Intelligent water and fertilizer adjusting method and system for soilless culture of vegetables

Abstract

The invention discloses an intelligent water and fertilizer adjusting method and system for soilless culture of vegetables, and the method comprises the steps: arranging an environmental parameter sensor and a nutrient solution state sensor, and transmitting the collected data to a central control system; processing the sensor data through a central control system, and establishing a prediction model through a machine learning algorithm; according to an output result of the prediction model, combining plant physiology knowledge and cultivation expert experience to obtain a water and fertilizer adjustment strategy, and issuing the water and fertilizer adjustment strategy to a water and fertilizer equipment controller through a data interface protocol; the regulation strategy is converted into a control instruction through the water and fertilizer equipment controller, and an execution mechanism is controlled to achieve water and fertilizer supply; in the adjusting strategy execution process, abnormal judgment is conducted on the environment parameters or the nutrient solution state, and if abnormal, an alarm mechanism is triggered. Intelligent and precise water and fertilizer management based on crop growth requirements is realized, the resource utilization efficiency can be remarkably improved, and the labor cost is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of vegetable soilless cultivation, and in particular relates to a water and fertilizer intelligent regulation method for vegetable soilless cultivation and a system thereof. Background Art

[0002] In the field of soilless vegetable cultivation, water and fertilizer management is a key link to ensure healthy crop growth and increase yield. Although there have been some attempts to improve water and fertilizer management through automation technology, existing technologies still face a series of challenges.

[0003] First, the environmental conditions and crop requirements of different cultivation areas vary greatly, which requires the water and fertilizer management system to be able to achieve unified regulation in multiple regions, but existing technologies often find it difficult to adapt to this diversity and complexity.

[0004] Secondly, existing technologies have limitations in sensor layout and data transmission. The accuracy and stability of sensors are insufficient, and the reliability and real-time performance of data transmission are difficult to guarantee. In addition, the processing power and algorithm optimization level of the central control system are insufficient, making it difficult to quickly process and analyze massive amounts of data and form an effective water and fertilizer regulation strategy.

[0005] Furthermore, the existing technology has compatibility issues in the issuance and execution of adjustment strategies. It is difficult to uniformly manage water and fertilizer equipment of different models and specifications, which affects the accurate execution of adjustment instructions.

[0006] Finally, the existing system lacks an effective stability and reliability assurance mechanism when dealing with emergencies such as equipment failures, extreme weather and other disturbances, which limits the long-term stable operation of the system and the continuous optimization of the crop growth environment. Summary of the invention

[0007] In order to solve the above technical problems, the present invention proposes a water and fertilizer intelligent regulation method and system for soilless vegetable cultivation to solve the problems existing in the above prior art.

[0008] In a first aspect, the present invention provides a method for intelligently regulating water and fertilizer for soilless cultivation of vegetables, comprising:

[0009] Arrange environmental parameter sensors and nutrient solution status sensors, and transmit the collected data to the central control system through a wireless sensor network;

[0010] After receiving the sensor data through the central control system, the sensor data is processed and a prediction model is established through a machine learning algorithm;

[0011] Based on the output of the prediction model, combined with plant physiology knowledge and cultivation expert experience, the water and fertilizer regulation strategy is obtained and sent to the water and fertilizer equipment controller through the data interface protocol;

[0012] The water and fertilizer equipment controller converts the regulation strategy into control instructions and controls the actuator to realize water and fertilizer supply;

[0013] During the execution of the adjustment strategy, the environmental parameters or the state of the nutrient solution are judged to be abnormal. If abnormal, an alarm mechanism is triggered and an emergency plan is initiated through the central control system.

[0014] Preferably, the steps of arranging sensors and transmitting data include:

[0015] Arrange temperature and humidity sensors, light intensity sensors, pH sensors, and EC value sensors in the cultivation area to obtain environmental data and nutrient solution status data;

[0016] Through wireless sensor network networking, the environmental data and the nutrient solution status data are transmitted in real time to a central control system for subsequent processing.

[0017] Preferably, the data processing and prediction model building steps include:

[0018] Cleaning the environmental data and the nutrient solution status data, and processing abnormal values ​​and missing values;

[0019] Integrate the processed data into multi-source heterogeneous data through data fusion methods;

[0020] Extracting key features related to crop growth conditions based on the multi-source heterogeneous data;

[0021] Based on the key features, a supervised learning algorithm is used to train and establish a prediction model.

[0022] Preferably, the water-fertilizer regulation strategy forming step comprises:

[0023] Acquire plant physiology knowledge and cultivation expert experience based on crop growth status data derived from prediction models;

[0024] By using a data fusion algorithm, the crop growth status data, the plant physiology knowledge and the cultivation expert experience are integrated to generate a water and fertilizer regulation strategy for each region;

[0025] According to the generated water-fertilizer regulation strategy, determine the set values ​​of irrigation time, irrigation amount and nutrient solution ratio;

[0026] The set irrigation time, irrigation amount and nutrient solution ratio are sent to the water and fertilizer equipment controllers in each area through a unified data interface protocol.

[0027] Preferably, the water and fertilizer equipment control step includes:

[0028] According to the regional division and the distribution of water and fertilizer equipment, a regional equipment mapping table is established to clarify the controllers and actuators corresponding to the regions;

[0029] Preset equipment model and specification parameters in the controller, including the flow curve of the valve and the pressure and flow characteristics of the pump;

[0030] Based on the adjustment strategy and the regional device mapping table, converting into device control instructions;

[0031] Based on the equipment control instructions, the irrigation valve and the fertilizer pump are controlled to realize the water and fertilizer supply.

[0032] Preferably, the process of converting into device control instructions includes:

[0033] The adjustment strategy issued by the host computer analyzes the quantitative parameters of irrigation and fertilization in each area;

[0034] Based on the regional equipment mapping table, the fertigation quantitative parameters are decomposed into each controller, and the fertigation quantitative parameters are converted into control instructions of corresponding equipment through the controller.

[0035] Preferably, the process of the central control system initiating the emergency plan includes:

[0036] During the execution of the regulation strategy, the central control system pre-processes the received data and compares it with the preset threshold to determine whether there is any abnormality;

[0037] If an abnormality is detected, the central control system triggers an alarm and initiates an emergency plan, executing control measures according to the severity of the abnormality; wherein the control measures include adjusting parameters of environmental control equipment and nutrient solution supply system to restore normal conditions;

[0038] The central control system continuously monitors the effects of adjustments and uses machine learning algorithms to evaluate and optimize emergency response strategies;

[0039] If the anomaly persists, the central control system takes further measures;

[0040] If the anomaly is resolved, the system returns to normal operating mode and records and analyzes abnormal events to optimize future responses.

[0041] In a second aspect, the present invention further provides a water and fertilizer intelligent regulation system for vegetable soilless cultivation, which is used to implement the water and fertilizer intelligent regulation method for vegetable soilless cultivation described in the first aspect, and the system comprises:

[0042] A data acquisition module is used to arrange environmental parameter sensors and nutrient solution status sensors, and transmit the collected data to the central control system through a wireless sensor network;

[0043] A data processing module is used to process the sensor data after receiving it through the central control system, and to establish a prediction model through a machine learning algorithm;

[0044] The strategy formulation module is used to obtain the water and fertilizer adjustment strategy based on the output results of the prediction model, combined with plant physiology knowledge and cultivation expert experience, and send it to the water and fertilizer equipment controller through the data interface protocol;

[0045] An execution control module is used to convert the regulation strategy into control instructions through the water and fertilizer equipment controller, and control the actuator to realize water and fertilizer supply;

[0046] The exception handling module is used to judge the abnormality of the environmental parameters or the state of the nutrient solution during the execution of the adjustment strategy. If there is an abnormality, an alarm mechanism is triggered and an emergency plan is started through the central control system.

[0047] Compared with the prior art, the present invention has the following advantages and technical effects:

[0048] The present invention provides an intelligent water and fertilizer regulation method for soilless cultivation of vegetables, comprising: firstly arranging environmental parameter sensors and nutrient solution state sensors, and transmitting the collected data to a central control system through a wireless sensor network; then, after receiving the sensor data through the central control system, processing the sensor data, and establishing a prediction model through a machine learning algorithm; again, according to the output result of the prediction model, combining plant physiological knowledge and cultivation expert experience, obtaining a water and fertilizer regulation strategy, and sending it to a water and fertilizer equipment controller through a data interface protocol; further, converting the regulation strategy into a control instruction through the water and fertilizer equipment controller, and controlling an actuator to realize water and fertilizer supply; finally, in the process of executing the regulation strategy, performing an abnormal judgment on the environmental parameter or the nutrient solution state, and if abnormal, triggering an alarm mechanism, and starting an emergency plan through the central control system.

[0049] The present invention transmits the collected data to the central control system through a wireless sensor network to achieve real-time monitoring of the environmental characteristics of the cultivation area and the growth needs of crops; establishes a prediction model through a machine learning algorithm to improve the computing power and algorithm optimization of data processing, and solves the problems of large data volume and slow processing speed in the prior art; sends it to the water and fertilizer equipment controller through a data interface protocol to achieve unified regulation of multiple regions, and solves the problem that different regions are difficult to uniformly regulate in the prior art; the present invention converts the regulation strategy into a control instruction, controls the irrigation valve, fertilizer pump and other actuators, realizes the water and fertilizer supply, is compatible with equipment of different models and specifications, ensures the accurate execution of the regulation instruction, and solves the equipment compatibility problem in the prior art; the present invention activates the emergency plan through the central control system to deal with emergencies such as equipment failure and extreme weather, ensures the stability and reliability of the system, and solves the problem of insufficient ability to deal with emergencies in the prior art;

[0050] The present invention realizes intelligent and precise water and fertilizer management based on crop growth needs, which can significantly improve resource utilization efficiency, reduce labor costs, and ensure crop yield and quality. At the same time, it has strong adaptability and scalability and can be widely used in various types of facility agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0052] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0053] Figure 2 Schematic diagram of a system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0055] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0056] Embodiment 1

[0057] like Figure 1 As shown, this embodiment provides a method for intelligent water and fertilizer regulation for soilless cultivation of vegetables, including:

[0058] S1. Arrange environmental parameter sensors and nutrient solution status sensors, and transmit the collected data to the central control system through a wireless sensor network;

[0059] S2. After receiving the sensor data through the central control system, the sensor data is processed and a prediction model is established through a machine learning algorithm;

[0060] S3. Based on the output results of the prediction model, combined with plant physiology knowledge and cultivation expert experience, the water and fertilizer adjustment strategy is obtained and sent to the water and fertilizer equipment controller through the data interface protocol;

[0061] S4, converting the regulation strategy into control instructions through the water and fertilizer equipment controller, and controlling the actuator to realize water and fertilizer supply;

[0062] S5. During the execution of the adjustment strategy, the environmental parameters or the state of the nutrient solution are judged to be abnormal. If abnormal, an alarm mechanism is triggered and an emergency plan is initiated through the central control system.

[0063] Specifically, in the tomato cultivation greenhouse, SHT31 temperature and humidity sensors, BH1750 light intensity sensors, pH sensors and EC sensors are reasonably arranged, among which the temperature and humidity sensors are arranged 5 meters away from the crops, the light sensors are arranged on the top, the pH and EC sensors are arranged at the nutrient solution return pipe, and the sensor sampling frequency is set to 10 minutes. Each sensor forms a wireless sensor network through the Zigbee protocol, with a data transmission rate of up to 250kbps and a network coverage radius of 100 meters. The central control system is based on the STM32 microcontroller and obtains sensor data from the wireless sensor gateway through the Modbus protocol. According to the set thresholds of temperature 25℃, humidity 60%, light intensity 20000lux, pH value 0, and EC value 8ms / cm, the fuzzy control algorithm is used for judgment. If the deviation exceeds 5%, the corresponding actuator is triggered for adjustment, such as adjusting the water pump flow through the frequency control method and adjusting the opening and closing degree of the roller shutter through the PID control algorithm. At the same time, the control system uses BP neural network algorithm, takes historical environmental parameters and nutrient solution status data as training samples, and establishes a prediction model for environmental factors, tomato growth rate, and fruit quality, with an average prediction accuracy of more than 90%. The system also uses the host computer software developed by QT to display the changes in temperature, humidity, and light intensity in real time with a curve chart, the changes in pH and EC values ​​with a bar chart, and the qualified rate of each parameter with a pie chart, providing intuitive data support for management personnel.

[0064] Furthermore, the steps of arranging sensors and transmitting data include:

[0065] Arrange temperature and humidity sensors, light intensity sensors, pH sensors, and EC value sensors in the cultivation area to obtain environmental data and nutrient solution status data;

[0066] Through wireless sensor network networking, the environmental data and the nutrient solution status data are transmitted in real time to a central control system for subsequent processing.

[0067] Furthermore, the data processing and prediction model building steps include:

[0068] Cleaning the environmental data and the nutrient solution status data, and processing abnormal values ​​and missing values;

[0069] Integrate the processed data into multi-source heterogeneous data through data fusion methods;

[0070] Extracting key features related to crop growth conditions based on the multi-source heterogeneous data;

[0071] Based on the key features, a supervised learning algorithm is used to train and establish a prediction model.

[0072] Specifically, data fusion technology is used to fuse environmental parameter data such as temperature, humidity, and light with nutrient solution status data such as pH value and EC value through the Kalman filter algorithm to obtain multi-dimensional time series data in a unified format. The maximum information coefficient method is used to extract four features with the highest correlation with crop growth conditions from the fused data, including average temperature, cumulative light time, daily average carbon dioxide concentration, and nutrient solution pH stability.

[0073] Furthermore, the water-fertilizer regulation strategy forming step includes:

[0074] Acquire plant physiology knowledge and cultivation expert experience based on crop growth status data derived from prediction models;

[0075] By using a data fusion algorithm, the crop growth status data, the plant physiology knowledge and the cultivation expert experience are integrated to generate a water and fertilizer regulation strategy for each region;

[0076] According to the generated water-fertilizer regulation strategy, determine the set values ​​of irrigation time, irrigation amount and nutrient solution ratio;

[0077] The set irrigation time, irrigation amount and nutrient solution ratio are sent to the water and fertilizer equipment controllers in each area through a unified data interface protocol.

[0078] Specifically, the irrigation time in this embodiment is 9 am, the irrigation amount is set to 10 liters per square meter, and the ratio of nitrogen, phosphorus and potassium in the nutrient solution is adjusted to 3:1:2.

[0079] Furthermore, the water and fertilizer equipment control step includes:

[0080] According to the regional division and the distribution of water and fertilizer equipment, a regional equipment mapping table is established to clarify the controllers and actuators corresponding to the regions;

[0081] Preset equipment model and specification parameters in the controller, including the flow curve of the valve and the pressure and flow characteristics of the pump;

[0082] Based on the adjustment strategy and the regional device mapping table, converting into device control instructions;

[0083] Based on the equipment control instructions, the irrigation valve and the fertilizer pump are controlled to realize the water and fertilizer supply.

[0084] Specifically, the regional equipment mapping table has five areas, marked as A, B, C, D, and E. Each area is equipped with a water and fertilizer controller and corresponding irrigation valves and fertilizer pumps. The mapping table records are as follows: Area A corresponds to controller A1, valve V1 and pump P1; Area B corresponds to controller B1, valve V2 and pump P2; and so on. The control instructions include valve opening and pump frequency.

[0085] Furthermore, the process of converting to device control instructions includes:

[0086] The adjustment strategy issued by the host computer analyzes the quantitative parameters of irrigation and fertilization in each area;

[0087] Based on the regional equipment mapping table, the fertigation quantitative parameters are decomposed into each controller, and the fertigation quantitative parameters are converted into control instructions of corresponding equipment through the controller.

[0088] Furthermore, the process of the central control system initiating the emergency plan includes:

[0089] During the execution of the regulation strategy, the central control system pre-processes the received data and compares it with the preset threshold to determine whether there is any abnormality;

[0090] If an abnormality is detected, the central control system triggers an alarm and initiates an emergency plan, executing control measures according to the severity of the abnormality; wherein the control measures include adjusting parameters of environmental control equipment and nutrient solution supply system to restore normal conditions;

[0091] The central control system continuously monitors the effects of adjustments and uses machine learning algorithms to evaluate and optimize emergency response strategies;

[0092] If the anomaly persists, the central control system takes further measures;

[0093] If the anomaly is resolved, the system returns to normal operating mode and records and analyzes abnormal events to optimize future responses.

[0094] Embodiment 2

[0095] like Figure 2 As shown, this embodiment provides a water and fertilizer intelligent regulation system for vegetable soilless cultivation, which is used to implement the water and fertilizer intelligent regulation method for vegetable soilless cultivation described in Example 1. The system includes:

[0096] A data acquisition module is used to arrange environmental parameter sensors and nutrient solution status sensors, and transmit the collected data to the central control system through a wireless sensor network;

[0097] A data processing module is used to process the sensor data after receiving it through the central control system, and to establish a prediction model through a machine learning algorithm;

[0098] The strategy formulation module is used to obtain the water and fertilizer adjustment strategy based on the output results of the prediction model, combined with plant physiology knowledge and cultivation expert experience, and send it to the water and fertilizer equipment controller through the data interface protocol;

[0099] An execution control module is used to convert the regulation strategy into control instructions through the water and fertilizer equipment controller, and control the actuator to realize water and fertilizer supply;

[0100] The exception handling module is used to judge the abnormality of the environmental parameters or the state of the nutrient solution during the execution of the adjustment strategy. If there is an abnormality, an alarm mechanism is triggered and an emergency plan is started through the central control system.

[0101] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for intelligently regulating water and fertilizer for soilless cultivation of vegetables, characterized in that: The following steps are involved: Arrange environmental parameter sensors and nutrient solution status sensors, and transmit the collected data to the central control system through a wireless sensor network; After receiving the sensor data through the central control system, the sensor data is processed and a prediction model is established through a machine learning algorithm; Based on the output of the prediction model, combined with plant physiology knowledge and cultivation expert experience, the water and fertilizer regulation strategy is obtained and sent to the water and fertilizer equipment controller through the data interface protocol; The water and fertilizer equipment controller converts the regulation strategy into control instructions and controls the actuator to realize water and fertilizer supply; During the execution of the adjustment strategy, the environmental parameters or the state of the nutrient solution are judged to be abnormal. If abnormal, an alarm mechanism is triggered and an emergency plan is initiated through the central control system.

2. The water and fertilizer intelligent regulation method for soilless cultivation of vegetables according to claim 1 is characterized in that: The steps of arranging sensors and transmitting data include: Arrange temperature and humidity sensors, light intensity sensors, pH sensors, and EC value sensors in the cultivation area to obtain environmental data and nutrient solution status data; Through wireless sensor network networking, the environmental data and the nutrient solution status data are transmitted in real time to a central control system for subsequent processing.

3. The water and fertilizer intelligent regulation method for soilless cultivation of vegetables according to claim 1 is characterized in that: The data processing and prediction model building steps include: Cleaning the environmental data and the nutrient solution status data, and processing abnormal values ​​and missing values; Integrate the processed data into multi-source heterogeneous data through data fusion methods; Extracting key features related to crop growth conditions based on the multi-source heterogeneous data; Based on the key features, a supervised learning algorithm is used to train and establish a prediction model.

4. The water and fertilizer intelligent regulation method for soilless cultivation of vegetables according to claim 1 is characterized in that: The steps of forming the water-fertilizer regulation strategy include: Acquire plant physiology knowledge and cultivation expert experience based on crop growth status data derived from prediction models; By using a data fusion algorithm, the crop growth data, the plant physiology knowledge and the cultivation expert experience are integrated to generate a water and fertilizer regulation strategy for each region; According to the generated water-fertilizer regulation strategy, determine the set values ​​of irrigation time, irrigation amount and nutrient solution ratio; The set irrigation time, irrigation amount and nutrient solution ratio are sent to the water and fertilizer equipment controllers in each area through a unified data interface protocol.

5. The water and fertilizer intelligent regulation method for soilless cultivation of vegetables according to claim 1 is characterized in that: The water and fertilizer equipment control step comprises: According to the regional division and the distribution of water and fertilizer equipment, a regional equipment mapping table is established to clarify the controllers and actuators corresponding to the regions; Preset equipment model and specification parameters in the controller, including the flow curve of the valve and the pressure and flow characteristics of the pump; Based on the adjustment strategy and the regional device mapping table, converting into device control instructions; Based on the equipment control instructions, the irrigation valve and the fertilizer pump are controlled to realize the water and fertilizer supply.

6. The water and fertilizer intelligent regulation method for soilless cultivation of vegetables according to claim 5 is characterized in that: The process of converting to device control instructions includes: The adjustment strategy issued by the host computer analyzes the quantitative parameters of irrigation and fertilization in each area; Based on the regional equipment mapping table, the fertigation quantitative parameters are decomposed into each controller, and the fertigation quantitative parameters are converted into control instructions of corresponding equipment through the controller.

7. The water and fertilizer intelligent regulation method for soilless cultivation of vegetables according to claim 1 is characterized in that: The process of the central control system initiating the emergency plan includes: During the execution of the regulation strategy, the central control system pre-processes the received data and compares it with the preset threshold to determine whether there is any abnormality; If an abnormality is detected, the central control system triggers an alarm and initiates an emergency plan, executing control measures according to the severity of the abnormality; wherein the control measures include adjusting parameters of environmental control equipment and nutrient solution supply system to restore normal conditions; The central control system continuously monitors the effects of adjustments and uses machine learning algorithms to evaluate and optimize emergency response strategies; If the anomaly persists, the central control system takes further measures; If the anomaly is resolved, the system returns to normal operating mode and records and analyzes abnormal events to optimize future responses.

8. An intelligent water and fertilizer regulation system for vegetable soilless cultivation, used to implement the intelligent water and fertilizer regulation method for vegetable soilless cultivation according to any one of claims 1 to 7, characterized in that: The system comprises: A data acquisition module is used to arrange environmental parameter sensors and nutrient solution status sensors, and transmit the collected data to the central control system through a wireless sensor network; A data processing module is used to process the sensor data after receiving it through the central control system, and to establish a prediction model through a machine learning algorithm; The strategy formulation module is used to obtain the water and fertilizer adjustment strategy based on the output results of the prediction model, combined with plant physiology knowledge and cultivation expert experience, and send it to the water and fertilizer equipment controller through the data interface protocol; An execution control module is used to convert the regulation strategy into control instructions through the water and fertilizer equipment controller, and control the actuator to realize water and fertilizer supply; The exception handling module is used to judge the abnormality of the environmental parameters or the state of the nutrient solution during the execution of the adjustment strategy. If there is an abnormality, an alarm mechanism is triggered and an emergency plan is started through the central control system.

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