Gobi solar greenhouse structure parameter dynamic optimization method combined with environmental perception

Abstract

The invention provides a gobi solar greenhouse structure parameter dynamic optimization method combined with environmental perception, and relates to the technical field of agricultural engineering, and the method comprises the steps: sensing the environmental information of a gobi solar greenhouse, generating an environmental perception parameter set, and defining a plurality of to-be-optimized targets according to the environmental perception parameter set; constructing a dynamic planning model according to the greenhouse structure parameters and the environment perception parameter set, and obtaining a plurality of dynamic data; introducing constraint conditions, integrating the multiple pieces of dynamic data according to the multiple to-be-optimized targets, and generating multiple structure adjustment coefficients; performing real-time layout based on the plurality of structure adjustment coefficients in combination with greenhouse structure parameters, and generating a layout decision; and executing a layout decision to perform simulation verification, updating a plurality of structure adjustment coefficients according to a verification result, and adaptively optimizing greenhouse structure parameters. The technical problems of insufficient structural design flexibility, difficulty in multi-target dynamic optimization and low resource utilization rate of a traditional greenhouse are solved, and the crop growth environment stability and the resource utilization rate are improved.

Description

Technical Field

[0001] The present application relates to the field of agricultural engineering technology, and in particular to a method for dynamically optimizing structural parameters of a Gobi solar greenhouse in combination with environmental perception. Background Art

[0002] As an innovative facility for agricultural production in arid areas, the Gobi solar greenhouse uses natural light and heat resources to provide a suitable growth environment for crops. Due to its unique geographical location and climatic conditions, the design and construction of the Gobi solar greenhouse needs to consider many special factors, such as light, temperature, humidity, etc.

[0003] Existing Gobi solar greenhouse structural parameter optimization methods are mainly focused on the design and construction stages of the greenhouse. By optimizing the greenhouse azimuth, span, ridge height, roof angle and other structural parameters, and selecting appropriate enclosure materials, the greenhouse's light energy utilization efficiency and thermal insulation performance are improved. These methods have certain limitations when dealing with extreme environmental conditions such as the Gobi. First, these methods are often static. Once the greenhouse structure is built, its parameters are difficult to adjust, and it is difficult to adapt to the complex and changeable environmental conditions in the Gobi region, resulting in environmental fluctuations in the greenhouse. Secondly, these methods rarely consider the real-time changes in the environment inside and outside the greenhouse. Under extreme climatic conditions such as the Gobi, the greenhouse's environmental regulation capacity is limited, and it is impossible to fully utilize the resources of production facilities, which limits the growth potential of crops. Summary of the invention

[0004] This application provides a method for dynamic optimization of Gobi solar greenhouse structural parameters combined with environmental perception, which solves the technical problem that traditional greenhouse structural design adopts static analysis and focuses on a single optimization goal, making it difficult to respond to environmental changes in real time and unable to fully utilize existing production resources, thus leading to large fluctuations in the greenhouse environment. It achieves the technical effect of improving the environmental adaptability of the greenhouse and the stability of the crop growth environment, and improving resource utilization.

[0005] In view of the above problems, the present application provides a method for dynamic optimization of structural parameters of a Gobi solar greenhouse combined with environmental perception, the method comprising: perceiving the environmental information of the Gobi solar greenhouse, generating an environmental perception parameter set, and defining multiple targets to be optimized according to the environmental perception parameter set; constructing a dynamic programming model according to the greenhouse structural parameters of the Gobi solar greenhouse and the environmental perception parameter set to obtain multiple dynamic data; introducing constraints, integrating the multiple dynamic data according to the multiple targets to be optimized, and generating multiple structural adjustment coefficients of the Gobi solar greenhouse; performing real-time layout based on the multiple structural adjustment coefficients in combination with the greenhouse structural parameters to generate layout decisions; executing the layout decisions for simulation verification, updating the multiple structural adjustment coefficients according to the verification results, and performing adaptive dynamic optimization adjustment on the greenhouse structural parameters.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The environmental information of the Gobi solar greenhouse is perceived, and an environmental perception parameter set is generated to provide a real-time data basis for subsequent optimization. Multiple targets to be optimized are defined according to the environmental perception parameter set, the direction and purpose of optimization are clarified, and it is ensured that the construction and application of the dynamic programming model can solve practical problems in a targeted manner. A dynamic programming model is constructed according to the greenhouse structural parameters of the Gobi solar greenhouse and the environmental perception parameter set to obtain multiple dynamic data, which provides a decision-making basis for optimization. Constraints are introduced to ensure that the optimization process is carried out within a realistic and feasible range. The multiple dynamic data are integrated according to the multiple targets to be optimized to generate multiple structural adjustment coefficients of the Gobi solar greenhouse. Based on the multiple structural adjustment coefficients combined with the greenhouse structural parameters, real-time layout is performed, and layout decisions are generated to guide the real-time adjustment of the greenhouse structure to adapt to environmental changes. The layout decision is executed for simulation verification, and the multiple structural adjustment coefficients are updated according to the verification results. The greenhouse structural parameters are adaptively and dynamically optimized and adjusted, realizing the closed-loop feedback of optimization and enhancing the regulation and control ability of the greenhouse.

[0008] In summary, this application improves the adaptability of the greenhouse to environmental changes by real-time perception of environmental information and dynamic adjustment of greenhouse structural parameters, maximizes the use of existing environmental resources, and reduces energy consumption; at the same time, the introduction of constraints and real-time layout decisions enhances the greenhouse's control capabilities, and through simulation verification and updating of structural adjustment coefficients, ensures the continuity and accuracy of the optimization process, thereby improving the stability of the crop growth environment.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of a process for dynamically optimizing the structural parameters of a Gobi solar greenhouse combined with environmental perception provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of a process for constructing a dynamic programming model in a method for dynamically optimizing structural parameters of a Gobi solar greenhouse combined with environmental perception provided in an embodiment of the present application;

[0012] Figure 3 A schematic diagram of the process of obtaining multiple dynamic data in the method for dynamic optimization of Gobi solar greenhouse structural parameters combined with environmental perception provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] The embodiment of the present application provides a method for dynamic optimization of Gobi solar greenhouse structural parameters combined with environmental perception, which solves the technical problem that traditional greenhouse structural design adopts static analysis and focuses on a single optimization goal, making it difficult to respond to environmental changes in real time and unable to fully utilize existing production resources, thus leading to large volatility in the greenhouse environment. The technical effect of improving the environmental adaptability of the greenhouse and the stability of the crop growth environment and improving resource utilization is achieved.

[0014] like Figure 1 As shown, the embodiment of the present application provides a method for dynamically optimizing structural parameters of a Gobi solar greenhouse in combination with environmental perception, the method comprising:

[0015] Step S1: sensing the environmental information of the Gobi solar greenhouse, generating an environmental sensing parameter set, and defining a plurality of targets to be optimized according to the environmental sensing parameter set.

[0016] Specifically, by installing various environmental sensors in the Gobi solar greenhouse, such as temperature sensors, humidity sensors, and light sensors, the environmental information of the Gobi solar greenhouse is sensed and environmental data inside and outside the greenhouse is collected. These data include temperature, humidity, light intensity, wind speed, soil moisture, etc. These environmental data are summarized to generate an environmental perception parameter set, which is a detailed description of the current environmental status.

[0017] Next, based on this set of environmental perception parameters, multiple optimization targets are defined. Optimization targets refer to performance indicators that need to be improved by adjusting greenhouse structural parameters. These targets include maximizing light utilization efficiency, maintaining a suitable indoor temperature range, minimizing energy consumption, etc. These targets will guide the subsequent optimization process to ensure that the structure and function of the greenhouse can meet specific agricultural needs. For example, if the perceived data indicates that the temperature in the greenhouse is too high, one optimization target can be to reduce the indoor temperature. Similarly, if the light intensity is insufficient, the optimization target can be to increase light reception.

[0018] Step S1 provides direction and data support for subsequent structural parameter optimization by real-time sensing of environmental status and defining optimization goals.

[0019] Step S2: constructing a dynamic programming model according to the greenhouse structural parameters of the Gobi solar greenhouse and the environmental perception parameter set to obtain a plurality of dynamic data.

[0020] Specifically, greenhouse structural parameters refer to various design parameters that affect greenhouse performance, including greenhouse geometric parameters (such as length, width, height, roof angle), material properties (such as thermal resistance of insulation materials, light transmittance of covering materials), ventilation equipment performance, etc. The dynamic programming model is an optimization algorithm used to deal with multi-stage decision-making problems, by decomposing the problem into sub-problems and solving these sub-problems, and finally finding the global optimal solution.

[0021] By consulting the design documents, we obtain the greenhouse structural parameters of the Gobi solar greenhouse, and combine the greenhouse structural parameters with the environmental perception parameter set obtained above. We build a dynamic programming model through the dynamic programming algorithm. The constructed dynamic programming model will combine these greenhouse structural parameters and environmental perception parameters to predict and simulate the greenhouse performance under different parameter settings. Through this model, we can obtain multiple dynamic data. These dynamic data are the greenhouse state data that changes over time in the dynamic programming model, reflecting the performance of the greenhouse under different environmental conditions and structural parameters, including structural state variables, environmental state variables, and crop state variables.

[0022] Step S2 can simulate the performance of the greenhouse under different environmental conditions and structural parameters by constructing a dynamic programming model, thereby providing decision support for structural parameter optimization and guiding the optimization direction.

[0023] Step S3: Introducing constraint conditions, integrating the multiple dynamic data according to the multiple targets to be optimized, and generating multiple structural adjustment coefficients of the Gobi solar greenhouse.

[0024] Specifically, constraints are restrictions that need to be met during the optimization process, such as greenhouse structural strength, cost budget, material availability, etc. Introducing constraints can ensure that the optimization process is carried out within a feasible range. These conditions limit the degrees of freedom in the optimization process and ensure that the final solution is practically feasible.

[0025] According to the multiple optimization targets defined in step S1, the multiple dynamic data obtained in step S2 are integrated, and different performance indicators and constraints are comprehensively considered to find a balance point so that the greenhouse can meet all constraints while reaching or approaching all optimization targets as much as possible. Through the multi-objective optimization algorithm, multiple structural adjustment coefficients of the Gobi solar greenhouse are finally generated. These structural adjustment coefficients are parameters that guide the structural adjustment of the greenhouse and determine the direction and degree of the structural adjustment of the greenhouse. For example, these coefficients may include the adjustment value of the roof angle, the control parameters of the ventilation system, etc.

[0026] Step S3 generates specific coefficients for guiding greenhouse structure adjustment by introducing constraints and comprehensive dynamic data. These coefficients are the basis for subsequent actual adjustment of the greenhouse structure and will guide the actual adjustment of the greenhouse structure to ensure that the greenhouse performance and crop growth environment reach the optimal state.

[0027] Step S4: Perform real-time layout based on the multiple structural adjustment coefficients combined with the greenhouse structural parameters to generate a layout decision.

[0028] Specifically, based on the multiple structural adjustment coefficients generated in step S3, combined with the existing structural parameters of the Gobi solar greenhouse, real-time layout is performed to dynamically adjust the structural settings of the greenhouse. For example, if the structural adjustment coefficient indicates that the angle of the roof needs to be changed to increase light, the control system will adjust the supporting structure of the roof according to these coefficients to change the angle of the roof. Similarly, if ventilation needs to be improved to reduce the temperature, the size or position of the vents is adjusted. Through real-time layout, a layout decision is generated, which is a specific adjustment plan, such as a set of specific operating instructions, to guide the control system to automatically adjust the structural parameters of the greenhouse to achieve the optimization goal. The layout decision may include specific adjustment values ​​for the roof angle, specific control strategies for the ventilation system, etc.

[0029] Step S4 applies the previously calculated structural adjustment coefficient to the actual greenhouse structure, and generates specific layout decisions based on the current environmental conditions and optimization goals to optimize the greenhouse performance in real time and achieve the purpose of optimizing the growth environment.

[0030] Step S5: executing the layout decision to perform simulation verification, updating the multiple structural adjustment coefficients according to the verification results, and performing adaptive dynamic optimization adjustment on the greenhouse structural parameters.

[0031] Specifically, a simulation environment is constructed through greenhouse environment simulation software to execute the layout decision generated in step S4 to test and verify the effectiveness of the layout decision. The simulation verification can simulate different environmental conditions (such as different light intensity, temperature, etc.) and the response after the greenhouse structure is adjusted. By comparing the simulation results with the optimization target, the effect of the layout decision can be evaluated.

[0032] According to the results of the simulation verification, the multiple structural adjustment coefficients generated in step S3 are updated. If the simulation results show that the layout decision fails to achieve the expected goal, the structural adjustment coefficients are adjusted continuously and a new round of simulation verification is performed until the optimization goal is met. This iterative process ensures the continuous optimization and adaptability of the structural adjustment coefficients. After the optimization goal is met, the greenhouse structural parameters are adaptively and dynamically optimized with the current structural adjustment coefficients, and the current layout decision is executed through the automated control system to change the actual structural settings of the greenhouse, such as adjusting the roof angle, ventilation system, etc., to optimize the greenhouse performance.

[0033] Step S5 realizes adaptive dynamic optimization adjustment of the Gobi solar greenhouse structural parameters through simulation verification and iterative update, ensuring that the greenhouse can continuously and efficiently adapt to changing environmental conditions.

[0034] Furthermore, step S1 of the embodiment of the present application further includes:

[0035] A sensor network is constructed based on multiple sensor devices, and the sensor network includes a collection frequency; the environmental information of the Gobi solar greenhouse is sensed by the sensor network according to the collection frequency to generate an environmental perception parameter set, and the environmental perception parameter set includes multiple external environmental parameters and multiple internal environmental parameters; the environmental perception parameter set is transmitted to a central server for analysis to generate an environmental fluctuation trend graph; the multiple external environmental parameters are optimized and analyzed according to the environmental fluctuation trend graph to determine multiple external targets to be optimized; the multiple internal environmental parameters are optimized and analyzed according to the environmental fluctuation trend graph to determine multiple internal targets to be optimized; the multiple external targets to be optimized and the multiple internal targets to be optimized are integrated to determine the multiple targets to be optimized.

[0036] Specifically, a comprehensive sensor network is built by deploying a variety of sensors inside and outside the Gobi solar greenhouse, such as temperature, humidity and light intensity sensors. A reasonable collection frequency is set for the sensor network to ensure the real-time and accuracy of the data. The sensor network collects environmental data inside and outside the greenhouse at the set frequency, obtains multiple external environmental parameters (such as temperature, humidity, wind speed, light intensity, etc.) and multiple internal environmental parameters (such as indoor temperature, humidity, CO2 concentration, etc.), and summarizes the collected multiple external environmental parameters and multiple internal environmental parameters to generate an environmental perception parameter set. The environmental perception parameter set is transmitted to the central server in real time through wireless transmission technology. On the central server, data analysis technology, such as time series analysis, is used to generate an environmental fluctuation trend graph. This trend graph shows the changes of various environmental parameters over time, which can be used to determine the pattern and trend of environmental changes.

[0037] Based on the environmental fluctuation trend chart, the fluctuation pattern of the greenhouse environment is analyzed, the optimization requirements of external and internal environmental parameters are identified, and multiple external and internal optimization targets are determined. For example, the analysis found that the temperature difference between the inside and outside of the greenhouse is large at night, which leads to drastic fluctuations in the internal temperature. It can be determined that the improvement of the nighttime insulation performance is an external optimization target; at the same time, it is observed that the internal humidity fluctuates greatly within a specific time period, affecting the growth of crops, and humidity control can be used as an internal optimization target.

[0038] Ultimately, by integrating external and internal optimization targets, a comprehensive set of optimization targets is formed, which provides clear guidance for the subsequent optimization adjustment of greenhouse structural parameters, ensuring that the greenhouse can effectively cope with the special environmental conditions in the Gobi region and provide the best growth environment for crops.

[0039] Further, such as Figure 2 As shown, step S2 of the embodiment of the present application also includes:

[0040] The greenhouse structural parameters of the Gobi solar greenhouse are determined based on the geometric parameters of the Gobi solar greenhouse combined with the material property information; the first state variable is constructed based on the interaction between the greenhouse structural parameters of the Gobi solar greenhouse and the multiple external environmental parameters; the second state variable is constructed based on the interaction between the greenhouse structural parameters of the Gobi solar greenhouse and the multiple internal environmental parameters; the structural state variables and the environmental state variables are determined based on the analysis of the first state variables and the second state variables; the stage objective function is introduced, the structural state variables and the environmental state variables are processed according to multiple time steps, and the dynamic programming model is constructed.

[0041] Specifically, the greenhouse structural parameters are determined based on the geometric parameters and material property information of the Gobi solar greenhouse. The geometric parameters include the length, width, height, roof angle, etc. of the greenhouse, while the material property information includes the light transmittance of the transparent materials used in the greenhouse, the thermal insulation performance of the walls, the opening and closing degree of the shading system, etc. For example, the light transmittance of the greenhouse covering material is set to 80%, and the thickness of the thermal insulation material is 50mm, so as to construct the basic structure of the greenhouse.

[0042] The interaction between greenhouse structural parameters and external environmental parameters is analyzed to construct the first state variable. For example, the increase in light intensity outside the greenhouse will affect the light inside the greenhouse through the light transmittance of the covering material, thereby affecting crop growth. This process is quantified and included in the first state variable. At the same time, the interaction between greenhouse structural parameters and internal environmental parameters is analyzed to construct the second state variable. For example, the temperature change inside the greenhouse will be affected by the thermal resistance of the insulation material and the external temperature. This process is quantified and included in the second state variable.

[0043] According to the first state variable and the second state variable, the greenhouse structural parameters and internal and external environmental parameters at the same time are determined to generate structural state variables and environmental state variables. The structural state variables describe the state of the greenhouse structure at the tth time, including geometric parameters and material properties, while the environmental state variables describe the environmental perception parameter set of the greenhouse at the tth time.

[0044] Next, the stage objective function is introduced. The stage objective function is a function in the dynamic programming model that describes the optimization goal that needs to be achieved in a specific time stage. It is usually defined based on the optimization goal, such as maximizing the efficiency of light utilization, minimizing energy consumption, etc. This function will guide the dynamic programming model to process the structural state variables and environmental state variables in multiple time steps to find the optimal solution.

[0045] Through the above steps, the greenhouse structural parameters are combined with the environmental parameters to obtain a series of simulation data, and a dynamic programming model is constructed to provide a flexible and efficient optimization framework for the Gobi solar greenhouse to adapt to the changing environmental conditions and optimize the crop growth environment.

[0046] Furthermore, in step S2 of the embodiment of the present application, a stage objective function is introduced, the structural state variables and the environmental state variables are processed according to multiple time steps, and the dynamic programming model is constructed, which also includes:

[0047] Call the historical operation status record archive of the Gobi solar greenhouse, determine a plurality of operation time stages based on the historical operation status record archive; divide the plurality of time steps according to the plurality of operation time stages; perform calculations based on the plurality of operation time stages in combination with the structural state variables and the environmental state variables, define the stage objective function, and there is a corresponding relationship between the stage objective function and the plurality of time steps; determine a time series based on the plurality of time steps, recursively perform the structural state variables and the environmental state variables according to the time series, and construct the dynamic programming model.

[0048] Specifically, the historical operating status record archive of the Gobi Solar Greenhouse is called. The historical operating status record archive contains the past operating data of the greenhouse, such as changes in temperature, humidity, light, etc. over time, as well as greenhouse structural parameters. Based on these historical data, the operating status of the greenhouse in different time stages in the past is analyzed, such as spring, summer, autumn and winter, or the early, middle and late growth stages of crops, etc., to determine the key time stages of greenhouse operation. The time steps are divided according to these key time stages. For example, every 10 days in spring is a time step, and in summer it may be shortened to every 5 days to adapt to more frequent environmental changes. Each time step represents a time point or time period.

[0049] The stage objective function is defined based on each time step and the structural state variables and environmental state variables of the greenhouse. This function will provide specific optimization goals for the model according to different time steps and corresponding operation stages. For example, in the early stage of crop growth in spring, the possible goal may be to minimize energy consumption while maintaining appropriate light intensity; while in the high temperature period in summer, the goal may be to control the internal temperature not exceeding the upper limit of crop growth by optimizing the ventilation and shading systems.

[0050] The time series is determined according to the time step. The time series is a series of time points arranged in chronological order. The structural state variables and environmental state variables are recursively processed according to this time series, that is, at each time step, the model will consider the previous optimization results, as well as the environmental conditions and greenhouse structural state in the current time step to determine the optimal structural parameter settings. For example, starting from the beginning of spring, the model will optimize the greenhouse structure and environmental parameters according to the first-stage objective function, and then enter the next time step, and continue to optimize until the optimal greenhouse operation state is reached.

[0051] Through the dynamic programming model constructed through the above steps, the greenhouse can dynamically adjust and optimize the greenhouse structure according to different time stages and environmental conditions to adapt to the special environment and crop growth cycle requirements of the Gobi region.

[0052] Further, such as Figure 3 As shown, step S2 of the embodiment of the present application also includes:

[0053] Performing operation analysis on the geometric parameters of the Gobi solar greenhouse in combination with the material property information according to the multiple time steps to determine structural dynamic data; performing environmental change analysis on the multiple external environmental parameters and the multiple internal environmental parameters according to the multiple time steps to determine environmental dynamic data; performing growth analysis based on the structural dynamic data in combination with the environmental dynamic data according to the multiple time steps to determine crop dynamic data; adding the structural dynamic data, the crop dynamic data, and the environmental dynamic data to the multiple dynamic data.

[0054] Specifically, the dynamic programming model is used to analyze the geometric parameters of the Gobi solar greenhouse in combination with the material property information at multiple time steps to simulate the structural performance of the greenhouse structure at different time points, such as the light distribution and insulation effect under different light and temperature conditions. These analysis results constitute the structural dynamic data, reflecting the changes of the greenhouse structure over time. For example, the light transmittance of the covering material is adjusted according to the increase or decrease of the light intensity, and the thermal resistance of the insulation material changes with the external temperature.

[0055] Next, multiple external environmental parameters and multiple internal environmental parameters are analyzed for environmental changes at multiple time steps. The external and internal environmental conditions at different time points are simulated and predicted through the dynamic programming model. These analysis results constitute environmental dynamic data, which reflects the changes in the greenhouse environment over time. For example, the external temperature gradually rises in spring, and the humidity and light intensity inside the greenhouse also change accordingly.

[0056] According to the determined structural dynamic data and environmental dynamic data, the growth of crops in the corresponding time step is simulated to determine the crop dynamic data. The crop dynamic data reflects the change of crop growth status over time. For example, in the early stage of crop growth in spring, as the light intensity and temperature inside the greenhouse increase, the crop growth rate accelerates.

[0057] Finally, structural dynamic data, crop dynamic data and environmental dynamic data are added to multiple dynamic data to provide a comprehensive data basis for dynamic optimization of greenhouses. Combined with dynamic data analysis, greenhouses can more accurately regulate environmental conditions, promote crop growth, and improve greenhouse operation efficiency and crop yields.

[0058] Furthermore, step S3 of the embodiment of the present application also includes:

[0059] Perform structural analysis based on the multiple external environmental parameters and define structural safety constraints; perform crop growth analysis based on the multiple internal environmental parameters and define crop growth environment constraints; extract structural targets to be optimized based on the structural safety constraints and in combination with the multiple targets to be optimized; extract crop growth targets to be optimized based on the crop growth environment constraints and in combination with the multiple targets to be optimized; assign weights to the structural targets to be optimized and the crop growth targets to be optimized to determine a first weight factor and a second weight factor; integrate the multiple dynamic data based on the structural targets to be optimized in combination with the first weight factor and based on the crop growth targets to be optimized in combination with the second weight factor to generate the multiple structural adjustment coefficients of the Gobi solar greenhouse.

[0060] Specifically, first, a structural analysis is performed based on multiple external environmental parameters to define structural safety constraints. These structural safety constraints are parameter limits that ensure the safety of the greenhouse structure, such as the maximum wind load that the greenhouse roof and walls can withstand, ensuring the stability and safety of the greenhouse structure under harsh environmental conditions. Next, a crop growth analysis is performed based on multiple internal environmental parameters to define crop growth environmental constraints. These crop growth environmental constraints are environmental parameter ranges that ensure the healthy growth of crops, such as suitable temperature, humidity, light intensity, etc., to meet the needs of crop growth.

[0061] According to the structural safety constraints and multiple targets to be optimized, the structural targets to be optimized are extracted. Similarly, according to the crop growth environment constraints and multiple targets to be optimized, the crop growth targets to be optimized are extracted. For example, the structural targets to be optimized may include the stability, durability and energy efficiency of the greenhouse, and the crop growth targets to be optimized may include the uniformity of light, temperature suitability and humidity control.

[0062] Weights are assigned to the structural optimization targets and crop growth optimization targets, and the first weight factor corresponding to the structural optimization targets and the second weight factor corresponding to the crop growth optimization targets are determined. The weight assignment takes into account the greenhouse operation priority and crop growth requirements, and is determined by experts based on experience and actual conditions, or obtained through data analysis. The weights can be adjusted based on the actual operation data of the greenhouse and the crop growth cycle. For example, during the critical period of crop growth, the weight of the crop growth optimization target is higher, and during the period of frequent strong winds, the weight of the structural optimization target is higher.

[0063] Finally, based on the structural optimization target and the first weight factor, as well as the crop growth optimization target and the second weight factor, multiple dynamic data are integrated, and the optimization results of different targets are synthesized through multi-objective optimization algorithms, such as weighted summation method or weighted product method, to calculate the optimization result of each optimization target after comprehensive consideration, and then generate multiple structural adjustment coefficients of the Gobi solar greenhouse. The structural adjustment coefficient reflects the adjustment direction and amplitude of the greenhouse structural parameters, such as the increase in the transmittance of the covering material and the optimization of the thermal resistance of the insulation material, to meet the dual needs of structural safety and crop growth. The integration process requires multiple iterations to find the optimal structural adjustment coefficient. In each iteration, adjustments are made according to the new data and weight factors until a satisfactory optimization result is achieved.

[0064] For example, a greenhouse in the Gobi region faces the dual challenges of strong winds and high temperatures, and needs to optimize structural safety and the crop growth environment. First, clarify the structural safety constraints, including maximum wind load and structural stability, and the crop growth environment constraints, including suitable temperature range and light intensity. Next, through a multi-objective optimization algorithm, such as NSGA-II, the structural safety objectives to be optimized and the crop growth objectives to be optimized are weighted to determine the priority of structure and crop growth, with a structural safety weight of 0.6 and a crop growth weight of 0.4. Construct an optimization model, set the mathematical model and parameters for structural safety and crop growth, and perform iterative solutions. Through optimization, the structural adjustment coefficient is obtained, including a 10% increase in the light transmittance of the covering material and a 5% reduction in the thermal resistance of the insulation material, in order to adapt to the special environment of the Gobi region while meeting the needs of crop growth.

[0065] The above steps combine structural safety and crop growth requirements with dynamic data and consider the weights of different objectives to generate coefficients that specifically guide greenhouse structural adjustments, achieve a dynamic balance between greenhouse structural safety and crop growth optimization, ensure efficient greenhouse operation and healthy crop growth, and thus comprehensively optimize greenhouse performance.

[0066] Furthermore, step S5 of the embodiment of the present application further includes:

[0067] Based on the layout decision, the greenhouse top layout parameters and greenhouse wall layout parameters of the Gobi solar greenhouse are extracted; based on the multiple external environmental parameters, the greenhouse top layout parameters are simulated and verified to obtain a first performance verification result; based on the multiple internal environmental parameters, the greenhouse wall layout parameters are simulated and verified to obtain a second performance verification result; whether the first performance verification result meets the first expected indicator is determined, and whether the second performance verification result meets the second expected indicator is determined; if the first performance verification result meets the first expected indicator, and the second performance verification result meets the second expected indicator, an update instruction is generated to update the multiple structural adjustment coefficients based on the greenhouse top layout parameters and the greenhouse wall layout parameters.

[0068] Specifically, the greenhouse top layout parameters and greenhouse wall layout parameters of the Gobi solar greenhouse in the layout decision are extracted. These parameters include roof angle, wall height, material type, etc. Then, the greenhouse top layout parameters are simulated and verified based on multiple external environmental parameters through greenhouse simulation software, and the greenhouse lighting efficiency, thermal insulation effect, etc. are evaluated to obtain the first performance verification result. At the same time, the greenhouse wall layout parameters are simulated and verified based on the internal environmental parameters, and the thermal insulation performance, structural strength, etc. are evaluated to obtain the second performance verification result.

[0069] The first performance verification result is compared with the first expected index to determine whether the greenhouse top layout meets expectations. Similarly, the second performance verification result is compared with the second expected index to determine whether the greenhouse wall layout meets expectations. If both the first and second performance verification results meet the expected index, it indicates that the greenhouse layout design is reasonable and can adapt to the specific environmental conditions of the Gobi region. Generate an update instruction based on the current layout decision, and update the structural adjustment coefficient based on the greenhouse top layout parameters and the greenhouse wall layout parameters to optimize the greenhouse structural performance and environmental control effect. For example, it may be necessary to adjust the inclination angle of the greenhouse top to improve lighting, or increase the wall thickness to enhance insulation. Among them, the first expected index is the performance expectation for the greenhouse top layout parameters, such as lighting efficiency, thermal insulation effect, etc. The second expected index is the performance expectation for the greenhouse wall layout parameters, such as thermal insulation performance, structural strength, etc.

[0070] The above steps are verified by simulation and iterative updates, ensuring that the optimization adjustment of greenhouse structural parameters is based on actual environmental conditions and design requirements, thereby improving the performance and adaptability of the greenhouse.

[0071] Further, in step S5 of the embodiment of the present application, determining whether the first performance verification result meets the first expected indicator and determining whether the second performance verification result meets the second expected indicator further include:

[0072] If the first performance verification result does not meet the first expected indicator, and the second performance verification result meets the second expected indicator, a first backtracking instruction is generated, and the greenhouse top layout parameters are backtracked through the first backtracking instruction to determine multiple abnormal top layout points; based on the multiple abnormal top layout points, an evaluation and analysis are performed, and first error correction information is generated according to the first evaluation result, and the greenhouse top layout parameters are corrected through the first error correction information; if the first performance verification result meets the first expected indicator, and the second performance verification result does not meet the second expected indicator, a second backtracking instruction is generated, and the greenhouse wall layout parameters are backtracked through the second backtracking instruction to determine multiple abnormal wall layout points; based on the multiple abnormal wall layout points, an evaluation and analysis are performed, and second error correction information is generated according to the second evaluation result, and the greenhouse wall layout parameters are corrected through the second error correction information; if the first performance verification result does not meet the first expected indicator, and the second performance verification result does not meet the second expected indicator, an abnormal alarm instruction is generated, and is sent to a remote terminal for abnormal analysis according to the greenhouse top layout parameters and the greenhouse wall layout parameters combined with the abnormal alarm instruction.

[0073] Specifically, if the first performance verification result does not meet the first expected indicator, and the second performance verification result meets the second expected indicator, then a first backtracking instruction will be generated. The greenhouse top layout parameters are backtracked through the first backtracking instruction to check and evaluate the top layout parameters that may cause performance to fail to meet the standards, and multiple top layout abnormal points are determined. Based on the determined top layout abnormal points, an in-depth evaluation is performed to determine the differences between the top layout abnormal points and the design requirements, and the first error correction information is generated based on the evaluation results. The first error correction information is used to guide the correction of the greenhouse top layout parameters. For example, it is found that the light transmittance of a certain area on the top of the greenhouse is lower than the design requirements, resulting in insufficient light. The generated correction measures include adjusting the top material type or thickness to improve lighting conditions.

[0074] Similarly, if the first performance verification result meets the first expected indicator, and the performance verification of the greenhouse wall layout parameters, that is, the second performance verification result, does not meet the second expected indicator, then a second backtracking instruction is generated to backtrack the greenhouse wall layout parameters and determine multiple abnormal wall layout points. For example, it may be found that the thermal insulation performance of a certain wall position in the greenhouse is lower than the design requirements. Based on the determined abnormal wall layout points, an in-depth evaluation is performed to determine the difference between the abnormal wall layout points and the design requirements, and a second error correction information is generated based on the evaluation results. The second error correction information is used to guide the correction of the greenhouse wall layout parameters. For example, increase the wall thickness or replace materials with better thermal insulation performance.

[0075] If both the first performance verification result and the second performance verification result do not meet their respective expected indicators, an abnormal alarm instruction is generated, and the greenhouse layout parameters and abnormal alarm information are sent to the remote terminal for in-depth analysis by a professional team, and a more comprehensive evaluation and adjustment of the greenhouse structure may be required.

[0076] The above steps can timely discover and resolve anomalies in the layout design through the layout parameter backtracking and correction process, ensure that the greenhouse structure and environmental control are in the optimal state, and improve the greenhouse operation efficiency and crop growth quality.

[0077] In summary, the method for dynamic optimization of Gobi solar greenhouse structural parameters combined with environmental perception provided in the embodiments of the present application has the following technical effects:

[0078] By building a sensor network to collect environmental information inside and outside the greenhouse, an environmental perception parameter set is generated and transmitted to the central server for analysis to generate an environmental fluctuation trend graph. According to the environmental fluctuation trend graph, the internal and external targets to be optimized are determined, providing direction and data support for the subsequent structural parameter optimization. The greenhouse structural parameters are determined by combining the geometric parameters and material properties of the greenhouse. The first state variable is constructed by the interaction between the external environmental parameters and the greenhouse structural parameters. The second state variable is constructed by the interaction between the internal environmental parameters and the greenhouse structural parameters. The stage objective function is introduced, and the processing is carried out according to multiple time steps. A dynamic programming model is constructed to determine multiple dynamic data, thereby simulating the performance of the greenhouse under different environmental conditions and structural parameters, thereby providing decision support for structural parameter optimization and guiding the optimization direction. Define structural safety and crop growth environment constraints. Introduce constraints, assign weights to the structural optimization targets and crop growth optimization targets among the multiple optimization targets, integrate dynamic data, and generate multiple structural adjustment coefficients for the Gobi solar greenhouse. These coefficients are the basis for the subsequent actual adjustment of the greenhouse structure, and will guide the actual adjustment of the greenhouse structure to ensure that the greenhouse performance and crop growth environment are in the best state. Based on the multiple structural adjustment coefficients combined with the greenhouse structural parameters, real-time layout is performed to generate layout decisions; based on the layout decisions, the greenhouse top and wall layout parameters are extracted for simulation verification to determine whether the performance verification results meet the expected indicators. The structural adjustment coefficients are updated according to the verification results, and error correction or abnormality analysis is performed to achieve adaptive dynamic optimization and adjustment of the Gobi solar greenhouse structural parameters, ensuring that the greenhouse can continuously and efficiently adapt to changing environmental conditions.

[0079] Overall, the embodiments of the present application realize dynamic optimization of the structural parameters of the Gobi solar greenhouse by integrating environmental perception, dynamic programming and real-time simulation verification, so that the Gobi solar greenhouse can quickly respond to environmental changes, maximize the use of existing environmental resources, and ensure the stability of the crop production environment, thereby improving the environmental adaptability of the greenhouse and crop quality, reducing energy consumption, and promoting the sustainable development of agriculture in harsh environments such as the Gobi.

[0080] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for dynamic optimization of Gobi solar greenhouse structural parameters combined with environmental perception, characterized in that: The method comprises: Perceiving environmental information of the Gobi solar greenhouse, generating an environmental perception parameter set, and defining a plurality of targets to be optimized according to the environmental perception parameter set; A dynamic programming model is constructed according to the greenhouse structural parameters of the Gobi solar greenhouse and the environmental perception parameter set to obtain a plurality of dynamic data; Introducing constraint conditions, integrating the plurality of dynamic data according to the plurality of targets to be optimized, and generating a plurality of structural adjustment coefficients of the Gobi solar greenhouse; Performing real-time layout based on the multiple structural adjustment coefficients combined with the greenhouse structural parameters to generate a layout decision; The layout decision is executed to perform simulation verification, the multiple structural adjustment coefficients are updated according to the verification results, and the greenhouse structural parameters are adaptively and dynamically optimized and adjusted.

2. The method for dynamic optimization of Gobi solar greenhouse structural parameters combined with environmental perception as claimed in claim 1, characterized in that: Environmental information of a Gobi solar greenhouse is sensed, an environmental perception parameter set is generated, and a plurality of targets to be optimized are defined according to the environmental perception parameter set. The method includes: Building a sensor network based on multiple sensor devices, wherein the sensor network includes a collection frequency; The environmental information of the Gobi solar greenhouse is sensed by the sensor network according to the collection frequency to generate an environmental perception parameter set, wherein the environmental perception parameter set includes multiple external environmental parameters and multiple internal environmental parameters; Transmitting the environment perception parameter set to a central server for analysis to generate an environment fluctuation trend graph; Optimizing and analyzing the plurality of external environmental parameters according to the environmental fluctuation trend diagram to determine a plurality of external targets to be optimized; Optimizing and analyzing the multiple internal environmental parameters according to the environmental fluctuation trend graph to determine multiple internal targets to be optimized; The multiple targets to be optimized are determined based on integration of the multiple external targets to be optimized and the multiple internal targets to be optimized.

3. The method for dynamic optimization of Gobi solar greenhouse structural parameters combined with environmental perception as claimed in claim 2, characterized in that: A dynamic programming model is constructed according to the greenhouse structural parameters of the Gobi solar greenhouse and the environmental perception parameter set, and the method includes: Determine the greenhouse structural parameters of the Gobi solar greenhouse based on the geometric parameters of the Gobi solar greenhouse combined with the material property information; Constructing a first state variable based on the interaction between the greenhouse structure parameters of the Gobi solar greenhouse and the plurality of external environmental parameters; Constructing a second state variable based on the interaction between the greenhouse structure parameters and the plurality of internal environmental parameters of the Gobi solar greenhouse; Analyze the first state variable and the second state variable to determine a structural state variable and an environmental state variable; A stage objective function is introduced, the structural state variables and the environmental state variables are processed according to multiple time steps, and the dynamic programming model is constructed.

4. The method for dynamic optimization of Gobi solar greenhouse structural parameters combined with environmental perception as claimed in claim 3 is characterized in that: Introducing a stage objective function, processing the structural state variables and the environmental state variables according to multiple time steps, and constructing the dynamic programming model, the method includes: Calling the historical operation status record file of the Gobi solar greenhouse, and determining a plurality of operation time stages based on the historical operation status record file; Dividing the plurality of time steps according to the plurality of runtime phases; Based on the multiple runtime stages, the structural state variables and the environmental state variables are combined to perform calculations to define the stage objective function, where the stage objective function corresponds to the multiple time steps; A time sequence is determined based on the multiple time steps, and the structural state variables and the environmental state variables are recursively calculated according to the time sequence to construct the dynamic programming model.

5. The method for dynamic optimization of Gobi solar greenhouse structural parameters combined with environmental perception as claimed in claim 4, characterized in that: Get multiple dynamic data, including: Performing operation analysis on the geometric parameters of the Gobi solar greenhouse in combination with the material property information according to the multiple time steps to determine structural dynamic data; Performing environmental change analysis on the multiple external environmental parameters and the multiple internal environmental parameters according to the multiple time steps to determine environmental dynamic data; Perform growth analysis based on the structural dynamic data in combination with the environmental dynamic data according to the multiple time steps to determine crop dynamic data; The structure dynamic data, the crop dynamic data, and the environment dynamic data are added to the plurality of dynamic data.

6. The method for dynamic optimization of Gobi solar greenhouse structural parameters combined with environmental perception as claimed in claim 2, characterized in that: Introducing constraint conditions, integrating the plurality of dynamic data according to the plurality of targets to be optimized, and generating a plurality of structural adjustment coefficients of the Gobi solar greenhouse, the method comprising: Performing structural analysis based on the multiple external environmental parameters to define structural safety constraints; Perform crop growth analysis based on the multiple internal environmental parameters and define crop growth environmental constraints; Extracting a structural target to be optimized based on the structural safety constraint conditions and combining the multiple targets to be optimized; Extracting a crop growth target to be optimized based on the crop growth environment constraint conditions and combining the multiple targets to be optimized; Allocating weights to the structural target to be optimized and the crop growth target to be optimized, and determining a first weight factor and a second weight factor; The multiple dynamic data are integrated based on the structural target to be optimized in combination with the first weight factor, and based on the crop growth target to be optimized in combination with the second weight factor to generate the multiple structural adjustment coefficients of the Gobi solar greenhouse.

7. The method for dynamic optimization of Gobi solar greenhouse structural parameters combined with environmental perception as claimed in claim 6, characterized in that: Executing the layout decision to perform simulation verification, and updating the multiple structure adjustment coefficients according to the verification result, the method includes: Extracting greenhouse top layout parameters and greenhouse wall layout parameters of the Gobi solar greenhouse based on the layout decision; Performing simulation verification on the greenhouse top layout parameters based on the multiple external environmental parameters to obtain a first performance verification result; Performing simulation verification on the greenhouse wall layout parameters based on the multiple internal environmental parameters, a second performance verification result; Determining whether the first performance verification result meets a first expected indicator, and determining whether the second performance verification result meets a second expected indicator; If the first performance verification result meets the first expected index, and the second performance verification result meets the second expected index, an update instruction is generated to update the multiple structural adjustment coefficients based on the greenhouse top layout parameters and the greenhouse wall layout parameters.

8. The method for dynamic optimization of Gobi solar greenhouse structural parameters combined with environmental perception as claimed in claim 7, characterized in that: Determining whether the first performance verification result meets a first expected indicator, and determining whether the second performance verification result meets a second expected indicator, the method includes: If the first performance verification result does not meet the first expected index, and the second performance verification result meets the second expected index, a first backtracking instruction is generated, and the greenhouse top layout parameters are backtracked through the first backtracking instruction to determine multiple top layout abnormal points; Performing an evaluation and analysis based on the multiple top layout abnormal points, generating first error correction information according to a first evaluation result, and correcting the greenhouse top layout parameters according to the first error correction information; If the first performance verification result meets the first expected index, and the second performance verification result does not meet the second expected index, a second backtracking instruction is generated, and the greenhouse wall layout parameters are backtracked through the second backtracking instruction to determine multiple wall layout abnormal points; Performing an evaluation and analysis based on the multiple abnormal points of the wall layout, generating second error correction information according to a second evaluation result, and correcting the greenhouse wall layout parameters according to the second error correction information; If the first performance verification result does not meet the first expected indicator, and the second performance verification result does not meet the second expected indicator, an abnormal alarm instruction is generated and sent to a remote terminal for abnormal analysis based on the greenhouse top layout parameters, the greenhouse wall layout parameters and the abnormal alarm instruction.

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