Irrigation area water resource scheduling method and equipment based on digital twinning and medium

Abstract

The invention discloses an irrigation district water resource scheduling method and device based on digital twinning and a medium, and the method comprises the steps: obtaining irrigation district data of a to-be-scheduled irrigation district, and carrying out the preprocessing of the irrigation district data, so as to obtain target irrigation district data; wherein the target irrigation area data comprises water resource data and environment data; based on environment data in the target irrigation area data, establishing a digital twinborn model corresponding to the irrigation area to be scheduled, and optimizing the digital twinborn model; wherein the digital twin model comprises a physical model and a data model; inputting water resource data in the target irrigation area data into the optimized digital twinborn model, and outputting water resource prediction information of the to-be-scheduled irrigation area in the next time period; and based on a preset intelligent scheduling algorithm, determining a water resource scheduling instruction corresponding to the water resource prediction information, and performing water resource scheduling on the to-be-scheduled irrigation area according to the water resource scheduling instruction.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and medium for water resource scheduling in an irrigation area based on digital twins. Background Art

[0002] In the current context of increasingly tight global water resources, the efficient scheduling and management of water resources in irrigation areas, as an important infrastructure for agricultural production, is particularly important. However, traditional irrigation water resource scheduling methods often rely on manual experience and simple data statistics, which are difficult to cope with the complex and changing water supply and demand conditions and the influence of environmental factors. With the rapid development of information technology, especially the widespread application of technologies such as the Internet of Things, big data, and cloud computing, new technical means and solutions have been provided for irrigation water resource scheduling.

[0003] In recent years, the information construction of irrigation areas has made significant progress. Various sensors and monitoring equipment have been widely used in the real-time monitoring of water level, flow, water quality and other parameters in irrigation areas, providing rich data support for water resource scheduling. However, these data are often multi-source and heterogeneous. How to effectively integrate and utilize these data has become a major challenge facing irrigation area water resource scheduling.

[0004] As an emerging digital technology, digital twin technology achieves comprehensive perception, accurate mapping and intelligent decision-making of physical objects by creating a virtual mirror of the physical world. In irrigation district water resource scheduling, digital twin technology can integrate multi-source heterogeneous data, realize comprehensive digital modeling of irrigation district infrastructure, and form accurate expression and mapping of virtual objects to physical objects in the information dimension. This not only improves the comprehensiveness and accuracy of data collection, but also provides more accurate and reliable decision-making support for water resource scheduling.

[0005] However, there are still some shortcomings in the application of existing digital twin technology in irrigation area water resource scheduling. For example, incomplete or inaccurate data collection leads to insufficient decision support; the model is not accurate enough to accurately predict the supply and demand of water resources; there is a lack of real-time monitoring and early warning mechanism, which makes it impossible to discover and solve problems in a timely manner; the operation and scheduling are not flexible enough to adapt to the complex changes in water resources; supervision and management are not in place, and the rational use of water resources cannot be guaranteed. These problems seriously restrict the widespread application and effectiveness of digital twin technology in irrigation area water resource scheduling. Summary of the invention

[0006] The embodiments of the present application provide an irrigation area water resources scheduling method, equipment and medium based on digital twins to solve the above-mentioned technical problems.

[0007] On the one hand, the embodiment of the present application provides a method for water resource scheduling in an irrigation area based on digital twin, including:

[0008] Acquire irrigation area data of the irrigation area to be scheduled, and pre-process the irrigation area data to obtain target irrigation area data; wherein the target irrigation area data includes water resource data and environmental data;

[0009] Based on the environmental data in the target irrigation area data, a digital twin model corresponding to the irrigation area to be scheduled is established, and the digital twin model is optimized; wherein the digital twin model includes a physical model and a data model;

[0010] Input the water resource data in the target irrigation area data into the optimized digital twin model, and output the water resource forecast information of the irrigation area to be scheduled in the next period;

[0011] Based on a preset intelligent scheduling algorithm, a water resource scheduling instruction corresponding to the water resource prediction information is determined, and water resource scheduling is performed on the irrigation area to be scheduled according to the water resource scheduling instruction.

[0012] In one implementation of the present application, the water resource data in the target irrigation area data is input into the optimized digital twin model, and the water resource forecast information of the irrigation area to be scheduled in the next period is output, specifically including:

[0013] Based on the data interface, a connection is established between the sensor data and the digital twin model, so as to synchronize the sensor data of the irrigation area to be scheduled to the digital twin model in real time;

[0014] Through the digital twin model, the irrigation area to be scheduled is monitored in real time, and the predicted water level, predicted water quality and predicted flow rate corresponding to the irrigation area to be scheduled in the next period are determined;

[0015] The predicted water level, the predicted water quality and the predicted flow are compared with corresponding preset thresholds to determine an early warning level corresponding to the sensor data.

[0016] In one implementation of the present application, based on a preset intelligent scheduling algorithm, a water resource scheduling instruction corresponding to the water resource forecast information is determined, and water resource scheduling is performed on the irrigation area to be scheduled according to the water resource scheduling instruction, specifically including:

[0017] Determine the actual dispatching demand corresponding to the irrigation area to be dispatched according to the warning level corresponding to the water resources forecast information, and the predicted water level, predicted water quality and predicted flow of the irrigation area to be dispatched in the next period of time;

[0018] Determine the water resource scheduling instructions corresponding to the water resource forecast information according to the actual scheduling demand, the types of crops in the irrigation area to be scheduled and the planting area of ​​each crop, and based on a preset intelligent scheduling algorithm;

[0019] According to the water resource scheduling instruction, a specified number of gates are opened in sequence to the target opening height, and the gates are kept open for the target opening time to complete the irrigation area water resource scheduling.

[0020] In one implementation of the present application, obtaining irrigation district data of the irrigation district to be scheduled specifically includes:

[0021] Determine the rainfall conditions corresponding to the irrigation area to be scheduled based on the weather forecast corresponding to the irrigation area to be scheduled, and determine the water conditions corresponding to the irrigation area to be scheduled based on sensor data corresponding to the water level sensor, water quality sensor and flow sensor preset in the irrigation area to be scheduled;

[0022] By using a camera preset in the irrigation area to be scheduled, an irrigation area image corresponding to the irrigation area to be scheduled is collected, and the irrigation area image is compared with a historical image in a preset irrigation area image library; wherein the irrigation area image library includes the land dryness degree corresponding to each historical image;

[0023] Determine the dryness of the land corresponding to the irrigation area to be scheduled according to the comparison result, and combine the comparison result with the sensor data to determine the soil moisture corresponding to the irrigation area to be scheduled;

[0024] The irrigation area image is analyzed to determine the corresponding irrigation area topography, irrigation area landform and water conservancy facilities, and to determine the types of crops corresponding to the irrigation area to be scheduled and the planting area corresponding to each crop.

[0025] In one implementation of the present application, the irrigation area data is preprocessed to obtain target irrigation area data, specifically including:

[0026] Filtering the irrigation district data to remove high-frequency noise in the irrigation district data, and identifying and deleting outliers and erroneous data in the irrigation district data;

[0027] The irrigation district data after filtering and deleting abnormal values ​​and erroneous data are smoothed, and the smoothed irrigation district data are fused to obtain the target irrigation district data corresponding to the irrigation district to be scheduled.

[0028] In one implementation of the present application, the smoothed irrigation district data is fused to obtain the target irrigation district data corresponding to the irrigation district to be scheduled, specifically including:

[0029] Determine the collection time corresponding to each irrigation area data, and fuse the irrigation area data after smoothing at different time points in chronological order from the front to the back;

[0030] Determine the spatial position corresponding to each irrigation district data, and fuse the plurality of irrigation district data into the same space by interpolation according to the corresponding spatial position;

[0031] The collection time corresponding to each sensor data is determined, and the sensor data corresponding to different sensors at the same time are fused to obtain a data set with multi-source data in the same space with time series.

[0032] In one implementation of the present application, a digital twin model corresponding to the irrigation area to be scheduled is established based on the environmental data in the target irrigation area data, specifically including:

[0033] According to the irrigation area topography, irrigation area landform and water conservancy facilities in the target irrigation area data, a physical model corresponding to the irrigation area to be scheduled is constructed; wherein the physical model is used to describe the water resource flow process and water resource storage process of the irrigation area to be scheduled;

[0034] Establishing a data model corresponding to the irrigation area to be scheduled, and storing the water resource data in the target irrigation area data through the data model;

[0035] The physical model and the data model are integrated to construct a digital twin model corresponding to the irrigation area to be scheduled; wherein the water resource data includes rainfall conditions, water conditions, sensor data and geographic information corresponding to water resources.

[0036] In one implementation of the present application, the digital twin model is optimized, specifically including:

[0037] Obtaining historical sensor data of the irrigation area to be scheduled through a data model corresponding to the irrigation area to be scheduled, and determining the water level, water quality and flow rate corresponding to the irrigation area to be scheduled in the next time period based on the historical sensor data;

[0038] Inputting the historical sensor data into the digital twin model for training, and outputting the predicted water level, predicted water quality and predicted flow corresponding to the historical sensor data;

[0039] If the predicted water level, predicted water quality and predicted flow rate corresponding to the historical sensor data do not match the water level, water quality and flow rate corresponding to the irrigation area to be scheduled in the next period, the model parameters in the digital twin model are adjusted according to the water level, water quality and flow rate corresponding to the irrigation area to be scheduled in the next period;

[0040] The training of the digital twin model is completed until the predicted water level, predicted water quality and predicted flow output by the optimized digital twin model match the water level, water quality and flow corresponding to the irrigation area to be scheduled in the next period.

[0041] On the other hand, the embodiment of the present application also provides an irrigation area water resources scheduling device based on digital twin, the device comprising:

[0042] at least one processor;

[0043] and, a memory communicatively coupled to the at least one processor;

[0044] Among them, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a water resources scheduling method for an irrigation area based on digital twins as described above.

[0045] On the other hand, an embodiment of the present application also provides a non-volatile computer storage medium storing computer executable instructions, which, when executed, implements a water resource scheduling method for an irrigation area based on digital twins as described above.

[0046] The embodiment of the present application provides a method, device and medium for water resource scheduling in an irrigation area based on digital twin, which at least has the following beneficial effects:

[0047] By preprocessing the acquired irrigation area data, outliers and redundant data can be eliminated, ensuring the accuracy and completeness of the target irrigation area data, which helps to understand the actual situation of the irrigation area more accurately; the establishment of the digital twin model enables the irrigation area to be replicated and simulated in a virtual form. By continuously optimizing the model, the actual environmental factors of the irrigation area, such as topography, climate, soil, etc., can be fully considered, and the operating status and environmental changes of the irrigation area can be more realistically reflected, thereby improving the accuracy and practicality of the model prediction; using advanced simulation technology, the changing trend of water resources can be predicted in advance, providing a scientific basis for the rational allocation and scheduling of water resources, helping to avoid water shortages or surpluses, and improving the efficiency and effectiveness of water resource management; through the application of intelligent scheduling algorithms, the automation and intelligence of water resource scheduling is realized, which can respond quickly according to real-time data and prediction results, improve scheduling efficiency and flexibility, better adapt to the complex and changeable water supply and demand situation, optimize the allocation of water resources, not only improve irrigation efficiency, but also save water resources and promote the sustainable development of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0049] Figure 1 A schematic diagram of a flow chart of a method for water resource scheduling in an irrigation area based on digital twins provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of the internal structure of an irrigation district water resources scheduling device based on digital twins provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0052] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0053] Figure 1 A flow chart of a method for water resource scheduling in an irrigation area based on digital twins is provided in an embodiment of the present application.

[0054] The analysis method involved in the embodiments of the present application can be implemented by a terminal device or a server, and the present application does not impose any special restrictions on this. For the convenience of understanding and description, the following embodiments are described in detail by taking a server as an example.

[0055] It should be noted that the server may be a single device or a system consisting of multiple devices, that is, a distributed server, and this application does not make any specific limitation on this.

[0056] like Figure 1 As shown, an irrigation district water resources scheduling method based on digital twins provided in an embodiment of the present application includes:

[0057] 101. Acquire the irrigation district data of the irrigation district to be scheduled, and pre-process the irrigation district data to obtain the target irrigation district data.

[0058] It should be noted that the target irrigation area data in the embodiment of the present application includes water resource data and environmental data.

[0059] Specifically, in one embodiment of the present application, obtaining irrigation district data of the irrigation district to be scheduled specifically includes:

[0060] Based on the weather forecast corresponding to the irrigation area to be dispatched, determine the rainfall conditions corresponding to the irrigation area to be dispatched, and based on the sensor data corresponding to the water level sensor, water quality sensor and flow sensor preset in the irrigation area to be dispatched, determine the water conditions corresponding to the irrigation area to be dispatched;

[0061] Through a preset camera in the irrigation area to be scheduled, an irrigation area image corresponding to the irrigation area to be scheduled is collected, and the irrigation area image is compared with a historical image in a preset irrigation area image library; wherein the irrigation area image library includes the land dryness degree corresponding to each historical image;

[0062] According to the comparison results, the dryness of the land corresponding to the irrigation area to be scheduled is determined, and the comparison results are combined with the sensor data to determine the soil moisture conditions corresponding to the irrigation area to be scheduled;

[0063] The irrigation area images are analyzed to determine the corresponding irrigation area terrain, irrigation area landforms and water conservancy facilities, and to determine the types of crops corresponding to the irrigation area to be scheduled and the corresponding planting area for each crop.

[0064] In one embodiment, first, the rainfall in the future is predicted by obtaining the weather forecast information corresponding to the irrigation area to be scheduled, so as to determine the rainfall conditions. At the same time, the water level sensor, water quality sensor and flow sensor preset in the irrigation area to be scheduled are used to collect and analyze sensor data in real time to understand the current water level, water quality and flow conditions and determine the water conditions.

[0065] Using the preset cameras in the irrigation area to be scheduled, collect irrigation area images regularly or in real time. Compare the collected irrigation area images with the historical images in the preset irrigation area image library. The irrigation area image library contains the land dryness information corresponding to each historical image, which is obtained through historical data and expert evaluation. Through comparison, you can intuitively understand the changes in the dryness of the land in the current irrigation area. Combined with the comparison results of the irrigation area images and sensor data, a comprehensive analysis of the soil moisture content of the irrigation area to be scheduled is conducted. The sensor data provides the specific value of the soil moisture content, while the image comparison results provide an intuitive display of the dryness of the land. The two complement each other. Based on the analysis results, it can be determined whether the soil needs irrigation, drainage or other management measures.

[0066] Further analysis of the collected irrigation area images can identify the topography and geomorphic features of the irrigation area as well as the layout and status of water conservancy facilities (such as gates, channels, pumping stations, etc.). By analyzing the vegetation characteristics in the irrigation area images, combined with the crop growth cycle and local planting habits, the types of crops in the irrigation area to be scheduled can be determined. Using image processing technology and geographic information systems (GIS), the planting area of ​​each crop can be accurately measured, providing an accurate basis for the formulation of irrigation plans.

[0067] Assume that before a water resource dispatch, the weather forecast for the irrigation area to be dispatched shows that there will be continuous rainfall in the next week. At the same time, sensor data shows that the current water level is high, but the water quality is good and the flow is stable. By comparing the irrigation area images collected by the camera with the historical images, it is found that the degree of land dryness has decreased, but local dry areas still need to be paid attention to. After comprehensive analysis, it is determined that the overall soil moisture is good, but attention should be paid to local drainage and irrigation. Further analysis of the irrigation area images clarified the terrain, landforms and water conservancy facilities, as well as the types of crops and planting areas. Finally, based on this information, an accurate water resource dispatch plan was formulated, including the opening and closing times of the gates, irrigation volume and drainage volume, etc.

[0068] In one embodiment of the present application, the irrigation area data is preprocessed to obtain the target irrigation area data, specifically including:

[0069] Filter the irrigation area data to remove high-frequency noise in the irrigation area data, identify and delete outliers and erroneous data in the irrigation area data;

[0070] The irrigation district data after filtering and deleting outliers and erroneous data are smoothed, and the smoothed irrigation district data are fused to obtain the target irrigation district data corresponding to the irrigation district to be scheduled.

[0071] In one embodiment, first, the collected irrigation data is preliminarily checked to identify and remove high-frequency noise. High-frequency noise is usually caused by excessive sensor sensitivity, environmental interference, or data transmission errors, which will mask the true trend of the data and affect the accuracy of subsequent analysis. Then, statistical methods or machine learning algorithms are used to identify and delete outliers and erroneous data in the irrigation data. Outliers may be caused by sensor failure, data entry errors, or extreme weather conditions, which will significantly deviate from the normal range of the data and interfere with the analysis results.

[0072] Smoothing is performed on the filtered irrigation area data to eliminate random fluctuations and minor changes in the data, making the data more stable and continuous. Smoothing can be performed using moving average, exponential smoothing, or low-pass filtering, and appropriate smoothing parameters can be selected based on the characteristics of the data and analysis requirements. The smoothed data can more clearly reflect the long-term trend and periodic changes of irrigation area water resources, providing more valuable information for subsequent data fusion and decision analysis.

[0073] The smoothed irrigation area data is fused with other related data to obtain more comprehensive target irrigation area data corresponding to the irrigation area to be scheduled. These data may include meteorological data, soil data, crop growth data, etc. They are closely related to irrigation area water resources and can provide a deeper analysis perspective. Data fusion can use weighted averaging, Bayesian estimation or Kalman filtering methods to assign different weights according to the reliability and importance of the data to ensure the accuracy and reliability of the fusion results.

[0074] Assume that before a water resource dispatch, multiple sensor data of the irrigation area to be dispatched are collected, including water level, water quality and flow data. First, these data are filtered to remove high-frequency noise and outliers to ensure the accuracy and reliability of the data. Then, the filtered data is smoothed to eliminate random fluctuations and small changes, making the data more stable and continuous. Next, the smoothed data is fused with other related data, such as meteorological data, soil data and crop growth data, to obtain more comprehensive target irrigation area data. Finally, based on the fused target irrigation area data, an accurate water resource dispatch plan is formulated, including irrigation volume, irrigation time and drainage strategy, to ensure the rational use of water resources in the irrigation area and the healthy growth of crops.

[0075] In one embodiment of the present application, the smoothed irrigation district data is fused to obtain target irrigation district data corresponding to the irrigation district to be scheduled, specifically including:

[0076] Determine the collection time corresponding to each irrigation area data, and fuse the irrigation area data after smoothing at different time points in chronological order from the front to the back;

[0077] Determine the spatial position corresponding to each irrigation district data, so as to merge multiple irrigation district data into the same space according to the corresponding spatial position and by interpolation;

[0078] The collection time corresponding to each sensor data is determined, and the sensor data corresponding to different sensors at the same time are fused to obtain a data set with multi-source data in the same space with time series.

[0079] In one embodiment, first, for each irrigation district data point, determine its corresponding collection time. This can be achieved by looking at the timestamp in the data record or using the log of the data collection system. Then, the irrigation district data after smoothing at different time points are fused in chronological order from front to back. This can be achieved by creating a time series database or data frame, where each row represents a time point and each column represents an irrigation district data indicator (such as water level, water quality, flow, etc.). When fusion, ensure that the time intervals between data points are uniform so that the change trend of the data can be accurately captured in subsequent analysis.

[0080] For each irrigation district data point, determine its corresponding spatial location, which is usually achieved through GPS coordinates or geographic information system (GIS). Since the sensors in the irrigation district may be unevenly distributed, it is necessary to use interpolation methods (such as nearest neighbor interpolation, linear interpolation, spline interpolation, etc.) to fuse multiple irrigation district data into the same space. In this way, even if the sensor positions are not completely consistent, the data distribution within the entire irrigation district can be obtained. After interpolation, a spatially continuous data set can be obtained, which provides a basis for subsequent spatial analysis and visualization.

[0081] Determine the collection time corresponding to each sensor data, which is also achieved by checking the timestamp in the data record. For sensor data corresponding to different sensors at the same time, fusion can be achieved by aligning the data according to the timestamp and then merging them into a data frame or database. When fusing, it is necessary to note that the units and dimensions of different sensor data may be different, so appropriate conversion and standardization are required to ensure the comparability and consistency of the data. Finally, a data set containing multi-source data with time series in the same space is obtained.

[0082] Assume that in a large irrigation area, multiple sensors are deployed to monitor data such as water level, water quality, and flow. First, the data of each sensor is timestamped and fused in chronological order to obtain a time series data set. Next, GIS technology is used to determine the spatial location of each sensor, and interpolation methods are used to fuse data from different locations onto the same spatial grid. Then, the data from different sensors at the same time are fused to obtain a data set containing multi-source data with time series in the same space.

[0083] 102. Based on the environmental data in the target irrigation area data, establish a digital twin model corresponding to the irrigation area to be scheduled, and optimize the digital twin model.

[0084] It should be noted that the digital twin model in the embodiment of the present application includes a physical model and a data model.

[0085] Specifically, in one embodiment of the present application, based on the environmental data in the target irrigation area data, a digital twin model corresponding to the irrigation area to be scheduled is established, which specifically includes:

[0086] According to the irrigation area topography, irrigation area landform and water conservancy facilities in the target irrigation area data, a physical model corresponding to the irrigation area to be scheduled is constructed; wherein the physical model is used to describe the water resource flow process and water resource storage process of the irrigation area to be scheduled;

[0087] Establish a data model corresponding to the irrigation area to be scheduled, and store the water resource data in the target irrigation area data through the data model;

[0088] The physical model and data model are integrated to construct a digital twin model corresponding to the irrigation area to be scheduled; among them, water resources data include rainfall conditions, water conditions, sensor data and geographical information corresponding to water resources.

[0089] In one embodiment, first, based on the irrigation area topography, irrigation area geomorphology and water conservancy facilities information in the target irrigation area data, a physical model corresponding to the irrigation area to be scheduled is constructed using geographic information system (GIS) and three-dimensional modeling technology. The physical model is intended to describe the water resource flow process and water resource storage process of the irrigation area to be scheduled. It takes into account the ups and downs of the terrain, the characteristics of the landform (such as rivers, lakes, wetlands, etc.), and the layout and operation of water conservancy facilities (such as gates, pumping stations, channels, etc.). Through the physical model, the flow path, flow rate and distribution of water resources under different conditions (such as rainfall, irrigation, drainage, etc.), as well as the storage capacity and changes of water resources can be simulated.

[0090] Next, a data model corresponding to the irrigation area to be scheduled is established to store and manage the water resource data in the target irrigation area data. The data model can be in the form of a relational database or a non-relational database, and the appropriate data structure and storage method should be designed according to the characteristics of the data and analysis requirements. The data model should include multi-dimensional data such as rainfall conditions, water conditions, sensor data, and geographic information corresponding to water resources to ensure data integrity and consistency.

[0091] Integrate the constructed physical model and data model to construct a digital twin model corresponding to the irrigation area to be scheduled. The digital twin model is a virtual model corresponding to the physical irrigation area. It can reflect the actual status of the irrigation area in real time and support dynamic simulation and prediction of water resources in the irrigation area. Through the digital twin model, the water resource flow and storage process in the physical model can be combined with the actual data in the data model to realize data visualization and dynamic analysis. The digital twin model can also be used to test and optimize water resource scheduling strategies, and provide support for decision-making by simulating the allocation and utilization of water resources under different schemes.

[0092] Suppose that an effective water resource scheduling plan needs to be formulated in an irrigation area to be scheduled. First, a physical model is constructed based on the terrain, landform and water conservancy facilities information in the target irrigation area data to simulate the flow and storage process of water resources. Next, a data model is established to store and manage multi-dimensional data such as rainfall conditions, water conditions, sensor data, and geographic information corresponding to water resources. Then, the physical model and the data model are integrated to construct a digital twin model. Through the digital twin model, the actual status of the irrigation area can be monitored in real time, and the allocation and utilization of water resources under different rainfall and irrigation conditions can be simulated. Finally, based on the simulation results and optimization algorithm, a water resource scheduling plan that meets the water needs of crops and saves water resources is formulated.

[0093] In one embodiment of the present application, the digital twin model is optimized, specifically including:

[0094] Obtain historical sensor data of the irrigation area to be scheduled through the data model corresponding to the irrigation area to be scheduled, and determine the water level, water quality and flow rate corresponding to the irrigation area to be scheduled in the next period based on the historical sensor data;

[0095] Input historical sensor data into the digital twin model for training, and output the predicted water level, predicted water quality, and predicted flow corresponding to the historical sensor data;

[0096] If the predicted water level, predicted water quality and predicted flow rate corresponding to the historical sensor data do not match the water level, water quality and flow rate corresponding to the irrigation area to be scheduled in the next period, the model parameters in the digital twin model are adjusted according to the water level, water quality and flow rate corresponding to the irrigation area to be scheduled in the next period;

[0097] The training of the digital twin model is completed until the predicted water level, predicted water quality and predicted flow output by the optimized digital twin model match the corresponding water level, water quality and flow of the irrigation area to be scheduled in the next period.

[0098] In one embodiment, the historical sensor data of the irrigation area is obtained through the data model corresponding to the irrigation area to be scheduled. These data include monitoring records of water level, water quality, flow rate and other aspects, which are an important basis for understanding the historical status of water resources in the irrigation area. Based on the historical sensor data obtained, the water level, water quality and flow rate corresponding to the irrigation area to be scheduled in the next period are determined by using time series analysis, statistical regression or machine learning methods. These predicted values ​​provide a reference for subsequent digital twin model training and verification.

[0099] The historical sensor data is input into the digital twin model for training. The digital twin model combines the physical model and the data model to simulate the water resource flow and storage process of the irrigation area, and outputs the predicted water level, predicted water quality and predicted flow corresponding to the historical sensor data. During the training process, the prediction results of the digital twin model are made as close as possible to the actual observed values ​​by adjusting the model parameters and optimizing the algorithm.

[0100] If the predicted water level, predicted water quality, and predicted flow rate corresponding to the historical sensor data do not match the water level, water quality, and flow rate corresponding to the irrigation area to be scheduled in the next period, it means that there are still certain errors in the digital twin model. At this time, the model parameters in the digital twin model are adjusted according to the actual observed values ​​of the water level, water quality, and flow rate corresponding to the irrigation area to be scheduled in the next period. The adjustment process may involve modifying the parameters of the physical model, optimizing the storage and retrieval methods of the data model, or improving the model algorithm.

[0101] Repeat the above training and adjustment process until the predicted water level, predicted water quality and predicted flow output by the optimized digital twin model match the corresponding water level, water quality and flow of the irrigation area to be scheduled in the next period. When the prediction accuracy of the digital twin model reaches the predetermined requirements, the model training is considered complete. At this point, the digital twin model can accurately simulate and predict the water resource status of the irrigation area, providing strong support for water resource scheduling and management.

[0102] 103. Input the water resource data in the target irrigation area data into the optimized digital twin model, and output the water resource forecast information of the irrigation area to be scheduled in the next period.

[0103] Specifically, in one embodiment of the present application, the water resource data in the target irrigation area data is input into the optimized digital twin model, and the water resource forecast information of the irrigation area to be scheduled in the next period is output, specifically including:

[0104] Based on the data interface, a connection is established between the sensor data and the digital twin model to synchronize the sensor data of the irrigation area to be scheduled to the digital twin model in real time;

[0105] Through the digital twin model, the irrigation area to be scheduled is monitored in real time, and the predicted water level, water quality and flow rate corresponding to the irrigation area to be scheduled in the next period are determined;

[0106] The predicted water level, predicted water quality and predicted flow are compared with the corresponding preset thresholds to determine the warning level corresponding to the sensor data.

[0107] In one embodiment, first, a special data interface is developed that is compatible with the data formats and communication protocols of various sensors in the irrigation area to be scheduled. Then, using the data interface, sensors (such as water level sensors, water quality sensors, and flow sensors) are connected to the digital twin model. In this way, the sensor data can be transmitted to the digital twin model in real time. Once the connection is established, the sensor will begin to collect real-time data of the irrigation area to be scheduled, including information such as water level, water quality, and flow. These data will be automatically synchronized to the digital twin model through the data interface at set time intervals (such as every minute, every hour, etc.) to ensure that the model can reflect the latest status of the irrigation area.

[0108] After receiving the sensor data, the digital twin model will immediately analyze and process the data. Through built-in algorithms and models, the digital twin model can monitor the status of the irrigation area to be scheduled in real time and predict the water level, water quality and flow rate in the next period based on current data and historical trends.

[0109] In order to detect potential water resource problems in a timely manner, this embodiment presets a series of thresholds, including water level thresholds, water quality thresholds, and flow thresholds. The digital twin model compares the predicted water level, water quality, and flow with these thresholds. If the predicted value exceeds or falls below a certain threshold, the model triggers the corresponding early warning mechanism. Depending on the degree of deviation between the predicted value and the threshold, the early warning level will be divided into different levels (such as low risk, medium risk, high risk, etc.) so that irrigation district managers can take corresponding countermeasures.

[0110] Assume that in a real-time monitoring, the digital twin model predicts that the water level of the irrigation area to be dispatched will reach the high-risk threshold in the next period. The model will immediately trigger a high-risk warning and notify the irrigation area managers through SMS, email or APP push. After receiving the warning, the managers can immediately view the detailed data and analysis reports provided by the digital twin model so that they can take timely dispatch measures (such as opening or closing gates, adjusting irrigation plans, etc.) to avoid flooding caused by excessively high water levels.

[0111] 104. Based on the preset intelligent scheduling algorithm, determine the water resource scheduling instructions corresponding to the water resource forecast information, and perform water resource scheduling on the irrigation area to be scheduled according to the water resource scheduling instructions.

[0112] Specifically, in one embodiment of the present application, based on a preset intelligent scheduling algorithm, a water resource scheduling instruction corresponding to the water resource prediction information is determined, and water resource scheduling is performed on the irrigation area to be scheduled according to the water resource scheduling instruction, specifically including:

[0113] According to the warning level corresponding to the water resources forecast information, as well as the predicted water level, predicted water quality and predicted flow rate of the irrigation area to be dispatched in the next period, the actual dispatch demand corresponding to the irrigation area to be dispatched is determined;

[0114] According to the actual dispatching demand, the types of crops in the irrigation area to be dispatched and the planting area of ​​each crop, and based on the preset intelligent dispatching algorithm, the water resource dispatching instructions corresponding to the water resource forecasting information are determined;

[0115] According to the water resource scheduling instructions, the specified number of gates are opened in sequence to the target opening height, and the gates are kept open for the target opening time to complete the irrigation area water resource scheduling.

[0116] In one embodiment, first, the digital twin model will output the water resource forecast information of the irrigation area to be scheduled in the next period, including the predicted water level, predicted water quality and predicted flow, and give the corresponding warning level. The irrigation area management personnel will determine the actual scheduling needs based on these forecast information and warning levels, combined with the actual situation of the irrigation area (such as irrigation demand, water storage capacity, drainage conditions, etc.). For example, if the predicted water level is too high, it may be necessary to increase the drainage volume; if the predicted water quality is poor, it may be necessary to adjust the irrigation water source or strengthen water quality treatment.

[0117] Next, managers consider the types of crops in the irrigation area to be scheduled and the planting area of ​​each crop. Different crops have different water requirements, so irrigation strategies will also be different. By analyzing the crop's growth cycle, water requirements, and current soil moisture conditions, managers can more accurately determine the amount and timing of irrigation.

[0118] After determining the actual scheduling needs and crop information, managers will use the preset intelligent scheduling algorithm to generate water resource scheduling instructions. The intelligent scheduling algorithm will comprehensively consider multiple factors, such as irrigation area topography, water conservancy facility layout, water resource supply and demand balance, etc., to allocate water resources in the most optimized way. The algorithm output includes specific instructions such as the number of gates to be opened, the opening height of each gate, and the opening time.

[0119] Finally, according to the instructions generated by the intelligent scheduling algorithm, the managers will operate the gates in sequence. The specified number of gates will be opened to the target opening height and kept open for the target opening time to ensure that water resources are scheduled according to the predetermined plan. During the execution process, the managers will also pay close attention to the operating status of the gates and the actual situation of the irrigation area so as to adjust the scheduling strategy in time.

[0120] Assume that in a water resource dispatch, the digital twin model predicts that the water level in the irrigation area to be dispatched will be low in the next period, and the water quality will be good. Based on this prediction information and the actual situation of the irrigation area (such as the current soil moisture, crop growth needs, etc.), the manager determines the actual dispatch demand for increasing the irrigation volume. Then, by analyzing the types of crops and planting areas in the irrigation area, it is found that rice is mainly planted and is currently in the critical growth period. Based on this information, the manager uses the intelligent dispatching algorithm to generate a water resource dispatching instruction, which requires opening three gates to a half-open state and keeping them open for 2 hours. Finally, the manager operates the gate according to the instruction and successfully completes the water resource dispatching task of the irrigation area.

[0121] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, the embodiment of this application also provides an irrigation area water resource scheduling device based on digital twin, whose structure is as follows: Figure 2 shown.

[0122] Figure 2 The internal structure diagram of a digital twin irrigation area water resource scheduling device provided in the embodiment of the present application. Figure 2 As shown, the device includes:

[0123] at least one processor;

[0124] and, a memory communicatively coupled to the at least one processor;

[0125] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor to enable the at least one processor to:

[0126] Acquire the irrigation area data of the irrigation area to be scheduled, and pre-process the irrigation area data to obtain the target irrigation area data; wherein the target irrigation area data includes water resource data and environmental data;

[0127] Based on the environmental data in the target irrigation area data, a digital twin model corresponding to the irrigation area to be scheduled is established, and the digital twin model is optimized; wherein the digital twin model includes a physical model and a data model;

[0128] Input the water resource data in the target irrigation area data into the optimized digital twin model, and output the water resource forecast information of the irrigation area to be scheduled in the next period;

[0129] Based on the preset intelligent scheduling algorithm, the water resource scheduling instructions corresponding to the water resource forecast information are determined, and water resource scheduling is carried out for the irrigation area to be scheduled according to the water resource scheduling instructions.

[0130] The present application also provides a non-volatile computer storage medium storing computer executable instructions. When the computer executable instructions are executed, they can:

[0131] Acquire the irrigation area data of the irrigation area to be scheduled, and pre-process the irrigation area data to obtain the target irrigation area data; wherein the target irrigation area data includes water resource data and environmental data;

[0132] Based on the environmental data in the target irrigation area data, a digital twin model corresponding to the irrigation area to be scheduled is established, and the digital twin model is optimized; wherein the digital twin model includes a physical model and a data model;

[0133] The water resource data in the target irrigation area data is input into the optimized digital twin model, and the water resource forecast information of the irrigation area to be scheduled in the next period is output;

[0134] Based on the preset intelligent scheduling algorithm, the water resource scheduling instructions corresponding to the water resource forecast information are determined, and water resource scheduling is carried out for the irrigation area to be scheduled according to the water resource scheduling instructions.

[0135] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0136] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0137] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0138] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0139] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0140] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0141] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0142] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0143] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0144] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0145] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0146] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for water resource scheduling in irrigation areas based on digital twins, characterized in that: The method comprises: Acquire irrigation area data of the irrigation area to be scheduled, and pre-process the irrigation area data to obtain target irrigation area data; wherein the target irrigation area data includes water resource data and environmental data; Based on the environmental data in the target irrigation area data, a digital twin model corresponding to the irrigation area to be scheduled is established, and the digital twin model is optimized; wherein the digital twin model includes a physical model and a data model; Input the water resource data in the target irrigation area data into the optimized digital twin model, and output the water resource forecast information of the irrigation area to be scheduled in the next period; Based on a preset intelligent scheduling algorithm, a water resource scheduling instruction corresponding to the water resource prediction information is determined, and water resource scheduling is performed on the irrigation area to be scheduled according to the water resource scheduling instruction.

2. According to the method of irrigation area water resources scheduling based on digital twins according to claim 1, it is characterized in that: The water resource data in the target irrigation area data is input into the optimized digital twin model, and the water resource forecast information of the irrigation area to be scheduled in the next period is output, specifically including: Based on the data interface, a connection is established between the sensor data and the digital twin model, so as to synchronize the sensor data of the irrigation area to be scheduled to the digital twin model in real time; Through the digital twin model, the irrigation area to be scheduled is monitored in real time, and the predicted water level, predicted water quality and predicted flow rate corresponding to the irrigation area to be scheduled in the next period are determined; The predicted water level, the predicted water quality and the predicted flow are compared with corresponding preset thresholds to determine an early warning level corresponding to the sensor data.

3. The irrigation area water resources scheduling method based on digital twin according to claim 1 is characterized in that: Based on a preset intelligent scheduling algorithm, a water resource scheduling instruction corresponding to the water resource forecast information is determined, and water resource scheduling is performed on the irrigation area to be scheduled according to the water resource scheduling instruction, specifically including: Determine the actual dispatching demand corresponding to the irrigation area to be dispatched according to the warning level corresponding to the water resources forecast information, and the predicted water level, predicted water quality and predicted flow of the irrigation area to be dispatched in the next period of time; Determine the water resource scheduling instructions corresponding to the water resource forecast information according to the actual scheduling demand, the types of crops in the irrigation area to be scheduled and the planting area of ​​each crop, and based on a preset intelligent scheduling algorithm; According to the water resource scheduling instruction, a specified number of gates are opened in sequence to the target opening height, and the gates are kept open for the target opening time to complete the irrigation area water resource scheduling.

4. The irrigation area water resources scheduling method based on digital twin according to claim 1 is characterized in that: Obtain the irrigation area data to be dispatched, including: Determine the rainfall conditions corresponding to the irrigation area to be scheduled based on the weather forecast corresponding to the irrigation area to be scheduled, and determine the water conditions corresponding to the irrigation area to be scheduled based on sensor data corresponding to the water level sensor, water quality sensor and flow sensor preset in the irrigation area to be scheduled; By using a camera preset in the irrigation area to be scheduled, an irrigation area image corresponding to the irrigation area to be scheduled is collected, and the irrigation area image is compared with a historical image in a preset irrigation area image library; wherein the irrigation area image library includes the land dryness degree corresponding to each historical image; Determine the dryness of the land corresponding to the irrigation area to be scheduled according to the comparison result, and combine the comparison result with the sensor data to determine the soil moisture corresponding to the irrigation area to be scheduled; The irrigation area image is analyzed to determine the corresponding irrigation area topography, irrigation area landform and water conservancy facilities, and to determine the types of crops corresponding to the irrigation area to be scheduled and the planting area corresponding to each crop.

5. The irrigation area water resources scheduling method based on digital twin according to claim 4 is characterized in that: Preprocessing the irrigation area data to obtain target irrigation area data specifically includes: Filtering the irrigation district data to remove high-frequency noise in the irrigation district data, and identifying and deleting outliers and erroneous data in the irrigation district data; The irrigation district data after filtering and deleting abnormal values ​​and erroneous data are smoothed, and the smoothed irrigation district data are fused to obtain the target irrigation district data corresponding to the irrigation district to be scheduled.

6. The irrigation area water resources scheduling method based on digital twin according to claim 5 is characterized in that: The smoothed irrigation area data is fused to obtain the target irrigation area data corresponding to the irrigation area to be scheduled, specifically including: Determine the collection time corresponding to each irrigation area data, and fuse the irrigation area data after smoothing at different time points in chronological order from the front to the back; Determine the spatial position corresponding to each irrigation area data, and fuse the plurality of irrigation area data into the same space by interpolation according to the corresponding spatial position; The collection time corresponding to each sensor data is determined, and the sensor data corresponding to different sensors at the same time are fused to obtain a data set with multi-source data in the same space with time series.

7. The irrigation area water resources scheduling method based on digital twin according to claim 1 is characterized in that: Based on the environmental data in the target irrigation area data, a digital twin model corresponding to the irrigation area to be scheduled is established, specifically including: According to the irrigation area topography, irrigation area landform and water conservancy facilities in the target irrigation area data, a physical model corresponding to the irrigation area to be scheduled is constructed; wherein the physical model is used to describe the water resource flow process and water resource storage process of the irrigation area to be scheduled; Establishing a data model corresponding to the irrigation area to be scheduled, and storing the water resource data in the target irrigation area data through the data model; The physical model and the data model are integrated to construct a digital twin model corresponding to the irrigation area to be scheduled; wherein the water resource data includes rainfall conditions, water conditions, sensor data and geographic information corresponding to water resources.

8. The irrigation area water resources scheduling method based on digital twin according to claim 1 is characterized in that: Optimizing the digital twin model includes: Obtaining historical sensor data of the irrigation area to be scheduled through a data model corresponding to the irrigation area to be scheduled, and determining the water level, water quality and flow rate corresponding to the irrigation area to be scheduled in the next time period based on the historical sensor data; Inputting the historical sensor data into the digital twin model for training, and outputting the predicted water level, predicted water quality and predicted flow corresponding to the historical sensor data; If the predicted water level, predicted water quality and predicted flow rate corresponding to the historical sensor data do not match the water level, water quality and flow rate corresponding to the irrigation area to be scheduled in the next period, the model parameters in the digital twin model are adjusted according to the water level, water quality and flow rate corresponding to the irrigation area to be scheduled in the next period; The training of the digital twin model is completed until the predicted water level, predicted water quality and predicted flow output by the optimized digital twin model match the water level, water quality and flow corresponding to the irrigation area to be scheduled in the next period.

9. An irrigation area water resources scheduling device based on digital twin, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; In which, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a digital twin-based irrigation area water resources scheduling method as described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: When the computer executable instructions are executed, a method for water resource scheduling in an irrigation area based on digital twins as described in any one of claims 1 to 8 is implemented.

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