A load heterogenous control system for greenhouse cultivation

Abstract

The application discloses a load heterogeneous control system for greenhouse planting, and relates to the technical field of agricultural engineering.The system comprises an environment data acquisition module, which is used for detecting the working state of load heterogeneous equipment based on real-time environment data; a control parameter setting module, which is used for automatically setting differentiated control parameters according to the types of crops in each region, so as to accurately meet the requirements of the corresponding growth stages; an optimization and control module, which is used for continuously optimizing the control logic through deep learning analysis of historical data, and realizing dynamic matching; and a feedback signal adjustment module, which is used for comprehensively feeding back signals by using multi-sensing technology, and adjusting the execution mechanism in real time.The load heterogeneous control system for greenhouse planting solves the problems of certain deficiencies in resource allocation and equipment control in the prior art, resource waste, influence on the yield and quality of crops, and obvious limitations in dealing with multi-variety planting and resource optimization allocation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of agricultural engineering, in particular to a load heterogeneity control system for greenhouse planting. BACKGROUND

[0002] In modern greenhouse planting, environmental control systems are an important part of ensuring the healthy growth of crops. These systems usually control key parameters such as light, humidity, and temperature to provide an ideal growing environment for plants. However, traditional control methods often use uniform standards to manage the entire greenhouse area, ignoring the specific needs of different plant species and crops in different areas, which is particularly evident in multi-species planting.

[0003] In the prior art, load heterogeneity control manages environmental factors in greenhouses through diverse control strategies and hardware combinations to better adapt to the growth requirements of different plants. However, due to the lack of precise control and real-time response capabilities, these systems have certain deficiencies in resource allocation and device control. For example, when multiple crops are planted in a greenhouse, the system has difficulty effectively identifying and adjusting environmental parameters in each area, resulting in an inability to provide the most suitable growing conditions for each crop. This imprecise response not only leads to resource waste but also can affect crop yield and quality. Existing load heterogeneity control systems have obvious limitations in dealing with multi-species planting and optimizing resource allocation. SUMMARY

[0004] The purpose of the present application is to provide a load heterogeneity control system for greenhouse planting, which solves the problem of existing load heterogeneity control systems having obvious limitations in dealing with multi-species planting and optimizing resource allocation.

[0005] To achieve the above purpose, the present application provides the following technical solution: a load heterogeneity control system for greenhouse planting, the system comprising:

[0006] An environmental data acquisition module for detecting the working state of load heterogeneity devices based on real-time environmental data;

[0007] A control parameter setting module connected to the environmental data acquisition module for automatically setting differentiated control parameters according to the types of crops in each area to accurately meet the needs of the corresponding growth stage;

[0008] An optimization and control module connected to the environmental data acquisition module and the control parameter setting module for continuously optimizing and controlling logic through deep learning analysis of historical data to realize dynamic matching;

[0009] A feedback signal adjustment module connected with the environment data acquisition module, the control parameter setting module and the optimization control module, for adjusting the execution mechanism in real time by using multi-sensing technology to comprehensively feedback signals.

[0010] Preferably, the environment data acquisition module detects the working state of the load-heterogeneous device based on real-time environment data, which includes:

[0011] judging whether the temperature in the greenhouse is higher than the set upper limit T based on real-time temperature data; if yes, turning on the cooling device;

[0012] monitoring the light intensity in real time and adjusting the light supplement time according to the photosensitivity index I;

[0013] adjusting the ventilation volume in real time by the carbon dioxide concentration to ensure that the CO2 concentration is within the appropriate range;

[0014] judging whether the soil humidity is lower than the preset lower limit H, if yes, activating the watering device;

[0015] Preferably, the judging whether the temperature in the greenhouse is higher than the set upper limit T based on real-time temperature data, if yes, turning on the cooling device further includes:

[0016] calculating the temperature rise rate K1 = ΔT / Δt according to the relationship between the temperature change ΔT and the time interval Δt;

[0017] if K1 > VT, (wherein VT is the speed threshold value), it is considered that the current greenhouse environment is rapidly rising, and the high-power ventilation cooling device is opened preferentially;

[0018] if the temperature rise is less than the temperature difference warning value ΔTmin, the air conditioner is turned off to save power consumption but the window is kept slightly open;

[0019] For the temperature rise rate of the current environment, conditional judgment is adopted, if K1 ≥ VTmin, (wherein VTmin is the greenhouse cooling warning value), the judgment condition result is true, and the corresponding operation is performed.

[0020] Preferably, for the temperature rise rate of the current environment, conditional judgment is adopted, if K1 ≥ VTmin, the judgment condition result is true, and the corresponding operation is performed, which more specifically includes:

[0021] comparing K1 with the greenhouse cooling warning value VTmin, if [K1 ≥ VTmin], it indicates that active cooling measures need to be taken, and the air conditioning device is started to perform forced cooling, otherwise, the cooling is performed only by natural ventilation;

[0022] a timing strategy is adopted to ensure the equipment operation efficiency, and when the temperature control condition is met, it is checked every 15 minutes whether the equipment needs to be adjusted;

[0023] Adjusting the air conditioning power and fan rotation rate ensures that the target temperature of the greenhouse is achieved, i.e. in every other cycle, i.e. t = n x cycle, if K1≥VTmin, the air conditioner is started to cool, wherein n = [1, 2, 3,...];

[0024] The setting condition prevents excessive energy consumption caused by extreme weather in a short period of time from changing the air conditioning settings before the temperature difference ΔT reaches the temperature difference compensation threshold ΔTc;

[0025] The setting condition prevents excessive energy consumption caused by extreme weather in a short period of time from changing the air conditioning settings before the temperature difference ΔT reaches the temperature difference compensation threshold ΔTc, and more specifically includes the following:

[0026] Record the temperature data Ts of the greenhouse under the current air conditioning operating state and the target set temperature Tr at the start of air conditioning operation;

[0027] The difference between the current ambient temperature Ts and the target set temperature Tr is calculated to obtain the real-time temperature difference ΔT = Ts−Tr;

[0028] Determine whether the real-time temperature difference reaches the preset temperature difference compensation threshold ΔTc, i.e. determine (|ΔT|≤|ΔTc|), the condition is not true, indicating that the device setting does not need to be changed, otherwise the device needs to be adjusted according to the current environmental conditions;

[0029] Set the air conditioning operation mode adjustment period C, and execute the above condition, when the period C ends and |ΔT|>|ΔTc|) or the system determines that the external temperature has changed, the operating state is adjusted to respond to the new demand.

[0030] Preferably, according to the judgment (|ΔT|≤|ΔTc|), the condition is not true, indicating that the device setting does not need to be changed, otherwise the device needs to be adjusted according to the current environmental conditions, and further includes:

[0031] Record the last adjustment time To and the current temperature Ts, and obtain the external reference temperature Te at this time;

[0032] Environmental assessment to determine when |Ts−Tr|>|ΔTc| and the current time t is greater than the minimum allowed adjustment period from the last adjustment time, triggering condition update, indicating that the control strategy should be reset;

[0033] According to the update, the set temperature or the device operating mode is dynamically changed to maintain the stability of the internal environment, and the update condition is set as |Ts−Tr|>|ΔTc|;

[0034] The above determination is executed and the corresponding adjustment is made, the latest state is recorded and the adjustment instruction is sent to the control parameter setting module when needed.

[0035] Preferably, the control parameter module automatically sets differentiated control parameters according to the crop types in each region to accurately meet the needs of the corresponding growth stages, which more specifically includes:

[0036] The classification data of crops in the planting area is combined with geographic information technology to determine the location distribution information of each crop;

[0037] The database is searched to retrieve the suitable growth condition range required by each growth period of the crop;

[0038] A control parameter setting rule file is automatically generated according to the specific needs of the growth stage in each region;

[0039] A differentiated irrigation and temperature regulation plan is implemented to simulate a man-made climate environment that is most suitable for the current plant type development needs;

[0040] Periodic calibration checks are performed on the specific needs of specific crops to ensure compliance with standards or update strategies to improve effectiveness.

[0041] Preferably, the control parameter module automatically sets differentiated control parameters according to the crop types in each region to accurately meet the needs of the corresponding growth stages, which more specifically includes:

[0042] The classification data of different crops in the greenhouse is combined with geographic information technology to determine the specific location distribution of each crop, using the formula: Li = GIS(Xi, Yi, Zi),

[0043] Where Li represents the spatial location of the ith crop, Xi, Yi, and Zi are geographic coordinates;

[0044] By searching the database, the suitable environmental conditions required by each crop at each growth stage are obtained. The condition data is stored in the form of a multi-dimensional matrix, representing the parameter requirements of the crop, using the formula:

[0045] Ci,j = Retrieve(Di, Gj),

[0046] Where Ci,j is the environmental condition required by crop i at growth stage j, Di is the crop data, and Gj is the growth stage;

[0047] According to the growth needs of the crop, a control parameter setting rule file is automatically generated, with light time and intensity settings, using the formula:

[0048] ,

[0049] Where Iij is the light intensity required by the ith crop at growth stage j, Imax,j is the optimal light intensity, Tij and Hij are the temperature and humidity, respectively, and Topt,j and Hopt,j are the optimal temperature and humidity;

[0050] The temperature control setting adopts the formula:

[0051]

[0052] where Tset,i is the target temperature for the ith crop, and ΔTi is the temperature deviation adjusted according to the current environmental conditions;

[0053] According to the generated control parameter rule file, the system implements differentiated irrigation and temperature regulation for different crops in different areas. The irrigation water quantity calculation adopts the formula:

[0054]

[0055] where Wi is the irrigation quantity of the ith crop, Wbase,i is the basic irrigation quantity, ki is the adjustment coefficient, and Si is the real-time reading of the soil humidity sensor;

[0056] The system adjusts the temperature according to the deviation of the real-time temperature data from the preset temperature target. The temperature adjustment adopts the formula:

[0057]

[0058] where ΔTadj,i is the temperature deviation that needs to be adjusted, and Tcurrent is the current environmental temperature;

[0059] For the specific needs of each crop, the system will conduct regular calibration checks to ensure that the control parameters are consistent with the actual needs of the crops. The calibration process is completed through the feedback signal adjustment module. Multi-sensing technology is used for real-time monitoring and feedback of environmental changes in the greenhouse. The formula is:

[0060]

[0061] where Fi is the comprehensive feedback signal, and Si,k is the kth sensor data of the ith crop.

[0062] Preferably, the optimization control module continuously optimizes the control logic through deep learning analysis of historical data, achieving dynamic matching and self-adaptation, including:

[0063] Collect long-term accumulated operation parameters and result output data sets as a historical database;

[0064] Establish an environmental control model and continuously train and adjust the prediction accuracy with newly added historical data;

[0065] Deeply analyze the past environmental change trends and their internal regularity of influence on yield;

[0066] Add an automated fault recovery function to ensure that the system can quickly restart the operation program in case of system crash caused by sudden accidents.​​​​

[0067] Online monitoring of the deviation between actual performance and theoretical optimal configuration, timely dynamic parameter adjustment, optimization of control performance and reduction of energy consumption level.

[0068] Preferably, the optimization control module continuously optimizes the control logic through deep learning analysis of historical data, realizes dynamic matching and self-adaptation, and further comprises:

[0069] Collecting operation parameters and result output data accumulated in the greenhouse for a long time as the basis for subsequent modeling and optimization, using the formula: Dhist={(Ti,Hi,Ii,Ci,Wi,Yi)∣i=1,2,…,n},

[0070] Wherein, Dhist represents the data set of the historical database, Ti, Hi, Ii, Ci, Wi respectively represent the temperature, humidity, light intensity, carbon dioxide concentration and irrigation amount collected for the i-th time, and Yi represents the corresponding crop yield or quality;

[0071] Using the historical database Dhist to establish an environment control model, the control model is in the form of:

[0072] Y=f(T,H,I,C,W)+ϵ,

[0073] Wherein, Y is the expected yield or quality of crops, f is the control model, and ϵ is the model error, T, H, I, C, W respectively represent the collected temperature, humidity, light intensity, carbon dioxide concentration and irrigation amount;

[0074] In the model training process, the system continuously uses the newly added historical data to retrain the model, updates the weights and parameters, and uses mean square error (MSE) to measure the deviation between the predicted value and the actual value in the training process. The specific formula is:

[0075] ,

[0076] Wherein, MSE is the mean square error, Yi is the actual value of the i-th sample, The predicted value of the i-th sample is represented by n, and n represents the total number of samples.

[0077] Through deep analysis of historical data, the system identifies the long-term impact of environmental parameter changes on crop yield and quality, extracts the inherent regularity, and uses the formula:

[0078] X(t)=ϕ1X(t−1)+ϕ2X(t−2)+⋯+θ0+ϵt,

[0079] Wherein, X(t) represents the environmental parameter at time t, and ϕ and θ are the coefficients of the model.

[0080] Based on the trend analysis results, the system predicts future environmental changes and adjusts control parameters in advance to optimize the climate conditions in the greenhouse.

[0081] Online monitoring of the working state of each module, when detecting abnormal or failure, automatically trigger fault recovery mechanism, fault detection through formula:

[0082] ,

[0083] Fault(t) indicates the fault state at time t, X(t) indicates the actual measurement value of the system at time t, Xset is the expected control parameter, δ is the allowable deviation range, when the fault occurs, automatically switch to standby mode, quickly recover the key control function, and restart and diagnose the fault reason in the background;

[0084] Real-time monitoring of the deviation between the environmental parameters in the greenhouse and the theoretically optimal configuration. By comparing the difference between the actual monitoring data Xactual and the optimal setting value Xopt, the control parameters are dynamically adjusted, and the formula for dynamically adjusting the control parameters is:

[0085] ΔX=Kp·(Xopt-Xactual)+Ki·∑(Xopt-Xactual)+Kd·(d(Xopt-Xactual) / dt),

[0086] Where ΔX is the amount of control parameters that need to be adjusted, Xopt is the theoretically optimal setting value, Xactual is the actual monitored environmental parameter value, Kp is the proportional control coefficient, Ki is the integral control coefficient, and Kd is the differential control coefficient.

[0087] Preferably, the feedback signal adjustment module uses multi-sensing technology to integrate feedback signals, and the real-time adjustment execution mechanism includes:

[0088] Set up information transmission interface compatible with various types of sensor input ports between various types of perception instruments;

[0089] Consider the importance and weight of different index types to build a unified feedback evaluation standard;

[0090] Independently detect and isolate signal interference sources on a single router;

[0091] Periodically correct the accuracy of all measurement tool readings;

[0092] Equipped with disaster recovery standby circuit.

[0093] Preferably, the feedback signal adjustment module uses multi-sensing technology to integrate feedback signals, and the real-time adjustment execution mechanism further includes:

[0094] Set a unified sensor information delivery interface, using the formula:

[0095] Ij=f(Sj, Pj),

[0096] Where Ij represents the information delivery interface of the jth sensor, Sj represents the model of the jth sensor, Pj represents the data format of the jth sensor, and f is the interface adaptation function;

[0097] Considering the sensitivity and volatility of various environmental indicators, appropriate weights are assigned to different indicators. Based on these weights, a unified feedback evaluation standard is constructed. The unified feedback evaluation standard is constructed using the formula:

[0098] ,

[0099] Where Feval is the unified feedback evaluation standard, m is the total number of environmental indicators, wk is the weight of the kth indicator, Xk is the current actual value of the kth indicator, Xset,k is the target set value of the kth indicator, and σk is the volatility or standard deviation of the kth indicator;

[0100] Independently detect and isolate single router signal interference sources. The signal interference detection determination formula is as follows:

[0101] ,

[0102] Where Dint(t) is the signal interference state at time t, 1 indicates that interference is detected, 0 indicates no interference, R(t) is the signal transmission rate at time t, and δR is the threshold value of signal interference;

[0103] Periodically correct the accuracy of all measurement tool readings. The calibration formula for periodic correction is:

[0104] Ck=Xk+Δk,

[0105] Where Ck is the calibrated reading of the kth sensor, Xk is the actual measurement value before calibration, and Δk is the calibration deviation;

[0106] Equipped with disaster recovery standby circuit, in case of failure of core components or severe environmental conditions, automatically switch to standby circuit. The determination and triggering condition of disaster recovery switching is as follows:

[0107] ,

[0108] Where Ssw(t) is the disaster recovery switching state at time t, 1 indicates that the standby circuit is triggered, 0 indicates normal operation, Fcrit(t) is the fault critical parameter at time t, and τ is the threshold value of disaster recovery switching.

[0109] From the above technical solutions, the present application has the following beneficial effects:

[0110] The load heterogeneity control system for greenhouse planting can detect the working state of the load heterogeneity equipment based on real-time environmental data through the environmental data acquisition module, automatically set differentiated control parameters according to the types of crops in each region by the control parameter setting module, accurately meet the needs of the corresponding growth stage, continuously optimize the control logic through deep learning analysis of historical data by the optimization control module, realize dynamic matching, and adjust the execution mechanism in real time by using the multi-sensing technology comprehensive feedback signal feedback signal adjustment module, which realizes the precise adjustment of key parameters such as light, humidity and temperature in the greenhouse. This kind of precise adjustment not only can respond to the growth needs of different plants in real time, but also can effectively optimize the allocation and utilization of resources, reduce unnecessary energy consumption, can maintain stable control effect in complex environment of multi-variety plants coexistence, avoid resource waste and crop quality decline caused by inaccurate response of traditional system, solve the problem of lack of accurate control and real-time response ability in prior art, there are certain deficiencies in resource allocation and equipment control, inaccurate response not only easily causes resource waste, but also may affect the yield and quality of crops, there are obvious limitations in dealing with multi-variety planting and resource optimization allocation. BRIEF DESCRIPTION OF DRAWINGS

[0111] Figure 1 The module connection diagram of the present application is shown in the figure. DETAILED DESCRIPTION

[0112] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0113] As Figure 1 shown, in order to solve the problem of inaccurate response of load heterogeneity control system to different plant growth needs in greenhouse planting, a new load heterogeneity control method for greenhouse planting is proposed. This system comprehensively uses real-time sensor data analysis and machine learning technology to realize fine management, precise irrigation and other agricultural operation optimization configuration, to ensure that different plant types can get the most optimized environmental regulation support in different growth stages. The system includes:

[0114] The environmental data acquisition module for load heterogeneous device working state detection based on real-time environmental data. In order to solve the problem of inaccurate response of load heterogeneous control system to different plant growth needs in greenhouse planting, a load heterogeneous control method for greenhouse planting is proposed. This method comprehensively uses real-time sensor data analysis and machine learning technology to realize fine management, precise irrigation and other agricultural operation optimization configuration through multiple steps, so as to ensure that different plant types can get the best environmental regulation support in different growth stages.

[0115] Next, the automatic setting of differentiated control parameters for specific areas to accurately meet the needs of the corresponding growth stage, the control parameter setting module connected with the environmental data acquisition module. In practical application, for example, in a comprehensive test greenhouse, there are small tomatoes that like shade and humidity, large tomatoes that need sufficient sunlight, and other crops. Under this premise, we need the system to automatically adjust the control parameters according to the characteristics of the crops, such as irrigation frequency, ventilation volume, lighting time and CO2 supply ratio, etc. This can meet their different requirements for water and light during growth. For example: the system identifies the planting area of large tomatoes, and will automatically adjust to increase the light during the day and reduce the water irrigation amount to adapt to the characteristics of heat tolerance.

[0116] Through deep learning analysis of historical data to continuously optimize the control logic, the optimization control module connected with the environmental data acquisition module and the control parameter setting module. Through analysis and arrangement of the relevant monitoring data accumulated in the past, the best greenhouse condition law required by specific types of vegetables from the germination stage to the harvesting state can be found. These experiences can be used to guide the adoption of more effective control schemes for the same or similar crops in the future. For example, in the process of summarizing the data of the last cycle of green onion planting, it is concluded that the first 35-45 days of the growth cycle of this type of plant is a rapid growth period, and maintaining a relatively high water and light supply can promote yield. Therefore, when planting the same crop again, timely adjustment strategies can be made to make the production process more effective.

[0117] Using multi-sensor technology to comprehensively feedback signals, real-time adjustment of execution mechanism and connection with environmental data acquisition module, control parameter setting module and optimization control module, every adjustment and control in this process cannot be separated from the comprehensive use and accurate feedback of multi-sensor. For example, in the process of monitoring irrigation, soil moisture sensors will constantly feedback the water state in the soil to determine when to perform the next water replenishment or stop spraying; air temperature and light level rely on infrared and ultraviolet sensing technology for tracking. In this way, the smooth operation of the entire control chain can be ensured, and a benign self-adaptive regulation cycle can be formed.

[0118] In summary, this innovative solution aims to improve the efficiency of intelligent greenhouse planting while achieving the purpose of cost saving. Through strict supervision of each variety and the use of advanced technical means, it ensures the growth of each variety under suitable conditions, ultimately making the agricultural industry further approach sustainable development.

[0119] The working state detection of the load heterogeneous device based on real-time environmental data includes:

[0120] Determine whether the temperature in the greenhouse is higher than the set upper limit T based on real-time temperature data; if it is, turn on the cooling device;

[0121] Based on the light intensity, adjust the light supplement time according to the photosensitivity index I in real time;

[0122] Adjust the ventilation volume in real time based on the carbon dioxide concentration to ensure that the CO2 concentration is within the appropriate range;

[0123] Determine whether the soil humidity is lower than the preset lower limit value H, if it is, activate the watering device;

[0124] Among them, the determination of whether the temperature in the greenhouse is higher than the set upper limit T based on real-time temperature data, if it is, turn on the cooling device further includes:

[0125] According to the relationship between temperature change ΔT and time interval Δt, calculate the heating rate K1 = ΔT / Δt;

[0126] If K1 > VT, (where VT is the speed threshold), it is considered that the current greenhouse environment is rapidly rising, and the high-power ventilation cooling device is opened preferentially;

[0127] If the temperature rise is less than the temperature difference warning value ΔTmin, turn off the air conditioner to save power consumption but keep the window slightly open;

[0128] For the current environment, the heating rate is determined by the condition, if K1 ≥ VTmin, (where VTmin is the greenhouse cooling warning value), the condition result is true, and the corresponding operation is performed.

[0129] The environmental data acquisition module monitors the temperature, light, carbon dioxide concentration, and soil moisture in the greenhouse in real time through various sensors. The system determines whether the temperature in the greenhouse exceeds the set upper limit T based on temperature data. If it does, the cooling equipment is turned on. The system also adjusts the light supplement time based on the light intensity and photosensitivity index I to ensure that the crops receive sufficient light. Real-time monitoring of carbon dioxide concentration and adjustment of ventilation volume ensure appropriate CO2 levels. When the soil moisture is below the lower limit H, the system automatically activates the watering equipment.

[0130] Through real-time monitoring and control, the system can respond in the shortest time to ensure that the greenhouse environment is always in the optimal state for crop growth. Calculating the temperature rise rate and combining different control strategies allows the system to efficiently cool while saving energy and avoiding excessive reliance on a single cooling method, improving the system's flexibility and energy efficiency.

[0131] The environmental data acquisition module can use other types of sensors, such as thermocouple sensors instead of traditional temperature sensors, or ultrasonic humidity sensors instead of capacitive humidity sensors, to cope with special environmental conditions. The calculation method of the photosensitivity index I can also be adjusted or replaced, such as using different spectral ranges or different calculation algorithms to adapt to the light requirements of specific crops.

[0132] The temperature rise rate for the current environment is compared with the greenhouse cooling alert value VTmin. If K1 ≥ VTmin, the condition is true, and the corresponding operation is performed. More specifically, it includes:

[0133] If K1 ≥ VTmin, it indicates that active cooling measures need to be taken, and the air conditioning equipment is started to force cooling. Otherwise, only natural ventilation is used for cooling.

[0134] A timing strategy is used to ensure equipment efficiency. When the temperature control conditions are met, every 15 minutes is checked to see if the equipment needs to be adjusted.

[0135] Adjusting the air conditioning power and fan rotation rate ensures that the greenhouse environment temperature control target is achieved. That is, every other cycle, i.e., t = n × cycle, if K1 ≥ VTmin, the air conditioning cooling is started, where n = [1, 2, 3,...].

[0136] A condition is set to prevent excessive energy consumption due to extreme weather in a short period of time. The air conditioning settings are not changed until the temperature difference ΔT reaches the temperature difference compensation threshold ΔTc.

[0137] The setting condition prevents excessive energy consumption caused by extreme weather in a short period of time and does not change the air conditioning setting before the temperature difference ΔT reaches the temperature difference compensation threshold ΔTc. More specifically, the setting condition includes the following:

[0138] Record the temperature data Ts of the greenhouse in the current air conditioning running state and the target set temperature Tr at the beginning of air conditioning running;

[0139] Calculate the real-time temperature difference ΔT = Ts - Tr by using the difference between the current environment temperature Ts and the target set temperature Tr;

[0140] Determine whether the real-time temperature difference reaches the preset temperature difference compensation threshold ΔTc, that is, determine (|ΔT|≤|ΔTc|). If the condition is not established, the device setting does not need to be changed, otherwise the device needs to be adjusted according to the current environmental conditions;

[0141] Set the air conditioning working mode adjustment period C, and execute the above condition. When the period C ends and |ΔT|>|ΔTc|, or the system determines that the external temperature changes, the working state is adjusted to respond to the new demand.

[0142] Calculate the temperature rise rate K1 of the current environment, and compare it with the greenhouse temperature drop warning value VTmin. The system determines whether immediate cooling measures need to be taken. If the condition is met, the air conditioner is started for forced cooling, otherwise natural ventilation is adopted. During the operation of the device, the system will check the temperature control effect regularly and adjust the air conditioning power and fan speed according to the temperature control target to ensure the stability of the greenhouse environment. In addition, the system sets conditions to prevent excessive energy consumption caused by extreme weather changes in a short period of time. Only when the temperature difference reaches the compensation threshold ΔTc will the air conditioning setting be changed. This implementation can effectively identify the emergency of environmental temperature change and take corresponding measures in time to ensure the stability of the greenhouse environment. Through the timing strategy and condition setting, the system not only improves the cooling effect, but also avoids energy waste, improves the overall operation efficiency and economy.

[0143] Under different environmental conditions, the temperature drop warning value VTmin can be adjusted according to actual needs, for example, a lower VTmin value is set in warmer climate conditions. At the same time, the system can integrate other control means, such as using a liquid cooling system as a supplementary cooling method, to improve the response to high temperature environments. The interval time of the timing strategy can also be adjusted according to the size of the greenhouse and the type of crops to ensure the adaptability and flexibility of the cooling strategy.

[0144] According to the judgment (|ΔT|≤|ΔTc|), if the condition is not established, the device setting does not need to be changed, otherwise the device needs to be adjusted according to the current environmental conditions, and it further includes:

[0145] Record the last adjustment time To and the current temperature Ts, and obtain the external reference temperature Te at this time;

[0146] Environmental assessment, determine when |Ts−Tr|>|ΔTc| and the current time t the last adjustment time interval is greater than the minimum allowed adjustment period, trigger condition update, indicating that the control strategy should be reset;

[0147] According to the update situation, dynamically change the set temperature or equipment operation mode to maintain the stability of the internal environment, and set the update condition as |Ts−Tr|>|ΔTc|;

[0148] Perform the above determination and make corresponding adjustments, record the latest state and send adjustment instructions to the control parameter setting module when necessary.

[0149] The system monitors the changes of the temperature inside and outside the greenhouse in real time, combines historical data records and environmental assessment, and determines whether the current temperature control device needs to be adjusted. First, record the current temperature Ts and the external temperature Te, and evaluate the difference between them and the set temperature Tr. If the temperature difference |Ts−Tr| exceeds the preset compensation threshold ΔTc, or the external temperature Te is lower than the reference temperature difference ΔTt, and the time interval since the last adjustment has exceeded the minimum allowed adjustment period, the system will re-evaluate and update the control strategy. According to the updated conditions, dynamically adjust the set temperature or the operation mode of the equipment to ensure the stability of the greenhouse environment. After the system performs the adjustment operation, it will record the latest state and send adjustment instructions to the control parameter setting module when necessary to ensure the automation and accuracy of the system.

[0150] This implementation enhances the system's adaptability to changes in the external environment, especially in the face of rapid changes in external temperature or other extreme weather. The system can quickly respond and make corresponding adjustments to avoid energy waste and maintain ideal growing conditions inside the greenhouse. Through recording and monitoring, the system can automatically adjust the settings when needed, avoiding frequent manual intervention, further improving the system's intelligent level and operational efficiency.

[0151] In different environments, the reference value of external temperature Te and the temperature difference threshold ΔTc can be adjusted according to specific needs. For example, in relatively stable climate conditions, these thresholds can be appropriately relaxed to reduce unnecessary frequent adjustments. In addition, the environmental assessment algorithm can be replaced by other more complex weather models to improve the prediction and response capabilities of complex weather conditions. The minimum allowed adjustment period C can also be flexibly set according to the size of the greenhouse and the needs of the crops to ensure that the system maintains optimal performance in different application scenarios.

[0152] The control parameter module automatically sets differentiated control parameters according to the types of crops in each region to accurately meet the needs of the corresponding growth stages, including:

[0153] The classification data of crops in the planting area is combined with geographic information technology to determine the location distribution information of each crop;

[0154] The joint database retrieves the appropriate growth condition range required by each growth cycle of the crop;

[0155] The control parameter setting rule file is automatically generated according to the specific needs of the growth stage in each area;

[0156] Differential irrigation and temperature regulation plans are implemented to simulate an artificial climate environment that is most suitable for the current plant species development needs;

[0157] Periodic calibration checks are performed on the specific needs of specific crops to ensure compliance with standards or update strategies to improve effectiveness.

[0158] The module first locates the specific location of different crops in the greenhouse through geographic information technology (GIS), combines these locations with crop classification data to form a crop distribution information map, then the system retrieves the appropriate conditions required by each type of crop during its growth cycle, such as temperature, humidity, light, etc., and generates corresponding control parameter setting rule files. According to these files, the system will automatically implement differential irrigation and temperature regulation to ensure that each crop is in the best growing environment. At the same time, the system will also periodically calibrate the control parameters to ensure that they are consistent with the actual growth needs, and update the strategy based on the latest data.

[0159] This implementation uses GIS technology and database retrieval functions to achieve precise positioning and environmental condition setting for different crops in the greenhouse, thereby improving the efficiency and quality of crop growth. The differential management approach allows the system to better adapt to the individual needs of different crops, reducing resource waste and optimizing environmental control. In addition, regular calibration and updating of strategies ensure the continuous optimization and efficient operation of the system.

[0160] In other implementations, the control parameter setting module can use more refined control strategies, such as introducing more environmental parameters (such as carbon dioxide concentration, soil pH, etc.) for multi-dimensional adjustment. The classification data of crops can also be combined with real-time monitoring systems such as image recognition technology to more accurately locate crop distribution. Calibration checks for specific crop needs can be performed by automated robots to improve efficiency and accuracy.

[0161] The control parameter module automatically sets differential control parameters according to the crop species in each area to accurately meet the needs of the corresponding growth stage, which includes:

[0162] The classification data of different crops in the greenhouse is combined with geographic information technology to determine the specific location distribution of each crop, and a formula Li=Gis(Xi,Yi,Zi) is used,

[0163] wherein Li represents the spatial position of the i-th crop, Xi, Yi, Zi are geographic coordinates;

[0164] By searching the database, the suitable environmental conditions required by each crop at each growth stage are obtained, and the condition data is stored in the form of a multi-dimensional matrix, representing the parameter requirements of the crop, and a formula Cij=Retrieve(Di,Gj) is used,

[0165]

[0166] wherein Cij is the environmental condition required by the i-th crop at the j-th growth stage, Di is the crop data, and Gj is the growth stage;

[0167] According to the growth requirements of the crops, a control parameter setting rule file is automatically generated, and the light time and intensity are set, and a formula Iij=Imax,j*Tij / Hij-Topt,j / Hopt,j is used,

[0168]

[0169] wherein Iij is the light intensity required by the i-th crop at the j-th growth stage, Imax,j is the optimal light intensity, Tij and Hij are the temperature and humidity, respectively, and Topt,j and Hopt,j are the optimal temperature and humidity;

[0170] The temperature control setting uses a formula Tset,i=Tij+ΔTi,

[0171]

[0172] wherein Tset,i is the target temperature of the i-th crop, and ΔTi is the temperature deviation adjusted according to the current environmental conditions;

[0173] According to the generated control parameter rule file, the system implements differentiated irrigation and temperature regulation for crops in different regions, and the irrigation water quantity calculation uses a formula Wi=Wbase,i*ki*Si,

[0174]

[0175] wherein Wi is the irrigation quantity of the i-th crop, Wbase,i is the basic irrigation quantity, ki is the adjustment coefficient, and Si is the real-time reading of the soil humidity sensor;

[0176] The system adjusts the temperature according to the deviation of the real-time temperature data from the preset temperature target, and the temperature adjustment uses a formula Tset,i=Tij+ΔTi,

[0177]

[0178] ​​​​​Where ΔTadj,i is the temperature deviation that needs to be adjusted, Tcurrent is the current environmental temperature.

[0179] For the specific needs of each crop, the system will conduct regular calibration checks to ensure that the control parameters are consistent with the actual needs of the crops, and the calibration process is completed through the feedback signal adjustment module. Multi-sensing technology is used for real-time monitoring and feedback of environmental changes in the greenhouse. The formula used is:

[0180]

[0181] Where Fi is the comprehensive feedback signal, and Si,k is the kth sensor data of the ith crop.

[0182] The control parameter module first locates the spatial position of different crops in the greenhouse through geographic information technology (GIS). The position of each crop is determined by its geographic coordinates (Xi, Yi, Zi). Then, the system retrieves data from the database to obtain the environmental conditions required by each crop at different growth stages. These condition data are stored in the form of a multi-dimensional matrix (Ci,j). Based on these data, the system generates differentiated control parameter rule files to set the required light intensity, temperature, humidity, and other parameters for each crop at different growth stages. Temperature control is set based on the comparison of real-time environmental data and preset targets. The system calculates the temperature deviation and makes corresponding adjustments through the formula. In addition, irrigation volume is also calculated through the formula and dynamically adjusted in combination with real-time soil moisture data. The system also conducts regular calibration checks through the feedback signal adjustment module, using multi-sensing technology to monitor environmental changes in real time, ensuring that the control parameters match the actual needs.

[0183] This embodiment achieves precise location positioning and individualized environment setting for different crops by combining GIS technology and database. Through multi-dimensional matrix parameter setting, the system can accurately control environmental factors such as light, temperature, and humidity in the greenhouse, allowing each crop to grow under optimal conditions. In addition, through dynamic irrigation and temperature regulation, the system improves resource utilization efficiency, reduces energy consumption, and ensures the stability and optimal control of the greenhouse environment.

[0184] In other embodiments, GIS technology can be replaced by other positioning technologies such as laser ranging or acoustic positioning to improve positioning accuracy or adapt to different greenhouse structures. The method of obtaining crop classification data can be changed from manual input to automatic collection using image recognition or unmanned aerial vehicle inspection technology. The control algorithm for irrigation and temperature regulation can be further optimized using adaptive control or fuzzy logic control to cope with complex environmental changes. Calibration checks can be performed by automated equipment to reduce human intervention and improve the self-management ability of the system.

[0185] ​The optimization control module continuously optimizes the control logic through deep learning analysis of historical data, achieving dynamic matching and self-adaptation, including:

[0186] Collecting long-term accumulated operation parameters and result output data sets as a historical database;

[0187] Establishing an environmental control model and continuously training and adjusting the prediction accuracy with newly added historical data;

[0188] Deeply analyzing past environmental change trends and their internal regularity of influence on yield;

[0189] Adding an automated fault recovery function to ensure that system crashes caused by sudden accidents can be quickly restarted and the operation program can be restarted;

[0190] Online monitoring of the deviation between actual performance and theoretical optimal configuration to dynamically adjust and optimize control performance and reduce energy consumption levels.

[0191] The optimization control module first collects long-term accumulated operation parameters and result data in the greenhouse, and constructs a historical database. Based on these data, the system establishes an environmental control model for predicting crop yield or quality. The model improves the accuracy of prediction through continuous training and updating. The system analyzes the environmental change trends in the historical data through deep learning, identifies the key factors affecting yield, and optimizes the control logic. The module also has an automated fault recovery function. When the system detects an anomaly, it can quickly trigger the fault recovery program to ensure stable operation of the system. In addition, the system monitors the deviation between the environmental parameters in the greenhouse and the theoretical optimal configuration in real time, dynamically adjusts the control parameters, and reduces energy consumption and improves control performance.

[0192] This embodiment realizes continuous optimization of environmental control logic through deep learning analysis of historical data, dynamically matches the needs of crops in different growth stages, improves yield and quality, and at the same time, the automated fault recovery function ensures high reliability of the system, which can quickly recover in emergency situations, reduce downtime and losses, and in addition, the real-time dynamic parameter adjustment function can reduce energy consumption and improve the operating efficiency of the greenhouse.

[0193] In other embodiments, the collection method of the historical database can be extended to include more environmental variables such as carbon dioxide concentration or soil pH, the environmental control model can use more complex machine learning algorithms such as deep neural networks or reinforcement learning to improve prediction accuracy and model adaptability, and the fault recovery function can integrate more sensors and monitoring systems to detect a wider range of potential faults, and the dynamic parameter adjustment function can be further optimized by using self-learning algorithms to more accurately adjust control parameters.

[0194] The optimization and control module continuously optimizes the control logic through deep learning analysis of historical data to achieve dynamic matching and self-adaptation, further including:

[0195] Collect the greenhouse's long-term accumulated operating parameters and output data as the basis for subsequent modeling and optimization, using the formula: Dhist={(Ti,Hi,Ii,Ci,Wi,Yi)|i=1,2,…,n},

[0196] Where Dhist represents the data set of the historical database, Ti, Hi, Ii, Ci, and Wi represent the temperature, humidity, light intensity, carbon dioxide concentration, and irrigation amount collected for the i-th time, respectively, and Yi represents the corresponding crop yield or quality;

[0197] The environmental control model is established using the historical database Dhist. The control model is in the form of:

[0198] Y=f(T,H,I,C,W)+ϵ,

[0199] Where Y is the expected yield or quality of the crop, f is the control model, ϵ is the model error, T, H, I, C, and W represent the collected temperature, humidity, light intensity, carbon dioxide concentration, and irrigation amount, respectively;

[0200] During the model training process, the system continuously uses newly added historical data to retrain the model and update weights and parameters. The loss function during the training process uses the mean square error (MSE) to measure the deviation between the predicted value and the actual value. The specific formula is:

[0201] ,

[0202] Where MSE is the mean square error, Yi is the actual value of the i-th sample, represents the predicted value of the i-th sample, and n represents the total number of samples;

[0203] Through in-depth analysis of historical data, the system identifies the long-term impact of changes in environmental parameters on crop yield and quality, extracts inherent regularities, and uses the formula:

[0204] X(t)=ϕ1X(t−1)+ϕ2X(t−2)+⋯+θ0+ϵt,

[0205] Where X(t) represents the environmental parameters at time t, and ϕ and θ are the coefficients of the model.

[0206] Based on the trend analysis results, the system predicts future environmental changes and adjusts control parameters in advance to optimize the climate conditions in the greenhouse;

[0207] Online monitoring of the working state of each module, when detecting abnormal or failure, automatically trigger fault recovery mechanism, fault detection through formula:

[0208] ,

[0209] Fault(t) indicates the fault state at time t, X(t) indicates the actual measurement value of the system at time t, Xset is the expected control parameter, δ is the allowable deviation range, when the fault occurs, automatically switch to standby mode, quickly recover the key control function, and restart and diagnose the fault reason in the background;

[0210] Real-time monitoring of the deviation between the environmental parameters in the greenhouse and the theoretically optimal configuration. By comparing the difference between the actual monitoring data Xactual and the optimal setting value Xopt, the control parameters are dynamically adjusted, and the formula for dynamically adjusting the control parameters is:

[0211] ΔX=Kp·(Xopt-Xactual)+Ki·∑(Xopt-Xactual)+Kd·(d(Xopt-Xactual) / dt),

[0212] Where ΔX is the amount of control parameters that need to be adjusted, Xopt is the theoretically optimal setting value, Xactual is the actual monitored environmental parameter value, Kp is the proportional control coefficient, Ki is the integral control coefficient, and Kd is the differential control coefficient.

[0213] In this embodiment, the optimization control module forms a historical database (Dhist) by collecting environmental data and crop yield data accumulated in the greenhouse for a long time, which includes temperature, humidity, light intensity, carbon dioxide concentration, irrigation amount and corresponding crop yield or quality (Yi). Based on these data, the system establishes an environmental control model, which predicts the yield or quality of crops through the formula Y=f(T,H,I,C,W)+ϵ. With the continuous addition of new data, the system uses these data to retrain the model to optimize the accuracy of prediction. The loss function in the training process uses mean square error (MSE) to measure the deviation between the predicted value and the actual value. Through deep analysis of historical data, the system identifies the long-term impact of environmental parameter changes on crop yield, and predicts future environmental changes through the trend analysis formula X(t)=ϕ1X(t−1)+Ϭ2X(t−2)+⋯+θ0+Ϭt. Based on these prediction results, the system adjusts the control parameters in the greenhouse in advance to optimize the climate conditions.

[0214] The system also monitors the working status of each module in real time. When an abnormality or failure is detected, the system will automatically trigger the failure recovery mechanism, automatically switch to the standby mode, ensure the continuity of the control function, and restart in the background and diagnose the failure cause. For real-time monitoring of environmental parameters, the system compares the actual data (Xactual) with the theoretically optimal setting value (Xopt), dynamically adjusts the control parameters according to the calculated deviation ΔX, to ensure that the environment in the greenhouse is always in the best state.

[0215] Through deep learning of historical data and continuous model optimization, the system can accurately predict and control the environmental conditions in the greenhouse, thereby improving the yield and quality of crops. Real-time monitoring and dynamic parameter adjustment ensure the accuracy of environmental control, reduce energy consumption, and improve the reliability and adaptability of the system. The automated failure recovery function enables the system to quickly respond and recover normal operation in the face of unexpected situations, reducing potential losses.

[0216] In other embodiments, the type and scope of the historical database can be further expanded to include more environmental and biological indicators such as air pressure, wind speed, and crop leaf area. The environmental control model can use other advanced machine learning algorithms such as support vector machines or Bayesian networks to improve the predictive ability and adaptability of the model. The failure detection mechanism can add additional redundant systems or multi-layer detection algorithms to enhance the accuracy and response speed of failure detection. The dynamic parameter adjustment algorithm can also incorporate a self-learning system to continuously optimize the control strategy during operation.

[0217] The feedback signal adjustment module uses multi-sensing technology to integrate feedback signals and adjust the execution mechanism in real time, including:

[0218] Set up information transmission interface compatible with various types of sensor input ports between various types of perception instruments;

[0219] Construct a unified feedback evaluation standard by considering the importance and weight of different index types;

[0220] Independently detect and isolate signal interference sources on a single router;

[0221] Periodically correct the accuracy of all measurement tool readings;

[0222] Equipped with disaster recovery standby circuit.

[0223] The feedback signal adjustment module sets a unified sensor information transmission interface, enabling different types and models of sensors to be compatible and work together. The module comprehensively analyzes the importance and volatility of different environmental indicators, assigns appropriate weights, and builds a unified feedback evaluation standard based on these weights. The module also has a signal interference detection function, which can independently detect and isolate the source of signal interference on a single router, ensuring the stability and accuracy of signal transmission. To ensure the reliability of measurement data, the module regularly calibrates all measurement tools to ensure the accuracy of readings. In addition, the module is equipped with a disaster recovery switching backup circuit that can automatically switch to the backup circuit when the system's core components fail or operate in harsh conditions, ensuring the system's continuous operation.

[0224] This embodiment achieves seamless integration and collaboration between different sensors through a unified sensor interface design, improving the system's compatibility and adaptability. The construction of the feedback evaluation standard takes into account the importance and volatility of different environmental indicators, improving the accuracy and reliability of the feedback signal. The signal interference detection and isolation function enhances the system's anti-interference ability, ensuring the stability of data transmission. Regular calibration of measurement tools ensures the accuracy of system measurement data, improving the precision and effectiveness of environmental control. The disaster recovery function provides high availability of the system at critical moments, ensuring the system's stability and continuous operation.

[0225] In other embodiments, the sensor interface can support more sensor types or use more advanced interface protocols to improve the system's scalability. The construction of the feedback evaluation standard can incorporate artificial intelligence algorithms to adjust the weights of different environmental indicators in real time to adapt to more complex environmental changes. Signal interference detection can incorporate more network security technologies, such as firewalls or encryption technologies, to improve the system's security and attack resistance. Calibration of measurement tools can be performed by automated robots to improve calibration efficiency and accuracy. The disaster recovery backup circuit can be designed as a multi-layer redundant structure to ensure the system's stable operation in extreme conditions.

[0226] The feedback signal adjustment module utilizes multi-sensor technology to integrate feedback signals, and real-time adjustment of the execution mechanism further includes:

[0227] A unified sensor information transmission interface is set, and the formula is:

[0228] Ij=f(Sj, Pj),

[0229] where Ij represents the information transmission interface of the jth sensor, Sj represents the model of the jth sensor, Pj represents the data format of the jth sensor, and f is the interface adaptation function;

[0230] The sensitivity and volatility of various environmental indicators are comprehensively considered, appropriate weights are assigned to different indicators, and a unified feedback evaluation standard is constructed based on these weights. The unified feedback evaluation standard is constructed using the formula:

[0231] ,

[0232] wherein Feval is the unified feedback evaluation standard, m is the total number of environmental indicators, wk is the weight of the kth indicator, Xk is the current actual value of the kth indicator, Xset,k is the target set value of the kth indicator, and σk is the volatility or standard deviation of the kth indicator;

[0233] The signal interference source on a single router is detected and isolated independently, and the signal interference detection determination formula is as follows:

[0234] ,

[0235] wherein Dint(t) is the signal interference state at time t, 1 indicates that interference is detected, and 0 indicates no interference, R(t) is the signal transmission rate at time t, and δR is the threshold value of signal interference;

[0236] Periodically correct the accuracy of readings of all measurement tools, and the calibration formula for periodic correction is:

[0237] Ck=Xk+Δk,

[0238] wherein Ck is the calibrated reading of the kth sensor, Xk is the actual measurement value before calibration, and Δk is the calibration deviation;

[0239] A disaster recovery switching backup circuit is provided, which automatically switches to the backup circuit in the event of failure of core components or severe environmental conditions. The determination and triggering conditions of disaster recovery switching are as follows:

[0240] ,

[0241] wherein Ssw(t) is the disaster recovery switching state at time t, 1 indicates that the backup circuit is triggered, 0 indicates normal operation, Fcrit(t) is the failure critical parameter at time t, and τ is the threshold value of disaster recovery switching.

[0242] The feedback signal adjustment module in this embodiment is mainly based on multi-sensing technology, which adjusts the execution mechanism in real time by receiving and processing environmental data from different types of sensors. The core of multi-sensing technology is to integrate multiple environmental parameters and use advanced algorithms to comprehensively evaluate and feedback sensor data in real time, ensuring that the environmental conditions in the greenhouse accurately match the growth needs of crops. The system sets a unified information transmission interface to ensure that different types of sensors can be seamlessly integrated. This interface is designed to be highly compatible and expandable, capable of adapting to different types of sensor inputs. At the same time, the system dynamically adjusts environmental parameters in the greenhouse by building a unified feedback evaluation standard, taking into account the importance and volatility of each environmental indicator, and optimizing the growth conditions for crops.

[0243] Among them, the information transmission interface ensures that different types and formats of sensor data can be uniformly received and processed, providing accurate environmental data for the system; the feedback evaluation standard integrates the importance and volatility of various environmental parameters to build a unified evaluation standard, ensuring that the feedback environmental information accurately reflects the actual situation; the signal interference detection module detects and isolates signal interference in real time by monitoring signal transmission rate, ensuring the accuracy and timeliness of sensor data; the disaster recovery switching circuit automatically switches to the backup circuit when detecting core component failure or adverse environmental impact, ensuring stable system operation.

[0244] The system first receives data from various sensors through the information transmission interface. These data are analyzed comprehensively by the unified feedback evaluation standard. When the system detects deviations in environmental parameters from preset values, the feedback signal adjustment module will immediately issue adjustment instructions to adjust the execution mechanism. In addition, the signal interference detection module continuously monitors the transmission rate of sensor signals to ensure accurate data transmission. If interference or failure occurs, the disaster recovery switching circuit will respond in the first time to ensure the continuous and stable operation of the system. Each module is interconnected through a data bus. The information transmission interface receives and integrates data from different sensors. The feedback evaluation standard evaluates the comprehensive impact of environmental data. The signal interference detection module monitors the stability of data transmission. The disaster recovery switching circuit automatically switches to the backup circuit when a fault is detected, ensuring the continuity and reliability of the system.

[0245] The present embodiment can more accurately control the environmental parameters in the greenhouse through multi-sensing technology and feedback evaluation criteria, thereby improving the growth environment adaptability of crops, enhancing crop yield and quality, and reducing the need for manual intervention through the use of automated feedback and adjustment mechanisms, optimizing resource allocation and energy utilization in the greenhouse, significantly improving the operating efficiency of the control system, and simplifying the integration and maintenance process of sensors through the design of a unified information transmission interface, allowing users to easily add or replace sensors, improving the operational convenience and maintainability of the system, and ensuring the system's rapid response and normal operation in the event of signal interference or equipment failure through the design of a signal interference detection module and a disaster recovery switching circuit, significantly improving the safety and stability of the system. The system has high expandability and can adapt to different types and models of sensors and ensure long-term stable operation of the system through the disaster recovery switching circuit, and can adapt to different sizes and types of greenhouse environments.

[0246] In the present embodiment, the material of the information transmission interface can be selected according to actual needs, such as high-conductivity metal or anti-interference optical fiber material, to improve the stability of data transmission, and the structure of the feedback signal adjustment module can be adjusted according to different types of greenhouses, such as a distributed structure in large greenhouses to allow simultaneous signal processing and feedback control in multiple areas. During the feedback signal adjustment process, the weight parameters of the signal processing algorithm can be adjusted to address sensor data deviations in different environments. This adjustment can be dynamically optimized based on historical data or real-time monitoring data trends. In the case of large fluctuations in greenhouse environmental conditions, the proportion of key environmental indicators can be increased by adjusting the weight parameters in the feedback evaluation criteria to ensure stable operation of the system in extreme environments. During the manufacturing process of the feedback signal adjustment module, modular design and production can be used to facilitate flexible deployment in different greenhouses and quick customization and upgrading as needed. The connection between modules can use wireless communication technology to reduce wiring complexity and ensure data transmission security through signal encryption technology. In the case of harsh or frequently changing greenhouse environments, the feedback signal adjustment module can be installed in a suspended or embedded manner to ensure the stability of the device and the reliability of data transmission.

[0247] The system can also be applied to other scenarios that require precise environmental control, such as biological laboratories and precision manufacturing workshops, by adjusting the feedback evaluation criteria and sensor types to adapt to different control requirements. The feedback evaluation algorithm used in the system can be replaced with more advanced machine learning algorithms, such as deep neural networks, to further improve the precision and response speed of environmental parameter control. In high-humidity or high-temperature environments, high-temperature-resistant and corrosion-resistant sensor materials can be selected, or heat dissipation or protection devices can be added to the feedback signal adjustment module to ensure stable operation of the system in harsh conditions.

[0248] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A load heterogeneous control system for greenhouse planting, characterized in that: The system comprises: Environmental data acquisition module, used to detect the working status of load heterogeneous equipment based on real-time environmental data; The control parameter setting module connected to the environmental data acquisition module is used to automatically set differentiated control parameters according to the crop types in each region to accurately meet the needs of the corresponding growth stage; The optimization and control module, connected to the environmental data acquisition module and the control parameter setting module, is used to continuously optimize the control logic through deep learning analysis of historical data and implement dynamic matching; A feedback signal adjustment module connected to the environmental data acquisition module, the control parameter setting module, and the optimization and regulation module is used to integrate feedback signals using multi-sensor technology and adjust the execution mechanism in real time; The environmental data acquisition module detects the working status of the load heterogeneous equipment based on real-time environmental data, including: Based on the real-time temperature data, determine whether the temperature in the greenhouse is higher than the set upper limit T; if it is higher, turn on the cooling equipment; Based on real-time monitoring of light intensity and adjustment of fill light time according to light sensitivity index I; Adjust ventilation volume in real time based on CO2 concentration to ensure CO2 concentration is within an appropriate range; Determine whether the soil moisture is lower than the preset lower limit H, and if so, activate the watering device; The method of determining whether the temperature in the greenhouse is higher than the set upper limit T based on the real-time temperature data, and if so, activating the cooling device further includes: According to the relationship between temperature change ΔT and time interval Δt, the heating rate is calculated as K1=ΔT / Δt; If K1>VT, where VT is the speed threshold, it is considered that the current greenhouse environment is rising rapidly, and the high-power ventilation and cooling device is turned on first; If the temperature change ΔT is less than the temperature difference warning value ΔTmin, the air conditioner is turned off to save electricity but the window is kept slightly open; For the current environment's heating rate, a conditional judgment is used. If K1 ≥ VTmin, where VTmin is the greenhouse cooling warning value, the judgment condition result is true and the corresponding operation is performed; For the heating rate of the current environment, if K1≥VTmin, the conditional judgment result is true, and the corresponding operation is performed, which specifically includes: Compare K1 with the greenhouse cooling warning value VTmin. If K1 ≥ VTmin, it means that active cooling measures need to be taken and the air conditioning equipment should be started for forced cooling. Otherwise, cooling should be carried out only through natural ventilation. A timing strategy is used to ensure equipment operation efficiency. When temperature control conditions are met, checks are conducted every 15 minutes to determine whether equipment adjustments are needed. Adjust the air conditioning power and fan rotation speed to ensure that the greenhouse environment temperature control target is achieved. That is, in every cycle, that is, t=n×cycle, if K1≥VTmin, start the air conditioning to cool down, where n=[1,2,3,...]; Set conditions to prevent excessive energy consumption due to extreme weather in a short period of time. Do not change the air conditioning settings until the temperature difference ΔT1 reaches the temperature difference compensation threshold ΔTc. The setting conditions for preventing excessive energy consumption due to extreme weather in a short period of time and not changing the air conditioning settings before the temperature difference ΔT1 reaches the temperature difference compensation threshold ΔTc are more specifically as follows: Record the greenhouse temperature data Ts under the current air-conditioning operation state and the target set temperature Tr when the air-conditioning operation starts; Using the difference between the current temperature Ts and the target set temperature Tr, the real-time temperature difference ΔT1 = Ts-Tr is calculated; Determine whether the real-time temperature difference reaches the preset temperature difference compensation threshold ΔTc, that is, determine whether |ΔT1| ≤ |ΔTc|. If the condition is not met, there is no need to change the device settings. Otherwise, the device needs to be adjusted according to the current environmental conditions. Set the air conditioning operating mode adjustment cycle C and execute it according to the above conditions. When cycle C ends and |ΔT1|>|ΔTc| or the system determines that the external temperature has changed, adjust the operating state to respond to the new demand; The judgment of |ΔT1|≤|ΔTc| further includes: Record the last adjustment time To and the current temperature Ts, and obtain the external reference temperature Te at this time; Environmental assessment,determines that when |Ts−Tr|>|ΔTc| and the last adjustment time interval at the current time t is greater than the minimum allowed adjustment period, the trigger condition is updated, indicating that the control strategy should be reset; Dynamically change the set temperature or device operating mode based on the update situation to maintain a stable internal environment. The update condition is set to |Ts−Tr|>|ΔTc|. Execute the above judgment and make corresponding adjustments, record the latest status and send adjustment instructions to the control parameter setting module when necessary.

2. A load heterogeneous control system for greenhouse cultivation according to claim 1, characterized in that: The control parameter setting module automatically sets differentiated control parameters based on the crop types in each region to accurately meet the needs of the corresponding growth stage, including: Combine the classification data of crops in the planting area with geographic information technology to determine the location distribution information of each crop; Combined database to search for the range of suitable growing conditions required for each growth cycle of the crop; Automatically generate control parameter setting rule files based on the specific needs of the growth stage in each area; Implement differentiated irrigation and temperature regulation programs to simulate artificial climate environments that best suit the developmental needs of the current plant species; Regular calibration checks are conducted to ensure compliance with the specific needs of specific crops or to update strategies for improved results.

3. A load heterogeneous control system for greenhouse cultivation according to claim 2, characterized in that: The control parameter setting module automatically sets differentiated control parameters according to the crop types in each region to accurately meet the needs of the corresponding growth stage. More details include: The classification data of different crops in the greenhouse are combined with geographic information technology to determine the specific location distribution of each crop. The formula is: L i =GIS(X i ,Y i ,Z i ), Among them, L i represents the spatial position of the i-th crop, X i , Y i , Z i is the geographic coordinate; By searching the database, we can obtain the suitable environmental conditions required by each crop at each growth stage. The condition data is stored in the form of a multidimensional matrix, which represents the various parameter requirements of the crop. The formula is: C i,j =Retrieve(D i ,G j ), Among them, C i,j is the environmental condition required by crop i at growth stage j, D i is the crop data, G j is the growth stage; According to the growth requirements of crops, the control parameter setting rule file is automatically generated, and the lighting time and intensity are set using the formula: ， Among them, I ij is the light intensity required by the i-th crop at growth stage j, I max,j is the optimal light intensity, T ij ,H ij are temperature and humidity, T opt,j ,H opt,j For the optimum temperature and humidity; The temperature control setting uses the formula: ， Among them, T set,i is the target temperature of the i-th crop, ΔT i is the temperature deviation adjusted according to the current environmental conditions, T opt,j is the optimum temperature; According to the generated control parameter rule file, the system implements differentiated irrigation and temperature regulation for crops in different areas. The irrigation water volume is calculated using the formula: ， Among them, W i is the irrigation amount of crop i, W base,i is the basic irrigation amount, k i is the adjustment coefficient, S i Real-time readings of soil moisture sensors; The system adjusts the temperature based on the deviation between the real-time temperature data and the preset temperature target. The temperature adjustment adopts the formula: ， Where ΔT adj,i is the temperature deviation that needs to be adjusted, T current is the current ambient temperature; To meet the specific needs of each crop, the system will conduct regular calibration checks to ensure that the control parameters are consistent with the actual needs of the crop. The calibration process is completed by the feedback signal adjustment module. Multi-sensor technology is used to monitor and feedback the environmental changes in the greenhouse in real time. The formula is: ， Among them, F i is the comprehensive feedback signal, S i,k is the kth sensor data of the i-th crop.

4. The load heterogeneous control system for greenhouse cultivation according to claim 1, characterized in that: The optimization and control module continuously optimizes the control logic through deep learning analysis of historical data to achieve dynamic matching and self-adaptation, including: Collect long-term accumulated operating parameters and output data sets as historical databases; Establish an environmental control model and continuously train and adjust the prediction accuracy using newly added historical data; In-depth analysis of past environmental change trends and their inherent regularity in affecting yields; Adding an automated fault recovery function ensures that the system can be quickly restarted in the event of a system crash caused by an unexpected accident; Online monitoring of the deviation between actual performance and theoretical optimal configuration enables timely dynamic parameter adjustment to optimize control performance and reduce energy loss levels.

5. A load heterogeneous control system for greenhouse cultivation according to claim 4, characterized in that: The optimization and control module continuously optimizes the control logic through deep learning analysis of historical data to achieve dynamic matching and self-adaptation, further including: The long-term accumulated operating parameters and output data of the greenhouse are collected as the basis for subsequent modeling and optimization, using the formula: Dhist={(Ti,Hi,Ii,Ci,Wi,Yi)|i=1,2,…,q}, Where Dhist represents the data set of the historical database, Ti, Hi, Ii, Ci, and Wi represent the temperature, humidity, light intensity, carbon dioxide concentration, and irrigation amount collected for the i-th time, respectively, and Yi represents the corresponding crop yield or quality; The environmental control model is established using the historical database Dhist. The control model is in the form of: Y=f(T,H,I,C,W)+e, Where Y is the expected yield or quality of the crop, f is the control model, e is the model error, T, H, I, C, and W represent the collected temperature, humidity, light intensity, carbon dioxide concentration, and irrigation amount, respectively; During the model training process, the system continuously uses newly added historical data to retrain the model and update weights and parameters. The loss function in the training process uses mean square error to measure the deviation between the predicted value and the actual value. The specific formula is: ， Where MSE is the mean square error, Yi is the actual value of the i-th sample, represents the predicted value of the i-th sample, and q represents the total number of samples; Through in-depth analysis of historical data, the system identifies the long-term impact of changes in environmental parameters on crop yield and quality, extracts inherent regularities, and uses the formula: X(t)=Φ1X(t-1)+Φ2X(t-2)+……+θ0+et; Among them, X(t) represents the environmental parameters at time t, Φ and θ are the coefficients of the model respectively; Based on the trend analysis results, the system predicts future environmental changes and adjusts control parameters in advance to optimize the climate conditions in the greenhouse; The working status of each module is monitored online. When an abnormality or fault is detected, the fault recovery mechanism is automatically triggered. The fault detection formula is: ， Fault(t) represents the fault state at time t, X(t) represents the actual measurement value of the system at time t, and X set is the desired control parameter, δ is the permissible deviation range, and when a fault occurs, it automatically switches to the backup mode, quickly recovers the critical control functions, and restarts in the background to diagnose the cause of the fault; Monitor the deviation between the environmental parameters in the greenhouse and the theoretical optimal configuration in real time. By comparing the difference between the actual monitoring data Xactual and the optimal setting value Xopt, dynamically adjust the control parameters. The dynamic adjustment of control parameters uses the formula: ΔX=Kp·(Xopt-Xactual)+Ki·∑(Xopt-Xactual)+Kd·(d(Xopt-Xactual) / dt), Among them, ΔX is the control parameter that needs to be adjusted, Xopt is the theoretical optimal setting value, Xactual is the actual monitored environmental parameter value, Kp is the proportional control coefficient, Ki is the integral control coefficient, and Kd is the differential control coefficient.

6. The load heterogeneous control system for greenhouse cultivation according to claim 1, characterized in that: The feedback signal adjustment module uses multi-sensor technology to integrate feedback signals and adjust the execution mechanism in real time, including: Set up information transmission interfaces between various types of sensing instruments to be compatible with various types of sensor input ports; Comprehensively and weighing the importance and weight of different indicator types to build a unified feedback evaluation standard; Independently detect and isolate signal interference sources on a single router; Regularly calibrate the reading accuracy of all measuring tools; Equipped with disaster recovery switching backup circuit.

7. The load heterogeneous control system for greenhouse cultivation according to claim 6, characterized in that: The feedback signal adjustment module utilizes multi-sensor technology to integrate feedback signals, and the real-time adjustment execution mechanism further includes: Set a unified sensor information transmission interface and use the formula: Ij=f(Sj,Pj), Where Ij represents the information transmission interface of the j-th sensor, Sj represents the model of the j-th sensor, Pj represents the data format of the j-th sensor, and f is the interface adaptation function; Taking into account the sensitivity and volatility of various environmental indicators, appropriate weights are assigned to different indicators. Based on these weights, a unified feedback evaluation standard is constructed. The formula for constructing a unified feedback evaluation standard is: ， Among them, F eval To unify the feedback evaluation standards, m is the total number of environmental indicators, w k is the weight of the kth indicator, X k is the current actual value of the kth indicator, X set,k is the target setting value of the kth indicator, σ k is the volatility or standard deviation of the kth indicator; Independently detect and isolate the signal interference source on a single router. The signal interference detection judgment formula is as follows: ， Among them, D int (t) is the signal interference state at time t, 1 indicates interference is detected, 0 indicates no interference, R(t) is the signal transmission rate at time t, δ R is the threshold of signal interference; Regularly calibrate the reading accuracy of all measuring tools. The calibration formula for regular calibration is: Ck=Xk+Δk, Where Ck is the reading of the kth sensor after calibration, Xk is the actual measurement value before calibration, and Δk is the calibration deviation; Equipped with a disaster recovery switchover backup circuit, it automatically switches to the backup circuit in the event of a core component failure or adverse environmental conditions. The judgment and triggering conditions for disaster recovery switching are as follows: ， Among them, S sw (t) is the disaster recovery switching state at time t, 1 indicates triggering the backup circuit, 0 indicates normal operation, F crit (t) is the key fault parameter at time t, and τ is the threshold for disaster recovery switching.

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