Agricultural intelligent cultivation control system and method based on strategy optimization

Abstract

The invention discloses an agricultural intelligent cultivation control system and method based on strategy optimization, and belongs to the technical field of agriculture. Related data and plant growth parameters in an agricultural environment are collected through a sensor assembly, and a growth state judgment model is constructed; importing the collected growth parameters into a growth state judgment model for growth state judgment, constructing an energy conversion evaluation model, importing respiration data in the monitored agricultural environment into the energy conversion evaluation model for evaluation, constructing a temperature matching analysis model, and performing temperature matching analysis according to a growth state judgment result and a dark respiration rate. Whether the current temperature is matched with plant growth and physiological activities or not is evaluated, a matched temperature range is analyzed according to a matching judgment result and a temperature optimization algorithm, and crop growth is promoted.

Description

Technical Field

[0001] The present invention belongs to the field of agricultural technology, and specifically relates to an agricultural intelligent cultivation control system and method based on strategy optimization. Background Art

[0002] In recent years, the development of the Internet of Things, big data and artificial intelligence technologies has promoted the development of intelligent agriculture. Through sensor networks, agricultural producers can obtain data such as soil, climate and plant growth status in real time, and realize precision agriculture by analyzing these data and combining them with control systems. Suitable temperature is an important factor in the growth of crops, affecting the photosynthesis, respiration and other physiological activities of crops. When analyzing the agricultural planting environment, the existing technology cannot accurately analyze the temperature required by plants in combination with plant growth and respiration data, which affects the normal growth of plants.

[0003] The present invention aims to develop an agricultural intelligent cultivation control system and method based on strategy optimization, which acquires data through sensors and conducts intelligent analysis to determine the temperature range suitable for crop growth, promote crop growth, and improve resource utilization efficiency. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes an agricultural intelligent cultivation control system and method based on strategy optimization. The present invention collects relevant data and plant growth parameters in an agricultural environment through a sensor component, constructs a growth state judgment model, imports the collected growth parameters into the growth state judgment model for growth state judgment, constructs an energy conversion evaluation model, imports the monitored respiratory data in the agricultural environment into the energy conversion evaluation model for evaluation, constructs a temperature matching analysis model, performs temperature matching analysis through the growth state judgment result and the dark respiration rate, evaluates whether the current temperature matches the plant growth and physiological activities, analyzes the matching temperature range according to the matching judgment result and the temperature optimization algorithm, and promotes the growth of crops.

[0005] To achieve the above object, the present invention provides the following technical solutions: An agricultural intelligent cultivation control system and method based on strategy optimization, comprising the following specific steps: Collect relevant data and plant growth parameters in agricultural environments through sensor components; Constructing a growth state judgment model, and importing the collected growth parameters into the growth state judgment model to judge the growth state; Construct an energy conversion assessment model and import the respiratory data in the monitored agricultural environment into the energy conversion assessment model for assessment; A temperature matching analysis model was constructed, and temperature matching analysis was performed through growth status judgment results and dark respiration rate to evaluate whether the current temperature matches plant growth and physiological activities. The matching temperature range was analyzed based on the matching judgment results and temperature optimization algorithm.

[0006] It should be specifically explained here that the collection of relevant data and plant growth parameters in the agricultural environment by the sensor assembly includes the following specific steps: S11, using a temperature sensor and a plant image analyzer to collect plant growth related parameters, wherein the plant growth related parameters include temperature, average stem height and average leaf area, and transmitting them to a database through a network; S12. Collect respiration-related data using an infrared gas analyzer, wherein the respiration-related data includes carbon dioxide concentration.

[0007] It should be specifically explained here that the construction of the growth state judgment model and the importation of the collected growth parameters into the growth state judgment model for growth state judgment include the following specific steps: S21, acquiring the growth status data of the previous cycle and the growth status data of the current cycle; S22, substituting the average leaf area and average stem height under the temperature of this cycle into the growth state abnormal value calculation formula to calculate the growth state abnormal value, wherein the growth state abnormal value calculation formula is: , where S is the average leaf area, H is the average stem height, is the average leaf area under normal conditions in the same cycle, is the average stem height under normal conditions of the same period, β1 and β2 are the corresponding characteristic coefficients, ε is the error term, G i The larger the value, the more abnormal the growth state, indicating that the growth parameters of the crop deviate from the normal range. The growth state of this cycle is compared with the previous cycle to determine the growth trend of the plant. If G i-1 >G i , it indicates that the crop growth status in this cycle is better than that in the previous cycle. i-1 <G i , it indicates that the growth state of crops in this cycle is more abnormal than that in the previous cycle.

[0008] It should be specifically explained here that the construction of the energy conversion evaluation model and the importation of the respiratory data in the monitored agricultural environment into the energy conversion evaluation model for evaluation include the following specific steps: S31, obtaining carbon dioxide concentration data of the previous cycle and the current cycle; S32. Substitute the carbon dioxide concentration into the dark respiration rate calculation formula: , where D is the change in carbon dioxide concentration, V is the volume of the greenhouse, A is the total leaf area, and t is the cycle duration; S33. Substitute the dark respiration rates of this cycle and the previous cycle into the dark respiration rate change calculation formula, and the formula is: , if the change rate is positive, it indicates that the dark respiration rate increases, suggesting enhanced physiological activities of the plant. If the change rate is negative, it indicates that the dark respiration rate decreases, suggesting weakened physiological activities of the plant.

[0009] Specifically, for the construction of the temperature matching analysis model, temperature matching analysis is performed through the growth state judgment result and the dark respiration rate. Analyzing the matching temperature range based on the matching judgment result and the temperature optimization algorithm includes the following specific steps: S41. Import the calculated growth state change outlier into the temperature matching analysis model. When G i < G, define the temperature at this time as mismatched and proceed to S42, where G is the normal growth state parameter; S42. Substitute the calculated dark respiration rate into the fluctuation outlier calculation formula: , where R i is the dark respiration rate at this temperature, and R is the normal dark respiration rate. Draw an outlier waveform diagram based on the calculated Q value, and substitute the fluctuation outlier into the fluctuation outlier variance calculation formula to evaluate the fluctuation degree of the deviation. The calculation formula is: , where N is the number of cycles, μ is the average value of all fluctuation outliers. The larger the variance, the greater the degree of dispersion of the data points around the mean value, indicating a larger change in the dark respiration rate at this temperature. The smaller the variance, the more concentrated the data points are near the mean value, indicating a smaller change rate of dark respiration and more stable physiological activities at this temperature; S43. Substitute the abnormal growth state data and respiration rate at the mismatched temperature into the adaptive temperature calculation formula to calculate the matching temperature range. Among them, the matching temperature calculation formula is: , where T i is the abnormal environmental temperature, k is the adjustment factor, G i is the growth state at the abnormal temperature, G is the normal growth state, R i is the dark respiration rate at the abnormal temperature, and R is the normal dark respiration rate. w1 and w2 are weights.

[0010] An agricultural intelligent cultivation control system based on policy optimization is implemented based on the above-mentioned agricultural intelligent cultivation control method based on policy optimization. Specifically, it includes: A data collection module for collecting relevant data in the agricultural environment and plant growth parameters; A growth status judgment module is used to import the collected growth parameters into a growth status judgment model to judge the growth status; Energy conversion assessment module, used to import the respiratory data in the monitored agricultural environment into the energy conversion assessment model for evaluation; The temperature matching analysis module is used to perform temperature matching analysis based on the growth status judgment results and dark respiration rate to evaluate whether the current temperature matches the plant growth and physiological activities; The matching temperature calculation module is used to obtain the matching temperature range.

[0011] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned agricultural intelligent cultivation control system method based on strategy optimization by calling the computer program stored in the memory.

[0012] A computer-readable storage medium, characterized in that it stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned agricultural intelligent cultivation control method based on strategy optimization.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention collects relevant data and plant growth parameters in an agricultural environment through a sensor component, constructs a growth state judgment model, imports the collected growth parameters into the growth state judgment model to judge the growth state, constructs an energy conversion evaluation model, imports the respiratory data in the monitored agricultural environment into the energy conversion evaluation model for evaluation, constructs a temperature matching analysis model, performs temperature matching analysis through the growth state judgment result and the dark respiration rate, evaluates whether the current temperature matches the plant growth and physiological activities, analyzes the matching temperature range based on the matching judgment result and the temperature optimization algorithm, and promotes the growth of crops. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a schematic diagram of the overall process of an agricultural intelligent cultivation control method based on strategy optimization of the present invention; Figure 2 This is a schematic diagram of the overall framework of an agricultural intelligent cultivation control system based on strategy optimization of the present invention; Figure 3 This is a schematic diagram of leaf data collection in an agricultural intelligent cultivation control method based on strategy optimization of the present invention. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0016] Example 1 An embodiment provided by the present invention: Figure 1 As shown, an agricultural intelligent cultivation control method based on strategy optimization includes the following specific steps: Collect relevant data and plant growth parameters in agricultural environments through sensor components; Constructing a growth state judgment model, and importing the collected growth parameters into the growth state judgment model to judge the growth state; Construct an energy conversion assessment model and import the respiratory data in the monitored agricultural environment into the energy conversion assessment model for assessment; A temperature matching analysis model was constructed, and temperature matching analysis was performed through growth status judgment results and dark respiration rate to evaluate whether the current temperature matches plant growth and physiological activities. The matching temperature range was analyzed based on the matching judgment results and temperature optimization algorithm.

[0017] It should be specifically noted in this embodiment that collecting relevant data and plant growth parameters in an agricultural environment through a sensor component includes the following specific steps: S11, using a temperature sensor and a plant image analyzer to collect plant growth related parameters, wherein the plant growth related parameters include temperature, average stem height and average leaf area, and transmitting them to a database through a network; In this embodiment, the specific steps of collecting plant growth related parameters by the plant image analyzer in S11 include: S111, using filtering technology to remove noise from the collected image, and adjusting the color contrast of the image to make the leaves and the background more clearly distinguishable; S112, segmenting the leaf from the background by setting a grayscale threshold, and accurately segmenting the leaf contour by using Canny edge detection; S113, calculating the pixel area of ​​the leaf contour extracted and segmented, and converting the number of pixels in the contour into the actual leaf area according to the camera resolution. The actual leaf area calculation formula is: , where c is the pixel area, a is the actual size, and b is the image size. The actual size and image size can be obtained through calibration; S114. Store the calculated blade area data into a database.

[0018] S12. Collect respiration-related data using an infrared gas analyzer, wherein the respiration-related data includes carbon dioxide concentration.

[0019] In this embodiment, it should be specifically explained that constructing a growth state judgment model and importing the collected growth parameters into the growth state judgment model to judge the growth state includes the following specific steps: S21, acquiring the growth status data of the previous cycle and the growth status data of the current cycle; S22, substituting the average leaf area and average stem height under the temperature of this cycle into the growth state abnormal value calculation formula to calculate the growth state abnormal value, wherein the growth state abnormal value calculation formula is: , where S is the average leaf area, H is the average stem height, is the average leaf area under normal conditions in the same cycle, is the average stem height under normal conditions of the same period, β1 and β2 are the corresponding characteristic coefficients, ε is the error term, G i The larger the value, the more abnormal the growth state, indicating that the growth parameters of the crop deviate from the normal range. The growth state of this cycle is compared with the previous cycle to determine the growth trend of the plant. If G i-1 >G i , it indicates that the crop growth status in this cycle is better than that in the previous cycle. i-1 <G i , it indicates that the crop growth status in this cycle is more abnormal than that in the previous cycle; In this embodiment, this step evaluates the growth status by using two parameters: average leaf area and stem height. It indicates the difference between the average blade area under this cycle temperature and the average blade area under the same cycle normal state. The higher the difference, the higher the deviation of the average blade area difference value. It indicates the ratio of the difference to the normal value. Similarly, due to the different degrees of influence of leaf area and stem height, the relative deviations of leaf area and stem height are weighted and summed according to the weights of the characteristic coefficients β1 and β2, and the comprehensive growth status anomaly value is obtained by adding the error term ε.

[0020] It should be specifically explained in this embodiment that constructing an energy conversion evaluation model and importing the respiratory data in the monitored agricultural environment into the energy conversion evaluation model for evaluation includes the following specific steps: S31, obtaining carbon dioxide concentration data of the previous cycle and the current cycle; S32. Substitute the carbon dioxide concentration into the dark respiration rate calculation formula: , where D is the concentration change of carbon dioxide, V is the volume of the greenhouse, A is the total area of ​​leaves, and t is the cycle length; S33, substituting the dark respiration rate of the current cycle and the previous cycle into the dark respiration rate change calculation formula, the formula is: If the change rate is positive, it means that the dark respiration rate increases, indicating that the physiological activity of the plant is enhanced. If the change rate is negative, it means that the dark respiration rate decreases, indicating that the physiological activity of the plant is weakened. In this embodiment, in this step, the change rate of the dark respiration rate is expressed as a percentage, reflecting the change of the dark respiration rate between the current cycle and the previous cycle. The numerator part of the formula calculates the absolute change amount of the dark respiration rate between the current cycle and the previous cycle, and compares the absolute change amount with the dark respiration rate of the previous cycle to calculate the relative change, indicating the degree of the change of the dark respiration rate relative to the previous cycle.

[0021] In this embodiment, it should be specifically noted that a temperature matching analysis model is constructed, and temperature matching analysis is carried out on the growth state judgment result and the dark respiration rate. According to the matching judgment result and the temperature optimization algorithm, the specific steps for analyzing the matching temperature range include the following: S41. Import the calculated abnormal value of the growth state change into the temperature matching analysis model. When G i < G, the temperature at this time is defined as mismatched, and S42 is performed, where G is the normal growth state parameter; S42. Substitute the calculated dark respiration rate into the fluctuation abnormal value calculation formula: , where R i is the dark respiration rate at this temperature, R is the normal dark respiration rate, and an abnormal value waveform diagram is drawn based on the calculated Q value. The fluctuation abnormal value is substituted into the fluctuation abnormal value variance calculation formula to evaluate the fluctuation degree of the deviation. The calculation formula is: , where N is the number of cycles, μ is the average value of all fluctuation abnormal values. The larger the variance, the greater the degree of dispersion of the data points around the mean value. At this temperature, the change of the dark respiration rate is larger. The smaller the variance, the more concentrated the data points are near the mean value. At this temperature, the change rate of the dark respiration is smaller and the physiological activity is more stable; In this embodiment, by calculating the variance between the fluctuation abnormal value of the dark respiration rate and the average fluctuation abnormal value, the abnormal fluctuation of the dark respiration rate affected by temperature can be identified, and the stability of the dark respiration rate of the crop at a specific temperature can be quantified. In this formula calculate the deviation between each fluctuation abnormal value and the average value. This deviation represents the degree of dispersion of each data point relative to the overall average level. Square the deviation to eliminate negative values and emphasize the influence of larger deviations. After summing all the squared deviations and dividing by the number of cycles N, the variance is obtained , reflecting the overall degree of dispersion; S43. Substitute the abnormal growth state data and respiration rate at the mismatched temperature into the matching temperature calculation formula to calculate the matching temperature range. Among them, the matching temperature calculation formula is: , where T i is the abnormal environmental temperature, k is the adjustment factor, G iis the growth state of abnormal temperature, G is the normal growth state, R i is the dark respiration rate at abnormal temperature, R is the normal dark respiration rate, w1 and w2 are weights, In this embodiment, the equation It indicates the difference between abnormal growth status and normal growth status. The higher the difference, the higher the deviation of the growth status difference value. It indicates the ratio of the difference to the normal value. Similarly, the standardized deviation is multiplied by the corresponding weights w1 and w2 to reflect the importance of growth status and respiratory rate in temperature adaptation. The weighted sum of the entire deviation is multiplied by the adjustment factor k, and the abnormal ambient temperature T is calculated. i Subtract it from the value to calculate the adaptation temperature.

[0022] Through this embodiment, it is possible to achieve the following: collect relevant data and plant growth parameters in an agricultural environment through sensor components, construct a growth state judgment model, import the collected growth parameters into the growth state judgment model to judge the growth state, construct an energy conversion evaluation model, import the respiratory data in the monitored agricultural environment into the energy conversion evaluation model for evaluation, construct a temperature matching analysis model, perform temperature matching analysis through the growth state judgment result and the dark respiration rate, evaluate whether the current temperature matches the plant growth and physiological activities, analyze the matching temperature range based on the matching judgment result and the temperature optimization algorithm, and promote the growth of crops.

[0023] Example 2 like Figure 2 As shown, an agricultural intelligent cultivation control system based on strategy optimization is implemented based on the above-mentioned agricultural intelligent cultivation control method based on strategy optimization, and specifically includes a data collection module, a growth status judgment module, an energy conversion evaluation module, a temperature matching analysis module and a matching temperature calculation module.

[0024] The data collection module is used to collect relevant data and plant growth parameters in the agricultural environment; the growth state judgment module is used to import the collected growth parameters into the growth state judgment model for growth state judgment; the energy conversion evaluation module is used to import the respiratory data in the monitored agricultural environment into the energy conversion evaluation model for evaluation; the temperature matching analysis module is used to perform temperature matching analysis through the growth state judgment result and the dark respiration rate to evaluate whether the current temperature matches the plant growth and physiological activities; the matching temperature calculation module is used to obtain the matching temperature range.

[0025] Example 3 This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned agricultural intelligent cultivation control method based on strategy optimization by calling the computer program stored in the memory.

[0026] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement a multimedia image quality evaluation method based on scene recognition provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface for data input and output. This embodiment will not be described in detail here.

[0027] Example 4 This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon; When the computer program runs on a computer device, the computer device executes the above-mentioned agricultural intelligent cultivation control method based on strategy optimization.

[0028] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0029] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0030] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0031] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments 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 or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0032] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0033] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0034] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one way of division, and there may be other ways of division in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0035] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0036] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0037] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0038] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An agricultural intelligent cultivation control method based on strategy optimization, characterized in that: It includes the following specific steps: Collect relevant data and plant growth parameters in agricultural environments through sensor components; Constructing a growth state judgment model, and importing the collected growth parameters into the growth state judgment model to judge the growth state; Construct an energy conversion assessment model and import the respiratory data in the monitored agricultural environment into the energy conversion assessment model for assessment; A temperature matching analysis model was constructed, and temperature matching analysis was performed based on the growth status judgment results and the dark respiration rate to evaluate whether the current temperature matched the plant growth and physiological activities, and the matching temperature range was analyzed based on the matching judgment results.

2. The agricultural intelligent cultivation control method based on strategy optimization as claimed in claim 1, characterized in that: The collecting of relevant data and plant growth parameters in an agricultural environment by means of a sensor assembly comprises the following specific steps: S11, using a temperature sensor component and a plant image analyzer to collect plant growth related parameters, wherein the plant growth related parameters include temperature, average stem height and average leaf area, and transmit them to a database; S12. Collect respiration-related data using a gas analyzer, wherein the respiration-related data includes a carbon dioxide concentration change curve, and transmit the data to a database.

3. The agricultural intelligent cultivation control method based on strategy optimization as claimed in claim 2, characterized in that: The step of constructing a growth state judgment model and importing the collected growth parameters into the growth state judgment model to judge the growth state includes the following specific steps: S21, acquiring the growth status data of the previous cycle and the growth status data of the current cycle; S22, substituting the average leaf area and average stem height under the temperature of this cycle into the growth state abnormal value calculation formula to calculate the growth state abnormal value, wherein the growth state abnormal value calculation formula is: , where S is the average leaf area, H is the average stem height, is the average leaf area under normal conditions in the same cycle, is the average stem height under normal conditions of the same period, β1 and β2 are the corresponding characteristic coefficients, ε is the error term, G i The larger the value, the more abnormal the growth state, indicating that the growth parameters of the crop deviate from the normal range. The growth state of this cycle is compared with the previous cycle to determine the growth trend of the plant. If Gi-1>Gi, it means that the growth state of the crop in this cycle is better than that in the previous cycle. If G i-1 <G i , it indicates that the growth state of crops in this cycle is more abnormal than that in the previous cycle.

4. The agricultural intelligent cultivation control method based on strategy optimization as claimed in claim 3 is characterized in that: The construction of the energy conversion evaluation model and importing the respiratory data in the monitored agricultural environment into the energy conversion evaluation model for evaluation includes the following specific steps: S31, obtaining carbon dioxide concentration data of the previous cycle and the current cycle; S32. Substitute the carbon dioxide concentration into the dark respiration rate calculation formula: , where D is the concentration change of carbon dioxide, V is the volume of the greenhouse, A is the total area of ​​leaves, and t is the cycle length; S33, substituting the dark respiration rate of the current cycle and the previous cycle into the dark respiration rate change calculation formula, the formula is: If the change rate is positive, it means that the dark respiration rate has increased, indicating that the physiological activity of the plant has increased. If the change rate is negative, it means that the dark respiration rate has decreased, indicating that the physiological activity of the plant has weakened.

5. The agricultural intelligent cultivation control method based on strategy optimization as claimed in claim 4 is characterized in that: The construction of the temperature matching analysis model, performing temperature matching analysis based on the growth state judgment result and the dark respiration rate, and analyzing the matching temperature range based on the matching judgment result and the temperature optimization algorithm include the following specific steps: S41. Import the calculated abnormal value of the growth state change into the temperature matching analysis model. When G i < G, define the temperature at this time as mismatched and proceed to S42, where G is the normal growth state parameter; S42, substituting the calculated dark respiration rate into the fluctuation abnormal value calculation formula: , where R i For this temperature, R is the dark respiration rate, and the normal dark respiration rate is plotted. The abnormal value waveform is plotted based on the calculated Q value. The fluctuation abnormal value is substituted into the fluctuation abnormal value variance calculation formula to evaluate the fluctuation degree of the deviation. The calculation formula is: , where N is the number of cycles, μ is the average value of all fluctuation anomalies, the larger the variance, the greater the dispersion of data points around the mean, at this temperature, the dark respiration rate changes more, the smaller the variance, the more concentrated the data points are around the mean, at this temperature, the dark respiration rate changes less, and the physiological activity is more stable; S43, substituting the abnormal growth state data and the respiratory rate at the mismatching temperature into the matching temperature calculation formula to calculate the matching temperature range, wherein the matching temperature calculation formula is: , where T i is the abnormal ambient temperature, k is the adjustment factor, G i is the growth state of abnormal temperature, G is the normal growth state, R i is the dark respiration rate at abnormal temperature, R is the normal dark respiration rate, and w1 and w2 are weights.

6. An agricultural intelligent cultivation control system based on strategy optimization, which is implemented based on the agricultural intelligent cultivation control method based on strategy optimization as claimed in any one of claims 1 to 5, characterized in that: Specifically include: Data collection module, used to collect relevant data and plant growth parameters in agricultural environments; A growth status judgment module is used to import the collected growth parameters into a growth status judgment model to judge the growth status; Energy conversion assessment module, used to import the respiratory data in the monitored agricultural environment into the energy conversion assessment model for evaluation; The temperature matching analysis module is used to perform temperature matching analysis based on the growth status judgment results and dark respiration rate to evaluate whether the current temperature matches the plant growth and physiological activities; The matching temperature calculation module is used to obtain the matching temperature range.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the agricultural intelligent cultivation control method based on strategy optimization as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute an agricultural intelligent cultivation control method based on strategy optimization as described in any one of claims 1-6.

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