-computer interaction interface, allowing planters to manually adjust environmental control strategies. The platform is equipped with a message push function, which sends notifications to planters' mobile phones when environmental parameters exceed set thresholds or equipment malfunctions.

[0013] Data storage and analysis steps: The collected environmental data, equipment operation data and black fungus growth data are stored in the database; the data in the database are analyzed regularly, and data mining algorithms are used to explore the relationship between environmental parameters and black fungus growth;

[0014] System optimization and update step: Based on the results obtained in the data storage and analysis step, the growth model and environmental control strategy are optimized and updated; when a new correlation between environmental parameters and black fungus growth is discovered, the parameters of the growth model are adjusted; when an optimized environmental control strategy is discovered, the objective function and constraints of the NSGA-II algorithm are updated to improve the control efficiency of the system;

[0015] Multi-base collaborative control steps: For enterprises or organizations with black fungus cultivation bases, the monitoring platforms of each base can be connected through the Internet of Things. A distributed collaborative optimization algorithm is used to achieve collaborative environmental control between bases based on the environmental conditions, planting varieties, and growth stages of each base.

[0016] Disaster warning and emergency response steps: Establish a disaster warning model, combine weather forecast data and real-time environmental data to issue early warnings for natural disasters and pest and disease disasters, and automatically activate the emergency response plan when the warning model determines that a disaster has occurred.

[0017] Furthermore, it also includes:

[0018] Sensor calibration steps: Regularly calibrate the sensors in the planting area, use standard reference equipment to compare each sensor, and calculate the deviation between the sensor measurement value and the standard value; for temperature and humidity sensors and light sensors, if the deviation exceeds the set threshold, calibrate them; during the calibration process, correct the sensor output according to the deviation value. The correction formula is: corrected =x measured +△x, where x corrected is the corrected value, x measured is the measured value, and Δx is the deviation value.

[0019] Black fungus quality assessment steps: Use image recognition technology and chemical analysis technology to evaluate the quality of black fungus; capture images of black fungus through a camera, use the convolutional neural network (CNN) algorithm to analyze the images, and extract the morphological characteristics of black fungus; at the same time, regularly collect black fungus samples for chemical analysis and test the nutritional components; establish a black fungus quality assessment model based on the morphological characteristics and nutritional component test results.

[0020] Energy management steps: Install energy monitoring equipment in the planting area to monitor the energy consumption of each environmental control equipment in real time; establish an energy consumption model to analyze the energy consumption patterns under different environmental control strategies; use optimization algorithms to adjust the operating time and power of the equipment to reduce energy consumption while meeting the growth needs of black fungus.

[0021] User feedback and improvement steps: Set up a user feedback module on the remote monitoring platform, and planting personnel use the module to feedback problems encountered during system operation and make improvement suggestions; system administrators regularly collect and analyze user feedback information, and improve and optimize the system based on the problems and suggestions raised by users.

[0022] Furthermore, it also includes:

[0023] Planting plan recommendation steps: Based on the planting goals entered by the planting personnel and the environmental conditions of the planting area, combined with the growth model and historical planting data, a planting plan is recommended to the planting personnel; the planting plan includes environmental parameter settings, cultivation techniques, and fertilization plan content.

[0024] Pest and disease prediction and prevention steps: In addition to using real-time environmental data for pest and disease warning, a pest and disease prediction model is also established; the model comprehensively considers historical pest and disease occurrence data, meteorological data, and soil conditions, and uses time series analysis and machine learning algorithms to predict the time, type, and severity of pest and disease occurrence; based on the prediction results, preventive measures are taken in advance.

[0025] Equipment fault diagnosis and maintenance steps: Install fault sensors on each environmental control equipment to monitor the equipment's operating status parameters in real time; establish an equipment fault diagnosis model and use machine learning algorithms to analyze the equipment's operating status parameters to determine whether the equipment has a fault and the type of fault; when an equipment fault is diagnosed, the system automatically sends fault information and repair suggestions to the equipment maintenance personnel, and displays the location and fault information of the faulty equipment on the remote monitoring platform.

[0026] Data security and privacy protection steps: Use encryption technology to encrypt the collected data and the transmitted data, set up a user rights management system on the remote monitoring platform, set different operating permissions for users of different levels, and comply with relevant data protection laws and regulations to keep the user's personal information and planting data confidential.

[0027] Product traceability steps: The black fungus cultivation process data is stored on the node, and the characteristics of distributed ledger technology are used to generate a traceability code for each batch of black fungus. Consumers can scan the traceability code to query the black fungus cultivation process information and understand the growth environment and quality assurance.

[0028] Compared with the existing technology, the beneficial effects of the present invention are:

[0029] In terms of environmental control, this method enables precise, real-time regulation of the black fungus cultivation environment. Using a variety of sensors distributed throughout the growing area, the system comprehensively and accurately collects environmental information, including temperature, humidity, light intensity, and carbon dioxide concentration. Based on the needs of the fungus at different growth stages, the system automatically adjusts environmental parameters to create the most suitable growth environment for the fungus, significantly improving its growth quality and yield.

[0030] In terms of pest and disease control, this method establishes a pest and disease prediction model, combining real-time environmental data with historical pest and disease occurrences to predict the onset, type, and severity of pests and diseases in advance. Based on these predictions, growers can take preventative measures, such as adjusting environmental parameters, spraying preventative pesticides, and releasing natural enemies. This effectively reduces the risk of pests and diseases to black fungus, reduces pesticide use, and ensures the food safety and ecological quality of the fungus.

[0031] In terms of management efficiency, this method establishes a remote monitoring platform, allowing growers to view various parameters of the growing environment, equipment operating status, and black fungus growth status anytime and anywhere via the internet. Furthermore, the platform supports remote operation and intelligent decision-making, allowing growers to control environmental control equipment from a distance and handle emergencies promptly, significantly improving management efficiency and reducing labor costs.

[0032] In terms of data utilization, this method stores and analyzes various collected data to explore the potential relationships between environmental parameters and black fungus growth. By analyzing and learning from historical data, it continuously optimizes growth models and environmental control strategies, providing growers with scientific planting plans and decision-making support, thereby improving the scientific nature and precision of planting.

[0033] In terms of market competitiveness, the black fungus produced by this method is of high quality, with stable yield and traceability. Consumers can scan the traceability code to understand the entire black fungus cultivation process, which increases consumer trust in the product, improves its market competitiveness, and helps promote the sustainable development of the black fungus cultivation industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a schematic block diagram of the black fungus intelligent planting environment control method based on the Internet of Things proposed by the present invention;

[0035] Figure 2 This is a schematic diagram showing the comparison of the growth rates of black fungus at different growth stages using the method for controlling the black fungus cultivation environment based on the Internet of Things proposed by the present invention;

[0036] Figure 3 This is a schematic diagram showing the comparison of the incidence of diseases and insect pests before and after the intelligent regulation of the black fungus planting environment regulation method based on the Internet of Things proposed in the present invention. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0039] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the accompanying drawings.

[0040] Reference Figure 1-Figure 3 :A specific implementation method of a black fungus intelligent planting environment control method based on the Internet of Things

[0041] 1. Environmental Information Collection Steps: Scientifically and rationally arrange various sensors within the black fungus cultivation area. Use high-precision digital temperature and humidity sensors, such as the SHT31, with measurement accuracies of ±0.1°C and ±1% RH, collecting data every 10 minutes. Choose a light sensor that can measure light intensity from 0 to 100,000 lux, such as the BH1750, with a collection frequency of 15 minutes. Choose the MG-811 carbon dioxide concentration sensor, with a measurement range of 0 to 5,000 ppm and an accuracy of ±50 ppm, collecting data every 20 minutes. Use a frequency domain reflectometry sensor, such as the TDR-300, with a measurement accuracy of ±3%, collecting data every 30 minutes. Use a soil pH sensor, such as the E-201-C, with a measurement range of pH 0-14 and an accuracy of ±0.05 pH, collecting data every hour. Each sensor transmits collected data to a gateway device via a ZigBee wireless communication module to ensure stable data transmission.

[0042] 2. Data preprocessing step: After the gateway device receives the sensor data, it first performs outlier detection. The 3σ criterion based on statistical analysis is used. That is, for a set of data, if a data point exceeds the range of ±3 times the standard deviation of the mean, it is determined to be an outlier. For outliers, the Lagrange interpolation method is used to repair them. The Lagrange interpolation formula is: Where x is the point to be interpolated, x i 、y i is a known data point. Then the data is normalized using the minimum-maximum normalization method, and the formula is: Where x is the original data, x min 、xmax are the minimum and maximum values ​​of the data set respectively. Through such processing, the accuracy and availability of the data are improved.

[0043]

[0044] As can be seen from the table, data preprocessing significantly reduces fluctuations, effectively addresses outliers, and makes the normalized data more convenient for subsequent analysis and processing. This helps improve the accuracy of the growth model and the precision of environmental control, providing more reliable data support for the optimal growth of black fungus.

[0045] 3. Growth model establishment steps: Utilize historical planting data, which includes environmental parameters (temperature, humidity, light, etc.) and black fungus growth status data (such as mycelial growth length, auricularia size, yield, etc.). Use the support vector regression (SVR) algorithm to establish a black fungus growth model. This model uses environmental parameters as input and black fungus growth indicators as output. By continuously adjusting the model parameters, the mean square error between the model prediction value and the actual growth data is minimized. The mean square error formula is: where y i is the actual value, is the predicted value, and n is the number of samples. Through multiple training and optimization, the growth model can accurately reflect the relationship between environmental parameters and black fungus growth.

[0046] Training phase Mean square error Prediction accuracy Initial training About 0.5 About 70% After multiple optimizations About 0.1 About 90%

[0047] As can be seen from the table, as training and optimization progressed, the mean square error of the growth model decreased significantly, and the prediction accuracy increased significantly. This demonstrates that the growth model established using the SVR algorithm can well fit historical data and provide a reliable basis for subsequent environmental regulation decisions.

[0048] 4. Environmental control decision-making steps: Based on the currently collected environmental data and the established growth model, predict the growth trend of black fungus in the current environment. If the prediction results show that the growth indicators do not meet expectations, formulate an environmental control strategy through a multi-objective optimization algorithm (such as the NSGA-II algorithm). This algorithm comprehensively considers multiple goals, such as the growth rate, quality and energy consumption of black fungus, and tries to reduce energy consumption as much as possible while meeting the growth needs of black fungus. The control target value of each environmental parameter is calculated through the optimization algorithm, such as the temperature control target value T target , humidity control target value H target During the calculation process, the relationship between different objectives is constantly weighed to achieve the optimal control effect.

[0049] Control targets Pre-regulatory growth indicators Expected growth indicators after regulation Changes in energy consumption Growth rate Slow growth Rapid growth Reduced by about 10% quality generally excellent -

[0050] The data in the table shows that the environmental control strategy developed through the multi-objective optimization algorithm not only improved the growth rate and quality of black fungus, but also reduced energy consumption. This demonstrates the scientific and effective nature of the control decision-making process, ensuring the growth of black fungus while achieving rational resource utilization.

[0051] 5. Equipment control execution steps: Send the environmental control target value to the corresponding environmental control equipment, including temperature control equipment (such as heating furnaces, air conditioners), humidity control equipment (such as humidifiers, dehumidifiers), light control equipment (such as fill lights, sunshade nets), carbon dioxide generators and irrigation equipment. The temperature control equipment uses the PID control algorithm to adjust the temperature according to the temperature control target value. The formula is: Where u(t) is the control quantity, e(t) is the error between the current temperature and the target temperature, K p , K i , K d These are the proportional, integral, and differential coefficients, respectively. Humidity control equipment, light regulation, CO2 generators, and irrigation systems also utilize similar control algorithms, achieving precise control based on their respective target values. Real-time monitoring and feedback ensure that environmental parameters quickly and accurately achieve their control targets.

[0052] Device Type Parameter deviation before adjustment Parameter deviation after adjustment Control time Temperature control equipment ±2℃ ±0.5℃ About 30 minutes Humidity control equipment ±3%RH ±1%RH About 20 minutes

[0053] As can be seen from the table, after each device implemented control, the deviation of environmental parameters was significantly reduced, and the control target was achieved in a relatively short time. This demonstrates that control algorithms such as PID can effectively achieve precise control of environmental control equipment and ensure a stable black fungus cultivation environment.

[0054] 6. Remote Monitoring and Interaction Steps: Build a web-based remote monitoring platform that growers can access via the internet to view various parameters of the growing environment, equipment operating status, and black fungus growth in real time. The platform uses an intuitive interface design, presenting information in the form of charts and data displays, allowing growers to quickly understand the situation. At the same time, the platform provides a human-computer interaction interface, allowing growers to manually adjust environmental control strategies, such as setting upper and lower limits for temperature and humidity. The platform also has a push notification function, which promptly sends text messages or push notifications to growers' mobile phones when environmental parameters exceed set thresholds or equipment malfunctions. In this way, growers can monitor and manage the growing environment anytime and anywhere.

[0055] Monitoring content Monitoring frequency Abnormal response time User operation convenience score (out of 10 points) Environmental parameters real time About 5 minutes 8 points Device Status real time About 5 minutes 8 points

[0056] The data in the table demonstrates that the remote monitoring platform accurately monitors the planting environment and equipment status in real time, responding quickly to anomalies. Furthermore, the high user-friendliness score indicates that the platform's design meets user needs and helps improve the efficiency of planting management.

[0057] 7. Data Storage and Analysis Step: Collected environmental data, equipment operation data, and black fungus growth data are stored in a database. The database utilizes a secure and reliable storage architecture to ensure data integrity and security. The database data is regularly analyzed, using data mining algorithms (such as association rule mining and cluster analysis) to identify potential relationships between environmental parameters and black fungus growth. For example, association rule mining revealed a correlation between specific temperature, humidity, and light combinations and high yields, providing a basis for optimizing planting strategies.

[0058] The analysis results are presented in the form of reports or visual charts, making it easier for planters to understand and apply them.

[0059] Analytical methods The number of mined relationships Contribution score to planting strategy optimization (out of 10 points) Association rule mining About 10 7 points Cluster analysis Found 3 growth patterns 7 points

[0060] As the table shows, data mining algorithms can uncover multiple correlations and growth patterns between environmental parameters and black fungus growth, significantly contributing to the optimization of planting strategies. This helps growers develop more scientific and rational planting plans, thereby improving black fungus yield and quality.

[0061] 8. System Optimization and Update Step: Based on the results from the data storage and analysis steps, the growth model and environmental control strategy are optimized and updated. When new correlations between environmental parameters and black fungus growth are discovered, the growth model parameters are adjusted promptly. When a more optimal environmental control strategy is discovered, the objective function and constraints of the multi-objective optimization algorithm are updated to improve the system's control accuracy and efficiency. Furthermore, the system's hardware is regularly inspected and maintained to ensure stable operation.

[0062] Optimization cycle Improved prediction accuracy of growth models Improved efficiency of control strategies Once a quarter About 5% About 8%

[0063] The data in the table shows that regular system optimization and updates have significantly improved the growth model’s prediction accuracy and the efficiency of the control strategy. This demonstrates that this step enables the system to continuously adapt to new situations and maintain good performance.

[0064] 9. Multi-base collaborative control steps: For enterprises or organizations with multiple black fungus cultivation bases, the monitoring platforms of each base can be connected through the Internet of Things. A distributed collaborative optimization algorithm is used to achieve coordinated environmental control across multiple bases based on the environmental conditions, planting varieties, and growth stages of each base. For example, when a base has excess lighting resources, the excess lighting resources can be used through data sharing and collaborative control to be used by other bases with insufficient lighting, thereby improving overall resource utilization. During the collaborative control process, environmental parameters and equipment operating status of each base are monitored in real time to ensure the effectiveness of the collaborative effect.

[0065] Before coordinated regulation After coordinated regulation Improved resource utilization Each base is independently controlled, resulting in obvious waste of resources Reasonable allocation of resources and efficient collaboration About 15%

[0066] As can be seen from the table, multi-base coordinated control can effectively solve the problem of resource waste when each base is independently controlled, realize the rational allocation and efficient utilization of resources, and improve the overall planting efficiency.

[0067] 10. Disaster Warning and Emergency Response Steps: A disaster warning model will be established, combining weather forecast data with real-time environmental data to provide early warnings for potential natural disasters (such as heavy rain, high winds, and high temperatures) and pest and disease damage. When the warning model identifies a potential disaster, the emergency response plan will be automatically activated. For example, if heavy rain is predicted, ventilation openings will be automatically closed and drainage systems strengthened. If signs of pests and diseases are detected, pesticide spraying equipment will be automatically activated or natural enemies will be released for control. Disaster warning information will also be promptly transmitted to plantation personnel to facilitate further action.

[0068] Disaster Type Early warning accuracy Emergency response effectiveness score (out of 10 points) Natural disasters About 85% 7 points pests and diseases About 90% 7 points

[0069] The data in the table show that the disaster warning model has a high accuracy rate, and the emergency response plan can effectively respond to various disasters and ensure the safety of black fungus cultivation. This helps to reduce the losses caused by disasters to black fungus cultivation and improve the stability of cultivation.

[0070] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for regulating and controlling the black fungus intelligent planting environment based on the Internet of Things, characterized in that: include: Environmental information collection steps: Place temperature and humidity sensors, light sensors, carbon dioxide concentration sensors, soil moisture sensors, and soil pH sensors in the black fungus planting area; Each sensor sends the collected data to the gateway device through the ZigBee wireless communication module; Data preprocessing steps: After the gateway device receives the sensor data, it first performs outlier detection on the data. The 3σ criterion based on statistical analysis is used to determine that data points that exceed the mean ±3 times the standard deviation are outliers. The Lagrange interpolation method is used to repair outliers. The formula is: Where x is the point to be interpolated, x i 、y i For known data points, the data is then normalized using the minimum-maximum normalization method to map the data to the [0,1] interval. The formula is: Where x is the original data, x min 、x max are the minimum and maximum values ​​of the group of data respectively; Growth model establishment steps: Using historical planting data, including environmental parameters and black fungus growth status data, the black fungus growth model was established using the support vector regression (SVR) algorithm. The model takes environmental parameters as input and black fungus growth indicators as output. By adjusting the model parameters, the mean square error between the model prediction value and the actual growth data is minimized. The mean square error formula is: where y i is the actual value, is the predicted value, n is the number of samples; Environmental control decision-making steps: Based on the currently collected environmental data and the established growth model, predict the growth trend of black fungus in the current environment; if the prediction results show that the growth indicators do not meet expectations, formulate an environmental control strategy using the NSGA-II algorithm; The control target values ​​of each environmental parameter, including the temperature control target value T, are calculated through the optimization algorithm. target , humidity control target value H target .

2. The black fungus intelligent planting environment control method based on Internet of Things according to claim 1, wherein Also includes: Equipment control execution steps: The environmental control target value is sent to the corresponding environmental control equipment, including temperature control equipment, humidity control equipment, light control equipment, carbon dioxide generator and irrigation equipment; The temperature control device adjusts the temperature according to the temperature control target value and uses the PID control algorithm to adjust the temperature. The formula is: Where u(t) is the control quantity, e(t) is the error between the current temperature and the target temperature, K p , K i , K d They are proportional, integral and differential coefficients respectively; humidity control equipment, light control equipment, carbon dioxide generator and irrigation equipment also use the same control algorithm and are controlled according to their respective control target values; Remote monitoring and interaction steps: Build a web-based remote monitoring platform. Planters access the platform via the internet to view various parameters of the planting environment, equipment operating status, and black fungus growth in real time. The platform also provides a human-computer interaction interface, allowing planters to manually adjust environmental control strategies. The platform is equipped with a message push function, which sends notifications to planters' mobile phones when environmental parameters exceed set thresholds or equipment malfunctions. Data storage and analysis steps: storing the collected environmental data, equipment operation data and black fungus growth data in a database; regularly analyzing the data in the database and using data mining algorithms to explore the relationship between environmental parameters and black fungus growth; System optimization and update step: Based on the results obtained in the data storage and analysis step, the growth model and environmental control strategy are optimized and updated; when a new correlation between environmental parameters and black fungus growth is discovered, the parameters of the growth model are adjusted; when an optimized environmental control strategy is discovered, the objective function and constraints of the NSGA-II algorithm are updated to improve the control efficiency of the system; Multi-base collaborative control steps: For enterprises or organizations with black fungus cultivation bases, the monitoring platforms of each base can be connected through the Internet of Things. A distributed collaborative optimization algorithm is used to achieve collaborative environmental control between bases based on the environmental conditions, planting varieties, and growth stages of each base. Disaster early warning and emergency response steps: Establish a disaster early warning model, combine weather forecast data and real-time environmental data to issue early warnings for natural disasters and pests and diseases, and automatically activate emergency response plans when the early warning model determines a disaster has occurred; Sensor calibration steps: Regularly calibrate the sensors in the planting area, use standard reference equipment to compare each sensor, and calculate the deviation between the sensor measurement value and the standard value; for temperature and humidity sensors and light sensors, if the deviation exceeds the set threshold, calibrate them; during the calibration process, correct the sensor output according to the deviation value. The correction formula is: corrected =x measured +Δx, where x corrected is the corrected value, x measured is the measured value, and Δx is the deviation value.

3. The black fungus intelligent planting environment control method based on Internet of Things according to claim 1, wherein Also includes: Black fungus quality assessment steps: Use image recognition technology and chemical analysis technology to assess the quality of black fungus; Images of black fungus are captured by a camera, and the convolutional neural network (CNN) algorithm is used to analyze the images and extract the morphological characteristics of the black fungus. At the same time, black fungus samples are collected regularly for chemical analysis and nutritional component testing. Based on the morphological characteristics and nutritional component test results, a black fungus quality assessment model is established.

4. The black fungus intelligent planting environment control method based on Internet of Things according to claim 1, wherein Also includes: Energy management steps: Install energy monitoring equipment in the planting area to monitor the energy consumption of each environmental control equipment in real time; Establish an energy consumption model and analyze the energy consumption patterns under different environmental control strategies; adopt an optimization algorithm to adjust the operating time and power of the equipment to reduce energy consumption while meeting the growth needs of black fungus.

5. The black fungus intelligent planting environment control method based on Internet of Things according to claim 1, wherein Also includes: User feedback and improvement steps: Set up a user feedback module on the remote monitoring platform, and planting personnel use the module to feedback problems encountered during system operation and make improvement suggestions; system administrators regularly collect and analyze user feedback information, and improve and optimize the system based on the problems and suggestions raised by users.

6. The black fungus intelligent planting environment control method based on Internet of Things according to claim 1, wherein Also includes: Planting plan recommendation steps: Based on the planting goals entered by the planting personnel and the environmental conditions of the planting area, combined with the growth model and historical planting data, a planting plan is recommended to the planting personnel; the planting plan includes environmental parameter settings, cultivation techniques, and fertilization plan content.

7. The black fungus intelligent planting environment control method based on the Internet of Things according to claim 1, wherein Also includes: Pest and disease prediction and prevention steps: In addition to using real-time collected environmental data for pest and disease early warning, a pest and disease prediction model is also established; The model comprehensively considers historical pest and disease occurrence data, meteorological data, and soil conditions, and uses time series analysis and machine learning algorithms to predict the time, type, and severity of pest and disease occurrence; based on the prediction results, preventive measures are taken in advance.

8. The black fungus intelligent planting environment control method based on the Internet of Things according to claim 1, wherein Also includes: Equipment fault diagnosis and maintenance steps: Install fault sensors on each environmental control equipment to monitor the equipment's operating status parameters in real time; Establish an equipment fault diagnosis model and use machine learning algorithms to analyze the equipment operating status parameters to determine whether the equipment has a fault and the type of fault. When an equipment fault is diagnosed, the system automatically sends fault information and repair suggestions to the equipment maintenance personnel, and displays the location of the faulty equipment and the cause of the fault on the remote monitoring platform.

9. The black fungus intelligent planting environment control method based on Internet of Things according to claim 1, wherein Also includes: Data security and privacy protection steps: Use encryption technology to encrypt the collected data and the transmitted data, set up a user rights management system on the remote monitoring platform, set different operating permissions for users of different levels, and comply with relevant data protection laws and regulations to keep the user's personal information and planting data confidential.

10. The black fungus intelligent planting environment control method based on the Internet of Things according to claim 1, characterized in that: Also includes: Product traceability steps: Data from the black fungus cultivation process is stored on the node, and the characteristics of distributed ledger technology are used to generate a traceability code for each batch of black fungus; Consumers can scan the traceability code to query information about the black fungus's cultivation process and understand the growth environment and quality assurance.

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