Multi-scale nested space-air-ground interaction integrated irrigation area water consumption intelligent monitoring method and system

Abstract

The invention discloses a multi-scale nested space-air-ground interaction integrated irrigation area water consumption intelligent monitoring method and system, and relates to the field of water consumption monitoring, and the method comprises the steps: obtaining multi-source data of a target irrigation area; the multi-source data comprises water volume data and image data of the air, the sky and the ground; performing water consumption classification on the multi-source data by adopting an extreme gradient lifting algorithm to obtain a water consumption classification result of the target irrigation area; performing multi-scale nested quadrat design on the multi-source data to obtain a space-space-ground water volume data spatial distribution map of the target irrigation area; constructing a water consumption remote sensing sample set of the target irrigation area according to a water consumption classification result and a space-air-ground water volume data spatial distribution map; the space-air-ground interaction water consumption of the target irrigation area is analyzed through the irrigation area water consumption remote sensing sample set, the water consumption monitoring result of the target irrigation area is obtained, intelligent monitoring of the water consumption of the irrigation area can be achieved, and therefore the irrigation efficiency and precision are improved.

Description

Technical Field

[0001] The present application relates to the field of water consumption monitoring, and in particular to a multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method and system. Background Art

[0002] An irrigation area refers to an irrigation area with a reliable water source and a water diversion, transmission and distribution channel system as well as corresponding drainage ditches. It is a product of human economic activities and develops with the development of social economy.

[0003] At present, the relevant monitoring schemes include: a real-time dynamic monitoring system for remote irrigation and drainage systems based on the Internet, including a data acquisition unit, including a meteorological element module set up in the field, a canal water level flow module, a field soil moisture module, a groundwater level module and a drainage flow module, which are used to obtain the parameters required for the irrigation and drainage system; the storage system includes a remote receiving terminal, a cloud storage module and a data visualization analysis module; the model system automatically obtains the required parameters through the cloud platform in the cloud storage module, and performs data processing, including a parameter calibration and inversion module, a crop water demand prediction model, an irrigation system optimization model, an irrigation area water balance calculation model and a water use management module, using modern information technology to realize the remote, intelligent and efficient water resources monitoring technology in irrigation areas and river basins, providing technical support for the modernization of water conservancy and the intelligent management of water resources, which is conducive to improving the level of modern management of irrigation areas and the efficiency of water resource utilization.

[0004] In actual use, the above-mentioned monitoring system can only realize real-time dynamic remote monitoring of water balance elements such as meteorological elements, canal water level flow, field soil moisture, groundwater level and drainage flow, but cannot realize the monitoring of irrigation area water consumption, which does not meet the existing needs. Therefore, how to realize intelligent monitoring of irrigation area water consumption has become a problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of this application is to provide a multi-scale nested air-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method and system, which can realize intelligent monitoring of irrigation area water consumption, thereby improving the efficiency and accuracy of irrigation.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In the first aspect, the present application provides a multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method, including:

[0008] Acquire multi-source data of the target irrigation area; the multi-source data includes: water volume data of the air, ground and space, and image data;

[0009] Using an extreme gradient boosting algorithm to classify the water consumption of the multi-source data, and obtaining a water consumption classification result of the target irrigation area;

[0010] Perform multi-scale nested sample plot design on the multi-source data to obtain spatial distribution mapping of water volume data in the air and space of the target irrigation area;

[0011] Constructing a remote sensing sample set of water consumption in the target irrigation area based on the water consumption classification results and spatial distribution mapping of water consumption data in the air, space and land;

[0012] The irrigation area water consumption remote sensing sample set is used to analyze the air-space-ground interactive water consumption of the target irrigation area to obtain the water consumption monitoring result of the target irrigation area.

[0013] Optionally, obtain multi-source data of the target irrigation area, including:

[0014] Acquire the initial multi-source data of the target irrigation area based on satellite remote sensing technology;

[0015] The initial multi-source data of the target irrigation area are preprocessed to obtain the multi-source data of the target irrigation area; the preprocessing includes: data cleaning, standardization, normalization, noise interference removal and data enhancement.

[0016] Optionally, a multi-scale nested sample plot design is performed on the multi-source data to obtain a spatial distribution map of water volume data in the air and space of the target irrigation area, specifically including:

[0017] Determine the spatial scale according to the image data in the multi-source data, and match the water volume data of the air, space and land in the multi-source data with the multi-source geographic elements at different spatial scales, and determine the matched data as the modeling sample;

[0018] According to the modeling samples at each spatial scale, a geographically weighted regression model is used to construct a statistical relationship model between the water volume data of the target irrigation area and the multi-source geographical elements;

[0019] Determine the spatial distribution map of water volume data in the air and space of the target irrigation area based on the statistical relationship model.

[0020] Optionally, the remote sensing sample set of water consumption in the irrigation area is used to analyze the air-ground-space interactive water consumption of the target irrigation area to obtain the water consumption monitoring result of the target irrigation area, specifically including:

[0021] Determine irrigation area data using the irrigation area water consumption remote sensing sample set;

[0022] Establishing a water consumption accounting model based on the irrigation area data, the meteorological data in the irrigation area water consumption remote sensing sample set, and the soil moisture characteristic data in the irrigation area water consumption remote sensing sample set;

[0023] interpreting the actual planting area and coverage of various crops in the target irrigation area according to the image data;

[0024] Inputting the actual planting area and coverage of crops in the target irrigation area into the water consumption calculation model to obtain water consumption data of the target irrigation area;

[0025] combining the water consumption data with the geographical location information to generate a water consumption distribution map;

[0026] The water consumption distribution map is used to determine the air-ground-land interactive water consumption in the target irrigation area; the air-ground-land interactive water consumption is used as the water consumption monitoring result.

[0027] Optionally, the irrigation area data is determined using the irrigation area water consumption remote sensing sample set, specifically including:

[0028] Extracting potential irrigation area based on remote sensing sample set of water consumption in the irrigation area;

[0029] Inverting and calculating the field surface soil moisture content during the crop growth period in the potential irrigation area according to the image data to obtain the field surface soil moisture content inversion value;

[0030] According to the inverted value of the field surface soil moisture content, the actual irrigation area is extracted from the potential irrigation area, and the actual irrigation area is used as the irrigation area data.

[0031] Optionally, interpreting the actual planting area and coverage of various crops in the target irrigation area according to the image data specifically includes:

[0032] Determining the band data of the satellite remote sensing image and the polarization data of the radar image in the image data;

[0033] Calculating a normalized vegetation index according to the band data, and extracting texture features according to the polarization data;

[0034] Calculating the importance scores of the band data, the polarization data, the normalized difference vegetation index and the texture feature in crop classification according to the Gini impurity contribution;

[0035] Performing weighted calculation according to the importance scores to determine the actual planting area of ​​each type of crop in the target irrigation area;

[0036] The coverage of each type of crop in the target irrigation area is determined according to the actual planting area of ​​each type of crop in the target irrigation area.

[0037] Optionally, the water volume data includes: meteorological data, soil moisture characteristic data, geological water consumption, evaporation water, irrigation water and crop water consumption; the irrigation area water consumption remote sensing sample set includes: meteorological data, soil moisture characteristic data, geographical location information, irrigation and drainage engineering information, agricultural planting structure information and irrigation system information.

[0038] In the second aspect, the present application provides a multi-scale nested air-ground-integrated irrigation area water consumption intelligent monitoring system, including:

[0039] A data acquisition module is used to acquire multi-source data of the target irrigation area; the multi-source data includes: water volume data of the air, ground and space, and image data;

[0040] A water consumption classification module, used for classifying the water consumption of the multi-source data by using an extreme gradient boosting algorithm to obtain a water consumption classification result of the target irrigation area;

[0041] A nested sample plot design module is used to perform multi-scale nested sample plot design on the multi-source data to obtain spatial distribution mapping of water volume data in the air and space of the target irrigation area;

[0042] A water consumption remote sensing sample set construction module, used to construct a water consumption remote sensing sample set of the target irrigation area according to the water consumption classification result and the spatial distribution mapping of water volume data in the air, space and land;

[0043] The water consumption monitoring module is used to analyze the air-space-ground interactive water consumption of the target irrigation area by using the irrigation area water consumption remote sensing sample set to obtain the water consumption monitoring result of the target irrigation area.

[0044] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described multi-scale nested, air-ground-space interactive integrated irrigation area intelligent water consumption monitoring methods.

[0045] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described multi-scale nested, space-ground, and integrated irrigation area water consumption intelligent monitoring methods.

[0046] According to the specific embodiments provided in this application, this application has the following technical effects:

[0047] The present application provides a method and system for intelligent monitoring of water consumption in an integrated irrigation area with multi-scale nested air-space-ground-interaction. The method uses air-space-ground-water data and image data as multi-source data, classifies the water consumption of the multi-source data by adopting an extreme gradient boosting algorithm, and obtains spatial distribution mapping of air-space-ground-water data by multi-scale nested sample design for the multi-source data. A water consumption remote sensing sample set is constructed according to the water consumption classification results and the spatial distribution mapping of air-space-ground-water data, thereby analyzing the air-space-ground-interaction water consumption of the target irrigation area and obtaining water consumption monitoring results. The present application realizes intelligent monitoring of water consumption in the irrigation area, thereby improving irrigation efficiency, accurately grasping the moisture status of the irrigation area, avoiding waste of resources caused by blind irrigation, improving the utilization rate of irrigation water, monitoring irrigation water consumption in real time, ensuring that limited water resources are used to the maximum benefit, and greatly improving the efficiency and accuracy of irrigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 This is an application environment diagram of a multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method in one embodiment of the present application;

[0050] Figure 2 A schematic flow chart of a multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method provided in one embodiment of the present application;

[0051] Figure 3 A schematic diagram of functional modules of a multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring system provided by another embodiment of the present application;

[0052] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. 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 creative work are within the scope of protection of this application.

[0054] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0055] In order to solve the problem that in actual use, only real-time dynamic remote monitoring of water balance elements such as meteorological elements, canal water level flow, field soil moisture, groundwater level and drainage flow can be achieved, but the monitoring of irrigation area water consumption cannot be achieved, this application proposes an integrated irrigation area water consumption intelligent monitoring solution with multi-scale nested space-ground interaction.

[0056] The multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the multi-source data of the target irrigation area to the server 104. After the server 104 receives the multi-source data of the target irrigation area, for the multi-source data of the target irrigation area, the server 104 uses the extreme gradient boosting algorithm to classify the water consumption of the multi-source data to obtain the water consumption classification result of the target irrigation area; multi-scale nested sample plot design is performed on the multi-source data to obtain the spatial distribution mapping of the water volume data of the air-space-ground-land of the target irrigation area; the water consumption remote sensing sample set of the target irrigation area is constructed according to the water consumption classification results and the spatial distribution mapping of the water volume data of the air-space-ground-land; the irrigation area water consumption remote sensing sample set is used to analyze the air-space-ground-ground interactive water consumption of the target irrigation area to obtain the water consumption monitoring result of the target irrigation area.

[0057] The server 104 can feed back the obtained water consumption monitoring result of the target irrigation area to the terminal 102. In addition, in some embodiments, the multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly process the multi-source data of the target irrigation area, or the server 104 can obtain the multi-source data of the target irrigation area from the data storage system and process the multi-source data of the target irrigation area.

[0058] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0059] In an exemplary embodiment, Figure 2 As shown, a multi-scale nested space-ground-ground integrated irrigation area water consumption intelligent monitoring method is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 205. Among them:

[0060] Step 201, obtaining multi-source data of a target irrigation area; the multi-source data includes: air, ground, water volume data and image data.

[0061] The water volume data include: meteorological data, soil moisture characteristic data, geological water consumption, evaporation water, irrigation water and crop water consumption.

[0062] Step 202: Use an extreme gradient boosting algorithm to classify the water consumption of the multi-source data to obtain a water consumption classification result of the target irrigation area.

[0063] In this step, the extreme gradient boosting algorithm is used to achieve fine classification of water consumption and obtain the fine classification results of water consumption in the target irrigation area.

[0064] Step 203, performing multi-scale nested sample design on the multi-source data to obtain spatial distribution mapping of water volume data in the sky and land of the target irrigation area.

[0065] Step 204: construct a remote sensing sample set of water consumption in the target irrigation area based on the water consumption classification result and spatial distribution mapping of water volume data in the air, space and land.

[0066] The irrigation area water consumption remote sensing sample set includes: meteorological data, soil moisture characteristic data, geographical location information, irrigation and drainage engineering information, agricultural planting structure information and irrigation system information.

[0067] Step 205: Use the irrigation area water consumption remote sensing sample set to analyze the air-space-ground interactive water consumption of the target irrigation area to obtain the water consumption monitoring result of the target irrigation area.

[0068] The implementation of the above steps 201 to 205 can realize intelligent monitoring of water consumption in the irrigation area, thereby improving the efficiency and accuracy of irrigation.

[0069] In another exemplary embodiment of the present application, step 201 specifically includes:

[0070] (1) Obtain initial multi-source data of the target irrigation area based on satellite remote sensing technology.

[0071] Among them, the air-space water volume data in the initial air-space-ground water volume data is obtained by using satellite remote sensing technology to monitor and perceive the large-scale global situation of the basin underlying surface, river and lake water bodies, meteorological data and engineering features in the target irrigation area. The ground water volume data in the initial air-space-ground water volume data is obtained by using the ground monitoring equipment of rain gauges, evaporation stations, groundwater stations, soil moisture stations and video monitoring stations to monitor the rainfall, evaporation, groundwater level and soil moisture conditions at the irrigation area points in real time. In addition, satellite remote sensing technology or various stations can also be used to monitor the growth period and type of crops in the target irrigation area, determine the crop type and growth period, and facilitate the subsequent calculation of the actual planting area.

[0072] (2) Preprocessing the initial multi-source data of the target irrigation area to obtain the multi-source data of the target irrigation area; the preprocessing includes: data cleaning, standardization, normalization, noise interference removal and data enhancement.

[0073] Among them, data cleaning includes: processing missing values ​​and outliers, by deleting samples or features with missing values, or using interpolation, mean, median, mode and other methods to fill missing values, so as to improve data quality and model performance. Data standardization includes: converting data into a distribution with a mean of 0 and a standard deviation of 1, which helps to reduce the impact of dimensional inconsistency between features. Data normalization includes scaling the data to a specific range.

[0074] Noise interference removal includes: removal of communication interference, white noise interference and random pulse interference. In this embodiment, data enhancement is performed on the data after interference removal, so that the characteristic parameters in the data after interference removal are enhanced.

[0075] This embodiment can improve data quality by preprocessing the collected air, ground and water volume data and image data of the target irrigation area, ensure data integrity and accuracy by cleaning missing values ​​and outliers, reduce the impact of dimensional inconsistency between features through standardization and normalization, improve model training efficiency and prediction accuracy, and improve model generalization and robustness through data enhancement and data balancing.

[0076] In another exemplary embodiment of the present application, step 203 specifically includes:

[0077] (1) Determine the spatial scale (i.e., spatial resolution) based on the image data in the multi-source data, and match the air-space, ground-water volume data in the multi-source data with multi-source geographic elements at different spatial scales, and determine the matched data as modeling samples.

[0078] (2) Based on the modeling samples at each spatial scale, a geographically weighted regression (GWR) model is used to construct a statistical relationship model between the air-space, land-ground water volume data of the target irrigation area and multi-source geographical elements; and the spatial distribution map of the air-space, land-ground water volume data of the target irrigation area is determined based on the statistical relationship model.

[0079] In practical applications, the water volume data of the target irrigation area in the air, space, and land are matched with modeling samples of multi-source geographic elements at multiple spatial scales of 1 km to 5 km, 250 m to 500 m, and 30 m. The GWR model is used as the basic model to model different sample numbers and spatial distributions, and the changing laws of the modeling accuracy and model stability are statistically analyzed to obtain a statistical relationship model that meets the modeling accuracy and model stability, thereby obtaining the spatial distribution mapping of water volume data in the air, space, and land.

[0080] In summary, this embodiment uses the GWR model to construct a statistical relationship model between the water volume data of the target irrigation area's space, land, and sky and multi-source geographical variables based on the modeling samples between the water volume data of the target irrigation area's space, land, and sky and multi-source geographical elements at multiple spatial scales, and carries out spatial distribution mapping based on the water volume data of the target irrigation area's space, land, and sky at multiple spatial scales.

[0081] In another exemplary embodiment of the present application, step 205 specifically includes:

[0082] (1) Determine the irrigation area data using the irrigation area water consumption remote sensing sample set. Specifically:

[0083] The potential irrigation area is extracted based on the remote sensing sample set of water consumption in the irrigation area; the field surface soil moisture content in the crop growth period within the potential irrigation area is inverted and calculated based on the image data to obtain the field surface soil moisture content inversion value; based on the field surface soil moisture content inversion value, different methods are used to extract the actual irrigation area from the potential irrigation area, and the actual irrigation area is used as the irrigation area area data.

[0084] Among them, the potential irrigation area is extracted according to the remote sensing sample set of water consumption in the irrigation area, specifically including: obtaining the irrigation areas with irrigation boundaries and the irrigation areas without irrigation boundaries; wherein, the cultivated land area within the irrigation areas with irrigation boundaries is taken as the potential irrigation area, and the forest and fruit land area and the facility agriculture area in the non-cultivated land within the irrigation areas with irrigation boundaries are taken as the potential irrigation area; the potential irrigation area is determined according to the distribution information of conventional irrigated crops in the irrigation areas with non-irrigation boundaries, and the potential irrigation area is determined according to the water consumption of major crops in the irrigation areas with non-irrigation boundaries during the growing period and the actual precipitation during the crop growing period of that year.

[0085] (2) A water consumption accounting model is established based on the irrigation area data, the meteorological data in the irrigation area water consumption remote sensing sample set, and the soil moisture characteristic data in the irrigation area water consumption remote sensing sample set. For example, the water consumption can be calculated by using the improved unit energy balance formula or the Penman formula combined calculation method. These models can accurately calculate the water consumption of different crops in different growth periods.

[0086] (3) Interpreting the actual planting area and coverage of various crops in the target irrigation area based on the image data. Specifically:

[0087] Determine the band data of the satellite remote sensing image and the polarization data of the radar image in the image data; calculate the Normalized Difference Vegetation Index (NDVI) according to the band data, and extract the texture features according to the polarization data; calculate the importance score of the band data, the polarization data, the Normalized Difference Vegetation Index and the texture features in the crop classification according to the Gini impurity contribution; perform weighted calculation and screening of dominant feature information according to the importance score of each feature, extract the crop category and the actual planting area of ​​each type of crop in the target irrigation area according to the dominant feature information; determine the coverage of each type of crop in the target irrigation area according to the actual planting area of ​​each type of crop in the target irrigation area. In addition, this embodiment also uses the yield data of different crops in the field samples to verify the actual planting area and coverage of each type of crop, and determine the effectiveness of using image data for interpretation.

[0088] This embodiment can more accurately grasp the actual water demand of the irrigation area by accurately calculating the actual planting area and irrigation area (i.e., irrigation area data), so as to reasonably dispatch water resources and avoid water waste. For example, based on the water measurement results, the water transfer capacity and water transfer loss of channels at all levels can be analyzed and calculated, and the water consumption of each section and the irrigation quota of various crops can be statistically calculated, thereby providing the utilization rate of canal water and field water. Accurate planting area and irrigation area data can help irrigation area managers formulate more scientific water use plans, reduce unnecessary irrigation, and thus save water resources. For example, according to the water use plan, the water diversion flow of channels at all levels can be adjusted and controlled to promote water conservation. Accurate planting area and irrigation area data can provide a reliable management basis for irrigation areas, help managers better plan and manage irrigation systems, provide higher accuracy, reduce measurement errors, ensure data accuracy, and improve the management level of irrigation areas: Accurate planting area and irrigation area data can provide irrigation areas with a reliable basis for economic water management and promote the improvement of irrigation area management level.

[0089] (4) The actual planting area and coverage of the crops in the target irrigation area are input into the water consumption accounting model to obtain the water consumption data of the target irrigation area. This solves the current problems of weak metering and monitoring capabilities of agricultural irrigation water in irrigation areas and inaccurate statistical data, and provides technical support for strengthening agricultural water management in irrigation areas.

[0090] (5) Combining the water consumption data with the geographical location information to generate a water consumption distribution map.

[0091] (6) The water consumption distribution map is used to determine the space-ground-air interactive water consumption in the target irrigation area, so as to intuitively display the water consumption situation of different locations in the irrigation area for further analysis. The space-ground-air interactive water consumption is used as the water consumption monitoring result.

[0092] This embodiment collects irrigation area data for preprocessing; performs fine classification of water consumption on the preprocessed data; obtains spatial distribution mapping of water volume data in the air and space of the irrigation area; constructs a remote sensing sample set of water consumption in the irrigation area; and obtains monitoring results of water consumption in the irrigation area. This embodiment solves the problem that the current monitoring of water consumption in the irrigation area cannot be realized, can improve irrigation efficiency, promote water-saving agriculture, accurately grasp the moisture conditions in the irrigation area, perform precise irrigation according to the actual water requirements of crops, avoid resource waste caused by blind irrigation, improve the utilization rate of irrigation water, reduce unnecessary water resource consumption, monitor and record irrigation water consumption in real time, scientifically evaluate the water supply and demand conditions in the irrigation area, provide a basis for formulating a reasonable water resource allocation plan, ensure that limited water resources are used to the maximum benefit, promote the sustainable use of water resources, improve the level of irrigation management, and greatly improve the efficiency and accuracy of irrigation management.

[0093] Based on the same inventive concept, the embodiment of the present application also provides a multi-scale nested space-ground-interaction integrated irrigation area water consumption intelligent monitoring system for realizing the multi-scale nested space-ground-interaction integrated irrigation area water consumption intelligent monitoring method involved in the above-mentioned. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more multi-scale nested space-ground-interaction integrated irrigation area water consumption intelligent monitoring system embodiments provided below can refer to the limitations of the multi-scale nested space-ground-interaction integrated irrigation area water consumption intelligent monitoring method above, and will not be repeated here.

[0094] In an exemplary embodiment, Figure 3 As shown, a multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring system is provided, including:

[0095] The data acquisition module 301 is used to acquire multi-source data of the target irrigation area; the multi-source data includes: water volume data of the air, ground and space, and image data.

[0096] The water consumption classification module 302 is used to classify the water consumption of the multi-source data using an extreme gradient boosting algorithm to obtain a water consumption classification result of the target irrigation area.

[0097] The nested sample plot design module 303 is used to perform multi-scale nested sample plot design on the multi-source data to obtain the spatial distribution mapping of water volume data in the air and space of the target irrigation area.

[0098] The water consumption remote sensing sample set construction module 304 is used to construct the water consumption remote sensing sample set of the target irrigation area according to the water consumption classification result and the spatial distribution mapping of water volume data in the air, space and land.

[0099] The water consumption monitoring module 305 is used to analyze the air-space-ground interactive water consumption of the target irrigation area by using the irrigation area water consumption remote sensing sample set to obtain the water consumption monitoring result of the target irrigation area.

[0100] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store multi-source data of the target irrigation area. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-scale nested space-ground-earth-space integrated irrigation area water consumption intelligent monitoring method is implemented.

[0101] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0102] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0103] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0104] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0105] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0106] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, etc., but is not limited thereto.

[0107] The multi-scale nested space-ground-interactive integrated irrigation area water consumption intelligent monitoring method and system of the present application monitors the water consumption of the irrigation area, improves irrigation efficiency, promotes water-saving agriculture, accurately grasps the moisture status of the irrigation area, and performs precise irrigation according to the actual water demand of the crop to avoid resource waste caused by blind irrigation. It not only improves the utilization rate of irrigation water, but also helps to develop water-saving agriculture and reduce unnecessary water resource consumption. It monitors and records the irrigation water consumption in real time, scientifically evaluates the water supply and demand status of the irrigation area, and provides a basis for formulating a reasonable water resource allocation plan. At the same time, the monitoring data can also help managers identify weak links in water resource utilization, adjust irrigation strategies in time, optimize water resource allocation, and ensure that limited water resources are used to the maximum benefit. Accurately controlling the irrigation volume can effectively prevent soil salinization and groundwater level rise caused by excessive irrigation and unreasonable irrigation. In addition, accurate measurement data can also provide a scientific basis for water resource protection projects such as ecological water replenishment and wetland protection, promote the sustainable use of water resources, improve the level of irrigation management, and greatly improve the efficiency and accuracy of irrigation management.

[0108] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0109] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method, characterized in that: The multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method includes: Acquire multi-source data of the target irrigation area; the multi-source data includes: water volume data of the air, ground and space, and image data; Using an extreme gradient boosting algorithm to classify the water consumption of the multi-source data, and obtaining a water consumption classification result of the target irrigation area; Perform multi-scale nested sample plot design on the multi-source data to obtain spatial distribution mapping of water volume data in the air and space of the target irrigation area; Constructing a remote sensing sample set of water consumption in the target irrigation area based on the water consumption classification results and spatial distribution mapping of water consumption data in the air, space and land; The irrigation area water consumption remote sensing sample set is used to analyze the air-space-ground interactive water consumption of the target irrigation area to obtain the water consumption monitoring result of the target irrigation area.

2. The multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method according to claim 1 is characterized in that: Obtain multi-source data of the target irrigation area, including: Acquire the initial multi-source data of the target irrigation area based on satellite remote sensing technology; The initial multi-source data of the target irrigation area are preprocessed to obtain the multi-source data of the target irrigation area; the preprocessing includes: data cleaning, standardization, normalization, noise interference removal and data enhancement.

3. The multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method according to claim 1 is characterized in that: The multi-source data are subjected to multi-scale nested sample design to obtain spatial distribution mapping of water volume data in the air and space of the target irrigation area, specifically including: Determine the spatial scale according to the image data in the multi-source data, and match the water volume data of the air, space and land in the multi-source data with the multi-source geographic elements at different spatial scales, and determine the matched data as the modeling sample; According to the modeling samples at each spatial scale, a geographically weighted regression model is used to construct a statistical relationship model between the water volume data of the target irrigation area and the multi-source geographical elements; Determine the spatial distribution map of water volume data in the air and space of the target irrigation area based on the statistical relationship model.

4. The multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method according to claim 1 is characterized in that: The water consumption remote sensing sample set of the irrigation area is used to analyze the air-ground-space interactive water consumption of the target irrigation area to obtain the water consumption monitoring result of the target irrigation area, specifically including: Determine irrigation area data using the irrigation area water consumption remote sensing sample set; Establishing a water consumption accounting model based on the irrigation area data, the meteorological data in the irrigation area water consumption remote sensing sample set, and the soil moisture characteristic data in the irrigation area water consumption remote sensing sample set; interpreting the actual planting area and coverage of various crops in the target irrigation area according to the image data; Inputting the actual planting area and coverage of crops in the target irrigation area into the water consumption calculation model to obtain water consumption data of the target irrigation area; combining the water consumption data with the geographical location information to generate a water consumption distribution map; The water consumption distribution map is used to determine the air-ground-land interactive water consumption in the target irrigation area; the air-ground-land interactive water consumption is used as the water consumption monitoring result.

5. The multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method according to claim 4 is characterized in that: The irrigation area data is determined by using the irrigation area water consumption remote sensing sample set, including: Extracting potential irrigation area based on remote sensing sample set of water consumption in the irrigation area; Inverting and calculating the field surface soil moisture content during the crop growth period in the potential irrigation area according to the image data to obtain the field surface soil moisture content inversion value; According to the inverted value of the field surface soil moisture content, the actual irrigation area is extracted from the potential irrigation area, and the actual irrigation area is used as the irrigation area data.

6. The multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method according to claim 4 is characterized in that: The actual planting area and coverage of various crops in the target irrigation area are interpreted according to the image data, specifically including: Determining the band data of the satellite remote sensing image and the polarization data of the radar image in the image data; Calculating a normalized vegetation index according to the band data, and extracting texture features according to the polarization data; Calculating the importance scores of the band data, the polarization data, the normalized difference vegetation index and the texture feature in crop classification according to the Gini impurity contribution; Performing weighted calculation according to the importance scores to determine the actual planting area of ​​each type of crop in the target irrigation area; The coverage of each type of crop in the target irrigation area is determined according to the actual planting area of ​​each type of crop in the target irrigation area.

7. The multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring method according to claim 1 is characterized in that: The water volume data includes: meteorological data, soil moisture characteristic data, geological water consumption, evaporation water, irrigation water and crop water consumption; the irrigation area water consumption remote sensing sample set includes: meteorological data, soil moisture characteristic data, geographical location information, irrigation and drainage engineering information, agricultural planting structure information and irrigation system information.

8. An integrated irrigation area water consumption intelligent monitoring system with multi-scale nested space-ground interaction, characterized in that: The multi-scale nested space-ground-ground interactive integrated irrigation area water consumption intelligent monitoring system includes: A data acquisition module is used to acquire multi-source data of the target irrigation area; the multi-source data includes: water volume data of the air, ground and space, and image data; A water consumption classification module, used for classifying the water consumption of the multi-source data by using an extreme gradient boosting algorithm to obtain a water consumption classification result of the target irrigation area; A nested sample plot design module is used to perform multi-scale nested sample plot design on the multi-source data to obtain spatial distribution mapping of water volume data in the air and space of the target irrigation area; A water consumption remote sensing sample set construction module, used to construct a water consumption remote sensing sample set of the target irrigation area according to the water consumption classification result and the spatial distribution mapping of water volume data in the air, space and land; The water consumption monitoring module is used to analyze the air-space-ground interactive water consumption of the target irrigation area by using the irrigation area water consumption remote sensing sample set to obtain the water consumption monitoring result of the target irrigation area.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-scale nested, space-ground-interactive, integrated irrigation area water consumption intelligent monitoring method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the multi-scale nested space-ground-integrated intelligent monitoring method for water consumption in irrigation areas as described in any one of claims 1 to 7.

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