Irrigation area underground water characteristic analysis method and system based on space-time evolution

Abstract

The invention discloses an irrigation area groundwater characteristic analysis method and system based on space-time evolution, and the method comprises the steps: collecting a time series data set of groundwater data of a target irrigation area in an arid and semi-arid region, and extracting a time series data set related to the water level value of groundwater from the time series data set; then, a data processing and analysis algorithm based on artificial intelligence and deep learning is introduced at the rear end to carry out time sequence aggregation analysis on the water level values, so that time sequence multi-scale semantic feature representation of the underground water level of the target irrigation area is captured; and on the basis of the underground water level time sequence characteristic representation, predicting an estimated value of the underground water level of the target irrigation area in the next 10 years. Thus, the dynamic change of the underground water level can be predicted based on the spatial-temporal dynamic evolution characteristics of the underground water in the target irrigation area, whether sustainable development of agricultural production and sustainable utilization of water resources are affected or not is determined according to the dynamic change, and more scientific and accurate support is provided for water resource management and decision making.

Description

Technical Field

[0001] The present application relates to the field of characteristic analysis, and more specifically, to a method and system for analyzing groundwater characteristics in irrigation areas based on spatiotemporal evolution. Background Art

[0002] Irrigation areas in arid and semi-arid regions usually face severe challenges in water and soil resource management, especially when groundwater is the main source of irrigation water. These areas are highly dependent on groundwater due to scarce precipitation, high evaporation and limited natural recharge capacity. However, long-term over-exploitation has led to a series of problems such as groundwater level decline and water quality deterioration, which not only affect the sustainability of agricultural production, but may also cause a series of environmental and socio-economic problems. Therefore, it is particularly important to scientifically manage and rationally utilize groundwater in irrigation areas.

[0003] Although traditional groundwater analysis methods in irrigation areas can provide some basic trend analysis, they have limitations when dealing with complex nonlinear groundwater dynamics. Specifically, traditional analysis schemes are usually based on static or quasi-static models, assuming that the groundwater system remains unchanged over a long period of time. However, in actual situations, due to the influence of factors such as climate change and human activities, the groundwater system is very dynamic, which makes it difficult for static models to accurately reflect the actual situation. In addition, traditional groundwater analysis methods rely on statistical analysis and assume that the changes in groundwater data are linear, but in fact the response of groundwater systems is often nonlinear, especially under extreme conditions (such as long-term drought or over-exploitation). This simplification may ignore important nonlinear characteristics, thereby underestimating or overestimating the importance of certain key processes, resulting in misjudgment of groundwater analysis.

[0004] Therefore, an optimized groundwater characteristics analysis scheme for irrigation areas is desired. Summary of the invention

[0005] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a method and system for analyzing groundwater characteristics in irrigation areas based on spatiotemporal evolution, which collects a time series data set of groundwater data in a target irrigation area located in an arid and semi-arid area, and extracts a time series data set of groundwater level values ​​therefrom, and then introduces a data processing and analysis algorithm based on artificial intelligence and deep learning at the back end to perform time series aggregation analysis on these water level values, so as to capture the time series multi-scale semantic feature representation of the groundwater level in the target irrigation area, and predict the estimated value of the groundwater level drop in the target irrigation area within the next 10 years based on the time series feature representation of the groundwater level. In this way, the dynamic changes of the groundwater level can be predicted based on the spatiotemporal dynamic evolution characteristics of the groundwater in the target irrigation area, and whether it will affect the sustainable development of agricultural production and the sustainable use of water resources, so as to provide more scientific and accurate support for water resources management and decision-making.

[0006] According to one aspect of the present application, a method for analyzing groundwater characteristics in an irrigation area based on spatiotemporal evolution is provided, which includes:

[0007] Acquire a time series data set of groundwater data of a target irrigation area, wherein the groundwater data includes water quantity data and water quality data, wherein the water quantity data includes water level value and water quantity value, and the water quality data includes mineralization, pH value, total hardness, salt content, ion composition, and nitrogen, phosphorus, and potassium;

[0008] Extracting water level values ​​from a time series data set of groundwater data in the target irrigation area to obtain a time series data set of water level values;

[0009] Performing singular spectrum decomposition on the time series data set of the water level value to obtain a set of groundwater level subsequences of the target irrigation area;

[0010] Extracting time series features based on groundwater level for each target irrigation area groundwater level subsequence in the set of target irrigation area groundwater level subsequences to obtain a set of target irrigation area groundwater level time series implicit coding features;

[0011] Performing graph walk significant aggregation analysis on the set of implicit coding features of the target irrigation area groundwater level time series to obtain multi-scale semantic coding features of the target irrigation area groundwater level time series;

[0012] Based on the multi-scale semantic coding characteristics of the groundwater level time series in the target irrigation area, groundwater characteristic analysis is performed to determine the estimated value of the groundwater level drop in the target irrigation area in the next 10 years.

[0013] According to another aspect of the present application, a system for analyzing groundwater characteristics in irrigation areas based on spatiotemporal evolution is provided, which comprises:

[0014] A data acquisition module is used to acquire a time series data set of groundwater data of a target irrigation area, wherein the groundwater data includes water quantity data and water quality data, wherein the water quantity data includes water level value and water quantity value, and the water quality data includes mineralization, pH value, total hardness, salt content, ion composition, and nitrogen, phosphorus, and potassium;

[0015] A water level value extraction module, used to extract water level values ​​from a time series data set of groundwater data of the target irrigation area to obtain a time series data set of water level values;

[0016] A singular spectrum decomposition module, used for performing singular spectrum decomposition on the time series data set of the water level value to obtain a set of subsequences of groundwater levels in the target irrigation area;

[0017] A time series feature extraction module, used for extracting time series features based on groundwater level for each target irrigation area groundwater level subsequence in the set of target irrigation area groundwater level subsequences to obtain a set of implicit coding features of the time series of groundwater level in the target irrigation area;

[0018] A significant aggregation analysis module is used to perform a graph walk significant aggregation analysis on the set of implicit coding features of the target irrigation area groundwater level time series to obtain multi-scale semantic coding features of the target irrigation area groundwater level time series;

[0019] The groundwater characteristic analysis module is used to perform groundwater characteristic analysis based on the multi-scale semantic coding characteristics of the groundwater level time series of the target irrigation area to determine the estimated value of the groundwater level drop in the target irrigation area in the next 10 years.

[0020] Compared with the prior art, the present application provides a method and system for analyzing groundwater characteristics in irrigation areas based on spatiotemporal evolution. The method collects a time series data set of groundwater data in a target irrigation area located in an arid or semi-arid area, extracts a time series data set of groundwater level values, and then introduces a data processing and analysis algorithm based on artificial intelligence and deep learning at the back end to perform time series aggregation analysis on these water level values, thereby capturing the time series multi-scale semantic feature representation of the groundwater level in the target irrigation area, and predicting the estimated value of the groundwater level drop in the target irrigation area within the next 10 years based on the time series feature representation of the groundwater level. In this way, the dynamic changes of the groundwater level can be predicted based on the spatiotemporal dynamic evolution characteristics of the groundwater in the target irrigation area, and whether it will affect the sustainable development of agricultural production and the sustainable use of water resources, providing more scientific and accurate support for water resources management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0022] Figure 1 It is a flow chart of a method for analyzing groundwater characteristics in an irrigation area based on spatiotemporal evolution according to an embodiment of the present application;

[0023] Figure 2 A schematic diagram of data flow of a method for analyzing groundwater characteristics in an irrigation area based on spatiotemporal evolution according to an embodiment of the present application;

[0024] Figure 3 It is a flowchart of sub-step S5 of the irrigation area groundwater characteristic analysis method based on spatiotemporal evolution according to an embodiment of the present application;

[0025] Figure 4 It is a block diagram of the irrigation area groundwater characteristic analysis system based on spatiotemporal evolution according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0027] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0028] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0029] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0030] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0031] It should be understood that since groundwater data contains many different types of data, such as water quantity data such as water level value and water volume value, as well as water quality data such as mineralization, pH value, total hardness, salt content, ion composition, and nitrogen, phosphorus and potassium, these data have different time series changes in different irrigation areas. At the same time, since the current arid and semi-arid areas mainly rely on groundwater for irrigation, but the problem of over-exploitation of groundwater has been serious in recent years, the groundwater system has shown complex spatiotemporal variation characteristics. If the current irrigation mode is maintained, the groundwater level will drop in the next 10 years, affecting the sustainability of agricultural production. Therefore, if you want to dynamically predict the groundwater in the irrigation area, you need to conduct spatiotemporal evolution of the spatiotemporal variation characteristics of the groundwater in the irrigation area.

[0032] Based on this, in the technical solution of the present application, a method for analyzing groundwater characteristics in irrigation areas based on temporal and spatial evolution is proposed. Figure 1 It is a flow chart of a method for analyzing groundwater characteristics in an irrigation area based on spatiotemporal evolution according to an embodiment of the present application. Figure 2 FIG. 1 is a data flow diagram of a method for analyzing groundwater characteristics in an irrigation area based on spatiotemporal evolution according to an embodiment of the present application. Figure 1 and Figure 2 As shown, according to the embodiment of the present application, the irrigation area groundwater characteristic analysis method based on spatiotemporal evolution includes the following steps: S1, obtaining a time series data set of groundwater data of a target irrigation area, wherein the groundwater data includes water quantity data and water quality data, wherein the water quantity data includes water level value and water quantity value, and the water quality data includes mineralization, pH value, total hardness, salt content, ion composition, and nitrogen, phosphorus, and potassium; S2, extracting water level values ​​from the time series data set of groundwater data of the target irrigation area to obtain a time series data set of water level values; S3, performing singular spectrum decomposition on the time series data set of water level values ​​to obtain a sub-dataset of groundwater level values ​​of the target irrigation area. S4, extracting time series features based on groundwater level for each target irrigation area groundwater level subsequence in the set of target irrigation area groundwater level subsequences to obtain a set of implicit coding features of target irrigation area groundwater level time series; S5, performing graph walk significant aggregation analysis on the set of implicit coding features of target irrigation area groundwater level time series to obtain multi-scale semantic coding features of target irrigation area groundwater level time series; S6, performing groundwater feature analysis based on the multi-scale semantic coding features of target irrigation area groundwater level time series to determine the estimated value of the groundwater level drop in the target irrigation area in the next 10 years.

[0033] In particular, S1 obtains a time series data set of groundwater data in the target irrigation area, wherein the groundwater data includes water quantity data and water quality data, wherein the water quantity data includes water level value and water quantity value, and the water quality data includes mineralization, pH value, total hardness, salt content, total ion composition, and nitrogen, phosphorus, and potassium. It should be understood that the water level value in the water quantity data is an important indicator for measuring the abundance of groundwater resources. Long-term monitoring of the changing trend of water levels can help identify whether there is over-exploitation and assess the contribution of natural recharge (such as rainfall) to the groundwater system. By analyzing the changes in water levels, future groundwater level dynamics can be predicted, providing a basis for water resource management and policy formulation. Water quantity reflects the total amount of groundwater resources available in a certain period of time, which plays a key role in assessing irrigation demand and planning water allocation. Combined with water level information, water quantity data helps to more accurately understand the storage and flow characteristics of groundwater and support reasonable water extraction strategies. In terms of water quality data, mineralization refers to the total amount of dissolved solids in water. High mineralization may indicate the presence of excessive minerals or pollutants, which will affect crop growth and cause soil salinization. Monitoring the mineralization level can warn of potential water quality deterioration and guide appropriate purification measures. The pH value indicates the acidity and alkalinity of the water body. Extreme pH values ​​can have a negative impact on the absorption of nutrients by plant roots and may also corrode irrigation facilities. Maintaining an appropriate pH range is very important for maintaining a healthy agricultural ecosystem and extending the life of the irrigation system. Total hardness is determined by the content of calcium and magnesium ions. Too high a content can lead to hard water, affect irrigation efficiency, increase energy consumption, and may cause scaling of pipes. Understanding total hardness can help optimize irrigation methods and select hard water resistant crop varieties suitable for local conditions. Salt content directly affects soil structure and plant health. Excessive salt can inhibit crop growth and even poison plants. Regular testing of salt content can timely adjust irrigation management plans and prevent land degradation. The presence and concentration distribution of specific ions (such as sodium, chloride, etc.) have a significant impact on the soil and water environment. Excessive concentrations of certain ions may cause ecological problems. By monitoring ion composition, we can better understand the interaction between groundwater and the surrounding environment and ensure ecological and environmental safety. Nitrogen, phosphorus and potassium are the main nutrients necessary for plant growth, but excessive fertilization may lead to eutrophication and pollute groundwater. Reasonable control of nitrogen, phosphorus and potassium input can not only improve fertilizer utilization, but also reduce the risk of groundwater pollution. Therefore, the temporal and spatial evolution characteristics of groundwater in the irrigation area can be fully grasped, providing solid data support for precision agriculture, environmental protection and resource management.

[0034] In one example, first of all, it is a crucial step to reasonably select monitoring points within the target irrigation area. These monitoring points should be able to represent the groundwater characteristics of the entire irrigation area and cover different types of geological structures and land use patterns. Considering factors such as population distribution, agricultural activities, and industrial facilities in the irrigation area, selecting monitoring points with broad representation can ensure the representativeness of the data. To achieve this, researchers need to comprehensively consider multiple factors such as geographical location, geological conditions, and hydrological characteristics to ensure that the selection of monitoring points is scientific and reasonable. For example, setting up monitoring points near frequently irrigated farmland can help capture groundwater level changes caused by agricultural water use; while setting up monitoring points in nature reserves far away from human activities can help understand undisturbed groundwater dynamics. After determining the location of the monitoring points, various types of sensors and measuring equipment are installed to collect groundwater data. At the selected monitoring points, water level meters are usually installed to monitor groundwater level changes in real time. These water level meters are usually placed in wells and can accurately record the changes in groundwater levels over time. In addition, flow meters are installed to measure the flow rate or flow of groundwater, which is crucial for assessing the amount of groundwater resources. At the same time, water quality analyzers equipped with conductivity probes, pH sensors, temperature sensors, etc. are also used to detect multiple parameters such as mineralization, pH value, total hardness, and salt content online. For some more detailed ion components (such as sodium, chlorine, etc.) and nitrogen, phosphorus, and potassium content, they can be taken back to the laboratory for determination by regular sampling. In this way, the quality status of groundwater and its changing trends can be fully grasped. In terms of selecting appropriate sampling frequency and cycle, a reasonable sampling strategy must be set according to the specific research purpose and actual situation. For key indicators such as water level changes, high-frequency data recording (such as once an hour) may be required to capture rapidly changing trends. For some relatively stable water quality parameters, lower-frequency monthly or quarterly sampling can be selected. This flexible sampling arrangement can not only meet the needs of scientific research, but also effectively utilize resources and avoid unnecessary waste. At the same time, considering the impact of seasonal changes on groundwater, the sampling plan should also fully consider the different characteristics of the rainy season and the dry season to ensure the integrity and continuity of data throughout the year.

[0035] In particular, S2 extracts water level values ​​from the time series data set of groundwater data in the target irrigation area to obtain a time series data set of water level values. As an important indicator for measuring the abundance of groundwater resources, the water level value directly reflects the state of the groundwater system. By extracting the water level value alone, we can focus more on the change trend of this key variable, avoid interference from other factors, and improve the accuracy of the analysis.

[0036] In one example, first, the raw data is cleaned. De-noising and outlier processing are important steps in data cleaning, including removing obviously erroneous data points, filling missing values ​​(through interpolation or other statistical methods), smoothing curves to reduce the impact of random fluctuations, etc., to ensure that the data used in subsequent analysis is accurate and reliable. Especially for water level data, any values ​​that deviate significantly from the normal range should be carefully reviewed to determine whether they are measurement errors or actual extreme events. In addition, since instruments of different brands and models may have measurement deviations, all water level data must also undergo a standardization and correction process. This usually involves converting the raw readings into a unified standard unit and regularly calibrating the instruments using known standard samples to ensure the consistency and comparability of long-term monitoring results. Through strict internal quality control and external quality assurance measures, the quality of the data is further improved to ensure its scientificity and reliability. Next, select fields related to water levels from the database. These fields may include, but are not limited to, timestamps, well numbers / location identifiers, and water level gauge readings. The timestamp records the specific time and date of each observation; the well number / location identifier is used to distinguish different monitoring points; and the water level gauge reading is the actual measurement of the groundwater level. In order to ensure that the extracted data only contains valid water level values, reasonable filtering conditions need to be set. For example, data records marked as invalid or uncertain can be excluded; at the same time, the extracted data set can be limited according to a specific time range or geographical area. This is done to obtain a more focused and representative sequence of water level changes. Then, the filtered water level values ​​are arranged in chronological order to form a complete water level value time series data set. Each data point contains a timestamp and the corresponding water level measurement value. For some cases where there are multiple monitoring points, it is also possible to consider creating multiple independent time series data sets, corresponding to the location of each monitoring point. This helps to better capture local features and facilitates subsequent comparative analysis of differences between different locations. If it is found that there are obvious discontinuities or missing time periods in the time series data set, these gaps can be filled by interpolation methods (such as linear interpolation, spline interpolation, etc.). Reasonable interpolation not only maintains the continuity of the data, but also helps to improve the effect of model training, especially when applying machine learning algorithms.

[0037] In particular, the S3 performs singular spectrum decomposition on the time series data set of the water level value to obtain a set of groundwater level subsequences of the target irrigation area. It should be understood that the change of the groundwater level usually contains long-term trends, such as slow rise or fall caused by climate change or human activities. In addition, the groundwater level may also show seasonal fluctuations or other periodic patterns. For example, the peak irrigation period, rainy season, etc. will cause periodic changes. Therefore, in the technical solution of the present application, the time series data set of the water level value is further subjected to singular spectrum decomposition to obtain a set of groundwater level subsequences of the target irrigation area. Through the singular spectrum decomposition operation, the time series data set of the water level value can be decomposed into a plurality of target irrigation area groundwater level subsequences (i.e., reconstructed time series) that are easier to understand and analyze, thereby reducing the complexity of the problem and facilitating the subsequent capture of the time series long-term trend and periodic changes of the groundwater level in the target irrigation area. Time series dynamic feature information, such as, so as to help more accurately predict and estimate the decline of the groundwater level in the next 10 years.

[0038] In particular, the S4 extracts the time series features based on the groundwater level for each target irrigation area groundwater level subsequence in the set of the target irrigation area groundwater level subsequences to obtain a set of implicit coding features of the target irrigation area groundwater level time series. That is, in the technical solution of the present application, each target irrigation area groundwater level subsequence in the set of the target irrigation area groundwater level subsequences is input into the groundwater level time series feature extractor based on the LSTM-RNN hybrid model to obtain a set of implicit coding feature vectors of the target irrigation area groundwater level time series as the set of implicit coding features of the target irrigation area groundwater level time series. It should be understood that the LSTM-RNN hybrid model combines the advantages of the two architectures, retaining the ability of LSTM to handle long-term dependencies and utilizing the sensitivity of RNN to short-term local features. Therefore, through the processing of the groundwater level time series feature extractor based on the LSTM-RNN hybrid model, LSTM and RNN can be used to capture the long-term dependency and short-term local time series characteristics of the groundwater level in the target irrigation area in the time dimension, so as to perceive and extract the time series implicit correlation characteristics of the groundwater level at different time scales, and then combine the multi-scale perception characteristics of the groundwater level time series of the two to provide data support for subsequent groundwater level prediction.

[0039] In particular, the S5 performs a graph walk significant aggregation analysis on the set of implicit coding features of the target irrigation area groundwater level time series to obtain the target irrigation area groundwater level time series multi-scale semantic coding features. Since each target irrigation area groundwater level time series implicit coding feature vector in the set of target irrigation area groundwater level time series implicit coding feature vectors respectively contains the time series feature information about the target irrigation area groundwater level in different time periods, the groundwater level time series features in these different time periods represent the feature semantic information of different nodes. The groundwater level in the target irrigation area will show dynamic change characteristics and trends in the time dimension. In order to be able to aggregate the semantics of the groundwater level time series features of these different nodes to provide a basis for subsequent groundwater level prediction, in the technical solution of the present application, the set of implicit coding features of the target irrigation area groundwater level time series is further subjected to a graph walk significant aggregation analysis to obtain the target irrigation area groundwater level time series multi-scale semantic coding features. Through the graph walk significant aggregation analysis, the discrete feature distribution of the graph walk technology and the autocorrelation gating mechanism can be used to perform the time series feature aggregation analysis of the groundwater level in the target irrigation area at different nodes to obtain the significant aggregation results of the discrete feature distribution. In a specific example of this application, Figure 3 As shown, the S5 includes: S51, extracting the wandering topological features of the target irrigation area groundwater level time series graph based on the set of implicit coding features of the target irrigation area groundwater level time series; S52, performing significant gated weighted aggregation on the set of implicit coding features of the target irrigation area groundwater level time series based on the wandering topological features of the target irrigation area groundwater level time series graph to obtain the multi-scale semantic coding features of the target irrigation area groundwater level time series.

[0040] Specifically, the S51 extracts the target irrigation area groundwater level time series graph walk topology features based on the set of the target irrigation area groundwater level time series implicit coding features. That is, in the embodiment of the present application, firstly, each target irrigation area groundwater level time series implicit coding feature vector in the set of the target irrigation area groundwater level time series implicit coding feature vectors is respectively input into the hyperbolic space mapper to obtain a set of the target irrigation area groundwater level time series implicit coding feature vectors after hyperbolic space mapping; then, based on the set of the target irrigation area groundwater level time series implicit coding feature vectors after the hyperbolic space mapping, the graph walk topology features are extracted to obtain the target irrigation area groundwater level time series graph walk topology feature matrix as the target irrigation area groundwater level time series graph walk topology features. In the technical solution of the present application, based on the set of implicitly coded feature vectors of the target irrigation area groundwater level time series after the hyperbolic space mapping, the specific process of extracting the graph walk topology features includes: calculating the Poincare distance between any two implicitly coded feature vectors of the target irrigation area groundwater level time series after the hyperbolic space mapping in the set of implicitly coded feature vectors of the target irrigation area groundwater level time series after the hyperbolic space mapping to obtain the target irrigation area groundwater level time series graph walk topology matrix; performing atrous convolution coding on the target irrigation area groundwater level time series graph walk topology matrix to obtain the target irrigation area groundwater level time series graph walk topology feature matrix. By introducing hyperbolic space mapping and Poincare distance calculation, the hierarchical structure and long-tail distribution in the high-dimensional groundwater level time series features can be better maintained, overcoming the problem that different target irrigation area groundwater level time series implicitly coded feature vectors are difficult to effectively measure in high-dimensional sparse space, and enhancing the distinguishing ability and robustness of the target irrigation area groundwater level time series feature representation. More specifically, based on the set of implicit coding features of the target irrigation area groundwater level time series, the wandering topological features of the target irrigation area groundwater level time series graph are extracted using the following feature extraction formula; wherein the feature extraction formula is:

[0041]

[0042]

[0043]

[0044] in, is the set of implicitly coded feature vectors of the groundwater level time series of the target irrigation area, , , , and are the first, second, and third implicit coding feature vectors of the groundwater level time series in the target irrigation area. , and The implicit coding feature vector of the groundwater level time series of the target irrigation area, and are the first weight matrix in hyperbolic space and the second weight matrix in hyperbolic space respectively, for The corresponding implicit encoding feature vector of the groundwater level time series in the target irrigation area after hyperbolic space mapping is: for The corresponding implicit encoding feature vector of the groundwater level time series in the target irrigation area after hyperbolic space mapping is: is the square of the vector-norm, is the inverse hyperbolic cosine function, For calculation and The Poincare distance between The target irrigation area groundwater level time series diagram walks in the topological matrix The eigenvalues ​​of the positions, It is the walk topological matrix of the groundwater level time series diagram of the target irrigation area.

[0045] Specifically, the S52, based on the walking topology feature of the target irrigation area groundwater level time series graph, performs significant gated weighted aggregation on the set of implicit coding features of the target irrigation area groundwater level time series to obtain the multi-scale semantic coding features of the target irrigation area groundwater level time series. That is, in an embodiment of the present application, first, based on the walking topology feature matrix of the target irrigation area groundwater level time series graph, the set of implicit coding feature vectors of the target irrigation area groundwater level time series after hyperbolic space mapping is subjected to contextual semantic enhancement processing to obtain a set of contextual semantic enhancement feature vectors of the target irrigation area groundwater level time series; in the technical solution of the present application, the specific process of performing contextual semantic enhancement processing on the set of implicit coding feature vectors of the target irrigation area groundwater level time series after hyperbolic space mapping includes: inputting the walking topology feature matrix of the target irrigation area groundwater level time series graph and the set of implicit coding feature vectors of the target irrigation area groundwater level time series after hyperbolic space mapping into a global contextual walking encoder based on a graph convolutional neural network model to obtain a set of contextual semantic enhancement feature vectors of the target irrigation area groundwater level time series. That is, by constructing a walk topology matrix of the target irrigation area groundwater level time series graph of the relevant graph structure, the intrinsic connection between the groundwater level time series features of different nodes can be more fully understood. By using the void convolution and the global context walk encoder, the network architecture can effectively capture the feature information of the time series semantics of the target irrigation area groundwater level at different scales, improve the modeling ability of the long-range dependency relationship between features, and thus improve the learning and understanding of the walk topology matrix of the target irrigation area groundwater level time series graph. Furthermore, based on the set of target irrigation area groundwater level time series context semantics enhanced feature vectors, the set of target irrigation area groundwater level time series implicit coding feature vectors is significantly gated and weighted aggregated to obtain the target irrigation area groundwater level time series multi-scale semantic coding features.In the technical solution of the present application, the specific process of performing significant gated weighted aggregation on the set of implicitly coded feature vectors of the target irrigation area groundwater level temporal series includes: inputting each corresponding set of the target irrigation area groundwater level temporal series context semantics reinforcement feature vectors and the target irrigation area groundwater level temporal series implicitly coded feature vectors in the set of the target irrigation area groundwater level temporal series context semantics reinforcement feature vectors and the target irrigation area groundwater level temporal series implicitly coded feature vectors into an autocorrelation gating unit to obtain a set of target irrigation area groundwater level temporal series autocorrelation gating significant confidence factors; The set of autocorrelation gated significant confidence factors of the target irrigation area groundwater level time series is input into the normalization unit based on the Softmax function to obtain the sequence of autocorrelation gated significant confidence weight factors of the target irrigation area groundwater level time series; based on the sequence of autocorrelation gated significant confidence weight factors of the target irrigation area groundwater level time series, the position-weighted sum of the set of implicit coding feature vectors of the target irrigation area groundwater level time series is calculated to obtain the multi-scale semantic coding feature vector of the target irrigation area groundwater level time series as the multi-scale semantic coding feature of the target irrigation area groundwater level time series. In particular, the autocorrelation gating mechanism ensures that the network can dynamically adjust the weight distribution according to the importance of the groundwater level time series features represented by each node, thereby highlighting the key node feature semantics in the aggregation process, suppressing irrelevant or noise features, and improving the quality of the final target irrigation area groundwater level time series multi-scale semantic coding feature representation, providing more abundant and accurate input information for the subsequent target irrigation area groundwater level prediction, and helping to formulate a more reasonable water resources management strategy. More specifically, based on the wandering topological characteristics of the target irrigation area groundwater level time series graph, the set of implicit coding features of the target irrigation area groundwater level time series is significantly gated and weighted aggregated using the following significant gated weighted aggregation formula to obtain the multi-scale semantic coding features of the target irrigation area groundwater level time series; wherein the significant gated weighted aggregation formula is:.

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] in, is the dilated convolutional coding, is the topological characteristic matrix of the groundwater level time series diagram of the target irrigation area, is the graph convolutional coding, It is the first in the set of semantic enhancement feature vectors of the temporal context of groundwater level in the target irrigation area. The semantic enhancement feature vector of the groundwater level temporal context in the target irrigation area, yes function, For positional subtraction, It is calculated and The autocorrelation gated significance confidence factor between for and The gated significant confidence factor of the groundwater level time series autocorrelation between the target irrigation areas, represents the exponential function value with the natural constant e as the base, is the number of the target irrigation area groundwater level time series autocorrelation gated significant confidence factors in the set of target irrigation area groundwater level time series autocorrelation gated significant confidence factors, yes The corresponding target irrigation area groundwater level time series autocorrelation gate significant confidence weight factor, is the number of vectors in the set of implicitly coded feature vectors of the groundwater level time series of the target irrigation area, For point-by-position multiplication, It is the multi-scale semantic coding feature vector of the groundwater level time series in the target irrigation area.

[0052] In particular, the S6 performs groundwater feature analysis based on the temporal multi-scale semantic coding features of the groundwater level in the target irrigation area to determine the estimated value of the groundwater level in the target irrigation area that will drop in the next 10 years. In the technical solution of the present application, the temporal multi-scale semantic coding feature vector of the groundwater level in the target irrigation area is input into a decoder-based groundwater feature analysis module to obtain an analysis result, and the analysis result is used to represent the estimated value of the groundwater level in the target irrigation area that will drop in the next 10 years. In other words, decoding regression is performed using the temporal multi-scale aggregation representation of the groundwater level in the target irrigation area to predict the estimated value of the groundwater level in the target irrigation area that will drop in the next 10 years. In this way, the dynamic changes of the groundwater level can be predicted based on the spatiotemporal dynamic evolution characteristics of the groundwater in the target irrigation area, and this can be used to determine whether it will affect the sustainable development of agricultural production and the sustainable use of water resources, providing more scientific and accurate support for water resources management and decision-making.

[0053] Considering that each target irrigation area groundwater level time series implicit coding feature vector in the set of target irrigation area groundwater level time series implicit coding feature vectors respectively represents the time series periodic coding characteristics of the target irrigation area groundwater level in different time series windows, when performing feature sequence aggregation analysis based on graph walk autocorrelation gating, the heterogeneity of the time series periodic characteristics in different time series windows will lead to the lack of aggregated feature instance determination of the target irrigation area groundwater level time series multi-scale semantic coding feature vector, thereby affecting the accuracy of the analysis result obtained by inputting it into the decoder-based groundwater feature analysis module.

[0054] Based on this, before inputting the target irrigation area groundwater level time series multi-scale semantic coding feature vector into the decoder-based groundwater feature analysis module, the target irrigation area groundwater level time series multi-scale semantic coding feature vector is first optimized, including the steps of:

[0055] The distance between each pair of eigenvalues ​​of the multi-scale semantic coding feature vector of the target irrigation area groundwater level time series is calculated, such as the L2 distance, and the square root of the distance is taken to obtain the multi-scale semantic coding distance representation matrix of the target irrigation area groundwater level time series, that is,

[0056]

[0057] in, represents the multi-scale semantic coding feature vector of the time series of groundwater level in the target irrigation area, and They represent the i-th and j-th eigenvalues ​​of the multi-scale semantic coding feature vector of the groundwater level time series in the target irrigation area, respectively. represents the distance between the i-th and j-th eigenvalues ​​of the multi-scale semantic encoding feature vector of the groundwater level time series of the target irrigation area, A multi-scale semantic encoding distance representation matrix representing the time series of groundwater level in the marked irrigation area;

[0058] The target irrigation area groundwater level time series multi-scale semantic coding autocorrelation matrix of the target irrigation area groundwater level time series multi-scale semantic coding feature vector as the row vector is obtained, that is, ;

[0059] The target irrigation area groundwater level temporal multi-scale semantic coding feature vector is matrix-multiplied with the target irrigation area groundwater level temporal multi-scale semantic coding distance representation matrix to obtain the target irrigation area groundwater level temporal multi-scale semantic coding primary mapping vector, that is, ;

[0060] The target irrigation area groundwater level temporal multi-scale semantic coding first-level mapping vector is matrix-multiplied with the target irrigation area groundwater level temporal multi-scale semantic coding distance representation matrix and the target irrigation area groundwater level temporal multi-scale semantic coding self-correlation matrix to obtain the target irrigation area groundwater level temporal multi-scale semantic coding multi-level mapping vector ;

[0061] The multi-level mapping vector of the target irrigation area groundwater level temporal multi-scale semantic coding is interpolated with the target irrigation area groundwater level temporal multi-scale semantic coding associated eigenvector composed of the eigenvalues ​​of the target irrigation area groundwater level temporal multi-scale semantic coding auto-association matrix to obtain the optimized target irrigation area groundwater level temporal multi-scale semantic coding feature vector, wherein interpolation or zero padding is performed when the eigenvalue is insufficient.

[0062] That is, through the linear target mapping representation based on the similarity distance representation matrix of the target irrigation area groundwater level time series multi-scale semantic coding feature vector, the self-correlation of the target irrigation area groundwater level time series multi-scale semantic coding feature vector is instantiated to perform a secondary target mapping representation based on a multi-level distribution hierarchy, and the correlation mismatch negative impact factor is compensated by the correlation fusion kernel bias to improve the instance determination degree of the eigenvalue of the target irrigation area groundwater level time series multi-scale semantic coding feature vector under the similarity restriction, that is, the significance of the eigenvalue as an instance for the decoding regression judgment, and improve the accuracy of the analysis result obtained by the groundwater feature analysis module based on the decoder when the target irrigation area groundwater level time series multi-scale semantic coding feature vector is input. In this way, the dynamic changes of the groundwater level decline in the next 10 years can be predicted based on the spatiotemporal dynamic evolution characteristics of the groundwater in the target irrigation area, and whether it will affect the sustainable development of agricultural production and the sustainable use of water resources, so as to provide more scientific and accurate support for water resources management and decision-making.

[0063] In summary, the irrigation area groundwater characteristic analysis method based on spatiotemporal evolution according to the embodiment of the present application is explained, which collects a time series data set of groundwater data in a target irrigation area located in an arid and semi-arid area, and extracts a time series data set of groundwater level values ​​therefrom, and then introduces a data processing and analysis algorithm based on artificial intelligence and deep learning at the back end to perform a time series aggregation analysis on these water level values, so as to capture the time series multi-scale semantic feature representation of the groundwater level in the target irrigation area, and predict the estimated value of the groundwater level drop in the target irrigation area within the next 10 years based on the groundwater level time series feature representation. In this way, the dynamic change of the groundwater level can be predicted based on the spatiotemporal dynamic evolution characteristics of the groundwater in the target irrigation area, and whether it will affect the sustainable development of agricultural production and the sustainable use of water resources, so as to provide more scientific and accurate support for water resources management and decision-making.

[0064] Furthermore, a groundwater characteristic analysis system for irrigation areas based on spatiotemporal evolution is also provided.

[0065] Figure 4 FIG. 1 is a block diagram of an irrigation area groundwater characteristic analysis system based on spatiotemporal evolution according to an embodiment of the present application. Figure 4 As shown, according to the irrigation area groundwater characteristic analysis system 300 based on spatiotemporal evolution according to the embodiment of the present application, it includes: a data acquisition module 310, which is used to acquire a time series data set of groundwater data of a target irrigation area, wherein the groundwater data includes water quantity data and water quality data, wherein the water quantity data includes water level value and water quantity value, and the water quality data includes mineralization, pH value, total hardness, salt content, ion composition and nitrogen, phosphorus and potassium; a water level value extraction module 320, which is used to extract water level values ​​from the time series data set of groundwater data of the target irrigation area to obtain a time series data set of water level values; a singular spectrum decomposition module 330, which is used to perform singular spectrum decomposition on the time series data set of water level values ​​to obtain a subsequence of groundwater level in the target irrigation area. a collection of groundwater level subsequences in the target irrigation area; a time series feature extraction module 340, used to extract time series features based on groundwater level for each target irrigation area groundwater level subsequence in the set of groundwater level subsequences in the target irrigation area to obtain a set of implicit coding features of the target irrigation area groundwater level time series; a significant aggregation analysis module 350, used to perform graph walk significant aggregation analysis on the set of implicit coding features of the target irrigation area groundwater level time series to obtain multi-scale semantic coding features of the target irrigation area groundwater level time series; a groundwater feature analysis module 360, used to perform groundwater feature analysis based on the multi-scale semantic coding features of the target irrigation area groundwater level time series to determine the estimated value of the groundwater level drop in the target irrigation area within the next 10 years.

[0066] As described above, the irrigation district groundwater characteristic analysis system 300 based on spatiotemporal evolution according to the embodiment of the present application can be implemented in various wireless terminals, such as a server with an irrigation district groundwater characteristic analysis algorithm based on spatiotemporal evolution. In a possible implementation, the irrigation district groundwater characteristic analysis system 300 based on spatiotemporal evolution according to the embodiment of the present application can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the irrigation district groundwater characteristic analysis system 300 based on spatiotemporal evolution can be a software module in the operating system of the wireless terminal, or can be an application developed for the wireless terminal; of course, the irrigation district groundwater characteristic analysis system 300 based on spatiotemporal evolution can also be one of the many hardware modules of the wireless terminal.

[0067] Alternatively, in another example, the irrigation area groundwater characteristic analysis system based on spatiotemporal evolution 300 and the wireless terminal may also be separate devices, and the irrigation area groundwater characteristic analysis system based on spatiotemporal evolution 300 may be connected to the wireless terminal via a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0068] In the specific example of this application, we will collect and organize technical data on water and soil resources in irrigation areas, use statistics and other methods to analyze the spatiotemporal evolution characteristics of the quantity and quality of surface and groundwater resources, analyze the dynamic changes in the supply and water use structure and water use efficiency, and reveal the changing trend of the degree of water resource development and utilization; use the land use dynamics analysis model to study the spatiotemporal dynamic evolution characteristics of land use and land cover in irrigation areas in the past 10 years from three aspects: degree, quantity and type transfer; find out the water supply and distribution process of irrigation and drainage in irrigation areas, monitor the changes in irrigation water utilization coefficient in irrigation areas, and build a multi-dimensional digital base based on environmental factor data (precipitation, evapotranspiration, temperature, runoff), basic irrigation area data (water diversion, drainage, canal buildings), and real-time monitoring data to provide support for the construction of digital irrigation areas.

[0069] This application intends to analyze the spatiotemporal evolution characteristics of surface water and groundwater resources at the irrigation area-irrigation district scale, analyze the dynamic changes in water supply and use structure and water use efficiency, and reveal the changing trend of water resource development and utilization; analyze the dynamic changes in water supply and use structure and water use efficiency, and reveal the changing trend of water resource development and utilization; measure the water quality of representative points in the irrigation area through field sampling and indoor experiments, analyze the spatiotemporal variation laws and evolution characteristics of surface and groundwater quality in the entire irrigation area, and provide a basis and data support for the rational development and sustainable utilization of water resources in the irrigation area and the optimal allocation of water resources.

[0070] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for analyzing groundwater characteristics in irrigation areas based on spatiotemporal evolution, characterized in that: include: Acquire a time series data set of groundwater data of a target irrigation area, wherein the groundwater data includes water quantity data and water quality data, wherein the water quantity data includes water level value and water quantity value, and the water quality data includes mineralization, pH value, total hardness, salt content, ion composition, and nitrogen, phosphorus, and potassium; Extracting water level values ​​from a time series data set of groundwater data in the target irrigation area to obtain a time series data set of water level values; Performing singular spectrum decomposition on the time series data set of the water level value to obtain a set of groundwater level subsequences of the target irrigation area; Extracting time series features based on groundwater level for each target irrigation area groundwater level subsequence in the set of target irrigation area groundwater level subsequences to obtain a set of target irrigation area groundwater level time series implicit coding features; The set of implicit coding features of the target irrigation area groundwater level time series is subjected to graph walk significant aggregation analysis to obtain the target irrigation area groundwater level time series multi-scale semantic coding features, including: extracting the target irrigation area groundwater level time series graph walk topology features based on the set of implicit coding features of the target irrigation area groundwater level time series; based on the target irrigation area groundwater level time series graph walk topology features, performing significant gated weighted aggregation on the set of implicit coding features of the target irrigation area groundwater level time series to obtain the target irrigation area groundwater level time series multi-scale semantic coding features; Based on the multi-scale semantic coding characteristics of the groundwater level time series in the target irrigation area, groundwater characteristic analysis is performed to determine the estimated value of the groundwater level drop in the target irrigation area in the next 10 years.

2. The method for analyzing groundwater characteristics in irrigation areas based on spatiotemporal evolution according to claim 1 is characterized in that: Performing groundwater level-based time series feature extraction on each target irrigation area groundwater level subsequence in the set of target irrigation area groundwater level subsequences to obtain a set of target irrigation area groundwater level time series implicit coding features, including: inputting each target irrigation area groundwater level subsequence in the set of target irrigation area groundwater level subsequences into a groundwater level time series feature extractor based on an LSTM-RNN hybrid model to obtain a set of target irrigation area groundwater level time series implicit coding feature vectors as the set of target irrigation area groundwater level time series implicit coding features.

3. The method for analyzing groundwater characteristics in irrigation areas based on spatiotemporal evolution according to claim 2 is characterized in that: Based on the set of implicit coding features of the groundwater level time series of the target irrigation area, the wandering topological features of the groundwater level time series graph of the target irrigation area are extracted, including: Inputting each target irrigation area groundwater level time series implicit coding feature vector in the set of target irrigation area groundwater level time series implicit coding feature vectors into a hyperbolic space mapper to obtain a set of target irrigation area groundwater level time series implicit coding feature vectors after hyperbolic space mapping; Based on the set of implicitly encoded feature vectors of the target irrigation area groundwater level time series after the hyperbolic space mapping, the graph walk topology features are extracted to obtain the target irrigation area groundwater level time series graph walk topology feature matrix as the target irrigation area groundwater level time series graph walk topology features.

4. The method for analyzing groundwater characteristics in irrigation areas based on spatiotemporal evolution according to claim 3 is characterized in that: Based on the set of implicitly encoded feature vectors of the target irrigation area groundwater level time series after the hyperbolic space mapping, the graph walk topology features are extracted to obtain the target irrigation area groundwater level time series graph walk topology feature matrix as the target irrigation area groundwater level time series graph walk topology features, including: Calculate the Poincare distance between any two implicitly coded feature vectors of the target irrigation area groundwater level time series after hyperbolic space mapping in the set of implicitly coded feature vectors of the target irrigation area groundwater level time series after hyperbolic space mapping to obtain a walk topology matrix of the target irrigation area groundwater level time series graph; The walk topology matrix of the target irrigation area groundwater level time series graph is subjected to dilated convolution coding to obtain the walk topology feature matrix of the target irrigation area groundwater level time series graph.

5. The method for analyzing groundwater characteristics in irrigation areas based on spatiotemporal evolution according to claim 4 is characterized in that: Based on the wandering topological features of the target irrigation area groundwater level time series graph, a set of implicit coding features of the target irrigation area groundwater level time series is significantly gated and weighted aggregated to obtain multi-scale semantic coding features of the target irrigation area groundwater level time series, including: Based on the walk topology feature matrix of the target irrigation area groundwater level time series graph, a set of implicitly encoded feature vectors of the target irrigation area groundwater level time series after the hyperbolic space mapping is subjected to contextual semantic enhancement processing to obtain a set of contextual semantic enhancement feature vectors of the target irrigation area groundwater level time series; Based on the set of contextual semantic enhancement feature vectors of the target irrigation area groundwater level time series, a significant gated weighted aggregation is performed on the set of implicit coding feature vectors of the target irrigation area groundwater level time series to obtain the multi-scale semantic coding features of the target irrigation area groundwater level time series.

6. The method for analyzing groundwater characteristics in irrigation areas based on spatiotemporal evolution according to claim 5 is characterized in that: Based on the walk topology feature matrix of the target irrigation area groundwater level time series graph, a set of implicitly encoded feature vectors of the target irrigation area groundwater level time series after the hyperbolic space mapping is subjected to contextual semantic enhancement processing to obtain a set of contextual semantic enhancement feature vectors of the target irrigation area groundwater level time series, including: The walk topological feature matrix of the target irrigation area groundwater level time series graph and the set of implicitly encoded feature vectors of the target irrigation area groundwater level time series after hyperbolic space mapping are input into the global context walk encoder based on the graph convolutional neural network model to obtain the set of context semantic enhancement feature vectors of the target irrigation area groundwater level time series.

7. The method for analyzing groundwater characteristics in irrigation areas based on spatiotemporal evolution according to claim 6 is characterized in that: Based on the set of semantically enhanced feature vectors of the target irrigation area groundwater level time series context, a significant gated weighted aggregation is performed on the set of implicitly encoded feature vectors of the target irrigation area groundwater level time series to obtain multi-scale semantic encoding features of the target irrigation area groundwater level time series, including: Inputting each corresponding set of the target irrigation area groundwater level time series context semantic enhancement feature vector and the target irrigation area groundwater level time series implicit coding feature vector in the set of the target irrigation area groundwater level time series context semantic enhancement feature vector and the target irrigation area groundwater level time series implicit coding feature vector into the autocorrelation gating unit to obtain a set of target irrigation area groundwater level time series autocorrelation gating significant confidence factors; Inputting the set of the time series autocorrelation gated significant confidence factors of the groundwater level in the target irrigation area into a normalization unit based on the Softmax function to obtain a sequence of the time series autocorrelation gated significant confidence weight factors of the groundwater level in the target irrigation area; Based on the sequence of autocorrelation gated significant confidence weight factors of the target irrigation area groundwater level time series, the position-weighted sum of the set of implicit coding feature vectors of the target irrigation area groundwater level time series is calculated to obtain the multi-scale semantic coding feature vector of the target irrigation area groundwater level time series as the multi-scale semantic coding feature of the target irrigation area groundwater level time series.

8. The method for analyzing groundwater characteristics in irrigation areas based on spatiotemporal evolution according to claim 7 is characterized in that: Based on the multi-scale semantic coding characteristics of the time series of groundwater levels in the target irrigation area, groundwater characteristic analysis is performed to determine the estimated value of the groundwater level drop in the target irrigation area within the next 10 years, including: inputting the multi-scale semantic coding feature vector of the time series of groundwater levels in the target irrigation area into a decoder-based groundwater characteristic analysis module to obtain an analysis result, and the analysis result is used to represent the estimated value of the groundwater level drop in the target irrigation area within the next 10 years.

9. A groundwater characteristic analysis system for irrigation areas based on spatiotemporal evolution, characterized in that: include: A data acquisition module is used to acquire a time series data set of groundwater data of a target irrigation area, wherein the groundwater data includes water quantity data and water quality data, wherein the water quantity data includes water level value and water quantity value, and the water quality data includes mineralization, pH value, total hardness, salt content, ion composition, and nitrogen, phosphorus, and potassium; A water level value extraction module, used to extract water level values ​​from a time series data set of groundwater data of the target irrigation area to obtain a time series data set of water level values; A singular spectrum decomposition module, used for performing singular spectrum decomposition on the time series data set of the water level value to obtain a set of subsequences of groundwater levels in the target irrigation area; A time series feature extraction module, used for extracting time series features based on groundwater level for each target irrigation area groundwater level subsequence in the set of target irrigation area groundwater level subsequences to obtain a set of implicit coding features of the time series of groundwater level in the target irrigation area; A significant aggregation analysis module is used to perform a graph walk significant aggregation analysis on the set of implicit coding features of the target irrigation area groundwater level time series to obtain multi-scale semantic coding features of the target irrigation area groundwater level time series; The groundwater characteristic analysis module is used to perform groundwater characteristic analysis based on the multi-scale semantic coding characteristics of the groundwater level time series of the target irrigation area to determine the estimated value of the groundwater level drop in the target irrigation area in the next 10 years.

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