Dynamic Optimization Method for Structural Parameters of Gobi Solar Greenhouse Combined with Environment Perception
Optimizing the Gobi sunlight greenhouse structure through environmental perception and dynamic planning solves the problem that traditional greenhouses are difficult to respond to environmental changes in real time, and achieves efficient utilization of resources and stability of crop growth environment.
Patent Information
- Application Number
- CN202411523875.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-30
- Publication Date
- 2025-07-25
- Estimated Expiration
- 2044-10-30
AI Technical Summary
The traditional Gobi sunlight greenhouse structure design is static and single, making it difficult to respond to environmental changes in real time and cannot make full use of resources, resulting in high environmental volatility and unstable crop growth environment.
Generate parameter sets through environment perception, build dynamic programming models, introduce constraints, generate structural adjustment coefficients, make real-time layout decisions, and update adjustment coefficients through simulation verification to achieve adaptive optimization.
It improves the adaptability of greenhouses to environmental changes, maximizes resource utilization, enhances regulatory capabilities, and ensures the stability and resource utilization of crop growth environment.
Smart Images
Figure CN119398430B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of agricultural engineering, and particularly to a method for dynamically optimizing the structural parameters of a gobi solar greenhouse combined with environmental perception. Background Art
[0002] As an innovative facility for agricultural production in arid regions, the gobi solar greenhouse utilizes 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 need to consider many special factors, such as light, temperature, humidity, etc.
[0003] The existing methods for optimizing the structural parameters of gobi solar greenhouses mainly focus on the design and construction stages of the greenhouse. By optimizing the structural parameters such as the azimuth angle, span, ridge height, and roof angle of the greenhouse, and selecting appropriate enclosure materials, the light energy utilization efficiency and heat preservation performance of the greenhouse are improved. However, these methods have certain limitations when dealing with extreme environmental conditions such as gobi. Firstly, these methods are often static. Once the greenhouse structure is built, it is difficult to adjust its parameters, making it difficult to adapt to the complex and changeable environmental conditions in the gobi region, resulting in fluctuations in the greenhouse environment. Secondly, these methods rarely consider the real-time changes in the internal and external environments of the greenhouse. Under extreme climatic conditions such as gobi, the environmental control ability of the greenhouse is limited, and the production facility resources cannot be fully utilized, restricting the growth potential of crops. Summary of the Invention
[0004] This application provides a method for dynamically optimizing the structural parameters of a gobi solar greenhouse combined with environmental perception, which solves the technical problems that the traditional greenhouse structure design uses 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, resulting in large fluctuations in the greenhouse environment, and achieves the technical effects of improving the environmental adaptability of the greenhouse and the stability of the crop growth environment, and improving the resource utilization rate.
[0005] In view of the above problems, this application provides a method for dynamically optimizing the structural parameters of a gobi solar greenhouse combined with environmental perception. The method includes: perceiving the environmental information of the gobi solar greenhouse to generate an environmental perception parameter set, and defining multiple optimization targets according to the environmental perception parameter set; constructing a dynamic programming model based on the greenhouse structural parameters of the gobi solar greenhouse and the environmental perception parameter set to obtain multiple dynamic data; introducing constraint conditions, integrating the multiple dynamic data according to the multiple optimization targets to generate multiple structural adjustment coefficients for the gobi solar greenhouse; performing real-time layout based on the multiple structural adjustment coefficients in combination with the greenhouse structural parameters to generate a layout decision; executing the layout decision for simulation verification, and updating the multiple structural adjustment coefficients according to the verification results to perform 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] Perceive the environmental information of the gobi solar greenhouse to generate an environmental perception parameter set, providing a real-time data basis for subsequent optimization. Define multiple optimization targets according to the environmental perception parameter set, clarify the direction and purpose of optimization, and ensure that the construction and application of the dynamic programming model can specifically solve practical problems. Construct a dynamic programming model based on the greenhouse structure parameters of the gobi solar greenhouse and the environmental perception parameter set to obtain multiple dynamic data, providing a decision-making basis for optimization. Introduce constraint conditions to ensure that the optimization process is carried out within a realistic and feasible range, integrate the multiple dynamic data according to the multiple optimization targets, generate multiple structure adjustment coefficients for the gobi solar greenhouse, and perform real-time layout based on the multiple structure adjustment coefficients in combination with the greenhouse structure parameters to generate a layout decision, guiding the real-time adjustment of the greenhouse structure to adapt to environmental changes. Execute the layout decision for simulation verification, update the multiple structure adjustment coefficients according to the verification results, and perform adaptive dynamic optimization adjustment on the greenhouse structure parameters, realizing a closed-loop feedback of optimization and enhancing the regulation ability of the greenhouse.
[0008] In summary, this application improves the adaptability of the greenhouse to environmental changes, maximizes the utilization of existing environmental resources, and reduces energy consumption by real-time perceiving environmental information and dynamically adjusting greenhouse structure parameters; at the same time, by introducing constraint conditions and real-time layout decisions, the regulation ability of the greenhouse is enhanced, and through simulation verification and the update of structure adjustment coefficients, the sustainability and accuracy of the optimization process are ensured, thereby improving the stability of the crop growth environment.
[0009] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. BRIEF DESCRIPTION OF THE DRAWINGS
[0010] Figure 1 It is a schematic flowchart of a method for dynamically optimizing the structure parameters of a gobi solar greenhouse combined with environmental perception provided by an embodiment of this application;
[0011] Figure 2 It is a schematic flowchart of constructing a dynamic programming model in a method for dynamically optimizing the structure parameters of a gobi solar greenhouse combined with environmental perception provided by an embodiment of this application;
[0012] Figure 3 It is a schematic flowchart of obtaining multiple dynamic data in a method for dynamically optimizing the structure parameters of a gobi solar greenhouse combined with environmental perception provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0013] By providing a method for dynamically optimizing the structural parameters of a Gobi solar greenhouse combined with environmental perception in the embodiments of the present application, the technical problem that traditional greenhouse structure design uses 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, resulting in large fluctuations in the greenhouse environment, is solved. The technical effects of improving the environmental adaptability of the greenhouse and the stability of the crop growth environment and increasing the resource utilization rate are achieved.
[0014] As Figure 1 shown, the embodiments of the present application provide a method for dynamically optimizing the structural parameters of a Gobi solar greenhouse combined with environmental perception, and the method includes:
[0015] Step S1: Perceive the environmental information of the Gobi solar greenhouse to generate an environmental perception parameter set, and define multiple optimization targets according to the environmental perception parameter set.
[0016] Specifically, by installing various environmental sensors in the Gobi solar greenhouse, such as temperature sensors, humidity sensors, light sensors, etc., the environmental information of the Gobi solar greenhouse is perceived, and the environmental data inside and outside the greenhouse is collected. These data include temperature, humidity, light intensity, wind speed, soil humidity, etc. Summarize these environmental data to generate an environmental perception parameter set, which is a detailed description of the current environmental state.
[0017] Then, according to this environmental perception parameter set, multiple optimization targets are defined. The optimization targets refer to the performance indicators that need to be improved by adjusting the greenhouse structure 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 shows that the temperature inside the greenhouse is too high, an optimization target can be to lower the indoor temperature. Similarly, if the light intensity is insufficient, the optimization target can be to increase light reception.
[0018] Step S1 provides the direction and data support for the subsequent optimization of the structural parameters by perceiving the environmental state in real time and defining the optimization targets.
[0019] Step S2: Construct a dynamic programming model based on the greenhouse structure parameters of the Gobi solar greenhouse and the environmental perception parameter set to obtain multiple 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] In step S3, by introducing constraint conditions and integrating dynamic data, coefficients for specifically guiding the greenhouse structure adjustment are generated. These coefficients are the basis for subsequent actual adjustment of the greenhouse structure, and will guide the actual adjustment work of the greenhouse structure to ensure that the greenhouse performance and the crop growth environment reach the optimal state.
[0027] Step S4: Based on the multiple structure adjustment coefficients and in combination with the greenhouse structure parameters, perform real-time layout to generate a layout decision.
[0028] Specifically, based on the multiple structure adjustment coefficients generated in step S3, in combination with the existing structure parameters of the gobi solar greenhouse, perform real-time layout to dynamically adjust the structure settings of the greenhouse. For example, if the structure adjustment coefficient indicates that the roof angle needs to be changed to increase sunlight, then the control system will adjust the support structure of the roof according to these coefficients to change the roof angle. Similarly, if ventilation needs to be improved to reduce the temperature, then adjust the size or position of the ventilation openings. Through real-time layout, a layout decision is generated. This layout decision is a specific adjustment plan, such as a set of specific operation instructions, to guide the control system to automatically adjust the greenhouse structure parameters 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 structure adjustment coefficients to the actual greenhouse structure, and according to the current environmental conditions and optimization goal, generates a specific layout decision to optimize the greenhouse performance in real time and achieve the purpose of optimizing the growth environment.
[0030] Step S5: Execute the layout decision for simulation verification, update the multiple structure adjustment coefficients according to the verification result, and perform adaptive dynamic optimization adjustment on the greenhouse structure parameters.
[0031] Specifically, through greenhouse environment simulation software, construct a simulation environment and 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 intensities, temperatures, etc.) and the response after the greenhouse structure adjustment. By comparing the simulation results with the optimization goal, the effect of the layout decision can be evaluated.
[0032] According to the result of the simulation verification, update the multiple structure adjustment coefficients generated in step S3. If the simulation result shows that the layout decision fails to achieve the expected goal, continue to adjust the structure adjustment coefficients and conduct a new round of simulation verification until the optimization goal is met. This iterative process ensures the continuous optimization and adaptability of the structure adjustment coefficients. After meeting the optimization goal, perform adaptive dynamic optimization adjustment on the greenhouse structure parameters with the current structure adjustment coefficients, and execute the current layout decision through the automated control system to change the actual structure settings of the greenhouse, such as adjusting the roof angle, ventilation system, etc., to optimize the greenhouse performance.
[0033] Step S5 realizes the adaptive dynamic optimization adjustment of the structure parameters of the gobi solar greenhouse through simulation verification and iterative update, ensuring that the greenhouse can continuously and efficiently adapt to the changing environmental conditions.
[0034] Furthermore, step S1 of the embodiment of the present application further includes:
[0035] Construct a sensing network based on multiple sensing devices, where the sensing network includes a collection frequency; sense the environmental information of the gobi solar greenhouse according to the collection frequency through the sensing network to generate an environmental perception parameter set, and the environmental perception parameter set includes multiple external environmental parameters and multiple internal environmental parameters; transmit the environmental perception parameter set to a central server for analysis to generate an environmental fluctuation trend chart; perform optimization analysis on the multiple external environmental parameters according to the environmental fluctuation trend chart to determine multiple external targets to be optimized; perform optimization analysis on the multiple internal environmental parameters according to the environmental fluctuation trend chart to determine multiple internal targets to be optimized; integrate based on the multiple external targets to be optimized and the multiple internal targets to be optimized to determine the multiple targets to be optimized.
[0036] Specifically, deploy a variety of sensors inside and outside the gobi solar greenhouse, such as temperature, humidity, and light intensity sensors, to construct a comprehensively covered sensing network. Set a reasonable collection frequency for the sensing network to ensure the real-time and accuracy of data. The sensing network collects the environmental data inside and outside the greenhouse according to the set frequency to obtain 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 summarize the collected multiple external environmental parameters and multiple internal environmental parameters to generate an environmental perception parameter set. Transmit the environmental perception parameter set to the central server in real time through wireless transmission technology. On the central server, use data analysis technology, such as time series analysis, to generate an environmental fluctuation trend chart. This trend chart shows the changes of various environmental parameters over time and can be used to determine the patterns and trends of environmental changes.
[0037] Based on the environmental fluctuation trend chart, analyze the fluctuation rules of the greenhouse environment, identify the optimization requirements of external and internal environmental parameters, and determine multiple external targets to be optimized and multiple internal targets to be optimized. For example, through analysis, it is found that the temperature difference between inside and outside the greenhouse is large at night, resulting in severe internal temperature fluctuations, so it can be determined that improving the night heat preservation performance is an external target to be optimized; at the same time, it is observed that the internal humidity fluctuates greatly during a specific period, affecting crop growth, so humidity control can be used as an internal target to be optimized.
[0038] Finally, by integrating the external and internal objectives to be optimized, a comprehensive set of objectives to be optimized is formed, providing a clear guiding direction for the subsequent optimization and adjustment of the greenhouse structure parameters, ensuring that the greenhouse can effectively cope with the special environmental conditions in the Gobi region and providing the best growth environment for crops.
[0039] Further, as Figure 2 shown, step S2 of the embodiment of the present application further includes:
[0040] Determine the greenhouse structure parameters of the Gobi solar greenhouse based on the geometric parameters of the Gobi solar greenhouse combined with the material property information; interact the greenhouse structure parameters of the Gobi solar greenhouse with the multiple external environmental parameters to construct a first state variable; interact the greenhouse structure parameters of the Gobi solar greenhouse with the multiple internal environmental parameters to construct a second state variable; analyze according to the first state variable and the second state variable to determine the structural state variable and the environmental state variable; introduce a stage objective function, process the structural state variable and the environmental state variable according to multiple time steps, and construct the dynamic programming model.
[0041] Specifically, determine the greenhouse structure parameters based on the geometric parameters of the Gobi solar greenhouse and the material property information. The geometric parameters include the length, width, height, roof angle, etc. of the greenhouse, and the material property information includes the light transmittance of the transparent material used in the greenhouse, the heat insulation performance of the wall, the opening and closing degree of the sunshade system, etc. For example, the light transmittance of the greenhouse covering material is set to 80%, and the thickness of the heat insulation material is 50 mm, so as to construct the basic structure of the greenhouse.
[0042] Analyze the interaction between the greenhouse structure parameters and the external environmental parameters to construct a first state variable. For example, the increase in the external light intensity of the greenhouse will affect the internal light of the greenhouse through the light transmittance of the covering material, and then affect the growth of crops. This process is quantified and incorporated into the first state variable. At the same time, analyze the interaction between the greenhouse structure parameters and the internal environmental parameters to construct a second state variable. For example, the temperature change inside the greenhouse is affected by the thermal resistance of the heat insulation material and the external temperature. This process is quantified and incorporated into the second state variable.
[0043] Analyze according to the first state variable and the second state variable to determine the greenhouse structure parameters and the internal and external environmental parameters at the same moment, and generate the structural state variable and the environmental state variable. The structural state variable describes the state of the greenhouse structure at the t-th moment, including geometric parameters and material properties, while the environmental state variable describes the set of environmental perception parameters of the greenhouse at the t-th moment.
[0044] Next, introduce the stage objective function. The stage objective function is a function in the dynamic programming model that describes the optimization objective to be achieved within a specific time stage. It is usually defined based on the optimization objective, such as maximizing the light utilization efficiency, minimizing the energy consumption, etc. This function will guide the dynamic programming model to process the structural state variables and environmental state variables over multiple time steps to find the optimal solution.
[0045] Through the above steps, the greenhouse structure parameters and environmental parameters are combined 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, and the structural state variables and the environmental state variables are processed according to multiple time steps to construct the dynamic programming model, which further includes:
[0047] Call the historical operation status record file of the gobi solar greenhouse, determine multiple operation time stages based on the historical operation status record file; divide the multiple time steps according to the multiple operation time stages; calculate based on the multiple 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 multiple time steps; determine a time series based on the multiple time steps, and recursively process the structural state variables and the environmental state variables according to the time series to construct the dynamic programming model.
[0048] Specifically, call the historical operation status record file of the gobi solar greenhouse. The historical operation status record file contains the past operation data of the greenhouse, such as the changes in temperature, humidity, light, etc. over time, as well as the greenhouse structure parameters. Based on these historical data, analyze the operation status of the greenhouse in different past time stages, such as spring, summer, autumn and winter, or the initial, middle and late stages of crop growth, etc., to determine the key time stages of the greenhouse operation. Divide the time steps according to these key time stages. For example, every 10 days in spring is used as 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] Define the stage objective function based on each time step and the structural state variables and environmental state variables of the greenhouse. This function will provide specific optimization objectives for the model according to different time steps and corresponding operation stages. For example, in the initial stage of crop growth in spring, the possible objective is to minimize energy consumption while maintaining an appropriate light intensity; while in the high-temperature period of summer, the objective may be to control the internal temperature not to exceed the upper limit of crop growth by optimizing the ventilation and shading systems.
[0050] Determine the time series according to the time step. The time series is a series of time points arranged in chronological order. Recursively process the structural state variables and environmental state variables 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 structure state within 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 objective function of the first stage, and then enter the next time step, continuously optimizing until the best greenhouse operating state is reached.
[0051] Through the dynamic programming model constructed by 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 of the Gobi region and the needs of the crop growth cycle.
[0052] Furthermore, as Figure 3 shown, step S2 of the embodiment of the present application further includes:
[0053] Perform operation analysis on the geometric parameters of the Gobi solar greenhouse combined with the material property information according to the multiple time steps to determine the structural dynamic data; perform environmental change analysis on the multiple external environmental parameters and the multiple internal environmental parameters according to the multiple time steps to determine the environmental dynamic data; perform growth analysis based on the structural dynamic data combined with the environmental dynamic data according to the multiple time steps to determine the crop dynamic data; add the structural dynamic data, the crop dynamic data, and the environmental dynamic data to the multiple dynamic data.
[0054] Specifically, through the dynamic programming model, perform operation analysis on the geometric parameters of the Gobi solar greenhouse combined with the material property information according to multiple time steps, and simulate the structural performance of the greenhouse structure at different time points, such as the light distribution and heat preservation effect under different light and temperature conditions. These analysis results constitute the structural dynamic data, reflecting the change of the greenhouse structure over time. For example, the light transmittance of the covering material is adjusted according to the increase and decrease of the light intensity, and the thermal resistance of the heat preservation material changes with the external temperature.
[0055] Then, perform environmental change analysis on multiple external environmental parameters and multiple internal environmental parameters according to multiple time steps. Through the dynamic programming model, simulate and predict the external environmental conditions and internal environmental conditions at different time points. These analysis results constitute the environmental dynamic data, reflecting the change of the environment where the greenhouse is located over time. For example, the external temperature gradually increases in spring, and the humidity and light intensity inside the greenhouse also change accordingly.
[0056] Based on the determined structural dynamic data and environmental dynamic data, simulate the growth of crops within the corresponding time step to determine the crop dynamic data. The crop dynamic data reflects the change of the crop growth state over time. For example, in the initial stage of spring crop growth, as the light intensity and temperature inside the greenhouse increase, the growth rate of the crops accelerates.
[0057] Finally, add the structural dynamic data, crop dynamic data, and environmental dynamic data to the multiple dynamic data to provide a comprehensive data basis for the dynamic optimization of the greenhouse. Combining the dynamic data analysis, the greenhouse can more accurately regulate the environmental conditions, promote crop growth, and improve the greenhouse operation efficiency and crop yield.
[0058] Furthermore, step S3 of the embodiment of the present application further includes:
[0059] Conduct a structural analysis based on the multiple external environmental parameters to define the structural safety constraint conditions; conduct a crop growth analysis based on the multiple internal environmental parameters to define the crop growth environmental constraint conditions; according to the structural safety constraint conditions, combine the multiple optimization targets to extract the structural optimization targets to be optimized; according to the crop growth environmental constraint conditions, combine the multiple optimization targets to extract the crop growth optimization targets to be optimized; allocate weights to the structural optimization targets to be optimized and the crop growth optimization targets to be optimized to determine the first weight factor and the second weight factor; based on the structural optimization targets to be optimized combined with the first weight factor and based on the crop growth optimization targets to be optimized combined with the second weight factor, integrate the multiple dynamic data to generate the multiple structural adjustment coefficients of the gobi solar greenhouse.
[0060] Specifically, first, conduct a structural analysis based on multiple external environmental parameters to define the structural safety constraint conditions. These structural safety constraint conditions are parameter limitations to ensure the structural safety of the greenhouse, 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. Then, conduct a crop growth analysis based on multiple internal environmental parameters to define the crop growth environmental constraint conditions. These crop growth environmental constraint conditions are the range of environmental parameters to ensure the healthy growth of crops, such as appropriate temperature, humidity, light intensity, etc., to meet the growth needs of crops.
[0061] Extract the structural optimization targets to be optimized according to the structural safety constraint conditions and the multiple optimization targets. Similarly, extract the crop growth optimization targets to be optimized according to the crop growth environmental constraint conditions and the multiple optimization targets. For example, the structural optimization targets to be optimized may include the stability, durability, and energy consumption efficiency of the greenhouse, and the crop growth optimization targets to be optimized may include light uniformity, temperature suitability, and humidity control.
[0062] Weigh the structural optimization target and the crop growth optimization target to determine the first weight factor corresponding to the structural optimization target and the second weight factor corresponding to the crop growth optimization target. The weight allocation takes into account the priority of greenhouse operation and crop growth requirements, which are determined by experts based on experience and actual situations, 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; during the period with 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, integrate multiple dynamic data. Through multi-objective optimization algorithms such as the weighted summation method or the weighted product method, comprehensively combine the optimization results of different objectives, calculate the optimization results of each optimization target after comprehensive consideration, and then generate multiple structural adjustment coefficients for the gobi solar greenhouse. The structural adjustment coefficients reflect the adjustment direction and amplitude of greenhouse structure parameters, such as an increase in the light transmittance of the covering material and an optimization of the thermal resistance of the thermal insulation material, to meet the dual requirements of structural safety and crop growth. The integration process requires multiple iterations to find the optimal structural adjustment coefficients. In each iteration, adjust according to the new data and weight factors until a satisfactory optimization result is achieved.
[0064] Exemplarily, a certain greenhouse in the gobi area faces the dual challenges of strong winds and high temperatures and needs to optimize structural safety and crop growth environment. First, clarify the structural safety constraint conditions, including the maximum wind load and structural stability, and the crop growth environment constraint conditions, including the suitable temperature range and light intensity. Next, through a multi-objective optimization algorithm such as NSGA-II, weigh the structural safety optimization target and the crop growth optimization target to determine the priorities of structure and crop growth. The structural safety weight is 0.6, and the crop growth weight is 0.4. Build an optimization model, set the mathematical models and parameters of structural safety and crop growth, and perform iterative solutions. Through optimization, obtain the structural adjustment coefficients, including a 10% increase in the light transmittance of the covering material and a 5% reduction in the thermal resistance of the thermal insulation material, to adapt to the special environment in the gobi area and meet the crop growth requirements at the same time.
[0065] The above steps combine structural safety and crop growth requirements with dynamic data, consider the weights of different objectives, generate coefficients for specifically guiding greenhouse structure adjustment, achieve the dynamic balance between greenhouse structural safety and crop growth optimization, ensure the efficient operation of the greenhouse and the healthy growth of crops, and thus comprehensively optimize the greenhouse performance.
[0066] Furthermore, step S5 of the embodiment of the present application further includes:
[0067] Extract the greenhouse top layout parameters and greenhouse wall layout parameters of the Gobi solar greenhouse based on the layout decision; perform simulation verification on the greenhouse top layout parameters based on the multiple external environmental parameters to obtain the first performance verification result; perform simulation verification on the greenhouse wall layout parameters based on the multiple internal environmental parameters to obtain the second performance verification result; determine whether the first performance verification result meets the first expected index, and determine whether the second performance verification result meets the second expected index; if the first performance verification result meets the first expected index and the second performance verification result meets the second expected index, generate an update instruction, and update the multiple structure adjustment coefficients based on the greenhouse top layout parameters and the greenhouse wall layout parameters.
[0068] Specifically, extract the greenhouse top layout parameters and greenhouse wall layout parameters of the Gobi solar greenhouse in the layout decision. These parameters include roof angle, wall height, material type, etc. Then, through greenhouse simulation software, perform simulation verification on the greenhouse top layout parameters based on multiple external environmental parameters, evaluate the greenhouse lighting efficiency, heat preservation effect, etc., to obtain the first performance verification result. At the same time, perform simulation verification on the greenhouse wall layout parameters based on the internal environmental parameters, evaluate the heat preservation performance, structural strength, etc., to obtain the second performance verification result.
[0069] Compare the first performance verification result with the first expected index to determine whether the greenhouse top layout meets the expectation. Similarly, compare the second performance verification result with the second expected index to determine whether the greenhouse wall layout meets the expectation. 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 in the Gobi area. Generate an update instruction according to the current layout decision, and update the structure adjustment coefficients based on the greenhouse top layout parameters and the greenhouse wall layout parameters to optimize the greenhouse structure 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 heat preservation. Among them, the first expected index is the performance expectation for the greenhouse top layout parameters, such as lighting efficiency, heat preservation effect, etc. The second expected index is the performance expectation for the greenhouse wall layout parameters, such as heat preservation performance, structural strength, etc.
[0070] The above steps ensure that the optimization adjustment of the greenhouse structure parameters is based on the actual environmental conditions and design requirements through simulation verification and iterative update, 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 index and determining whether the second performance verification result meets the second expected index further includes:
[0072] 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. Through the first backtracking instruction, the greenhouse top layout parameters are traversed back to determine multiple top layout abnormal points. Based on the multiple top layout abnormal points, an evaluation and analysis are carried out, and first error correction information is generated according to the first evaluation result. The greenhouse top layout parameters are corrected through 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. Through the second backtracking instruction, the greenhouse wall layout parameters are traversed back to determine multiple wall layout abnormal points. Based on the multiple wall layout abnormal points, an evaluation and analysis are carried out, and second error correction information is generated according to the second evaluation result. 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 index and the second performance verification result does not meet the second expected index, an abnormal alarm instruction is generated, and the greenhouse top layout parameters, the greenhouse wall layout parameters and the abnormal alarm instruction are sent to a remote terminal for abnormal analysis in combination.
[0073] Specifically, if the first performance verification result does not meet the first expected index while the second performance verification result meets the second expected index, a first backtracking instruction will be generated. The first backtracking instruction is used to traverse the greenhouse top layout parameters backward to check and evaluate the top layout parameters that may cause substandard performance, and determine multiple top layout abnormal points. Based on the determined top layout abnormal points, an in-depth evaluation is carried out to determine the differences between the top layout abnormal points and the design requirements. First error correction information is generated according to the evaluation result, and this 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 at the top of the greenhouse is lower than the design requirement, resulting in insufficient light. The generated correction measures include adjusting the type or thickness of the top material to improve the light condition.
[0074] Similarly, if the first performance verification result meets the first expected index and the performance verification of the greenhouse wall layout parameters, that is, the second performance verification result fails to meet the second expected index, a second backtracking instruction is generated to traverse the greenhouse wall layout parameters backward to determine multiple wall layout abnormal points. For example, it may be found that the heat preservation performance of a certain wall position in the greenhouse is lower than the design requirement. Based on the determined wall layout abnormal points, an in-depth evaluation is carried out to determine the differences between the wall layout abnormal points and the design requirements. Second error correction information is generated according to the evaluation result, and this second error correction information is used to guide the correction of the greenhouse wall layout parameters. Such as increasing the wall thickness or replacing the material with better heat preservation performance.
[0075] If neither the first performance verification result nor the second performance verification result meets their respective expected indicators, an abnormal alarm instruction is generated, and the greenhouse layout parameters and the abnormal alarm information are sent to the remote terminal for in-depth analysis by a professional team. It may be necessary to conduct a more comprehensive evaluation and adjustment of the greenhouse structure.
[0076] Through the layout parameter backtracking and correction process, the above steps can timely detect and solve the abnormalities in the layout design, ensure that the greenhouse structure and environmental control reach the optimal state, and improve the greenhouse operation efficiency and crop growth quality.
[0077] In summary, the dynamic optimization method for the structure parameters of the gobi solar greenhouse combined with environmental perception provided by the embodiments of the present application has the following technical effects:
[0078] By constructing a sensing network to collect the internal and external environmental information of the greenhouse, generating an environmental perception parameter set, and transmitting it to the central server for analysis to generate an environmental fluctuation trend graph. Determine the internal and external optimization targets to be optimized according to the environmental fluctuation trend graph, providing direction and data support for the subsequent structure parameter optimization. Determine the greenhouse structure parameters in combination with the geometric parameters and material properties of the greenhouse, construct the first state variable through the interaction between the external environmental parameters and the greenhouse structure parameters, construct the second state variable through the interaction between the internal environmental parameters and the greenhouse structure parameters, introduce the stage objective function, process according to multiple time steps, construct a dynamic programming model, determine multiple dynamic data, so as to simulate the performance of the greenhouse under different environmental conditions and structure parameters, thus providing decision support for the structure parameter optimization and guiding the optimization direction. Define the structural safety and crop growth environment constraint conditions. Introduce the constraint conditions, allocate weights to the structural optimization target and the crop growth optimization target among the multiple optimization targets to be optimized, integrate the dynamic data, and generate multiple structure adjustment coefficients for the gobi solar greenhouse. These coefficients are the basis for the subsequent actual adjustment of the greenhouse structure, which will guide the actual adjustment work of the greenhouse structure to ensure that the greenhouse performance and the crop growth environment reach the best state. Based on the multiple structure adjustment coefficients, combine with the greenhouse structure parameters to perform real-time layout, generate layout decisions; extract the greenhouse top and wall layout parameters based on the layout decisions for simulation verification, and judge whether the performance verification results meet the expected indicators. Update the structure adjustment coefficients according to the verification results, perform error correction or abnormal analysis, realizing the adaptive dynamic optimization adjustment of the gobi solar greenhouse structure parameters, and ensuring that the greenhouse can continuously and efficiently adapt to the changing environmental conditions.
[0079] Overall, through the integration of environmental perception, dynamic programming, and real-time simulation verification, the embodiments of the present application achieve the dynamic optimization of the structural parameters of the gobi solar greenhouse, enabling the gobi solar greenhouse to quickly respond to environmental changes, maximize the utilization of existing environmental resources, ensure the stability of the crop growth environment, thereby improving the environmental adaptability of the greenhouse and the crop quality, reducing energy consumption, and promoting the sustainable development of agriculture in harsh environments such as 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 obvious to those skilled in the art, and the general principles defined herein can 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 rather to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A dynamic optimization method for the structural parameters of a Gobi solar greenhouse combined with environmental perception, characterized in that, The method includes: Perceiving the environmental information of the gobi solar greenhouse to generate an environmental perception parameter set, and defining multiple optimization targets to be optimized according to the environmental perception parameter set; Constructing a dynamic programming model based on the greenhouse structure parameters of the gobi solar greenhouse and the environmental perception parameter set to obtain multiple dynamic data; Introducing constraint conditions, integrating the multiple dynamic data according to the multiple optimization targets to be optimized to generate 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 structure parameters to generate a layout decision; Executing the layout decision for simulation verification, updating the multiple structural adjustment coefficients according to the verification results, and performing adaptive dynamic optimization adjustment on the greenhouse structure parameters; Perceiving the environmental information of the gobi solar greenhouse to generate an environmental perception parameter set, and defining multiple optimization targets to be optimized according to the environmental perception parameter set. The method includes: Constructing a sensing network based on multiple sensing devices, and the sensing network includes a collection frequency; Perceiving the environmental information of the gobi solar greenhouse according to the collection frequency through the sensing network to generate an environmental perception parameter set, and the environmental perception parameter set includes multiple external environmental parameters and multiple internal environmental parameters; Transmitting the environmental perception parameter set to a central server for analysis to generate an environmental fluctuation trend chart; Performing optimization analysis on the multiple external environmental parameters according to the environmental fluctuation trend chart to determine multiple external optimization targets to be optimized; Performing optimization analysis on the multiple internal environmental parameters according to the environmental fluctuation trend chart to determine multiple internal optimization targets to be optimized; Integrating based on the multiple external optimization targets to be optimized and the multiple internal optimization targets to be optimized to determine the multiple optimization targets to be optimized; Constructing a dynamic programming model based on the greenhouse structure parameters of the gobi solar greenhouse and the environmental perception parameter set. The method includes: Determining the greenhouse structure parameters of the gobi solar greenhouse based on the geometric parameters of the gobi solar greenhouse in combination with material characteristic information; Interacting based on the greenhouse structure parameters of the gobi solar greenhouse and the multiple external environmental parameters to construct a first state variable; Interacting based on the greenhouse structure parameters of the gobi solar greenhouse and the multiple internal environmental parameters to construct a second state variable; Performing analysis according to the first state variable and the second state variable to determine a structural state variable and an environmental state variable; Introducing a stage objective function, processing the structural state variable and the environmental state variable according to multiple time steps to construct the dynamic programming model.
2. The dynamic optimization method for the structural parameters of a Gobi solar greenhouse combined with environmental perception according to claim 1, characterized in that, Introducing a stage objective function, processing the structural state variable and the environmental state variable according to multiple time steps to construct the dynamic programming model. The method includes: Invoking the historical operation status record file of the gobi solar greenhouse, and determining multiple operation time stages based on the historical operation status record file; Dividing the multiple time steps according to the multiple operation time stages; Calculating based on the multiple operation time stages in combination with the structural state variable and the environmental state variable to define the stage objective function, and the stage objective function has a corresponding relationship with the multiple time steps; A time series is determined based on the multiple time steps, and the structural state variables and the environmental state variables are recursively processed according to the time series to construct the dynamic programming model.
3. The dynamic optimization method for the structural parameters of the gobi solar greenhouse combined with environmental perception according to claim 2, characterized in that Obtain a plurality of dynamic data, the method comprising: Perform an 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 the structural dynamic data; Perform an environmental change analysis on the multiple external environmental parameters and the multiple internal environmental parameters according to the multiple time steps to determine the environmental dynamic data; Perform a growth analysis based on the structural dynamic data in combination with the environmental dynamic data according to the multiple time steps to determine the crop dynamic data; Add the structural dynamic data, the crop dynamic data, and the environmental dynamic data to the multiple dynamic data.
4. The dynamic optimization method for the structural parameters of a Gobi solar greenhouse combined with environmental perception according to claim 1, characterized in that Introduce constraint conditions, and integrate the multiple dynamic data according to the multiple optimization targets to generate multiple structural adjustment coefficients for the gobi solar greenhouse, the method comprising: Perform a structural analysis based on the multiple external environmental parameters and define the structural safety constraint conditions; Perform a crop growth analysis based on the multiple internal environmental parameters and define the crop growth environment constraint conditions; Extract the structural optimization targets to be optimized according to the structural safety constraint conditions in combination with the multiple optimization targets; Extract the crop growth optimization targets to be optimized according to the crop growth environment constraint conditions in combination with the multiple optimization targets; Perform a weight assignment on the structural optimization targets to be optimized and the crop growth optimization targets to be optimized to determine a first weight factor and a second weight factor; Integrate the multiple dynamic data based on the structural optimization targets to be optimized in combination with the first weight factor and based on the crop growth optimization targets to be optimized in combination with the second weight factor to generate the multiple structural adjustment coefficients for the gobi solar greenhouse.
5. The dynamic optimization method for the structural parameters of a gobi solar greenhouse combined with environmental perception according to claim 4, characterized in that, Execute the layout decision for simulation verification, and update the multiple structural adjustment coefficients according to the verification results, the method comprising: Extract the greenhouse top layout parameters and the greenhouse wall layout parameters of the gobi solar greenhouse based on the layout decision; Perform a simulation verification on the greenhouse top layout parameters based on the multiple external environmental parameters to obtain a first performance verification result; Perform a simulation verification on the greenhouse wall layout parameters based on the multiple internal environmental parameters to obtain a second performance verification result; Judge whether the first performance verification result meets a first expected index, and judge whether the second performance verification result meets a second expected index; If the first performance verification result meets the first expected index and the second performance verification result meets the second expected index, generate an update instruction, and update the multiple structural adjustment coefficients based on the greenhouse top layout parameters and the greenhouse wall layout parameters.
6. The dynamic optimization method for the structural parameters of a Gobi solar greenhouse combined with environmental perception according to claim 5, characterized in that Judge whether the first performance verification result meets the first expected index, and judge whether the second performance verification result meets the second expected index, the method comprising: 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 traversed back through the first backtracking instruction to determine multiple top layout abnormal points; Based on the multiple top layout abnormal points, an evaluation and analysis are carried out, 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 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 traversed back through the second backtracking instruction to determine multiple wall layout abnormal points; Based on the multiple wall layout abnormal points, an evaluation and analysis are carried out, 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 index and the second performance verification result does not meet the second expected index, an abnormal alarm instruction is generated, and the greenhouse top layout parameters, the greenhouse wall layout parameters and the abnormal alarm instruction are sent to a remote terminal for abnormal analysis.