Vegetable greenhouse environment management method and system, storage medium and program product

Abstract

The invention discloses a vegetable greenhouse environment management method and system, a storage medium and a program product, and the method comprises the steps: obtaining historical environment parameters of a vegetable greenhouse, vegetable growth data and the growth state of vegetables in the vegetable greenhouse at the current moment; establishing a multistable growth model according to the historical environmental parameters and the vegetable growth data, wherein the multistable growth model comprises a plurality of stable growth points; positioning a growth state in the multistable growth model, and determining a dominant stable growth point closest to the growth state; establishing an attractor potential field, setting an advantage stable growth point as an attractor center, and calculating a potential energy gradient from a growth state to the attractor center; based on the potential energy gradient, a progressive regulation and control path of vegetable greenhouse environment parameters is constructed, and a staged environment regulation and control instruction is generated; and the staged environment regulation and control instruction is sent to the environment control equipment, so that the environment control equipment carries out environment regulation according to the staged environment regulation and control instruction and the progressive regulation and control path, and the timeliness and the accuracy of farming operation and environment regulation are improved.

Description

Technical Field

[0001] The present application relates to the field of agricultural Internet services, and in particular to a vegetable greenhouse environment management method, system, storage medium and program product. Background Art

[0002] With the development of modern agricultural technology, vegetable greenhouse production has become an important way to ensure vegetable supply and improve vegetable quality. However, traditional vegetable greenhouse management mainly relies on manual experience and lacks real-time monitoring and scientific regulation of vegetable growth status and greenhouse environment, making it difficult to further improve vegetable yield and quality.

[0003] In order to solve the above problems, relevant technologies can obtain vegetable images and environmental information of facility greenhouses through inspection robots, and then obtain the growth physiological information of vegetables based on the vegetable images, input the growth physiological information and environmental information into the health scoring model, and score the current growth status of the vegetables and the facility greenhouse environment. Based on the scoring results, agricultural operations are performed on the vegetables and the facility greenhouse environment is adjusted.

[0004] However, this health scoring model only considers the static characteristics of vegetable growth and the instantaneous state of the greenhouse environment, ignoring the dynamic change trends during vegetable growth and the cumulative effects of environmental factors. This may lead to deviations between the scoring results and the actual growth status of the vegetables and long-term environmental impacts, thereby reducing the timeliness and accuracy of agricultural operations and environmental adjustments. Summary of the invention

[0005] The present application provides a vegetable greenhouse environment management method, system, storage medium and program product for improving the timeliness and accuracy of agricultural operations and environmental adjustments.

[0006] In the first aspect, the present application provides a vegetable greenhouse environment management method, which obtains historical environmental parameters of the vegetable greenhouse, vegetable growth data and the growth status of vegetables in the vegetable greenhouse at the current moment, the historical environmental parameters include temperature, humidity, light intensity and carbon dioxide concentration, and the vegetable growth data includes plant height, leaf area, stem thickness and yield; A multi-stable growth model is established based on historical environmental parameters and vegetable growth data. The multi-stable growth model includes several stable growth points, each of which corresponds to a set of environmental parameter combinations. Locate the growth state in a multi-stable growth model and determine the dominant stable growth point closest to the growth state; Establish an attractor potential field, set the dominant stable growth point as the attractor center, and calculate the potential energy gradient from the growth state to the attractor center; Based on the potential energy gradient, a gradual control path of the environmental parameters of the vegetable greenhouse is constructed, and phased environmental control instructions are generated; The phased environmental control instructions are sent to the environmental control device, so that the environmental control device adjusts the environment according to the phased environmental control instructions along a progressive control path.

[0007] By adopting the above technical solution, a growth model that can reflect the multi-stable characteristics of vegetable growth is established. The multi-stable growth model contains several stable growth points, each of which corresponds to a set of optimal environmental parameter combinations. By locating the current growth state in the multi-stable growth model, determining the dominant stable growth point closest to the current growth state, establishing an attractor potential field centered on the dominant stable growth point, and calculating the potential energy gradient required to reach the dominant stable growth point from the current growth state, a progressive control path from the current growth state to the dominant stable growth point can be obtained. Based on the progressive control path, a staged environmental control instruction is generated, and the instruction is sent to the environmental control device, so that the environmental control device can perform staged adjustments to the vegetable greenhouse environment according to the progressive control path, guide the vegetable growth state to move closer to the optimal environmental parameter combination corresponding to the dominant stable growth point, and improve the timeliness and accuracy of agricultural operations and environmental regulation.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, a multi-stable growth model is established according to historical environmental parameters and growth data, specifically including: The historical environmental parameters and vegetable growth data were segmented into time series to obtain the key stages of vegetable growth; Constructing a phase space, and generating environmental parameter trajectories and vegetable growth trajectories in the phase space that correspond one to one with historical environmental parameters and vegetable growth data; According to the environmental parameter trajectory and the vegetable growth trajectory, the stable growth point is determined. The stable growth point is the convergence point where the Lyapunov index is negative. The convergence point is the intersection of the environmental parameter trajectory and the vegetable growth trajectory. Determine the attraction domain range and attraction strength of each stable growth point; Calculate the comprehensive advantage coefficient of each stable growth point; The stable growth point, attraction domain range, attraction intensity and comprehensive advantage coefficient are integrated to obtain a multi-stable growth model.

[0009] By adopting the above technical solution, the historical environmental parameters and vegetable growth data are segmented in time series, and the key stages of vegetable growth can be obtained. The environmental parameter trajectory and vegetable growth trajectory are generated in the phase space, and the intersection of the two trajectories is used as the stable growth point. The determination of the stable growth point is based on the Lyapunov index. Only the point near which the trajectory shows a convergence trend is a stable growth point, which improves the scientificity and reliability of the stable growth point. After determining the stable growth point, the attraction domain range, attraction intensity and comprehensive advantage coefficient of each stable growth point are further calculated, and they are integrated into the multi-stable growth model, so that the model can more comprehensively and accurately reflect the multi-stable characteristics and dynamic change laws in the vegetable growth process.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, establishing an attractor potential field specifically includes: A local potential field function is constructed with each stable growth point as the potential field center. The potential field intensity in the local potential field function decays exponentially with the distance from the potential field center, and the decay coefficient is determined by the stability of the stable growth point. Set the weight coefficient according to the advantage coefficient of each stable growth point; All local potential fields are weighted superimposed according to the weight coefficient to obtain the attractor potential field.

[0011] By adopting the above technical solution, a local potential field function is constructed with each stable growth point as the potential field center, and the potential field strength in the local potential field function is exponentially decayed with the distance from the potential field center, and the decay coefficient is determined by the stability of the stable growth point. The potential field strength decays exponentially with distance, reducing the impact on points far from the stable point, making environmental control more accurate in the local space. The stability and continuity of environmental control are achieved, reducing the impact on vegetable growth caused by sudden changes in environmental parameters.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, after establishing the multi-stable growth model according to historical environmental parameters and vegetable growth data, the method further includes: According to historical environmental parameters and vegetable growth data, the entropy value of the environmental parameter set at each growth stage and the entropy value of the vegetable growth state set are calculated; Calculate the mutual information between the entropy value of the environmental parameter set and the entropy value of the vegetable growth state set to obtain the correlation strength between the environmental parameters and the vegetable growth state within the growth stage; The weight coefficients of environmental parameter regulation in each growth stage are determined based on the correlation strength, and the weight coefficients are introduced into the multi-stable growth model.

[0013] By adopting the above technical solution, mutual information is an indicator that can measure the correlation between two random variables. It reduces the uncertainty of the random variables themselves and more accurately reflects the degree of correlation between them. Determining the weight coefficients of environmental parameter regulation at each growth stage based on the strength of correlation, and introducing the weight coefficients into the multi-stable growth model, the model can adapt to the dynamic changes in the importance of environmental parameters in different growth stages. When regulating environmental parameters, higher weights are given to key parameters to highlight their influence, while non-key parameters are correspondingly reduced to avoid over-regulation, thereby achieving stage-by-stage differentiation of environmental regulation and further improving the accuracy and effectiveness of environmental regulation.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the entropy value of the environmental parameter set and the entropy value of the vegetable growth state set at each growth stage specifically includes: Calculate the probability distribution function of the environmental parameter set and the vegetable growth state set; Substitute the probability distribution function into the preset entropy formula to calculate the entropy values ​​of the environmental parameter set and the vegetable growth state set in each growth stage.

[0015] By adopting the above technical solution, the probability distribution function of the environmental parameter set and the vegetable growth state set is calculated, and the probability distribution function is substituted into the preset entropy formula, the uncertainty of the environmental parameters and vegetable growth state in each growth stage can be quantitatively evaluated. The larger the entropy value, the higher the degree of disorder inside the set, the more dispersed the element distribution, and the stronger the uncertainty; conversely, the smaller the entropy value, the more ordered the set, the more concentrated the element distribution, and the weaker the uncertainty. The introduction of entropy value analysis can reveal the changing characteristics of environmental parameters and vegetable growth states in different growth stages, and find out the stage with the highest uncertainty, which has important guiding significance for targeted environmental parameter regulation. By comparing the entropy value of the environmental parameter set with the entropy value of the vegetable growth state set, the uncertainty difference between the two can be judged, and then the impact of environmental parameter changes on the stability of vegetable growth states can be inferred.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after determining the weight coefficients of environmental parameter regulation at each growth stage based on the association strength, the method further includes: Taking environmental factors as control variables and vegetable growth state parameters as state variables, a state feedback equation for environmental parameter regulation is established; The optimal controller is designed in combination with the weight coefficient to minimize the deviation of vegetable growth status under the condition of satisfying the environmental parameter constraints in each growth stage; Solve the state feedback equation to obtain the optimal control value of each environmental parameter; Generate a multi-stage environmental control strategy based on the optimal control quantity.

[0017] By adopting the above technical solution and introducing state feedback, the change information of vegetable growth state parameters can be obtained in real time, and a direct mathematical relationship can be established between it and the control amount of environmental parameters, so that environmental control can be dynamically adjusted according to the immediate feedback of vegetable growth state, thereby improving the adaptability and effectiveness of environmental control. On the basis of the state feedback equation, the optimal controller is designed in combination with the weight coefficients of environmental parameter control in each growth stage. By solving the state feedback equation, the optimal control amount of each environmental parameter can be obtained, and then a multi-stage environmental control strategy can be generated. By constructing a closed-loop optimization of environmental parameters and vegetable growth state through state feedback, the relationship between the two is dynamically balanced, and the best effect of environmental control is sought, which is more in line with the objective law of vegetable growth and has a positive effect on improving vegetable yield and quality.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, solving the state feedback equation to obtain the optimal control amount of each environmental parameter specifically includes: Transform the state feedback equation into the objective function of a convex optimization problem; According to the variation range of environmental parameters in each growth stage, the boundary constraints of decision variables are set; Introducing boundary constraints into the objective function, we obtain the Lagrangian function; Derivative the Lagrangian function and transform it into an unconstrained optimization problem; The gradient descent algorithm is used to search for the optimal solution of the unconstrained optimization problem and obtain the optimal control value of each environmental parameter.

[0019] By adopting the above technical solution, the state feedback equation is transformed into the objective function of the convex optimization problem, and the boundary constraints of the decision variables are set according to the range of changes of the environmental parameters in each growth stage, and then the boundary constraints are introduced into the objective function to obtain the Lagrangian function. By deriving the Lagrangian function, the original constrained optimization problem can be transformed into an equivalent unconstrained optimization problem. On the basis of the unconstrained optimization problem, the gradient descent algorithm is used for iterative search to find the optimal solution of the objective function, that is, the optimal control amount of the environmental parameters. By transforming the complex state feedback equation solution problem into a convex optimization and unconstrained optimization problem, existing mature algorithms such as the Lagrange multiplier method and the gradient descent method can be used to improve the solution efficiency and accuracy and reduce the computational complexity. The introduction of boundary constraints can ensure that the optimal control amount of the environmental parameters is always within the reasonable range of changes in each growth stage, avoiding excessive or insufficient control of the environmental parameters, and improving the feasibility and safety of the environmental control scheme.

[0020] In the second aspect, an embodiment of the present application provides a vegetable greenhouse environmental management system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call computer instructions to enable the system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a method for managing the environment of a vegetable greenhouse, and establishes a growth model that can reflect the multi-stable characteristics of the vegetable growth process. The multi-stable growth model includes several stable growth points, and each stable growth point corresponds to a set of optimal environmental parameter combinations. By locating the current growth state in the multi-stable growth model, determining the dominant stable growth point closest to the current growth state, establishing an attractor potential field centered on the dominant stable growth point, and calculating the potential energy gradient required to reach the dominant stable growth point from the current growth state, a progressive control path from the current growth state to the dominant stable growth point can be obtained. Based on the progressive control path, a phased environmental control instruction is generated, and the instruction is sent to the environmental control device, so that the environmental control device can perform phased adjustments to the vegetable greenhouse environment according to the progressive control path, guiding the vegetable growth state to move closer to the optimal environmental parameter combination corresponding to the dominant stable growth point, and improving the timeliness and accuracy of agricultural operations and environmental regulation.

[0024] 2. The present application provides a method for managing the environment of a vegetable greenhouse. Mutual information is an indicator that can measure the correlation between two random variables. It reduces the uncertainty of the random variables themselves and more accurately reflects the degree of correlation between them. The weight coefficients for regulating environmental parameters at each growth stage are determined based on the strength of association, and the weight coefficients are introduced into the multi-stable growth model. This allows the model to adapt to the dynamic changes in the importance of environmental parameters at different growth stages. When regulating environmental parameters, higher weights are given to key parameters to highlight their influence, while non-key parameters are weighted accordingly to avoid over-regulation, thereby achieving phased differentiation of environmental regulation and further improving the accuracy and effectiveness of environmental regulation.

[0025] 3. The present application provides a method for managing the environment of a vegetable greenhouse, which converts the state feedback equation into the objective function of a convex optimization problem, sets the boundary constraints of the decision variables according to the range of variation of the environmental parameters in each growth stage, and then introduces the boundary constraints into the objective function to obtain the Lagrangian function. By deriving the Lagrangian function, the original constrained optimization problem can be converted into an equivalent unconstrained optimization problem. On the basis of the unconstrained optimization problem, the gradient descent algorithm is used for iterative search to find the optimal solution of the objective function, that is, the optimal control amount of the environmental parameters. The complex state feedback equation solution problem is converted into a convex optimization and unconstrained optimization problem, and existing mature algorithms such as the Lagrangian multiplier method, the gradient descent method, etc. can be used to improve the solution efficiency and accuracy and reduce the computational complexity. The introduction of boundary constraints can ensure that the optimal control amount of the environmental parameters is always within the reasonable range of variation of each growth stage, avoid excessive or insufficient control of the environmental parameters, and improve the feasibility and safety of the environmental control scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a vegetable greenhouse environment management method in an embodiment of the present application.

[0027] Figure 2 It is another flow chart of a vegetable greenhouse environment management method in an embodiment of the present application.

[0028] Figure 3 It is a schematic diagram of the physical device structure of a vegetable greenhouse environmental management system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0030] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0031] The following uses an embodiment and combines Figure 1 , a vegetable greenhouse environment management method in an embodiment of the present application is described: See also Figure 1 , which is a flow chart of a vegetable greenhouse environment management method in an embodiment of the present application.

[0032] S101, obtaining historical environmental parameters of the vegetable greenhouse, vegetable growth data, and the growth status of vegetables in the vegetable greenhouse at the current moment; The system obtains the historical environmental parameters of the vegetable greenhouse, vegetable growth data and the growth status of vegetables in the vegetable greenhouse at the current moment. The historical environmental parameters include temperature, humidity, light intensity and carbon dioxide concentration. The vegetable growth data includes plant height, leaf area, stem diameter and yield.

[0033] In this step, the system first needs to obtain various historical data of the vegetable greenhouse and the current vegetable growth status data. Among them, historical environmental parameters include key environmental factors that can affect vegetable growth, such as temperature, humidity, light intensity and carbon dioxide concentration. These parameters can be obtained by deploying various sensors in the greenhouse and continuously collecting them to form a complete historical environmental parameter data set. Vegetable growth data includes various indicators related to vegetable growth and development, such as plant height, leaf area, stem thickness and yield. These data can be obtained through manual measurement or image recognition and other technical means. In addition, the system also needs to obtain the real-time growth status of vegetables in the vegetable greenhouse at the current moment as an important basis for subsequent environmental regulation.

[0034] During the acquisition process, the system can perform necessary preprocessing on the original data, such as denoising, smoothing, interpolation, etc., to improve data quality. At the same time, the system can also summarize and count the data at certain time intervals to generate data indicators of different time scales such as daily averages, weekly averages, and monthly averages for subsequent analysis. In addition, considering that there may be a certain time lag effect between environmental parameters and vegetable growth status, the system can obtain environmental parameter data in the recent period while obtaining the current vegetable growth status, which is used to comprehensively evaluate the impact of environmental conditions on vegetable growth.

[0035] S102, establishing a multi-stable growth model based on historical environmental parameters and vegetable growth data; A multi-stable growth model is established based on historical environmental parameters and vegetable growth data. The multi-stable growth model includes several stable growth points, each of which corresponds to a set of environmental parameter combinations. Specifically: A multi-stable growth model is established based on historical environmental parameters and growth data, including: The historical environmental parameters and vegetable growth data were segmented into time series to obtain the key stages of vegetable growth; Constructing a phase space, and generating environmental parameter trajectories and vegetable growth trajectories in the phase space that correspond one to one with historical environmental parameters and vegetable growth data; According to the environmental parameter trajectory and the vegetable growth trajectory, the stable growth point is determined. The stable growth point is the convergence point where the Lyapunov index is negative. The convergence point is the intersection of the environmental parameter trajectory and the vegetable growth trajectory. Determine the attraction domain range and attraction strength of each stable growth point; Calculate the comprehensive advantage coefficient of each stable growth point; The stable growth point, attraction domain range, attraction intensity and comprehensive advantage coefficient are integrated to obtain a multi-stable growth model.

[0036] The core of this step is to use the acquired historical data to construct a multi-stable growth model that can reflect the growth law of vegetables. The so-called multi-stable state means that under different combinations of environmental parameters, vegetable growth may present several relatively stable states, and each stable state corresponds to a set of specific environmental conditions and growth indicators. These stable states are called stable growth points and represent the ideal equilibrium state in the vegetable growth process. The establishment of a multi-stable growth model can help the system fully understand the dynamic characteristics of the vegetable growth process and grasp the correspondence between environmental conditions and growth states during the growth process.

[0037] In the modeling process, the system first needs to segment the historical environmental parameters and vegetable growth data in time series to identify the key stages of vegetable growth. Then, the system can generate environmental parameter trajectories and vegetable growth trajectories that correspond to the historical data in a high-dimensional phase space to describe the evolution of environmental conditions and growth status over time. By analyzing the environmental parameter trajectories and vegetable growth trajectories, the system can find and extract stable growth points. The Lyapunov exponents corresponding to these stable points are negative, indicating that under the corresponding environmental conditions, the vegetable growth state can remain stable for a long time. On the basis of identifying stable growth points, the system also needs to further determine the attraction domain range and attraction intensity of each stable point, and evaluate the difficulty of maintaining the stability of the vegetable growth state when the environmental conditions near the stable point are disturbed. In addition, the system can also calculate the comprehensive advantage coefficient of each stable growth point to measure the advantages and disadvantages between different stable points.

[0038] Finally, the system integrates factors such as stable growth points, attraction domain range, attraction intensity and comprehensive advantage coefficient to form a complete multi-stable growth model. This model can overall describe the multi-stable characteristics of the vegetable growth process, reveal the inherent laws of vegetable growth under different environmental conditions, and provide a theoretical basis and decision-making basis for intelligent environmental regulation.

[0039] S103, locating the growth state in the multi-stable growth model, and determining the dominant stable growth point closest to the growth state; After establishing the multi-stable growth model, this step needs to locate the current vegetable growth state in the model and find the stable growth point closest to the current state. Since the multi-stable growth model describes the topological structure of the state space during vegetable growth and contains several scattered stable growth points, the system can map the current vegetable growth state to a certain position in the state space. Then, the system needs to evaluate the closeness between the current growth state and each stable point, and find one or several stable points closest to the current state by calculating the distance or similarity measure between two points in the state space.

[0040] After determining the nearest stable point, the system needs to further determine whether the stable point is an advantageous stable point. The so-called advantageous stable point refers to the stable point with the highest comprehensive advantage coefficient among all stable points, which represents the ideal target state for vegetable growth under the current environmental conditions. By comparing the comprehensive advantage coefficients of different stable points, the system can select advantageous stable points from the nearest stable points as reference targets for subsequent environmental regulation.

[0041] It should be noted that since the growth state of vegetables changes dynamically, the system needs to continuously track and update the position of the current state in the multi-stable growth model based on the real-time vegetable growth state data, and adjust the selection of the dominant stable growth point accordingly. At the same time, considering the continuity of the vegetable growth process, the transition between the current state and the dominant stable point usually requires a gradual process. Therefore, after determining the dominant stable point, the system also needs to plan a gradual trajectory from the current state to the dominant stable point, and formulate a phased environmental control strategy based on this.

[0042] S104, establishing an attractor potential field, setting the dominant stable growth point as the attractor center, and calculating the potential energy gradient from the growth state to the attractor center; The system establishes an attractor potential field, sets the dominant stable growth point as the attractor center, and calculates the potential energy gradient from the growth state to the attractor center. The specific steps of establishing the attractor potential field are: constructing a local potential field function with each stable growth point as the potential field center, in which the potential field strength and the distance from the potential field center are exponentially decayed, and the decay coefficient is determined by the stability of the stable growth point; Set the weight coefficient according to the advantage coefficient of each stable growth point; All local potential fields are weighted superimposed according to the weight coefficient to obtain the attractor potential field.

[0043] After determining the dominant stable growth point, this step requires constructing an attractor potential field in the state space, with the dominant stable point as the attractor center. The so-called attractor potential field is a dynamic model that describes the evolution trend of the system state. It defines the potential energy level of each point in the space and gives the movement direction and speed of the state point under the action of the potential energy gradient. By introducing the attractor potential field, the system can quantitatively describe the interaction and evolution relationship between the current vegetable growth state and the dominant stable point, and accordingly plan the specific path of environmental regulation.

[0044] When constructing an attractor potential field, the system first needs to construct a local potential field function with each stable growth point as the center of the potential field. In the local potential field, the potential field strength is exponentially decayed with the distance from the center of the potential field, and the decay coefficient is determined by the stability of the stable growth point. The more stable the point, the smaller its decay coefficient, which means that its attraction range is wider and its influence on the surrounding state points is greater. Then, the system needs to set different weight coefficients according to the advantage coefficient of each stable growth point. The higher the advantage coefficient of the stable point, the larger its weight coefficient is, and it occupies a dominant position in the attractor potential field. Finally, the system weights and superimposes all local potential fields according to the weight coefficient to form a complete attractor potential field.

[0045] After the attractor potential field is established, the system needs to calculate the potential energy gradient from the current vegetable growth state to the center of the attractor (i.e., the dominant stable point). The potential energy gradient characterizes the direction and magnitude of the force on the state point in the potential field, which determines the trend and rate of evolution of the state point. By solving the potential energy gradient, the system can obtain the motion equation of the current growth state in the attractor potential field and predict its future evolution trajectory. At the same time, the potential energy gradient also provides important guidance information for environmental regulation. The system can guide the vegetable growth state to evolve to the ideal area where the dominant stable point is located by applying an environmental regulation force in the same direction as the potential energy gradient, thereby achieving the purpose of intelligent environmental regulation.

[0046] Since the attractor potential field is defined in the state space, the potential energy gradient is usually a high-dimensional vector. In order to facilitate its use in actual environmental control, the system can reduce the dimensionality of the potential energy gradient, extract the components corresponding to each environmental parameter, and form a set of multi-parameter collaborative control strategies. In addition, the system can also dynamically adjust the intensity of environmental regulation according to the size of the potential energy gradient, reduce the intensity of regulation when the state point is close to the center of the attractor, and appropriately increase the intensity of regulation when the state point deviates from the center of the attractor, thereby achieving more flexible and adaptive environmental regulation.

[0047] S105, constructing a progressive control path of the environmental parameters of the vegetable greenhouse based on the potential energy gradient, and generating phased environmental control instructions; After calculating the potential energy gradient, this step needs to construct a progressive control path for the environmental parameters of the vegetable greenhouse based on the potential energy gradient, and generate corresponding phased environmental control instructions. The so-called progressive control path refers to a continuous evolution trajectory from the current growth state to the dominant stable point in the state space. This trajectory represents the process in which the growth state of vegetables gradually approaches the ideal growth state under the control of the external environment. Since vegetable growth is a gradual and dynamic process, environmental control also needs to be carried out in stages and steps. Therefore, constructing a progressive control path is crucial to realizing intelligent environmental control.

[0048] When constructing a progressive control path, the system needs to make full use of the information provided by the potential energy gradient. Since the potential energy gradient gives the direction and magnitude of the force on the state point, the system can use it as the basic basis for environmental control. The system can coordinately control various environmental parameters along the direction of the potential energy gradient so that the state point can continue to move closer to the dominant stable point. At the same time, since the size of the potential energy gradient reflects the distance between the state point and the dominant stable point, the system can also dynamically adjust the step size and intensity of environmental control according to the size of the gradient. When the gradient is large, it means that there is still a large gap between the state point and the dominant stable point, so the system can take a larger range of environmental control; when the gradient is small, it means that the state point is close to the dominant stable point, so the system can reduce the control range and make more precise adjustments.

[0049] On the basis of the progressive control path, the system also needs to generate corresponding phased environmental control instructions. These instructions clearly specify the specific set values ​​and adjustment strategies of each environmental parameter in different control stages to ensure that environmental conditions can accurately evolve along the progressive control path. When generating control instructions, the system can comprehensively consider factors such as the coupling relationship between environmental parameters, control costs, and energy consumption, and use multi-objective optimization methods to find the optimal control solution that can save energy and increase efficiency while meeting the control needs. In addition, since the response of vegetable growth to environmental conditions has a certain lag, the system also needs to consider the delay characteristics of the control effect when formulating control instructions, and improve the control accuracy and stability through technologies such as predictive control.

[0050] It should be noted that the progressive control path and phased control instructions are not static, but need to be dynamically adjusted according to the real-time feedback of the vegetable growth status. On the one hand, due to the random fluctuations in environmental conditions and vegetable growth itself, the potential energy gradient and the position of the dominant stable point may also change, so the system needs to update the progressive control path in time to adapt to the new environmental situation; on the other hand, during the control process, due to factors such as modeling errors and control delays, the vegetable growth state may deviate from the expected trajectory, so the system also needs to introduce a feedback correction mechanism to correct the control instructions in real time according to the state deviation to ensure the robustness and effectiveness of the control process.

[0051] S106: Send the phased environmental control instruction to the environmental control device, so that the environmental control device adjusts the environment according to the phased environmental control instruction and the progressive control path.

[0052] After generating the phased environmental control instructions, this step needs to send the instructions to the various environmental control devices in the vegetable greenhouse. The control devices adjust the greenhouse environment accordingly based on the received instructions, so that the environmental parameters can evolve according to the progressive control path. In order to achieve precise control, the system first needs to establish a reliable communication link with the environmental control device to ensure that the instructions can be delivered to each device in a timely and accurate manner. This can be achieved through wired or wireless communication networks, such as CAN bus, ZigBee, LoRa, etc. At the same time, in order to improve communication efficiency and reliability, the system can adopt a variety of measures, such as data compression, error checking, multi-path transmission, etc., to reduce delays and errors in the communication process.

[0053] After the communication link is established, the system can send phased environmental control instructions to the environmental control equipment in batches according to the predetermined time intervals or trigger conditions. Each instruction should contain clear parameter setting values, execution time, duration and other information for the equipment to accurately control. After the instruction is sent, the system also needs to monitor and manage the execution status of the equipment, and understand the progress and effect of the instruction execution by receiving the status data returned by the equipment to ensure the smooth progress of the control process. If the equipment execution is found to be abnormal or deviant, the system can intervene in time to perform remote diagnosis or adjust the instruction to ensure the continuity and consistency of environmental regulation.

[0054] After the environmental control devices receive the control instructions, they need to work together according to the requirements of the instructions to complete the task of adjusting the environmental parameters. This requires high coordination and interoperability between the devices, and the ability to autonomously divide the work, coordinate and cooperate according to a unified control strategy. For example, when adjusting the temperature, ventilation equipment, refrigeration equipment, heating equipment, etc. need to work closely together to dynamically adjust their respective working states and output power according to the needs of temperature changes to achieve the target temperature value. When adjusting the humidity, the humidification equipment and the dehumidification equipment need to complement each other, and according to the changing trend of the air humidity, they are turned on and off alternately to maintain a constant humidity level. When adjusting the light, the lighting control system needs to work with the shading system to dynamically adjust the light intensity and light cycle according to the light requirements of plant growth to create a suitable light environment.

[0055] In the above embodiment, a growth model that can reflect the multi-stable characteristics of the vegetable growth process is established. The multi-stable growth model includes a number of stable growth points, each of which corresponds to a set of optimal environmental parameter combinations. By locating the current growth state in the multi-stable growth model, determining the dominant stable growth point closest to the current growth state, establishing an attractor potential field centered on the dominant stable growth point, and calculating the potential energy gradient required to reach the dominant stable growth point from the current growth state, a progressive control path from the current growth state to the dominant stable growth point can be obtained. Based on the progressive control path, a staged environmental control instruction is generated, and the instruction is sent to the environmental control device, so that the environmental control device can perform staged adjustments to the vegetable greenhouse environment according to the progressive control path, guide the vegetable growth state to move closer to the optimal environmental parameter combination corresponding to the dominant stable growth point, and improve the timeliness and accuracy of agricultural operations and environmental regulation.

[0056] The above embodiment realizes the gradual control of the vegetable greenhouse environment by establishing a multi-stable growth model and an attractor potential field. In order to further improve the accuracy and scientificity of environmental control and establish a closer relationship between environmental parameters and vegetable growth status, the following is combined with another embodiment and Figure 2, another vegetable greenhouse environment management method in the embodiment of the present application is described: See also Figure 2 , is another flow chart of a vegetable greenhouse environment management method in an embodiment of the present application.

[0057] S201, calculating the entropy value of the environmental parameter set and the entropy value of the vegetable growth state set at each growth stage according to the historical environmental parameters and vegetable growth data; The system calculates the entropy value of the environmental parameter set and the entropy value of the vegetable growth state set at each growth stage based on the historical environmental parameters and vegetable growth data. Specifically: the system calculates the probability distribution function of the environmental parameter set and the vegetable growth state set. Substitute the probability distribution function into the preset entropy formula to calculate the entropy values ​​of the environmental parameter set and the vegetable growth state set in each growth stage.

[0058] The system first obtains the historical environmental parameter data of the vegetable greenhouse and the vegetable growth status data from the database. The environmental parameter data includes multiple dimensions such as temperature, humidity, light intensity, and carbon dioxide concentration, while the vegetable growth status data includes indicators such as plant height, leaf area index, and fruit quantity. The system classifies and organizes these historical data according to the different growth stages of vegetables to form several data subsets.

[0059] For each subset of environmental parameter data and growth state data at each growth stage, the system calculates their probability distribution functions respectively. A feasible method is to use the kernel density estimation method, which can approximate the true probability distribution by smoothing the observed data. After obtaining the probability distribution function, the system substitutes it into the preset entropy formula for calculation, thereby obtaining the entropy value of the environmental parameter set and the growth state set at this stage. The entropy formula used here can be Shannon entropy, Tsallis entropy, etc., all of which can quantitatively describe the degree of order of a system.

[0060] S202, calculating the mutual information between the entropy value of the environmental parameter set and the entropy value of the vegetable growth state set, and obtaining the correlation strength between the environmental parameters and the vegetable growth state within the growth stage; Based on the calculated entropy of the environmental parameters and the growth state set at each growth stage, the system further calculates the mutual information between the two sets. Mutual information is an important concept in information theory, which measures the amount of information shared between two random variables. The calculation formula for mutual information is the difference between the joint entropy of the two variables and their individual entropy.

[0061] By calculating the mutual information between the environmental parameter set and the growth state set, the system can quantitatively evaluate the strength of the association between the two. The greater the mutual information, the stronger the dependence between the change in environmental parameters and the change in vegetable growth state, that is, changing environmental parameters is more likely to cause changes in vegetable growth state. This strength of association can serve as an important basis for regulating environmental parameters to optimize plant growth.

[0062] In practical applications, the system can choose different methods for calculating mutual information, such as the histogram method, the nearest neighbor method, the kernel density estimation method, etc. For high-dimensional data, the system can also first perform independent component analysis (ICA) to map the data to a low-dimensional space, eliminate the correlation between different dimensions, and then calculate the mutual information to improve calculation efficiency and accuracy. Considering that there may be deviations in the estimation of mutual information for finite samples, the system can correct the deviations by introducing methods such as Jackknife resampling. In addition, the system can also calculate the differences in the mutual information between environmental parameters and growth status at different growth stages, revealing the dynamic changes in the relationship between the two in different periods, and providing targeted guidance for phased environmental regulation.

[0063] S203, determining the weight coefficients of environmental parameter regulation at each growth stage based on the correlation strength, and introducing the weight coefficients into the multi-stable growth model; The system determines the weight coefficients of environmental regulation at different stages based on the strength of the correlation between environmental parameters and vegetable growth status at each growth stage. One feasible strategy is to normalize the mutual information value to obtain a weight coefficient ranging from 0 to 1. The greater the mutual information, the greater the weight coefficient, indicating that the environmental parameters are more closely related to the plant growth status at this growth stage, so a greater weight needs to be given in the environmental regulation process.

[0064] Introducing weight coefficients into the multi-stable growth model can make the model closer to the actual needs of vegetable growth. The multi-stable growth model is a type of kinetic model that describes the plant growth process, taking into account multiple factors that affect plant growth, such as temperature, humidity, light, etc. On the basis of the original model, the system applies different control intensities to each environmental factor according to the weight coefficient of the environmental parameter. Factors with large weights have greater control strength, while factors with small weights have relatively weaker control strength. This mutual information weight-based control method can adaptively balance multiple environmental factors according to the characteristics of the plant growth stage, and better meet the dynamic needs of plant growth.

[0065] In terms of technical implementation, the system can incorporate the weight coefficient into the model equation in a weighted average manner. Assume that the control term of the i-th environmental factor in the original model is fi(x), and the weight coefficient is wi, then the control term after the introduction of the weight can be expressed as wi·fi(x). The addition of the weight coefficient makes the model more flexible and changeable, and can automatically optimize the control strategy according to changes in environmental conditions. In addition, considering that the weight coefficient is estimated based on historical data, in order to improve the robustness of the system, the weight coefficient can be regularly updated online using newly collected environmental and growth data. At the same time, the upper and lower limits of the weight coefficient are set to avoid excessive or small weight values ​​affecting the stability of the system. By dynamically updating and reasonably limiting the weight coefficient, the multi-stable growth model can be precisely controlled in a complex and changeable environment, providing a strong guarantee for the high quality and high yield of vegetables.

[0066] S204, using environmental factors as control variables and vegetable growth state parameters as state variables to establish a state feedback equation for environmental parameter regulation; After clarifying the quantitative relationship between environmental factors and vegetable growth status at each growth stage, the system further established a state feedback equation for environmental parameter regulation. State feedback control is a common closed-loop control method that monitors system state variables in real time and feeds back deviation signals to the controller to continuously adjust the control variables so that the system state approaches the expected value.

[0067] In the environmental control of vegetable greenhouses, the system uses environmental factors such as temperature, humidity, and light as control variables, and changes environmental parameters by adjusting actuators such as heating equipment, ventilation equipment, and fill lights. On the other hand, the system selects key indicators that can reflect the growth status of vegetables as state variables, such as plant height, leaf area, and number of fruits. The changes in state variables reflect the response of plants to environmental conditions and are an important basis for optimizing control strategies.

[0068] With the help of modern control theory, the system establishes a state feedback equation between environmental parameters and vegetable growth status. This equation describes how the vegetable growth status will evolve under given environmental conditions and the possible impact of adjusting environmental parameters. Generally speaking, the state feedback equation can be expressed in the form of a differential equation or a difference equation, that is, there is a functional relationship between the rate of change or amount of change of the state variable and the current state variable and the control variable.

[0069] In practical applications, the system can derive specific state feedback equations based on crop growth models, such as logistics models and Richards models. Model parameters can be estimated by fitting historical data. For complex systems, factors such as time lag, nonlinearity, and random disturbances can also be considered to establish a more sophisticated state feedback model. The system can also establish multiple state feedback equations for different vegetable varieties and different growth stages to improve the applicability and accuracy of the model. After obtaining the state feedback equation, various control algorithms, such as PID control, optimal control, adaptive control, and intelligent control, can be used to achieve dynamic adjustment of environmental parameters and make the vegetable growth state approach the ideal level. The establishment of the state feedback equation transforms the complex environment-plant interaction relationship into a computable and optimizable mathematical model, providing an important tool for quantitative expression and refined regulation of the vegetable growth process.

[0070] S205, designing an optimal controller in combination with the weight coefficient, so as to minimize the deviation of the vegetable growth state under the condition of satisfying the environmental parameter constraints in each growth stage; On the basis of establishing the state feedback equation, the system further designs the optimal controller to achieve dynamic optimization and adjustment of environmental parameters. The goal of the optimal controller is to minimize the degree to which the growth state of vegetables deviates from the ideal value under the premise of meeting the constraints of environmental parameters at each growth stage, so that the plants are always in the best growth state.

[0071] The system introduces the environmental parameter weight coefficients calculated previously into the design of the optimal controller. Specifically, a weighted quadratic performance index function can be constructed, which is the weighted sum of squares of plant growth state deviations. The larger the weight coefficient, the stronger the correlation between the environmental factor and the plant response at the current growth stage, and the greater the weight in the performance index function. The optimal control problem is transformed into minimizing the performance index function under the constraints of environmental parameters, which is a typical quadratic programming problem.

[0072] In setting constraints, the system fully considers the adaptability of plant growth to environmental conditions. For example, the temperature cannot be too high or too low, otherwise it will cause temperature stress to crops; the humidity cannot be too dry or too wet, otherwise it will cause problems such as pests and diseases. These constraints are determined according to the characteristics of vegetable varieties and growth stages, and can be obtained through expert experience or data analysis. In addition, the physical characteristics of the actuator, such as adjustment range and response speed, are also taken into account in the constraints.

[0073] S206, solving the state feedback equation to obtain the optimal control amount of each environmental parameter; Solve the state feedback equation to obtain the optimal control value of each environmental parameter. Specifically: transform the state feedback equation into the objective function of the convex optimization problem; According to the variation range of environmental parameters in each growth stage, the boundary constraints of decision variables are set; Introducing boundary constraints into the objective function, we obtain the Lagrangian function; Derivative the Lagrangian function and transform it into an unconstrained optimization problem; The gradient descent algorithm is used to search for the optimal solution of the unconstrained optimization problem and obtain the optimal control value of each environmental parameter.

[0074] After designing the optimal controller, the system further solves the state feedback equation to obtain the optimal control value of each environmental parameter. This step is the key to achieve dynamic optimization and adjustment of environmental parameters, that is, according to the real-time monitored vegetable growth status information, calculate the specific control instructions of each actuator to keep the environmental conditions within the optimal range.

[0075] First, the system combines the state feedback equation with the optimal controller to build a complete closed-loop control system. The state feedback equation describes the dynamic relationship between environmental parameters and plant growth status, while the optimal controller determines the optimization strategy for adjusting environmental parameters based on performance indicator functions and constraints. Combining the two, a state feedback optimal control system is obtained, which can automatically calculate the optimal control input based on the current state and desired goals.

[0076] In order to facilitate numerical solution, the system usually discretizes the state feedback equation and converts it into the form of a difference equation. The discretized time step needs to be reasonably selected based on factors such as the time scale of environmental parameter changes and the response speed of the actuator, in order to ensure the real-time control and avoid system oscillation caused by too frequent adjustments. After discretization, the state feedback optimal control problem can be further converted into a convex optimization problem, that is, minimizing the performance index function at each time step while satisfying the state equation and constraints.

[0077] Convex optimization problems can be solved efficiently using a variety of numerical algorithms, such as the interior point method, the effective set method, and the gradient projection method. These algorithms continuously approach the optimal solution through iterative search until certain convergence conditions are met. In practical applications, factors such as the numerical stability and robustness of the algorithm also need to be considered. If necessary, regularization techniques such as Tikhonov regularization can be introduced to improve the reliability of the solution. The intermediate variables generated during the solution process, such as the predicted values ​​of state variables and the search directions of control variables, also provide useful information for monitoring and diagnosing the operating status of the system.

[0078] The optimal control quantity of environmental parameters obtained by solving the state feedback equation is a direct instruction for realizing intelligent management of vegetable greenhouses. The system converts the control quantity obtained by optimization calculation into specific actuator control instructions, such as the opening of ventilation equipment, the power of fill light, etc., and sends them to the corresponding control unit. These control instructions, through the action of the actuator, ultimately change the environmental conditions for vegetable growth, making them approach the optimal state. Through the organic combination of solving the state feedback equation and generating the optimization control instructions, the system realizes a complete closed-loop control process from environmental information collection, growth status evaluation to environmental condition optimization and adjustment, providing a full range of intelligent management solutions for vegetable production, effectively improving yield and quality, and promoting the development of facility agriculture towards digitalization, networking, and intelligence.

[0079] S207. Generate a multi-stage environmental control strategy based on the optimal control quantity.

[0080] After obtaining the optimal control amount of environmental parameters at each growth stage, the system further generates a multi-stage environmental control strategy for the entire growth cycle. This strategy comprehensively considers the dynamic changes in environmental requirements during vegetable growth and the optimal ratio of environmental conditions at different growth stages, aiming to create a consistently suitable growth environment for crops and achieve a coordinated improvement in yield and quality.

[0081] The generation process of the multi-stage environmental control strategy is as follows: First, based on plant physiological knowledge and cultivation experience, the system divides the growth process of vegetables into several key stages, such as seedling stage, bud stage, flowering stage, fruit expansion stage, and maturity stage. The stage division of different vegetable varieties may be different, and the system can be flexibly adjusted based on data analysis and expert advice. Then, for each growth stage, the system extracts the environmental parameter control amount obtained by the previous optimization calculation to form a phased environmental control plan. The plan clearly stipulates the optimal value range and dynamic adjustment strategy of each environmental factor in this stage to meet the growth needs of plants at different developmental stages.

[0082] On the basis of generating a phased control plan, the system further optimizes the connection and coordination between each stage to form a continuous multi-stage environmental control strategy. Specifically, the system analyzes the changing trends of environmental conditions in adjacent stages and smoothly transitions the control parameters to avoid adverse effects on plant growth caused by drastic environmental fluctuations when switching stages. At the same time, the system also needs to weigh the priority and resource allocation of environmental control at different stages, taking into account factors such as energy consumption and cost while meeting the needs of plant growth, and optimizing the overall benefits of environmental control.

[0083] Once the multi-stage environmental control strategy is generated, it becomes an action program to guide the entire process of vegetable production. According to the strategy requirements, the system implements corresponding environmental control measures at different growth stages, dynamically monitors the growth status of vegetables, and evaluates the control effect. If necessary, the established strategy can be corrected online according to special circumstances that occur during the actual planting process, such as extreme weather, pests and diseases, to ensure that environmental control is always synchronized with the growth status of plants. Through the formulation and implementation of multi-stage environmental control strategies, the system can achieve a leap from qualitative management to quantitative decision-making, and a transition from experience judgment to data-driven, truly realizing the refined and intelligent management of the vegetable production process.

[0084] In the above embodiment, mutual information is an indicator that can measure the correlation between two random variables, which reduces the uncertainty of the random variables themselves and more accurately reflects the degree of correlation between them. Based on the correlation strength, the weight coefficients of environmental parameter regulation in each growth stage are determined, and the weight coefficients are introduced into the multi-stable growth model, so that the model can adapt to the dynamic changes in the importance of environmental parameters in different growth stages. When regulating environmental parameters, higher weights are given to key parameters to highlight their influence, while non-key parameters are correspondingly reduced to avoid excessive regulation, thereby achieving stage-by-stage differentiation of environmental regulation and further improving the accuracy and effectiveness of environmental regulation.

[0085] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of a vegetable greenhouse environment management system provided in an embodiment of the present application.

[0086] It should be noted that Figure 3 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0087] like Figure 3 As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0088] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0089] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0090] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0091] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0092] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0093] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0094] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0095] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0096] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A vegetable greenhouse environment management method, characterized in that: include: Obtaining historical environmental parameters of the vegetable greenhouse, vegetable growth data and the growth status of the vegetables in the vegetable greenhouse at the current moment, wherein the historical environmental parameters include temperature, humidity, light intensity and carbon dioxide concentration, and the vegetable growth data includes plant height, leaf area, stem diameter and yield; Establishing a multi-stable growth model according to the historical environmental parameters and the vegetable growth data, wherein the multi-stable growth model includes a plurality of stable growth points, each of which corresponds to a set of environmental parameter combinations; Positioning the growth state in the multi-stable growth model and determining a dominant stable growth point closest to the growth state; Establishing an attractor potential field, setting the dominant stable growth point as the attractor center, and calculating the potential energy gradient from the growth state to the attractor center; Constructing a progressive control path of the environmental parameters of the vegetable greenhouse based on the potential energy gradient, and generating phased environmental control instructions; The phased environmental control instruction is sent to the environmental control device, so that the environmental control device performs environmental adjustment according to the phased environmental control instruction and the progressive control path.

2. The method according to claim 1, characterized in that: The establishing of a multi-stable growth model according to the historical environmental parameters and the growth data specifically includes: Performing time-series segmentation on the historical environmental parameters and the vegetable growth data to obtain key stages of vegetable growth; Constructing a phase space, and generating an environmental parameter trajectory and a vegetable growth trajectory in the phase space that correspond one-to-one to the historical environmental parameters and the vegetable growth data; Determine a stable growth point according to the environmental parameter trajectory and the vegetable growth trajectory, the stable growth point is a convergence point where the Lyapunov index is a negative number, and the convergence point is an intersection of the environmental parameter trajectory and the vegetable growth trajectory; Determine the attraction domain range and attraction strength of each of the stable growth points; Calculating the comprehensive advantage coefficient of each of the stable growth points; The stable growth point, the attraction domain range, the attraction intensity and the comprehensive advantage coefficient are integrated to obtain a multi-stable growth model.

3. The method according to claim 1, characterized in that The establishing of the attractor potential field specifically includes: A local potential field function is constructed with each of the stable growth points as the potential field center, wherein the potential field intensity in the local potential field function is in an exponential decay relationship with the distance from the potential field center, and the decay coefficient is determined by the stability of the stable growth point; Setting a weight coefficient according to the advantage coefficient of each of the stable growth points; All the local potential fields are weightedly superimposed according to the weight coefficient to obtain the attractor potential field.

4. The method according to claim 1, characterized in that: After establishing the multi-stable growth model according to the historical environmental parameters and the vegetable growth data, the method further comprises: Calculate the entropy value of the environmental parameter set and the entropy value of the vegetable growth state set at each growth stage according to the historical environmental parameters and the vegetable growth data; Calculate the mutual information between the entropy value of the environmental parameter set and the entropy value of the vegetable growth state set to obtain the correlation strength between the environmental parameters and the vegetable growth state in the growth stage; The weight coefficients for regulating the environmental parameters of each of the growth stages are determined based on the correlation strength, and the weight coefficients are introduced into the multi-stable growth model.

5. The method according to claim 4, characterized in that The step of calculating the entropy value of the environmental parameter set at each growth stage and the entropy value of the vegetable growth state set specifically includes: Calculate the probability distribution function of the environmental parameter set and the vegetable growth state set; Substitute the probability distribution function into a preset entropy formula to calculate the entropy values ​​of the environmental parameter set and the vegetable growth state set in each growth stage.

6. The method according to claim 4, characterized in that After determining the weight coefficients of the environmental parameter regulation at each growth stage based on the correlation strength, the method further includes: Taking environmental factors as control variables and vegetable growth state parameters as state variables, a state feedback equation for regulating the environmental parameters is established; Designing an optimal controller in combination with the weight coefficients to minimize the deviation of the vegetable growth state under the condition of satisfying the environmental parameter constraints in each growth stage; Solving the state feedback equation to obtain the optimal control value of each environmental parameter; A multi-stage environmental control strategy is generated according to the optimal control quantity.

7. The method according to claim 6, characterized in that The step of solving the state feedback equation to obtain the optimal control value of each environmental parameter specifically includes: Converting the state feedback equation into an objective function of a convex optimization problem; According to the variation range of the environmental parameters in each growth stage, setting the boundary constraints of the decision variables; Introducing the boundary constraint condition into the objective function to obtain a Lagrangian function; Deriving the Lagrangian function and converting it into an unconstrained optimization problem; The gradient descent algorithm is used to search for the optimal solution of the unconstrained optimization problem to obtain the optimal control value of each environmental parameter.

8. A vegetable greenhouse environmental management system, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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