Daylight greenhouse multi-environment factor optimization decision method and system based on crop growth model

Abstract

The application discloses a sunlight greenhouse multi-environment factor optimization decision method and system based on a crop growth model, relates to the technical field of intelligent control of facility agriculture, and comprises the following steps: collecting multi-source data of a sunlight greenhouse and performing preprocessing and exception processing to obtain current environment state data; determining a current growth stage of crops and corresponding initial target environment parameters based on a preset crop growth model; dynamically adjusting target environment parameters based on the current environment state data and in combination with external climate conditions to obtain target environment states matched with current environment conditions; comparing the current environment state data with the target environment states item by item to obtain deviation results of each environment factor; and performing multi-target optimization based on the deviation results, crop growth requirements, energy consumption and resource utilization rate. The application improves the precision of environment regulation and control of the sunlight greenhouse by constructing a multi-environment factor collaborative optimization decision mechanism based on the crop growth model.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for facility agriculture, and in particular to a multi-environmental factor optimization decision-making method and system for solar greenhouses based on crop growth models. Background Technology

[0002] With the rapid development of facility agriculture, solar greenhouses, as an important agricultural production facility, play a vital role in increasing crop yields and improving the crop growing environment. Current technologies typically rely on environmental sensors to monitor multiple environmental factors such as temperature, humidity, light intensity, and carbon dioxide concentration in real time, and control equipment such as ventilation, heating, shading, and irrigation based on preset thresholds or empirical rules. Some improved technologies further introduce automatic control strategies, such as methods based on fuzzy control, PID control, or simple model predictive control, to achieve automatic adjustment of single or multiple environmental factors.

[0003] Existing methods for environmental control in solar greenhouses still have certain limitations in the synergistic regulation of multiple environmental factors. Specifically, current technologies typically use fixed thresholds or static target parameters for control, lacking a refined characterization of the differences in needs at different crop growth stages, which may lead to deviations between environmental control targets and actual growth requirements. Furthermore, under the combined influence of multiple environmental factors, there are coupling relationships between them. Traditional control methods often employ decentralized control or single-objective optimization strategies, making it difficult to simultaneously ensure crop growth needs while considering energy consumption and resource utilization efficiency. Consequently, it is difficult to achieve the overall optimal control effect under complex environmental conditions. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models, which solves the problem that existing solar greenhouse environmental control lacks the ability to coordinate and optimize multiple environmental factors based on crop growth needs.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a multi-environmental factor optimization decision-making method for solar greenhouses based on a crop growth model, comprising, Collect multi-source data from the solar greenhouse and perform preprocessing and anomaly handling to obtain current environmental status data; Acquire crop information data and determine the current growth stage of the crop and the corresponding initial target environmental parameters based on the preset crop growth model; Based on the current environmental status data and combined with external climate conditions, the target environmental parameters are dynamically adjusted to obtain a target environmental status that matches the current environmental conditions. The current environmental status data is compared with the target environmental status item by item to obtain the deviation results of each environmental factor; Based on the deviation results, multi-objective optimization is carried out by combining crop growth requirements, energy consumption and resource utilization to generate corresponding environmental regulation decision schemes. The environmental control decision-making scheme is used to control the environmental control equipment in the solar greenhouse to carry out environmental regulation, and the regulation feedback data is collected and saved to the database.

[0007] As a preferred embodiment of the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models described in this invention, the step of obtaining current environmental state data includes the following steps: Collect environmental and crop data of the solar greenhouse and preprocess them to obtain multi-source data of the solar greenhouse; The validity of multi-source data from solar greenhouses is tested and anomalies are handled to obtain current environmental status data.

[0008] As a preferred embodiment of the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models described in this invention, the step of determining the current growth stage of the crop and the corresponding initial target environmental parameters based on a preset crop growth model includes the following steps: Crop information data is extracted from multi-source data of solar greenhouses, and a crop growth model is constructed based on the crop growth cycle pattern and the environmental demand relationship corresponding to each crop growth stage. Based on the crop type and growth time, and combined with the crop growth model, the current growth stage of the crop and the corresponding initial target environmental parameters are obtained.

[0009] As a preferred embodiment of the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models described in this invention, the step of dynamically adjusting the target environmental parameters to obtain a target environmental state that matches the current environmental conditions includes the following steps: Acquire climate data from outside the greenhouse, integrate and analyze the current environmental status data with the external climate data, and assess the degree of impact of the external environment on the internal environment of the greenhouse. Based on the degree of impact, the initial target environmental parameters are modified to determine the adjustment direction and magnitude of each environmental factor; The initial target environmental parameters are adjusted according to the adjustment direction and adjustment range to obtain the adjusted target environmental parameters; The adjusted target environmental parameters are subjected to reasonable constraints to obtain a target environmental state that matches the current environmental conditions.

[0010] As a preferred embodiment of the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models described in this invention, the step of obtaining the deviation results of each environmental factor includes the following steps: Based on the current environmental status data and the target environmental status, extract the corresponding environmental factor parameters; Match each environmental factor to establish the correspondence between the current environmental parameters and the target environmental parameters.

[0011] For each environmental factor, the difference between the current environmental parameter and the target environmental parameter is calculated to obtain the deviation value of each environmental factor; Based on the deviation values ​​of each environmental factor, the direction and magnitude of the deviation are determined, and the environmental factor deviation results are obtained.

[0012] The bias results of all environmental factors are integrated to generate a bias data set.

[0013] As a preferred embodiment of the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models described in this invention, the step of performing multi-objective optimization based on deviation results, combined with crop growth requirements, energy consumption, and resource utilization to generate a corresponding environmental control decision-making scheme includes the following steps: From the deviation data set, the deviation direction and magnitude of each environmental factor are extracted as input parameters for regulation; Based on the growth requirements corresponding to the current growth stage of the crop, establish the target regulation priority of each environmental factor and determine the control weight of different environmental factors.

[0014] Based on the control of input parameters, a multi-objective optimization function is constructed with the optimization objectives of minimizing environmental deviation, minimizing energy consumption, and maximizing resource utilization.

[0015] Based on a multi-objective optimization function and combined with the operating characteristics of greenhouse environmental control equipment, constraints are established. Under constraints, the multi-objective optimization function is solved to obtain the optimal parameter combination that satisfies the multi-objective constraints; Based on the optimal combination of control parameters, a corresponding environmental control decision scheme is generated.

[0016] As a preferred embodiment of the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models described in this invention, the step of controlling the environmental regulation equipment in the solar greenhouse according to the environmental regulation decision scheme, and collecting and saving the regulation feedback data to the database, includes the following steps: Based on the environmental control decision-making plan, corresponding equipment control instructions are generated and sent to each environmental control device in the solar greenhouse.

[0017] The environmental control equipment is controlled according to control commands to perform corresponding adjustment operations to regulate the environment inside the greenhouse; During the environmental regulation process, real-time data on changes in various environmental factors within the greenhouse are collected to obtain regulation feedback data. The adjustment feedback data is structured and uploaded to the database for storage.

[0018] Secondly, this invention provides a multi-environmental factor optimization decision system for solar greenhouses based on a crop growth model, comprising: The data acquisition and processing module collects multi-source environmental data from the greenhouse and performs preprocessing and anomaly removal to obtain current environmental status data. The growth stage identification module determines the current growth stage of the crop based on crop information data and a preset growth model, and outputs the corresponding initial target environmental parameters. The target environment generation module dynamically adjusts the initial target environment parameters based on the current environmental status and external climate conditions. The deviation analysis module compares the current environmental state with the target environmental state item by item to obtain the deviation results of each environmental factor. The optimization decision-making module performs multi-objective optimization based on deviation results, combined with growth requirements, energy consumption, and resource utilization, to generate environmental regulation decision-making schemes. The control execution feedback module controls the environmental control equipment to perform adjustments according to the environmental control decision plan, and collects and stores the feedback data.

[0019] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models as described in the first aspect of the present invention.

[0020] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models as described in the first aspect of the present invention.

[0021] The beneficial effects of this invention are as follows: By determining the growth stage and initial target environmental parameters through a crop growth model, the physiological needs of crops at different growth stages are integrated into the environmental regulation process. This transforms environmental regulation from a purely parameter-driven approach to a dynamic control method oriented towards crop needs, enabling better matching of crop growth patterns and improving the targeting and adaptability of environmental regulation. Furthermore, by performing multi-objective optimization based on deviation results and in conjunction with growth needs, energy consumption, and resource utilization, collaborative decision-making and comprehensive balancing of multiple environmental factors are achieved. This ensures that environmental regulation not only focuses on correcting environmental deviations but also considers operating costs and resource utilization efficiency, improving the rationality and feasibility of the regulation strategy. Finally, by combining multi-source data acquisition, dynamic target adjustment, and a regulation feedback mechanism, a complete closed-loop control process is formed, enhancing the responsiveness and continuous optimization capabilities to environmental changes. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models.

[0024] Figure 2 This is a schematic diagram of a multi-environmental factor optimization decision system for a solar greenhouse based on a crop growth model.

[0025] Figure 3 Flowchart for adjusting target environment parameters.

[0026] Figure 4 To obtain a flowchart of the environmental regulation decision-making scheme. Detailed Implementation

[0027] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0028] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0029] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0030] Reference Figures 1-4 This is one embodiment of the present invention, which provides a multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models, including the following steps: Collect multi-source data from the solar greenhouse and perform preprocessing and anomaly handling to obtain current environmental status data.

[0031] Specifically, environmental and crop data of the solar greenhouse are collected and preprocessed to obtain multi-source data of the solar greenhouse; The validity of multi-source data from solar greenhouses is tested and anomalies are handled to obtain current environmental status data.

[0032] By collecting and preprocessing environmental and crop data from solar greenhouses, unified integration and standardized processing of multi-source heterogeneous data were achieved, making data from different sources comparable and usable. Through the steps of validity detection and anomaly handling of multi-source data, abnormal data was identified and removed, avoiding interference from invalid or erroneous data in environmental status judgment and improving the accuracy and stability of environmental status data.

[0033] Acquire crop information data and determine the current growth stage of the crop and the corresponding initial target environmental parameters based on a preset crop growth model.

[0034] Specifically, crop information data is extracted from multi-source data of solar greenhouses, and a crop growth model is constructed based on the crop growth cycle pattern and the environmental demand relationship corresponding to each crop growth stage. Based on the crop type and growth time, and combined with the crop growth model, the current growth stage of the crop and the corresponding initial target environmental parameters are obtained.

[0035] By extracting crop information data from multi-source data in solar greenhouses and constructing crop growth models, the crop growth process is transformed from experience-based judgment to model-driven analysis, enhancing the theoretical support for regulation and decision-making. By combining crop type and growth time to determine the current growth stage and obtain corresponding initial target environmental parameters, the environmental regulation target is dynamically matched with the crop growth process, making the environmental settings more aligned with the actual needs of the plants and improving the pertinence and rationality of environmental regulation.

[0036] Based on current environmental data and combined with external climate conditions, the target environmental parameters are dynamically adjusted to obtain a target environmental state that matches the current environmental conditions.

[0037] Specifically, acquire climate data from outside the greenhouse, integrate and analyze the current environmental status data with the external climate data, and assess the degree of impact of the external environment on the internal environment of the greenhouse. Based on the degree of impact, the initial target environmental parameters are modified to determine the adjustment direction and magnitude of each environmental factor; The initial target environmental parameters are adjusted according to the adjustment direction and adjustment range to obtain the adjusted target environmental parameters; The adjusted target environmental parameters are subjected to reasonable constraints to obtain a target environmental state that matches the current environmental conditions.

[0038] By acquiring external climate data of the solar greenhouse and integrating it with current environmental status data, a comprehensive assessment of external environmental influencing factors was achieved. This transformed greenhouse environmental control from closed-loop management to collaborative internal and external perception, enhancing the completeness of environmental perception. Based on the degree of impact, initial target environmental parameters were corrected, and the adjustment direction and magnitude were determined, enabling dynamic adaptation of target parameters. This allows environmental control to be optimized in response to changing external trends. Furthermore, by constraining the adjusted target environmental parameters, the control results were standardized and limited, preventing unreasonable adjustments from adversely affecting the system, ensuring the stability of greenhouse operation, and improving the overall safety and reliability of control.

[0039] The current environmental status data is compared with the target environmental status item by item to obtain the deviation results of each environmental factor.

[0040] Specifically, based on the current environmental status data and the target environmental status, the corresponding environmental factor parameters are extracted; Match each environmental factor to establish the correspondence between the current environmental parameters and the target environmental parameters.

[0041] For each environmental factor, the difference between the current environmental parameter and the target environmental parameter is calculated to obtain the deviation value of each environmental factor; Based on the deviation values ​​of each environmental factor, the direction and magnitude of the deviation are determined, and the environmental factor deviation results are obtained.

[0042] The bias results of all environmental factors are integrated to generate a bias data set.

[0043] By extracting environmental factor parameters based on current environmental state data and target environmental state and establishing corresponding relationships, the mapping relationship between various environmental factors is clarified, enabling one-to-one matching and unified analysis of different environmental parameters, thus improving the consistency of data analysis. By calculating the difference between each environmental factor and determining the direction and magnitude of the deviation, the degree of deviation of the environmental state is quantitatively characterized, transforming environmental changes from fuzzy judgments to quantifiable analysis, thereby enhancing the clarity and operability of the basis for regulation.

[0044] Based on the deviation results, multi-objective optimization is carried out by combining crop growth requirements, energy consumption and resource utilization rate to generate corresponding environmental regulation decision schemes.

[0045] Specifically, the direction and magnitude of deviations of each environmental factor are extracted from the deviation data set and used as input parameters for regulation. Based on the growth requirements corresponding to the current growth stage of the crop, establish the target regulation priority of each environmental factor and determine the control weight of different environmental factors.

[0046] Based on the deviation values ​​of each environmental factor, and combined with the regulatory weights of each environmental factor, a weighted average is applied to construct an environmental deviation function. The operating status and intensity of each environmental control device during the control process are statistically analyzed, and an energy consumption function is established based on the energy consumption characteristics of different devices. The usage of various production factors during greenhouse regulation is analyzed, and a resource utilization rate function is constructed by combining crop absorption and utilization efficiency. Based on the controlled input parameters, a multi-objective optimization function is constructed with the optimization objectives of minimizing environmental deviation, minimizing energy consumption, and maximizing resource utilization. The formula is as follows: ; in, To synthesize multi-objective optimization functions, For environmental deviation function, Let the energy consumption function be... Let the resource utilization rate function be... , , These are the weighting coefficients for the environmental deviation function, energy consumption function, and resource utilization function, respectively.

[0047] Based on a multi-objective optimization function and combined with the operating characteristics of greenhouse environmental control equipment, constraints are established. Under constraints, the multi-objective optimization function is solved to obtain the optimal parameter combination that satisfies the multi-objective constraints; Based on the optimal combination of control parameters, a corresponding environmental control decision scheme is generated.

[0048] By prioritizing environmental factors and determining control weights based on the current growth stage of the crop, differentiated treatment of different environmental factors during the regulation process was achieved. This enabled the regulation strategy to highlight key growth needs and enhance the pertinence and adaptability of environmental regulation. By constructing a multi-objective optimization function with environmental deviation, energy consumption, and resource utilization as objectives, and establishing constraints based on equipment operating characteristics for solution, a synergistic balance among multiple regulation objectives was achieved. This transformed the regulation strategy from a single-objective driven approach to a comprehensive optimization decision, improving the overall performance of the greenhouse. By generating an environmental regulation decision scheme based on the optimal parameter combination, the optimization results were effectively transformed into actual control, ensuring the executability of the regulation strategy.

[0049] The environmental control decision-making scheme is used to control the environmental control equipment in the solar greenhouse to carry out environmental regulation, and the regulation feedback data is collected and saved to the database.

[0050] Specifically, based on the environmental control decision-making plan, corresponding equipment control instructions are generated and sent to each environmental control device in the solar greenhouse.

[0051] The environmental control equipment is controlled according to control commands to perform corresponding adjustment operations to regulate the environment inside the greenhouse; During the environmental regulation process, real-time data on changes in various environmental factors within the greenhouse are collected to obtain regulation feedback data. The adjustment feedback data is structured and uploaded to the database for storage.

[0052] By generating equipment control commands based on environmental regulation decision-making schemes and sending them to various environmental regulation devices, the optimization decision results are effectively transformed into specific execution actions. In practical applications, this reduces human intervention and improves the efficiency of regulation execution. By controlling the equipment to perform regulation operations and collecting environmental factor change data in real time during the process, dynamic monitoring of the environmental regulation process is achieved, enhancing the responsiveness to environmental changes and improving the controllability of the regulation process.

[0053] This embodiment also provides a multi-environmental factor optimization decision system for solar greenhouses based on crop growth models, including: The data acquisition and processing module collects multi-source environmental data from the greenhouse and performs preprocessing and anomaly removal to obtain current environmental status data. The growth stage identification module determines the current growth stage of the crop based on crop information data and a preset growth model, and outputs the corresponding initial target environmental parameters. The target environment generation module dynamically adjusts the initial target environment parameters based on the current environmental status and external climate conditions. The deviation analysis module compares the current environmental state with the target environmental state item by item to obtain the deviation results of each environmental factor. The optimization decision-making module performs multi-objective optimization based on deviation results, combined with growth requirements, energy consumption, and resource utilization, to generate environmental regulation decision-making schemes. The control execution feedback module controls the environmental control equipment to perform adjustments according to the environmental control decision plan, and collects and stores the feedback data.

[0054] This embodiment also provides a computer device applicable to the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models as proposed in the above embodiment.

[0055] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0056] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models, as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0057] In summary, this invention, through the steps of determining growth stages and initial target environmental parameters based on crop growth models, integrates the physiological needs of different crop growth stages into the environmental regulation process. This transforms environmental regulation from a purely parameter-driven approach to a dynamic control method oriented towards crop needs, better matching crop growth patterns and enhancing the targeting and adaptability of environmental regulation. Furthermore, through the steps of multi-objective optimization based on deviation results and combined with growth needs, energy consumption, and resource utilization, it achieves collaborative decision-making and comprehensive balancing of multiple environmental factors. This ensures that environmental regulation not only focuses on correcting environmental deviations but also considers operating costs and resource utilization efficiency, improving the rationality and feasibility of the regulation strategy. Finally, by combining multi-source data acquisition, dynamic target adjustment, and a regulation feedback mechanism, a complete closed-loop control process is formed, enhancing the responsiveness and continuous optimization capabilities to environmental changes.

[0058] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models, characterized in that: include, Collect multi-source data from the solar greenhouse and perform preprocessing and anomaly handling to obtain current environmental status data; Acquire crop information data and determine the current growth stage of the crop and the corresponding initial target environmental parameters based on the preset crop growth model; Based on the current environmental status data and combined with external climate conditions, the target environmental parameters are dynamically adjusted to obtain a target environmental status that matches the current environmental conditions. The current environmental status data is compared with the target environmental status item by item to obtain the deviation results of each environmental factor; Based on the deviation results, multi-objective optimization is carried out by combining crop growth requirements, energy consumption and resource utilization to generate corresponding environmental regulation decision schemes. The environmental control decision-making scheme is used to control the environmental control equipment in the solar greenhouse to carry out environmental regulation, and the regulation feedback data is collected and saved to the database.

2. The multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models as described in claim 1, characterized in that: Obtaining the current environmental status data includes the following steps: Collect environmental and crop data of the solar greenhouse and preprocess them to obtain multi-source data of the solar greenhouse; The validity of multi-source data from solar greenhouses is tested and anomalies are handled to obtain current environmental status data.

3. The multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models as described in claim 2, characterized in that: The process of determining the current growth stage of the crop and the corresponding initial target environmental parameters based on a preset crop growth model includes the following steps: Crop information data is extracted from multi-source data of solar greenhouses, and a crop growth model is constructed based on the crop growth cycle pattern and the environmental demand relationship corresponding to each crop growth stage. Based on the crop type and growth time, and combined with the crop growth model, the current growth stage of the crop and the corresponding initial target environmental parameters are obtained.

4. The multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models as described in claim 3, characterized in that: The process of dynamically adjusting the target environmental parameters to obtain a target environmental state that matches the current environmental conditions includes the following steps: Acquire climate data from outside the greenhouse, integrate and analyze the current environmental status data with the external climate data, and assess the degree of impact of the external environment on the internal environment of the greenhouse. Based on the degree of impact, the initial target environmental parameters are modified to determine the adjustment direction and magnitude of each environmental factor; The initial target environmental parameters are adjusted according to the adjustment direction and adjustment range to obtain the adjusted target environmental parameters; The adjusted target environmental parameters are subjected to reasonable constraints to obtain a target environmental state that matches the current environmental conditions.

5. The multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models as described in claim 4, characterized in that: The process of obtaining the deviation results for each environmental factor includes the following steps: Based on the current environmental status data and the target environmental status, extract the corresponding environmental factor parameters; Match each environmental factor to establish the correspondence between the current environmental parameters and the target environmental parameters; For each environmental factor, the difference between the current environmental parameter and the target environmental parameter is calculated to obtain the deviation value of each environmental factor; Based on the deviation values ​​of each environmental factor, the direction and magnitude of the deviation are determined, and the environmental factor deviation results are obtained. The bias results of all environmental factors are integrated to generate a bias data set.

6. The multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models as described in claim 5, characterized in that: The process of generating corresponding environmental regulation decision-making schemes based on deviation results, combined with crop growth requirements, energy consumption, and resource utilization, includes the following steps: From the deviation data set, the deviation direction and magnitude of each environmental factor are extracted as input parameters for regulation; Based on the growth requirements corresponding to the current growth stage of the crop, establish the target regulation priority of each environmental factor and determine the control weight of different environmental factors. Based on the control input parameters, a multi-objective optimization function is constructed with the optimization objectives of minimizing environmental deviation, minimizing energy consumption, and maximizing resource utilization. Based on a multi-objective optimization function and combined with the operating characteristics of greenhouse environmental control equipment, constraints are established. Under constraints, the multi-objective optimization function is solved to obtain the optimal parameter combination that satisfies the multi-objective constraints; Based on the optimal combination of control parameters, a corresponding environmental control decision scheme is generated.

7. The multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models as described in claim 6, characterized in that: The process of controlling the environmental regulation equipment in the solar greenhouse according to the environmental regulation decision-making scheme, and collecting and saving the regulation feedback data to the database, includes the following steps: Based on the environmental control decision-making plan, corresponding equipment control instructions are generated and sent to each environmental control device in the solar greenhouse; The environmental control equipment is controlled according to control commands to perform corresponding adjustment operations to regulate the environment inside the greenhouse; During the environmental regulation process, real-time data on changes in various environmental factors within the greenhouse are collected to obtain regulation feedback data. The adjustment feedback data is structured and uploaded to the database for storage.

8. A multi-environmental factor optimization decision system for a solar greenhouse based on a crop growth model, based on the multi-environmental factor optimization decision method for a solar greenhouse based on a crop growth model as described in any one of claims 1 to 7, characterized in that: include, The data acquisition and processing module collects multi-source environmental data from the greenhouse and performs preprocessing and anomaly removal to obtain current environmental status data. The growth stage identification module determines the current growth stage of the crop based on crop information data and a preset growth model, and outputs the corresponding initial target environmental parameters. The target environment generation module dynamically adjusts the initial target environment parameters based on the current environmental status and external climate conditions. The deviation analysis module compares the current environmental state with the target environmental state item by item to obtain the deviation results of each environmental factor. The optimization decision-making module performs multi-objective optimization based on deviation results, combined with growth requirements, energy consumption, and resource utilization, to generate environmental regulation decision-making schemes. The control execution feedback module controls the environmental control equipment to perform adjustments according to the environmental control decision plan, and collects and stores the feedback data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the multi-environmental factor optimization decision-making method for solar greenhouses based on crop growth models as described in any one of claims 1 to 7.

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