Greenhouse environment coordinated regulation and control method and system based on crop stem diameter change

Abstract

The invention discloses a greenhouse environment coordinated regulation and control method and system based on crop stem diameter change, and the method comprises the following steps: collecting greenhouse internal environment data, greenhouse external environment data, environment regulation and control equipment operation data and crop physiological and ecological data, and constructing a data set; constructing a crop stem diameter dynamic change prediction model based on the data set; and constructing a target optimization function based on the stem diameter dynamic change prediction model, the optimal stem diameter change range in the fixed period and environmental factor constraint conditions, and obtaining a cooperative regulation target value of the air environmental factors in the greenhouse by using a group optimization algorithm to obtain an environment regulation equipment operation strategy. According to the greenhouse environment coordinated regulation and control method provided by the embodiment of the invention, how to regulate and control the running state of environment control equipment in the greenhouse in real time based on the crop stem diameter change in greenhouse production so as to achieve an environment factor regulation and control target is provided.

Description

Technical Field

[0001] The present invention relates to the field of smart agricultural technology, and more specifically, to a greenhouse environment coordinated regulation method and system based on changes in crop stem diameter. Background Art

[0002] Crop growth has strict requirements on environmental conditions. Factors such as greenhouse temperature and light directly affect the growth rate, fruit development and final yield of crops. In recent years, with the rapid development of facility agriculture, greenhouse environmental regulation based on crop growth has become an important direction of modern agricultural research. However, the greenhouse environmental regulation methods in the prior art are usually controlled manually based on experience, with low intelligence and dependence on manual experience. Summary of the invention

[0003] The purpose of the present invention is to provide a new technical solution of a greenhouse environment collaborative control method and system based on changes in crop stem diameter, which can realize real-time control of the operating status of greenhouse environmental control equipment based on changes in crop stem diameter in greenhouse production to achieve the goal of environmental factor control and improve the level of intelligence.

[0004] The first aspect of the present invention provides a method for collaboratively controlling the greenhouse environment based on changes in crop stem diameter, comprising the following steps: collecting greenhouse environmental data, greenhouse environmental data, environmental control equipment operation data and crop physiological and ecological data to construct a data set; constructing a crop stem diameter dynamic change prediction model based on the data set; constructing a target optimization function based on the stem diameter dynamic change prediction model, the optimal stem diameter change range within a fixed period, and environmental factor constraints, and using a swarm optimization algorithm to obtain the target value for collaborative control of air environmental factors in the greenhouse, thereby obtaining an environmental control equipment operation strategy.

[0005] Optionally, an intelligent learning method based on a combination of an improved SSA algorithm and Transformer-XL is used to construct the crop stem diameter dynamic change prediction model.

[0006] Optionally, the crop stem diameter dynamic change prediction model is a LightGBM-SSA-Transformer-XL model, and constructing the crop stem diameter dynamic change prediction model comprises the following steps: S1. Divide the sample data set into multiple data set types according to the preset environmental change rules, and divide each type into training sample sets and test sample sets; S2. The input parameter variables of the training sample set are respectively subjected to feature extraction using the LightGBM algorithm to screen out characteristic factors that affect the growth rate of crop stem diameter; S3. Using the characteristic factors in the training sample set that have been screened by the LightGBM algorithm, a Transformer-XL model is constructed, and the SSA algorithm is used to optimize the hyperparameters of the Transformer-XL model to obtain a LightGBM-SSA-Transformer-XL model; S4. Select the characteristic factors in the test sample set that have been screened by the LightGBM algorithm, test the LightGBM-SSA-Transformer-XL model, and obtain the optimal LightGBM-SSA-Transformer-XL model; S5. Collect greenhouse environment data, greenhouse environment data, environmental control equipment operation data and crop physiological and ecological data in real time, and input them into the optimal LightGBM-SSA-Transformer-XL model to predict the future dynamic change trend of crop stem diameter.

[0007] Optionally, in step S3, optimizing the hyperparameters of the Transformer-XL model using the SSA algorithm includes the following steps: S31. Initialize SSA parameters, including population size and maximum number of iterations; S32. Convert the hyperparameters of the Transformer-XL model into the position coordinates of salps and calculate the fitness of each salp; S33. Sort the fitness values ​​of salps, take the position of the best salp as the position of the food source, take the first a% of the salp chain as the leader, and the last (100-a)% as the followers, and update the positions of the salp leader and followers respectively, a% is less than 50%; S34. Repeat step S31 to step S33, and when the maximum number of iterations is reached, the optimal values ​​of the number of encoder layers, batch size, and learning rate of the Transformer-XL model are obtained.

[0008] Optionally, the first 30% of the salp chain are used as leaders and the last 70% as followers, and the positions of the salp leaders and followers are updated separately.

[0009] Optionally, in step S1, the sample data set is divided into three data set types: low temperature and high humidity in winter, high temperature in summer, and heat preservation in spring and autumn according to seasonal changes and weather types.

[0010] Optionally, the greenhouse environmental data include air temperature, air humidity, light intensity, photosynthetically active radiation and carbon dioxide concentration; the greenhouse external environmental data include outdoor air temperature, air humidity, photosynthetically active radiation, rainfall and wind speed; the environmental control equipment operation data include equipment operation mode and operation time data; the crop physiological and ecological data include leaf temperature, leaf humidity and stem diameter change data.

[0011] The second aspect of the present invention provides a greenhouse environment collaborative control system based on crop stem diameter changes, including: a data acquisition module, the data acquisition module collects greenhouse environment data, greenhouse environment data, environmental control equipment operation data and crop physiological and ecological data to construct a data set; a cloud platform service module, the cloud platform service module includes a stem diameter change prediction module and an environmental factor collaborative control module, the stem diameter change prediction module constructs a crop stem diameter dynamic change prediction model based on the data set, the environmental factor collaborative control module constructs a target optimization function based on the stem diameter dynamic change prediction model, the optimal stem diameter change range within a fixed period, and environmental factor constraints, and uses a swarm optimization algorithm to obtain the target value of the collaborative control of air environmental factors in the greenhouse, and obtains the environmental control equipment operation strategy; a device execution module, the device execution module sends execution instructions to the control device in real time based on the generated environmental control strategy.

[0012] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor and a memory, wherein computer program instructions are stored in the memory, wherein when the computer program instructions are executed by the processor, the processor executes the steps of the above method.

[0013] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the steps of the above method.

[0014] According to the greenhouse environment collaborative control method based on crop stem diameter changes in an embodiment of the present invention, a target optimization function is constructed with crop stem diameter growth rate as the main control indicator, and then a control strategy for facility crop growth environment factors is obtained, which can provide a reliable technical method for greenhouse crop growth environment control and management.

[0015] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0017] Figure 1 Schematic diagram of the structure of the greenhouse environment collaborative control system according to an embodiment of the present invention; Figure 2 This is a flow chart of a method for collaboratively regulating environmental factors in a greenhouse based on changes in crop stem diameter according to an embodiment of the present invention; Figure 3 is a flow chart of a crop stem diameter change prediction method according to an embodiment of the present invention; Figure 4 is a schematic diagram of an electronic device according to an embodiment of the present invention.

[0018] Reference numerals: Electronic device 200; Processor 201; Memory 202; operating system 2021; application program 2022; Network interface 203; Input device 204; Hard disk 205; Display device 206. DETAILED DESCRIPTION

[0019] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

[0020] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0021] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0022] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0023] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0024] The following first describes in detail the greenhouse environment coordinated control method based on crop stem diameter changes according to an embodiment of the present invention with reference to the accompanying drawings.

[0025] like Figures 1 to 3As shown, the greenhouse environment collaborative control method based on crop stem diameter change according to an embodiment of the present invention includes the following steps: Collect greenhouse internal and external environmental data, environmental control equipment operation data and crop physiological and ecological data to construct a data set. Among them, greenhouse internal and external environmental data include greenhouse internal environmental data and greenhouse external environmental data. Greenhouse internal environmental data can also be defined as greenhouse internal environmental factor data, for example, greenhouse internal environmental data includes air temperature, air humidity, light intensity, photosynthetically active radiation and carbon dioxide concentration, etc. Greenhouse external environmental data can also be defined as greenhouse external environmental factor data or outdoor meteorological data, for example, greenhouse external environmental data includes outdoor air temperature, air humidity, photosynthetically active radiation, rainfall and wind speed, etc. Environmental control equipment operation data can also be defined as equipment operation data, for example, environmental control equipment operation data includes equipment operation mode and operation time data, etc. Crop physiological and ecological data can be defined as crop growth data, for example, crop physiological and ecological data includes leaf temperature, leaf humidity and stem diameter change data.

[0026] Optionally, when constructing a data set, data processing may be performed, for example, the collected data may be transmitted to a cloud platform server for data anomaly elimination, data filling, and normalization.

[0027] A crop stem diameter dynamic change prediction model is constructed based on the data set, for example, based on an artificial intelligence algorithm and using the cleaned data set to construct a crop stem diameter dynamic change prediction model.

[0028] Based on the prediction model of stem diameter dynamic change, the optimal range of stem diameter change in a fixed period, and environmental factor constraints, the target optimization function is constructed, and the group optimization algorithm is used to obtain the target value of the coordinated regulation of air environmental factors in the greenhouse, and the operation strategy of the environmental control equipment is obtained. In other words, taking the micro-change of stem diameter as the core control indicator, the target optimization function is constructed based on the prediction model of stem diameter dynamic change, the optimal range of stem diameter change in a fixed period, and environmental factor constraints, and the group optimization algorithm is used to obtain the target value of the coordinated regulation of air environmental factors in the greenhouse, and then the operation strategy of the environmental control equipment is obtained.

[0029] In addition, in crop cultivation, the change in stem diameter is one of the key indicators for evaluating the growth of crops, and its monitoring method is convenient and does not harm the crops. By real-time monitoring of small changes in the diameter of crop stems, the growth rate, growth trend and physiological state of the plants can be reflected in a timely manner, providing a scientific basis for environmental regulation. With the breakthrough of sensor and phenotypic monitoring technology, it is possible to use the change in crop stem diameter as a basis for regulating environmental factors in the greenhouse. Therefore, the embodiment of the present invention provides a greenhouse environment collaborative regulation method based on the change in crop stem diameter, which is beneficial to ensure that the greenhouse environment is always in the best state for crop growth, which is of great significance, not only to improve crop yield and quality, but also to promote the sustainable development of agricultural production.

[0030] It should be noted that the greenhouse environment coordinated control method based on crop stem diameter changes of the embodiment of the present invention can be used for vine plants or vine crops such as tomatoes, sweet peppers, and cucumbers.

[0031] Therefore, according to the greenhouse environment collaborative control method based on crop stem diameter changes according to an embodiment of the present invention, a target optimization function is constructed with crop stem diameter growth rate as the main control indicator, and then a control strategy for facility crop growth environment factors is obtained, which can provide a reliable technical method for greenhouse crop growth environment control and management.

[0032] Optionally, the target optimization function is constructed as follows: The environmental factor constraints are: In formula (6) and formula (7), F(x) is the target optimization function; f(x) is the stem diameter change prediction model function; S obj , the optimal setting value for the micro-change in crop stem diameter within a specific period; S max , the maximum change of stem diameter within a specific period; S min , the minimum change of stem diameter within a specific period; t min , minimum temperature; t max , maximum temperature; t aim , temperature control target value; I aim , target value of light control; h aim , humidity control target value; S (t, i, h), stem diameter change prediction model function; St, constraint condition set.

[0033] In formula (6) and formula (7), the stem diameter change prediction model function and the optimal setting value for micro-variation of crop stem diameter within a specific period Arranged into target optimization function The setting range of stem diameter change within a specific period ( ) comes from the production experience data of crop cultivation. The number of environmental factor constraints is consistent with the actual input parameters of the prediction model, among which the temperature control target value It needs to meet the pre-set reasonable control range ( );Lighting control target value It needs to meet the pre-set reasonable control range ( );Humidity control target value It needs to meet the pre-set reasonable control range ( ).

[0034] Moreover, the optimization goal is to make the function infinitely approach the optimal set value of the stem diameter growth change within a fixed time period while satisfying the constraints. The second-generation non-dominated sorting genetic algorithm (NSGA-II) is selected, and the results are verified based on the measured environment and stem diameter change data in the greenhouse. Finally, the environmental control strategy in the greenhouse during the entire growth period of crops is obtained.

[0035] According to one embodiment of the present invention, an intelligent learning method based on an improved SSA (Salp Swarm Algorithm) algorithm combined with Transformer-XL is used to construct a prediction model for dynamic changes in crop stem diameter. That is, in this embodiment, the method for predicting changes in crop stem diameter in a greenhouse adopts an intelligent learning method based on an improved salp swarm algorithm combined with Transformer-XL, and predicts the trend of crop stem diameter changes in real time based on the improved SSA and Transformer-XL combined model. The improved SSA algorithm may include a dynamically adaptive SSA algorithm.

[0036] In some specific embodiments of the present invention, the crop stem diameter dynamic change prediction model is a LightGBM-SSA-Transformer-XL model, such as Figure 3 As shown in FIG. 1 , constructing a crop stem diameter dynamic change prediction model includes the following steps: S1. Divide the sample data set into multiple data set types according to the preset environmental change rules, and divide each type into training sample sets and test sample sets; S2. The input parameter variables of the training sample set are respectively subjected to feature extraction by the LightGBM algorithm to screen out the characteristic factors that affect the growth rate of crop stem diameter, that is, the input parameter variables are subjected to feature extraction by LightGBM. The LightGBM algorithm is a Light Gradient Boosting Machine (LightGBM) algorithm. In step S2, the Light Gradient Boosting Machine algorithm is used to obtain the feature importance of the input data, screen out the main characteristic factors that affect the growth rate of crop stem diameter, and thus improve the prediction accuracy and running speed of the model.

[0037] S3. The Transformer-XL model is constructed by using the characteristic factors selected by the LightGBM algorithm in the training sample set, and the hyperparameters of the Transformer-XL model are optimized by using the improved SSA algorithm to obtain the LightGBM-SSA-Transformer-XL model; that is, the crop stem diameter dynamic change prediction model based on the Transformer-XL model is constructed by using the training sample set. The improved Salp Swarm Algorithm (SSA) is used to optimize the hyperparameters of the Transformer-XL model, such as the number of encoder layers, batch size, and learning rate.

[0038] Among them, in step S3, Transformer-XL is an improved method for the traditional Transformer model, which introduces a loop mechanism and relative position encoding, and is mainly optimized for the problem of long sequence modeling. During training, Transformer-XL processes the sequence segment by segment to avoid the problem that the complexity of global self-attention calculation increases sharply with the increase of sequence length. At the same time, the traditional Transformer model uses absolute position encoding to calculate the attention score, which makes the model unable to adapt to the changes in relative distances between different positions and makes it difficult to capture the long-term dependencies of time series problems. The Transformer-XL used in this embodiment uses relative position encoding to effectively solve the above problems.

[0039] In addition, the salp swarm algorithm in step S3 is a new swarm intelligence optimization algorithm. The idea of ​​the algorithm comes from the aggregation behavior of salps, that is, the salp chain. In SSA, the salp chain consists of two types of salps, the leader and the follower. The leader is located at the front of the salp chain, and the other individuals are followers.

[0040] The salp swarm algorithm is described in detail below.

[0041] First, in the algorithm, the location of the food source is the target location of all salps, and the leader’s position update formula is: (1); in, is the current iteration number, For the current salp leader in The position of the dimensional space, For food source The position of the dimensional space; , Respectively The upper and lower limits of the dimensional space; , is a random number uniformly distributed between (0, 1). is the maximum number of iterations, It decreases adaptively as the number of iterations increases, and its value is: (2); Second, the follower updates the position formula as follows: (3); in, and The previous generation of salps followers , In the dimensional space position.

[0042] In order to better balance the exploration and development capabilities of the salp swarm algorithm and avoid falling into the local optimal solution, the present invention proposes a dynamic adaptive weighting method so that the followers in the early stage of the algorithm search have a stronger exploration ability, and the specific development ability of the followers in the later stage of the algorithm search can be improved. Among them, the algorithm improvement part is shown in the following formula: (4); (5); In formula (4) and formula (5), is the weight.

[0043] S4. Select the feature factors in the test sample set that have been screened by the LightGBM algorithm, test the LightGBM-SSA-Transformer-XL model, and obtain the optimal LightGBM-SSA-Transformer-XL model. In other words, select the test sample set, test the Transformer-XL model with the above optimized hyperparameters, and obtain the optimal Transformer-XL model.

[0044] S5. Collect greenhouse environment data, greenhouse environment data, environmental control equipment operation data and crop physiological and ecological data in real time, and input them into the optimal LightGBM-SSA-Transformer-XL model to predict the future dynamic change trend of crop stem diameter. In other words, collect greenhouse internal and external environmental factor data and crop body number data in real time, and input the real-time data into the optimal LightGBM-SSA-Transformer-XL model to predict the future dynamic change trend of crop stem diameter.

[0045] According to one embodiment of the present invention, optimizing the hyperparameters of the Transformer-XL model using the SSA algorithm in step S3 includes the following steps: S31. Initialize the parameters of the Salp Swarm Algorithm (SSA), including the population size and the maximum number of iterations; S32. Convert the hyperparameters of the Transformer-XL model into the position coordinates of salps and calculate the fitness of each salp; S33. Sort the fitness values ​​of salps, take the position of the best salp as the position of the food source, take the first a% of the salp chain as the leader, and the last (100-a)% as the followers, and update the positions of the salp leader and followers respectively, a% is less than 50%; S34. Repeat step S31 to step S33, and when the maximum number of iterations is reached, the optimal values ​​of the number of encoder layers, batch size, and learning rate of the Transformer-XL model are obtained.

[0046] In this embodiment, the improved salp swarm algorithm is used to optimize the hyperparameters of the Transformer-XL model, such as the number of encoder layers, batch size, and learning rate.

[0047] In some specific embodiments of the present invention, the first 30% of the salp chain is used as the leader and the last 70% as the followers. The positions of the salp leader and followers are updated separately, which can improve decision-making efficiency and group collaboration, enable leaders to quickly formulate directions and strategies, and reduce differences in the decision-making process, while most followers focus on execution to ensure coordinated group actions.

[0048] According to one embodiment of the present invention, in step S1, the sample data set is divided into three data set types: low temperature and high humidity in winter, high temperature in summer, and heat preservation in spring and autumn according to seasonal changes and weather types. That is, the sample data set is divided into three data set types: low temperature and high humidity in winter, high temperature in summer, and heat preservation in spring and autumn according to seasonal changes and weather types, and a training set and a test set are divided for each type. The above seasonal division can enable the model to focus on specific climate conditions and improve its adaptability and prediction accuracy to each season.

[0049] In some specific embodiments of the present invention, the environmental data inside the greenhouse include air temperature, air humidity, light intensity, photosynthetically active radiation and carbon dioxide concentration; the environmental data outside the greenhouse include outdoor air temperature, air humidity, photosynthetically active radiation, rainfall and wind speed; the operating data of the environmental control equipment includes equipment operation mode and operation time data; the crop physiological and ecological data include leaf temperature, leaf humidity and stem diameter change data. In this embodiment, by selecting the above data at the same time, the growth changes of crop stem diameter can be predicted more accurately.

[0050] like Figure 1 As shown, the present invention also provides a greenhouse environment collaborative control system based on crop stem diameter changes, including: a data acquisition module, a stem diameter change prediction module, an environmental factor collaborative control module and a device execution module. That is, the greenhouse environment collaborative control system based on crop stem diameter changes includes: a data acquisition module, a cloud platform service module and a device execution module, wherein the cloud platform service module includes a stem diameter change prediction module and an environmental factor collaborative control module.

[0051] Specifically, the data acquisition module mainly collects greenhouse internal and external environmental data, environmental control equipment operation data, crop physiological and ecological data, etc., to construct a data set; that is, the data acquisition module can obtain greenhouse internal and external environmental data and crop growth data, etc. Among them, the greenhouse environmental factor data mainly include air temperature, air humidity, light intensity, light effective radiation, carbon dioxide concentration, etc.; the greenhouse external environmental factor data include air temperature, air humidity, light intensity, light effective radiation, wind speed, wind direction and rainfall, etc.; the equipment operation data include the operating status and operation time of the environmental control equipment; the crop physiological and ecological data include crop leaf temperature, leaf humidity, stem diameter change and other information. By using the above data at the same time, the growth change of crop stem diameter can be predicted more accurately.

[0052] The cloud platform service module includes a stem diameter change prediction module and an environmental factor coordinated control module. The stem diameter change prediction module builds a crop stem diameter dynamic change prediction model based on the data set. The environmental factor coordinated control module builds a target optimization function based on the stem diameter dynamic change prediction model, the optimal stem diameter change range within a fixed period, and the environmental factor constraint conditions, and uses a swarm optimization algorithm to obtain the target value of the coordinated control of the air environmental factors in the greenhouse, and obtains the operation strategy of the environmental control equipment.

[0053] Among them, the cloud platform service module uses the Internet of Things, sensors, etc. to process the collected data, such as technical transmission, storage and cleaning of massive data; and it can accurately and dynamically predict future short-term stem diameter changes based on artificial intelligence algorithms, and use stem diameter changes as the basis for regulation. By constructing a target optimization function, it obtains the coordinated regulation thresholds of multiple environmental factors within a fixed crop growth period (for example, one week), and then generates an environmental regulation strategy.

[0054] The device execution module sends execution instructions to the control device in real time based on the generated environmental control strategy.

[0055] That is to say, the present invention also provides a greenhouse environmental factor coordinated control system based on crop stem diameter changes, which mainly includes a data acquisition module, a cloud platform service module and a device execution module. The greenhouse environmental coordinated control system based on crop stem diameter changes according to an embodiment of the present invention can correspond to the method of the above embodiment, and can realize intelligent management and control of the crop growth environment, which will not be described in detail here.

[0056] Optionally, the cloud platform service module also includes a data storage and cleaning module, that is, the cloud platform service module includes a data storage and cleaning module, a stem diameter change prediction module and an environmental factor coordinated regulation module.

[0057] The greenhouse environment coordinated control method and system based on crop stem diameter changes according to an embodiment of the present invention will be described in detail below in conjunction with specific embodiments.

[0058] Example 1 In Example 1, tomatoes are selected as the crop type. The greenhouse environment collaborative control system based on crop stem diameter changes in Example 1 is a greenhouse environment collaborative control system driven by crop growth. The system includes a data acquisition module, a cloud platform service module and a device execution module.

[0059] Embodiment 1 specifically comprises the following steps: 1) Collect greenhouse environment data, greenhouse environment data, environmental control equipment operation data and crop physiological and ecological data within a predetermined time period.

[0060] 2) All the above data are cleaned and processed, and the processed sample sets are divided into three types of data sets according to seasonal changes and weather types: low temperature and high humidity in winter, high temperature in summer, and heat preservation in spring and autumn. At the same time, training sets and test sets are divided for each type.

[0061] 3) Using the training set and test set, a crop stem diameter change prediction model (LightGBM-SSA-Transformer-XL model) was constructed based on the lightweight gradient boosting machine (LightGBM), the improved salp swarm (SSA) algorithm and the Transformer variant (Transformer-XL) model respectively.

[0062] 4) Combined with the LightGBM-SSA-Transformer-XL model, the objective optimization function is constructed, and the swarm intelligence optimization algorithm (second-generation non-dominated sorting genetic algorithm) is used to complete the numerical solution of the objective function and obtain the target threshold of the coordinated regulation of crop growth environment parameters. In addition, based on the target value of the coordinated regulation of environmental factors, the environmental regulation strategy is generated according to the current state of the environmental regulation equipment.

[0063] It can be seen that Example 1 provides a greenhouse environment collaborative control method based on crop stem diameter changes, takes stem diameter growth changes as the core control indicator, forms a set of facility tomato environmental parameter control strategies driven by crop growth, and realizes intelligent management and control of the crop growth environment.

[0064] The present invention further provides an electronic device 200, comprising: a processor 201 and a memory 202, wherein the memory 202 stores computer program instructions, wherein when the computer program instructions are executed by the processor 201, the processor 201 executes the steps of the method in the above embodiment.

[0065] Furthermore, if Figure 4 As shown, the electronic device 200 further includes a network interface 203 , an input device 204 , a hard disk 205 , and a display device 206 .

[0066] The above-mentioned interfaces and devices can be interconnected through a bus architecture. The bus architecture can include any number of interconnected buses and bridges. Specifically, one or more central processing units 201 (CPUs) represented by the processor 201 and various circuits of one or more memories 202 represented by the memory 202 are connected together. The bus architecture can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits together. It can be understood that the bus architecture is used to achieve connection and communication between these components. In addition to the data bus, the bus architecture also includes a power bus, a control bus, and a status signal bus, which are all well known in the art, so they will not be described in detail herein.

[0067] The network interface 203 can be connected to a network (such as the Internet, a local area network, etc.), obtain relevant data from the network, and save it in the hard disk 205.

[0068] The input device 204 can receive various instructions input by the operator and send them to the processor 201 for execution. The input device 204 can include a keyboard or a pointing device (for example, a mouse, a trackball, a touch pad or a touch screen, etc.).

[0069] The display device 206 can display the result obtained by the processor 201 executing the instruction.

[0070] The memory 202 is used to store programs and data necessary for the operation of the operating system 2021, as well as data such as intermediate results during the calculation process of the processor 201.

[0071] It is understood that the memory 202 in the embodiment of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. The memory 202 of the apparatus and method described herein is intended to include, but is not limited to, these and any other suitable types of memory 202.

[0072] In some implementations, the memory 202 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system 2021 and application programs 2022 .

[0073] The operating system 2021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 2022 includes various application programs 2022, such as a browser, etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present invention can be included in the application 2022.

[0074] The processor 201 executes the steps of the method according to the above embodiment when calling and executing the application 2022 and data stored in the memory 202, specifically, the program or instructions stored in the application 2022.

[0075] The method disclosed in the above embodiment of the present invention can be applied to the processor 201, or implemented by the processor 201. The processor 201 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 201 or the instruction in the form of software. The above processor 201 can be a general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general processor can be a microprocessor or the processor 201 can also be any conventional processor 201, etc. The steps of the method disclosed in conjunction with the embodiment of the present invention can be directly embodied as a hardware decoding processor to be executed, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 202, and the processor 201 reads the information in the memory 202 and completes the steps of the above method in combination with its hardware.

[0076] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions of the present invention or a combination thereof.

[0077] For software implementation, the technology herein can be implemented by modules (e.g., procedures, functions, etc.) that perform the functions herein. The software code can be stored in the memory 202 and executed by the processor 201. The memory 202 can be implemented in the processor 201 or outside the processor 201.

[0078] Specifically, the processor 201 is also used to read the computer program and execute the following steps: the method predicts and outputs the answer to the question asked by the user.

[0079] The present invention further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by the processor 201, the processor 201 executes the steps of the method in the above embodiment.

[0080] In the several embodiments provided by the present invention, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as 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.

[0081] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may be physically included separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0082] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform some steps of the sending and receiving methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store program codes.

[0083] Although some specific embodiments of the present invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It will be appreciated by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A greenhouse environment coordinated control method based on crop stem diameter changes, characterized in that: The steps include: Collect greenhouse environment data, greenhouse environment data, environmental control equipment operation data and crop physiological and ecological data to build a data set; Constructing a crop stem diameter dynamic change prediction model based on the data set; Based on the dynamic change prediction model of stem diameter, the optimal change range of stem diameter within a fixed period, and environmental factor constraints, a target optimization function is constructed, and a swarm optimization algorithm is used to obtain the target value of the coordinated control of air environmental factors in the greenhouse, thereby obtaining the operation strategy of the environmental control equipment.

2. The greenhouse environment coordinated control method based on crop stem diameter change according to claim 1 is characterized in that: The crop stem diameter dynamic change prediction model is constructed by using an intelligent learning method based on a combination of an improved SSA algorithm and Transformer-XL.

3. The greenhouse environment coordinated control method based on crop stem diameter change according to claim 2 is characterized in that: The crop stem diameter dynamic change prediction model is a LightGBM-SSA-Transformer-XL model. Constructing the crop stem diameter dynamic change prediction model includes the following steps: S1. Divide the sample data set into multiple data set types according to the preset environmental change rules, and divide each type into training sample sets and test sample sets; S2. The input parameter variables of the training sample set are respectively subjected to feature extraction using the LightGBM algorithm to screen out characteristic factors that affect the growth rate of crop stem diameter; S3. Using the characteristic factors in the training sample set that have been screened by the LightGBM algorithm, a Transformer-XL model is constructed, and the SSA algorithm is used to optimize the hyperparameters of the Transformer-XL model to obtain a LightGBM-SSA-Transformer-XL model; S4. Select the characteristic factors in the test sample set that have been screened by the LightGBM algorithm, test the LightGBM-SSA-Transformer-XL model, and obtain the optimal LightGBM-SSA-Transformer-XL model; S5. Collect greenhouse environment data, greenhouse environment data, environmental control equipment operation data and crop physiological and ecological data in real time, and input them into the optimal LightGBM-SSA-Transformer-XL model to predict the future dynamic change trend of crop stem diameter.

4. The greenhouse environment coordinated control method based on crop stem diameter change according to claim 3 is characterized in that: In step S3, the SSA algorithm is used to optimize the hyperparameters of the Transformer-XL model, including the following steps: S31. Initialize SSA parameters, including population size and maximum number of iterations; S32. Convert the hyperparameters of the Transformer-XL model into the position coordinates of salps and calculate the fitness of each salp; S33. Sort the fitness values ​​of salps, take the position of the best salp as the position of the food source, take the first a% of the salp chain as the leader, and the last (100-a)% as the followers, and update the positions of the salp leader and followers respectively, a% is less than 50%; S34. Repeat step S31 to step S33, and when the maximum number of iterations is reached, the optimal values ​​of the number of encoder layers, batch size, and learning rate of the Transformer-XL model are obtained.

5. The greenhouse environment coordinated control method based on crop stem diameter change according to claim 4 is characterized in that: The first 30% of the salp chain are taken as leaders and the last 70% as followers, and the positions of the salp leaders and followers are updated separately.

6. The greenhouse environment coordinated control method based on crop stem diameter change according to claim 3, characterized in that: In step S1, the sample data set is divided into three types of data sets: low temperature and high humidity in winter, high temperature in summer, and heat preservation in spring and autumn according to seasonal changes and weather types.

7. The greenhouse environment coordinated control method based on crop stem diameter change according to claim 1, characterized in that: The greenhouse environment data include air temperature, air humidity, light intensity, photosynthetically active radiation and carbon dioxide concentration; the greenhouse environment data include outdoor air temperature, air humidity, photosynthetically active radiation, rainfall and wind speed; The environmental control equipment operation data includes equipment operation mode and operation time data; the crop physiological and ecological data includes leaf temperature, leaf humidity and stem diameter change data.

8. A greenhouse environment coordinated control system based on crop stem diameter changes, characterized in that: include: A data collection module, wherein the data collection module collects greenhouse environment data, greenhouse environment data, environmental control equipment operation data and crop physiological and ecological data to construct a data set; A cloud platform service module, the cloud platform service module includes a stem diameter change prediction module and an environmental factor collaborative control module, the stem diameter change prediction module constructs a crop stem diameter dynamic change prediction model based on the data set, the environmental factor collaborative control module constructs a target optimization function based on the stem diameter dynamic change prediction model, the optimal stem diameter change range within a fixed period, and environmental factor constraints, and uses a swarm optimization algorithm to obtain a target value for collaborative control of air environmental factors in a greenhouse, thereby obtaining an environmental control equipment operation strategy; The device execution module sends execution instructions to the control device in real time based on the generated environment control strategy.

9. An electronic device, characterized in that: include: A processor and a memory, wherein computer program instructions are stored in the memory, wherein when the computer program instructions are executed by the processor, the processor is caused to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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