Irrigation area measurement and control integrated gate intelligent scheduling system and method thereof

Abstract

The invention relates to the technical field of intelligent agriculture, in particular to an irrigation area measurement and control integrated gate intelligent scheduling system and method, and the system comprises a data collection module, a gate control module, a data processing and analysis module, an intelligent scheduling module and a monitoring and early warning module. By integrating a water level sensor, a flow meter, a weather station data interface, a soil humidity sensor and an agricultural satellite data interface, various agricultural data and hydrological data in an irrigation area are collected in real time, and the crop growth condition, the water demand, the rainfall capacity, the evaporation capacity and the like are analyzed by utilizing historical data and models; therefore, an accurate irrigation scheduling strategy is formulated and executed in the intelligent scheduling module, water supply needed by crop growth is ensured, meanwhile, the system monitors agricultural data and hydrological data in an irrigation area in real time through the monitoring and early warning module, a reasonable threshold range is set, early warning can be triggered in time once an abnormal condition occurs, and the system is safe and reliable. And a reliable decision basis is provided for irrigation area management.

Description

Technical Field

[0001] The present invention relates to the field of intelligent agricultural technology, and in particular to an irrigation area measurement and control integrated gate intelligent dispatching system and method thereof. Background Art

[0002] In recent years, with the continuous expansion of agricultural production in irrigation areas, efficient and water-saving water resource management has become particularly important. Traditional irrigation scheduling methods mostly rely on experience or manual observation, which has problems such as delayed response and imprecise decision-making, resulting in low efficiency of water resource utilization, and the hydrological environment of the irrigation area is prone to change, causing damage to crop growth.

[0003] Existing irrigation area monitoring and scheduling methods based on remote sensing technology mainly focus on using satellite images to obtain crop growth information and hydrological data, but these methods are often limited to the prediction of crop growth stages and lack solutions for real-time dynamic monitoring and precise scheduling of irrigation area water resources. At the same time, the existing irrigation area water resources management system lacks a unified data integration platform, which makes it difficult to use the dispersed data and implement intelligent scheduling strategies.

[0004] Based on the above background, the present invention aims at the shortcomings of the existing irrigation area integrated measurement and control gate intelligent dispatching system, and proposes an irrigation area measurement and control integrated gate intelligent dispatching system based on data collection, processing and analysis, intelligent dispatching, monitoring and early warning. The system can realize accurate monitoring of irrigation area water resources, data fusion analysis, intelligent dispatching strategy formulation and execution, and real-time early warning and timely response, effectively improving the utilization efficiency of irrigation area water resources and ensuring the sustainable development of agricultural production in irrigation area. Summary of the invention

[0005] In order to overcome the problems raised in the above-mentioned background technology, the present invention proposes an irrigation area measurement and control integrated gate intelligent dispatching system and method thereof.

[0006] The technical solution of the present invention is an irrigation area measurement and control integrated gate intelligent dispatching system, comprising: The data collection module is used to collect agricultural data and hydrological data in the irrigation area, wherein the agricultural data includes the crop growth situation, and the hydrological data includes the water level, flow, rainfall and soil moisture; A gate control module, used to control the opening and closing of the gate to adjust the water flow through the gate; The data processing and analysis module is used to pre-process, store and analyze the collected agricultural and hydrological data to obtain the specific agricultural and hydrological conditions in the irrigation area; Intelligent scheduling module, used to formulate and implement intelligent scheduling strategies based on specific agricultural and hydrological conditions; The monitoring and early warning module is used to monitor agricultural data and hydrological data and trigger early warning when abnormal conditions are detected.

[0007] Preferably, the data acquisition module includes a water level sensor, a flow meter, a weather station data interface, a soil moisture sensor and an agricultural satellite data interface; The water level sensor is used to collect water level data in the irrigation area, the flow meter is used to collect flow data in the irrigation area, the meteorological station data interface is used to connect with the meteorological station and obtain meteorological data in the irrigation area, the soil moisture sensor is used to collect soil moisture data in the irrigation area, and the agricultural satellite data interface is used to connect with the meteorological satellite and obtain satellite image data in the irrigation area.

[0008] Preferably, the data processing and analysis module pre-processes the collected agricultural data and hydrological data, including the following steps: S11: Data collection and integration, collect data from different sources and integrate the data according to the data collection time and source; S12: Data cleaning: identifying and correcting duplicate values, outliers, and missing values ​​in the data, and converting the data into a unified format; S13: Data formatting, including converting data into uniform units, ensuring uniform naming conventions for data, and scaling data to the same scale.

[0009] Preferably, the data processing and analysis module analyzes the collected agricultural data and hydrological data to obtain the specific agricultural conditions in the irrigation area, including the following steps: S21: historical data retrieval, retrieval of crop planting conditions in the irrigation area and ideal growth conditions of various crops in historical records; S22: Crop growth analysis: obtaining remote sensing image data from agricultural satellites, obtaining crop image data in the irrigation area, and identifying and analyzing the crop image data to determine the growth status, state and crop density of the crops; S23: difference analysis, comparing the obtained crop growth conditions, states and crop density with the ideal growth conditions of the crops, determining the differences between the crops and the ideal conditions, and using the differences as specific agricultural conditions.

[0010] Preferably, the data processing and analysis module obtains the remote sensing image data of the agricultural satellite, obtains the crop image data in the irrigation area, and identifies and analyzes the crop image data to determine the growth status, state and crop density of the crop, including: Use image segmentation algorithms to segment image data, remove useless images, and obtain multiple groups of crop images. Then process the crop images to identify the growth status, state, and crop density of the crops. Among them, the crop images are processed to identify the growth status, state and crop density of the crops, including: A21: Crop height recognition: each group of crop images is processed separately to obtain crop height data. The principle formula is: ; Where H is the height of the crop, L is the length of the shadow of the crop under sunlight, is the solar altitude angle; A22: Crop density recognition, which counts the number of plants in the crop image for density statistics. The principle formula is: ; Where D is the crop density, k is the correction factor obtained from historical data, N is the number of plants, and A is the actual area size corresponding to the crop area; A23: Crop color recognition: Perform separate color recognition on each group of crop images. The principle formula is: ; Where M×N represents the size of the image, the number of pixels, and C represents the color set. Represents the color value of pixel (i, j) in the image, express, When it is equal to color c, its value is 1, otherwise it is 0. Indicates the proportion of color c in the image; A24: Crop type identification, extracting feature vectors from crop images, and determining and verifying crop types based on feature vectors and historical data.

[0011] Preferably, the intelligent scheduling module formulates and implements intelligent scheduling strategies based on specific agricultural and hydrological conditions, including the following steps: First, the demand for water is determined based on the specific agricultural conditions. Then, the meteorological data within the preset time is obtained through the weather station data interface. Finally, the current water volume in the irrigation area is calculated based on the water level sensor, flow meter and soil moisture sensor. Finally, the required irrigation volume in the irrigation area is determined.

[0012] As a preferred embodiment, when the intelligent scheduling module formulates and implements the intelligent scheduling strategy based on the specific agricultural and hydrological conditions, the principle formula adopted is: ; Among them, R is the required irrigation volume, D represents the water demand of crops, C represents the current water volume in the irrigation area, P represents the predicted future rainfall, and E represents the future water evaporation.

[0013] Preferably, the monitoring and early warning module monitors the agricultural data and hydrological data collected by the data collection module, and triggers an early warning when an abnormal situation is detected, including the following steps: S31: data reception, receiving real-time data transmitted by the data acquisition module, including crop growth, water level, flow, rainfall and soil moisture; S32: Threshold setting and comparison: according to historical data, expert experience and crop growth requirements, reasonable threshold ranges are set for various agricultural data and hydrological data, and real-time data is compared with the set thresholds to determine whether the data is within the normal range; S33: Anomaly detection: when the real-time data exceeds the set threshold range, it is judged as abnormal data and triggers an early warning.

[0014] Preferably, the monitoring and early warning module is also used to monitor the amount of water in stock in real time and determine whether the amount of water in stock is sufficient according to the demand of crops, specifically including: First, obtain the inventory water data, then calculate the crop demand, and then predict the rainfall within the preset period based on meteorological data and historical meteorological data. Compare the inventory water data, crop demand and predicted rainfall to determine whether the inventory water is sufficient. If the inventory water is not sufficient, an early warning will be triggered.

[0015] On the other hand, the embodiment of the present disclosure provides an irrigation district measurement and control integrated gate intelligent scheduling method, comprising the following steps: S41: Data collection, using the water level sensor, flow meter, weather station data interface, soil moisture sensor and agricultural satellite data interface in the data collection module to collect agricultural data and hydrological data in the irrigation area in real time; S42: Data preprocessing: the data processing and analysis module preprocesses the collected agricultural data and hydrological data; S43: Crop growth and hydrological analysis: using historical data and real-time data to analyze crop growth, including crop height recognition, crop density recognition, crop color recognition and crop type recognition. At the same time, the hydrological conditions in the irrigation area are analyzed, including the prediction of current water volume, future rainfall and future evaporation. S44: Intelligent scheduling strategy formulation, based on the data results of the data processing and analysis module, formulate and execute the intelligent scheduling strategy; S45: Monitoring and early warning. The monitoring and early warning module performs real-time monitoring and early warning on agricultural data and hydrological data in the irrigation area.

[0016] Beneficial effects of the present invention: 1. By integrating water level sensors, flow meters, weather station data interfaces, soil moisture sensors and agricultural satellite data interfaces, various agricultural data and hydrological data in the irrigation area are collected in real time, and pre-processed, integrated, cleaned and formatted in the data processing module. Historical data and models are used to analyze crop growth conditions, water demand, rainfall and evaporation, so as to formulate and implement accurate irrigation scheduling strategies in the intelligent scheduling module to ensure the water supply required for crop growth. At the same time, the system uses the monitoring and early warning module to monitor agricultural data and hydrological data in the irrigation area in real time, set a reasonable threshold range, and trigger an early warning in time if an abnormal situation occurs, providing a reliable decision-making basis for irrigation area management and realizing accurate and efficient use of irrigation area resources; 2. Compared with the existing technology that only relies on ground observation and manual recording to analyze the agricultural situation in the irrigation area, there are shortcomings such as incomplete data collection, inaccurate analysis and long time consumption. This solution adopts a data processing and analysis module, which retrieves historical data and uses agricultural satellite image data to perform crop growth analysis and irrigation analysis. In particular, it introduces image segmentation algorithms to accurately process crop images and identify the growth status, state, density and type of crops. This solution not only realizes comprehensive data collection and efficient analysis, but also significantly improves the accuracy and efficiency of analysis through automated image recognition technology, providing strong data support for precise irrigation and crop management in irrigation areas, and effectively promoting the sustainable development of agricultural production; 3. Compared with the existing technology that only relies on manual inspections and simple records to monitor the storage water volume, it is difficult to accurately judge whether the water volume meets the crop demand and the early warning is not timely. This solution adopts a monitoring and early warning module. Through real-time monitoring of storage water volume data, combined with crop demand calculation and meteorological forecast, a comprehensive assessment is made on whether the storage water volume is sufficient. This solution not only realizes the dynamic management and accurate early warning of storage water volume, but also can foresee the risk of insufficient water volume in advance and trigger the early warning mechanism in time, providing a scientific basis for irrigation scheduling and water resources management in irrigation areas, and effectively ensuring the normal growth of crops and the rational use of water resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Shown is a schematic diagram of the structure of the irrigation area measurement and control integrated gate intelligent dispatching system of the present invention; Figure 2 What is shown is a flow chart of the intelligent dispatching method of the irrigation area integrated measurement and control gate of the present invention. DETAILED DESCRIPTION

[0018] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0019] Embodiment 1 See also Figure 1The present invention provides an embodiment: an irrigation area measurement and control integrated gate intelligent dispatching system, comprising: The data collection module is used to collect agricultural data and hydrological data in the irrigation area, wherein the agricultural data includes the crop growth situation, and the hydrological data includes the water level, flow, rainfall and soil moisture; A gate control module, used to control the opening and closing of the gate to adjust the water flow through the gate; The data processing and analysis module is used to pre-process, store and analyze the collected agricultural and hydrological data to obtain the specific agricultural and hydrological conditions in the irrigation area; Intelligent scheduling module, used to formulate and implement intelligent scheduling strategies based on specific agricultural and hydrological conditions; The monitoring and early warning module is used to monitor agricultural data and hydrological data and trigger early warning when abnormal conditions are detected.

[0020] As mentioned above, this irrigation area measurement and control integrated gate intelligent dispatching system collects agricultural and hydrological data such as crop growth, water level, flow, rainfall and soil moisture in real time through the data acquisition module. After preprocessing, storage and analysis by the data processing and analysis module, these data are used by the intelligent dispatching module to formulate and implement precise irrigation strategies. At the same time, the monitoring and early warning module continuously monitors these data, and triggers an early warning once an abnormality is found, ensuring that water resources in the irrigation area are managed efficiently and safely.

[0021] Preferably, the data acquisition module includes a water level sensor, a flow meter, a meteorological station data interface, a soil moisture sensor and an agricultural satellite data interface. The water level sensor is used to collect water level data in the irrigation area, the flow meter is used to collect flow data in the irrigation area, the meteorological station data interface is used to connect with the meteorological station to realize data transmission and obtain meteorological data in the irrigation area, the soil moisture sensor is used to collect soil moisture data in the irrigation area, and the agricultural satellite data interface is used to connect with the meteorological satellite and obtain satellite image data in the irrigation area.

[0022] As mentioned above, this data acquisition module integrates water level sensor, flow meter, weather station data interface, soil moisture sensor and agricultural satellite data interface. Among them, the water level sensor accurately measures the water level change in the irrigation area; the flow meter records the water flow in real time; the weather station data interface seamlessly connects with the weather station to obtain real-time meteorological data; the soil moisture sensor monitors the soil moisture status; the agricultural satellite data interface receives high-resolution satellite image data from the meteorological satellite, fully covering the agricultural and hydrological monitoring needs of the irrigation area.

[0023] Preferably, the gate control module comprises: A11: Motor controller, used to provide sufficient torque to start the gate hoist, and has overload protection and short circuit protection functions; A12: Gate hoist, used to convert the motor's rotational motion into the gate's linear motion to drive the gate to open and close; A13: Limit switch, used to detect the opening and closing position of the gate to ensure that the gate moves within the specified range.

[0024] As mentioned above, this gate control module integrates a motor controller, a gate hoist and a limit switch. The motor controller provides sufficient torque to drive the gate hoist, and is equipped with overload and short-circuit protection mechanisms; the gate hoist is responsible for converting the motor's rotational kinetic energy into linear motion of the gate, achieving reliable opening and closing of the gate; the limit switch accurately monitors the gate's opening and closing position to ensure that it operates within the preset safety range, thereby ensuring the stable operation and safety of the entire gate system.

[0025] Preferably, the data processing and analysis module comprises the following steps when preprocessing the collected data: S11: Data collection and integration, collect data from different sources and integrate the data according to the data collection time and source; S12: Data cleaning: identifying and correcting duplicate values, outliers, and missing values ​​in the data, and converting the data into a unified format; S13: Data formatting, including converting data into uniform units, ensuring uniform naming conventions for data, and scaling data to the same scale.

[0026] As mentioned above, the preprocessing steps of the above data processing and analysis modules ensure that data from different sources can be effectively collected, integrated, cleaned and formatted, which not only eliminates redundancy, anomalies and missing problems in the data, but also realizes the unified format, unit and naming specifications of the data, providing a high-quality and standardized data foundation for subsequent in-depth analysis, and significantly improving data processing efficiency and the accuracy of analysis results.

[0027] Preferably, the data processing and analysis module analyzes the collected agricultural data and hydrological data to obtain the specific agricultural conditions in the irrigation area, including the following steps: S21: historical data retrieval, retrieval of crop planting conditions in the irrigation area and ideal growth conditions of various crops in historical records; S22: Crop growth analysis: obtaining remote sensing image data from agricultural satellites, obtaining crop image data in the irrigation area, and identifying and analyzing the crop image data to determine the growth status, state and crop density of the crops; S23: difference analysis, comparing the obtained crop growth conditions, states and crop density with the ideal growth conditions of the crops, determining the differences between the crops and the ideal conditions, and using the differences as specific agricultural conditions.

[0028] Preferably, before the data processing and analysis module acquires the remote sensing image data of the agricultural satellite, obtains the crop image data in the irrigation area, and identifies and analyzes the crop image data to determine the growth status, state and crop density of the crop: Use image segmentation algorithms to segment image data, remove useless images, and obtain multiple groups of crop images. Then process the crop images to identify the growth status, state, and crop density of the crops. Among them, when processing crop images and identifying the growth status, state and crop density of crops, it includes: A21: Crop height recognition: each group of crop images is processed separately to obtain crop height data. The principle formula is: ; Where H is the height of the crop, L is the length of the shadow of the crop under sunlight, is the solar altitude angle; A22: Crop density recognition, which counts the number of plants in the crop image for density statistics. The principle formula is: ; Where D is the crop density, k is the correction factor obtained from historical data, N is the number of plants, and A is the actual area size corresponding to the crop area; A23: Crop color recognition: Perform separate color recognition on each group of crop images. The principle formula is: ; Where M×N represents the size of the image, the number of pixels, and C represents the color set. Represents the color value of pixel (i, j) in the image, express, When it is equal to color c, its value is 1, otherwise it is 0. Indicates the proportion of color c in the image; A24: Crop type identification, extracting feature vectors from crop images, and determining and verifying crop types based on feature vectors and historical data.

[0029] Among them, the stem and leaf density of crops changes with the different growth stages of crops. Under standardized planting conditions, the image changes of the land under the crops in the same area are small. Therefore, the change in stem and leaf density is not an abnormal variable in color judgment. When identifying crop density, only the planting area and the historical planting conditions of crops need to be considered. When identifying crop color, only the impact of crop color changes on the overall image needs to be considered based on crop density.

[0030] As mentioned above, compared with the existing technology that only relies on ground observation and manual recording to analyze the agricultural situation in the irrigation area, there are shortcomings such as incomplete data collection, inaccurate analysis and long time consumption. This solution adopts a data processing and analysis module, which retrieves historical data and uses agricultural satellite image data to perform crop growth analysis and irrigation analysis, especially introduces image segmentation algorithms to accurately process crop images and identify crop growth conditions, states, density and types. This solution not only realizes comprehensive data collection and efficient analysis, but also significantly improves the accuracy and efficiency of analysis through automated image recognition technology, providing strong data support for precise irrigation and crop management in irrigation areas, and effectively promoting the sustainable development of agricultural production.

[0031] Preferably, the intelligent scheduling module formulates and implements intelligent scheduling strategies based on specific agricultural and hydrological conditions, including the following steps: First, the demand for water is determined based on the specific agricultural conditions. Then, the meteorological data within the preset time is obtained through the weather station data interface. Finally, the current water volume in the irrigation area is calculated based on the water level sensor, flow meter and soil moisture sensor. Finally, the required irrigation volume in the irrigation area is determined.

[0032] Among them, when identifying the specific status of crops and judging the demand for water, different crops have different water requirements at different growth stages. The specific judgment is made based on crop color, crop height, crop density, and crop type. Among them, a neural network algorithm is used to judge the status of crops.

[0033] As a preferred embodiment, when the intelligent scheduling module formulates and implements the intelligent scheduling strategy based on the specific agricultural and hydrological conditions, the principle formula adopted is: ; Among them, R is the required irrigation volume, D represents the water demand of crops, C represents the current water volume in the irrigation area, P represents the predicted future rainfall, and E represents the future water evaporation.

[0034] As mentioned above, compared with the existing technology that relies on manual experience and regular inspections to formulate irrigation strategies, which has the disadvantages of delayed response, inaccurate scheduling and waste of water resources, this solution adopts an intelligent scheduling module, which automatically determines the water demand of crops by integrating the results of the data processing and analysis module, combines real-time meteorological data, the current water volume in the irrigation area, and future rainfall and evaporation forecasts, and uses principle formulas to accurately calculate the required irrigation volume, and formulates and implements intelligent scheduling strategies accordingly. This solution not only significantly improves the timeliness and accuracy of irrigation scheduling, but also effectively avoids excessive or insufficient use of water resources, realizes the optimal allocation and efficient use of water resources, and promotes the intelligent and sustainable development of agricultural production.

[0035] Preferably, the monitoring and early warning module monitors the agricultural data and hydrological data collected by the data collection module, and triggers an early warning when an abnormal situation is detected, including the following steps: S31: data reception, receiving real-time data transmitted by the data acquisition module, including crop growth, water level, flow, rainfall and soil moisture; S32: Threshold setting and comparison: according to historical data, expert experience and crop growth requirements, reasonable threshold ranges are set for various agricultural data and hydrological data, and real-time data is compared with the set thresholds to determine whether the data is within the normal range; S33: Anomaly detection: when the real-time data exceeds the set threshold range, it is judged as abnormal data and triggers an early warning.

[0036] As mentioned above, compared with the problem that the existing technology lacks real-time data monitoring and early warning mechanisms, which makes it difficult to timely discover and handle abnormal situations in irrigation areas, this solution adopts a monitoring and early warning module, which receives data transmitted by the data acquisition module in real time, and sets a reasonable threshold range for comparison based on historical data, expert experience and crop growth requirements. Once data anomalies are detected, an early warning is immediately triggered. This technical solution not only realizes all-weather, real-time monitoring of agricultural data and hydrological data in irrigation areas, but also can quickly discover and respond to abnormal situations, effectively prevent potential risks and losses, and improve the intelligent level of irrigation area management and emergency response capabilities.

[0037] Preferably, the monitoring and early warning module is also used to monitor the amount of water in stock in real time and determine whether the amount of water in stock is sufficient according to the demand of crops, specifically including: First, obtain the inventory water data, then calculate the crop demand, and then predict the rainfall within the preset period based on meteorological data and historical meteorological data. Compare the inventory water data, crop demand and predicted rainfall to determine whether the inventory water is sufficient. If the inventory water is not sufficient, an early warning will be triggered.

[0038] As described above, compared with the existing technology that relies only on manual inspections and simple records to monitor the amount of water in storage, it is difficult to accurately judge whether the water volume meets the needs of crops and the early warning is not timely. This solution adopts a monitoring and early warning module to conduct a comprehensive assessment of whether the amount of water in storage is sufficient by real-time monitoring of the water volume in storage, combined with crop demand calculation and meteorological forecast. This solution not only realizes the dynamic management and accurate early warning of the amount of water in storage, but also can foresee the risk of insufficient water in advance and trigger the early warning mechanism in time, providing a scientific basis for irrigation scheduling and water resources management in irrigation areas, and effectively ensuring the normal growth of crops and the rational use of water resources.

[0039] Embodiment 2 See also Figure 2The present invention provides an embodiment: an intelligent dispatching method for an irrigation area integrated measurement and control gate, comprising the following steps: S41: Data collection, using the water level sensor, flow meter, weather station data interface, soil moisture sensor and agricultural satellite data interface in the data collection module to collect agricultural data and hydrological data in the irrigation area in real time; S42: Data preprocessing, preprocessing the collected agricultural data and hydrological data; S43: Crop growth and hydrological analysis: using historical data and real-time data to analyze crop growth, including crop height recognition, crop density recognition, crop color recognition and crop type recognition. At the same time, the hydrological conditions in the irrigation area are analyzed, including the prediction of current water volume, future rainfall and future evaporation. S44: Intelligent scheduling strategy formulation, based on the data results of the data processing and analysis module, formulate and execute the intelligent scheduling strategy; S45: Real-time monitoring and early warning of agricultural and hydrological data within the irrigation area.

[0040] As described above, this irrigation district integrated measurement and control gate intelligent scheduling method first obtains comprehensive data such as water level, flow, meteorology, soil moisture and satellite images in real time through the data acquisition module, and then the data processing and analysis module performs data preprocessing, and then deeply analyzes the crop growth status (including height, density, color and type) and hydrological conditions (current water volume, future rainfall and evaporation forecasts). The intelligent scheduling module formulates and implements accurate irrigation strategies based on this. At the same time, the monitoring and early warning module monitors data changes throughout the process, and immediately triggers an early warning once an abnormality is found, ensuring efficient and intelligent irrigation district management.

[0041] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.

Claims

1. Irrigation area integrated measurement and control gate intelligent dispatching system; its characteristics are: include: The data collection module is used to collect agricultural data and hydrological data in the irrigation area, wherein the agricultural data includes the crop growth situation, and the hydrological data includes the water level, flow, rainfall and soil moisture; A gate control module, used to control the opening and closing of the gate to adjust the water flow through the gate; The data processing and analysis module is used to pre-process, store and analyze the collected agricultural and hydrological data to obtain the specific agricultural and hydrological conditions in the irrigation area; Intelligent scheduling module, used to formulate and implement intelligent scheduling strategies based on specific agricultural and hydrological conditions; The monitoring and early warning module is used to monitor agricultural data and hydrological data and trigger early warning when abnormal conditions are detected.

2. The irrigation area measurement and control integrated gate intelligent dispatching system according to claim 1 is characterized by: The data acquisition module includes a water level sensor, a flow meter, a weather station data interface, a soil moisture sensor, and an agricultural satellite data interface; The water level sensor is used to collect water level data in the irrigation area, the flow meter is used to collect flow data in the irrigation area, the meteorological station data interface is used to connect with the meteorological station and obtain meteorological data in the irrigation area, the soil moisture sensor is used to collect soil moisture data in the irrigation area, and the agricultural satellite data interface is used to connect with the meteorological satellite and obtain satellite image data in the irrigation area.

3. The irrigation area measurement and control integrated gate intelligent dispatching system according to claim 2 is characterized by: The data processing and analysis module pre-processes the collected agricultural data and hydrological data, including the following steps: S11: Data collection and integration, collect data from different sources and integrate the data according to the data collection time and source; S12: Data cleaning: identifying and correcting duplicate values, outliers, and missing values ​​in the data, and converting the data into a unified format; S13: Data formatting, including converting data into uniform units, ensuring uniform naming conventions for data, and scaling data to the same scale.

4. The irrigation area measurement and control integrated gate intelligent dispatching system according to claim 3 is characterized by: The data processing and analysis module analyzes the collected agricultural data and hydrological data to obtain the specific agricultural conditions in the irrigation area, including the following steps: S21: historical data retrieval, retrieval of crop planting conditions in the irrigation area and ideal growth conditions of various crops in historical records; S22: Crop growth analysis: obtaining remote sensing image data from agricultural satellites, obtaining crop image data in the irrigation area, and identifying and analyzing the crop image data to determine the growth status, state and crop density of the crops; S23: difference analysis, comparing the obtained crop growth conditions, states and crop density with the ideal growth conditions of the crops, determining the differences between the crops and the ideal conditions, and using the differences as specific agricultural conditions.

5. The irrigation area measurement and control integrated gate intelligent dispatching system according to claim 4 is characterized by: The data processing and analysis module obtains the remote sensing image data of agricultural satellites, obtains the crop image data in the irrigation area, and identifies and analyzes the crop image data to determine the growth status, state and crop density of the crops, including: Use image segmentation algorithms to segment image data, remove useless images, and obtain multiple groups of crop images. Then process the crop images to identify the growth status, state, and crop density of the crops. Among them, the crop images are processed to identify the growth status, state and crop density of the crops, including: A21: crop height recognition, each group of crop images is processed separately to obtain the height data of the crops. The principle formula is: ; Where H is the height of the crop, L is the length of the shadow of the crop under sunlight, is the solar altitude angle; A22: Crop density recognition, which counts the number of plants in the crop image for density statistics. The principle formula is: ; Where D is the crop density, k is the correction factor obtained from historical data, N is the number of plants, and A is the actual area size corresponding to the crop area; A23: Crop color recognition: Perform separate color recognition on each group of crop images. The principle formula is: ; Where M×N represents the size of the image, the number of pixels, and C represents the color set. Represents the color value of pixel (i, j) in the image, express, When it is equal to color c, its value is 1, otherwise it is 0. Indicates the proportion of color c in the image; A24: Crop type identification, extracting feature vectors from crop images, and determining and verifying crop types based on feature vectors and historical data.

6. The irrigation area measurement and control integrated gate intelligent dispatching system according to claim 5 is characterized by: The intelligent scheduling module formulates and implements intelligent scheduling strategies based on specific agricultural and hydrological conditions, including the following steps: First, the demand for water is determined based on the specific agricultural conditions. Then, the meteorological data within the preset time is obtained through the weather station data interface. Finally, the current water volume in the irrigation area is calculated based on the water level sensor, flow meter and soil moisture sensor. Finally, the required irrigation volume in the irrigation area is determined.

7. The irrigation area measurement and control integrated gate intelligent dispatching system according to claim 6 is characterized by: The principle formula used by the intelligent scheduling module when formulating and implementing intelligent scheduling strategies based on specific agricultural and hydrological conditions is: ; Among them, R is the required irrigation volume, D represents the water demand of crops, C represents the current water volume in the irrigation area, P represents the predicted future rainfall, and E represents the future water evaporation.

8. The irrigation area measurement and control integrated gate intelligent dispatching system according to claim 7 is characterized by: The monitoring and early warning module monitors the agricultural data and hydrological data collected by the data acquisition module and triggers an early warning when an abnormal situation is detected, including the following steps: S31: data reception, receiving real-time data transmitted by the data acquisition module, including crop growth, water level, flow, rainfall and soil moisture; S32: Threshold setting and comparison: according to historical data, expert experience and crop growth requirements, reasonable threshold ranges are set for various agricultural data and hydrological data, and real-time data is compared with the set thresholds to determine whether the data is within the normal range; S33: Anomaly detection: when the real-time data exceeds the set threshold range, it is judged as abnormal data and triggers an early warning.

9. The irrigation area measurement and control integrated gate intelligent dispatching system according to claim 8 is characterized by: The monitoring and early warning module is also used to monitor the amount of water in stock in real time and determine whether the amount of water in stock is sufficient based on the crop demand, including: First, obtain the inventory water data, then calculate the crop demand, and then predict the rainfall within the preset period based on meteorological data and historical meteorological data. Compare the inventory water data, crop demand and predicted rainfall to determine whether the inventory water is sufficient. If the inventory water is not sufficient, an early warning will be triggered.

10. An intelligent dispatching method for integrated gate measurement and control in irrigation areas, characterized by: The following steps are involved: S41: Data collection, using the water level sensor, flow meter, weather station data interface, soil moisture sensor and agricultural satellite data interface in the data collection module to collect agricultural data and hydrological data in the irrigation area in real time; S42: Data preprocessing, preprocessing the collected agricultural data and hydrological data; S43: Crop growth and hydrological analysis: using historical data and real-time data to analyze crop growth, including crop height recognition, crop density recognition, crop color recognition and crop type recognition. At the same time, the hydrological conditions in the irrigation area are analyzed, including the prediction of current water volume, future rainfall and future evaporation. S44: Intelligent scheduling strategy formulation, based on the data results of the data processing and analysis module, formulate and execute the intelligent scheduling strategy; S45: Monitoring and early warning. The monitoring and early warning module performs real-time monitoring and early warning on agricultural data and hydrological data in the irrigation area.

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