A groundwater level modeling method and system based on HASM

Abstract

The present application relates to the field of spatial information simulation technology, and provides a groundwater level modeling method, system, computer-readable storage medium and electronic device based on HASM. The scheme first obtains multi-temporal groundwater level observation data of the study area, and identifies whether the groundwater level observation data is missing or abnormal one by one according to the observation time and observation site, so as to distinguish valid observation data points from invalid observation data points; then, the spatial position relationship and time relationship between the valid observation data points and the invalid observation data points are used to calculate the corresponding virtual observation values ​​of the invalid observation data points, and then the observation values ​​of the valid observation data points and the virtual observation values ​​of the invalid observation data points are used to construct the observation vector, and on this basis, the high-accuracy surface modeling (HASM) method is used to carry out multi-temporal simulation of the groundwater level in the study area, obtain the simulation results corresponding to each time, and then obtain the multi-temporal average simulation results of the study area by mean calculation.

Description

Technical Field

[0001] The present application relates to the field of spatial information simulation technology, and in particular to a HASM-based groundwater level modeling method, system, computer-readable storage medium and electronic device. Background Art

[0002] The detection of groundwater level can be done by drilling, that is, drilling wells on the ground through technical means, and detecting the groundwater level downward to obtain the collection information of the point. However, drilling requires a lot of manpower and material resources, and it is often impossible to complete the drilling work in a certain area in a short time. The time of groundwater level detection at different locations is different. Especially when the study area is large, the data collection time of multiple points may be long, and the groundwater level has a relatively obvious time change characteristic (such as changes with the month). Secondly, considering the cost limit, the number of drillings is limited and may not cover the entire groundwater system; furthermore, due to the objective influence of operating conditions such as geological factors, drilling may be restricted under certain special geological conditions, and thus the groundwater level information of the area cannot be obtained. When facing a large study area, due to the existence of the time and space misalignment problem in the above-mentioned groundwater level detection process, it becomes difficult to study the groundwater level change in the entire study area. Therefore, how to spatially model the drilling information collected at different time points to fully reflect the spatiotemporal change law of groundwater level is currently an important scientific issue in this field.

[0003] Therefore, it is necessary to provide an improved technical solution to address the above-mentioned deficiencies in the prior art. Summary of the invention

[0004] The purpose of this application is to provide a HASM-based groundwater level modeling method, system, computer-readable storage medium and electronic device. By selecting a suitable method, the drilling information collected at different observation time points and different locations is fully utilized to model the groundwater level, so as to solve or alleviate the problems existing in the above-mentioned prior art.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] In a first aspect, the present application provides a groundwater level modeling method based on HASM, comprising:

[0007] Obtaining multi-temporal groundwater level observation data in the study area; in the study area, there are multiple observation stations for observing groundwater levels;

[0008] Determine valid observation data points and invalid observation data points according to the groundwater level observation data; wherein the invalid observation data points refer to observation data missing or observation data abnormal, otherwise they are valid observation data points;

[0009] According to the spatial position relationship and the time relationship between the valid observation data point and the invalid observation data point, the observation value of the valid observation data point is processed to obtain the virtual observation value corresponding to the invalid observation data point;

[0010] For each phase in the multiple phases, construct an observation vector of the phase using the observation value of the valid observation data point and the virtual observation value of the invalid observation data point to obtain the observation vectors of all phases;

[0011] The observation values ​​of the effective observation data points are used to generate the initial trend surface of the high-precision surface modeling HASM method, and the observation vectors corresponding to each time are used as the optimization control conditions for each time phase simulation. Multi-time phase HASM simulation of the groundwater level in the study area is carried out to obtain the simulation results corresponding to each time; the simulation results corresponding to each time are averaged to obtain the multi-time phase average simulation results.

[0012] In the above scheme, outliers and missing values ​​in groundwater level observation data are detected to determine valid observation data points and invalid observation data points, wherein the outliers of groundwater level observation data are detected, including the following steps:

[0013] For any observation station of groundwater level, the maximum value of all valid observation data of the observation station is subtracted from the minimum value to obtain the variation range of the observation data;

[0014] Determine the effective observation range according to the variation range of the observation data;

[0015] Observation values ​​that are not within the valid observation range among all observation data of the observation site are marked as abnormal values.

[0016] In the above scheme, the observation values ​​of the valid observation data points are processed according to the spatial position relationship and time relationship between the valid observation data points and the invalid observation data points to obtain the virtual observation values ​​corresponding to the invalid observation data points, including the following steps:

[0017] Take any invalid observation data point as the current invalid observation data point;

[0018] According to the spatial position relationship between the current invalid observation data point and each observation site of the first valid observation data point set, the spatial component of the virtual observation value of the current invalid observation data point is calculated; wherein the first valid observation data point set is a set of valid observation data points with the same observation time as the current invalid observation data point but with different observation sites;

[0019] Calculate the time component of the virtual observation value according to the observation time of the current invalid observation data point and the observation time of the second valid observation data point set; wherein the second valid observation data point set is a set of valid observation data points with the same observation site as the current invalid observation data point but with different observation time;

[0020] The spatial component and the temporal component are weighted averaged to obtain a virtual observation value of the current invalid observation data point.

[0021] In the above scheme, according to the spatial position relationship between the current invalid observation data point and each observation station of the first valid observation data point set, the spatial component of the virtual observation value of the current invalid observation data point is calculated, including:

[0022] Calculate the spatial distance between the current invalid observation data point and each observation site in the first valid observation data point set to obtain a spatial distance vector;

[0023] Calculate a spatial distance weight vector based on the spatial distance vector;

[0024] The spatial distance weight vector and the observation value of the first valid observation data point set are weightedly summed to obtain the spatial component of the virtual observation value of the current invalid observation data point.

[0025] In the above scheme, the time component of the virtual observation value is calculated according to the observation time of the current invalid observation data point and the observation time of the second valid observation data point set, including:

[0026] Calculate the time distance between the observation time of the current invalid observation data point and different observation times of the second set of valid observation data points to obtain a time distance vector;

[0027] According to the time distance vector, a time distance weight vector is calculated;

[0028] The time distance weight vector is weightedly summed with the observation value of the second valid observation data point set to obtain the time component of the virtual observation value of the current invalid observation data point.

[0029] In the above scheme, the initial trend surface of the high-precision surface modeling HASM method is generated by using the observation values ​​of the effective observation data points, including:

[0030] For each observation site, the observation values ​​of the valid observation data points of the observation site are averaged to obtain the average value of the observation data corresponding to the observation site;

[0031] Combine the average values ​​of the observation data corresponding to each observation station to generate an average value sequence;

[0032] Based on the mean value sequence, the Kriging interpolation method is used to perform interpolation processing to obtain the grid surface of the study area, and the grid surface is used as the initial trend surface of the HASM method.

[0033] In the above scheme, the observation vector corresponding to each time is used as the optimization control condition for each time phase simulation, and a multi-time phase HASM simulation is carried out on the groundwater level in the study area to obtain the simulation results corresponding to each time, including:

[0034] The multiple phases are sorted in chronological order, and the following steps are performed in sequence from the start phase according to the sorting result to carry out HASM simulation:

[0035] Determine the position of the current phase in the sorting result. If the current phase is the starting phase, use the observation vector corresponding to the starting phase as the optimization control condition, use the initial trend surface as the driving field, conduct HASM simulation on the groundwater level in the study area, and obtain the simulation result of the starting phase.

[0036] If the current phase is other than the starting phase, the simulation result of the previous phase of the current phase in the sorting result is used as the driving field, and the observation vector corresponding to the current phase is used as the optimization control condition to carry out HASM simulation on the groundwater level of the study area to obtain the simulation result of the current phase;

[0037] Repeat the above steps until the simulation results of groundwater levels in all phases are obtained.

[0038] In a second aspect, this embodiment provides a groundwater level modeling system based on HASM, including:

[0039] An acquisition unit is configured to acquire groundwater level observation data of multiple phases in a study area; wherein, in the study area, there are multiple observation sites for observing groundwater levels;

[0040] A determination unit, configured to determine valid observation data points and invalid observation data points according to the groundwater level observation data; wherein the invalid observation data points refer to observation data missing or observation data abnormal, otherwise they are valid observation data points;

[0041] A processing unit configured to process the observation values ​​of the valid observation data points according to the spatial position relationship and the time relationship between the valid observation data points and the invalid observation data points, so as to obtain virtual observation values ​​corresponding to the invalid observation data points;

[0042] A construction unit is configured to construct, for each phase in the multiple phases, an observation vector of the phase using the observation value of the valid observation data point and the virtual observation value of the invalid observation data point, so as to obtain respective observation vectors of all phases;

[0043] The simulation unit is configured to generate an initial trend surface of a high-precision surface modeling HASM method using the observation values ​​of the effective observation data points, and use the observation vectors corresponding to each time as the optimization control conditions for each time phase simulation to carry out multi-time phase HASM simulation of the groundwater level in the study area to obtain simulation results corresponding to each time; and perform mean processing on the simulation results corresponding to each time to obtain a multi-time phase average simulation result.

[0044] In a third aspect, this embodiment provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0045] In a fourth aspect, this embodiment provides an electronic device, comprising: a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements the method described in any of the above embodiments when executing the program.

[0046] Beneficial effects:

[0047] In the technical solution of the present embodiment, firstly, the multi-temporal groundwater level observation data of the study area are obtained, and the missing or abnormal groundwater level observation data are identified one by one according to the observation time and the observation site, so as to distinguish the valid observation data points from the invalid observation data points; then, the spatial position relationship and the time relationship between the valid observation data points and the invalid observation data points are used to calculate the virtual observation values ​​corresponding to the invalid observation data points, and then the observation values ​​of the valid observation data points and the virtual observation values ​​of the invalid observation data points are used to construct the observation vector, and on this basis, the high-accuracy surface modeling (HASM) method is used to carry out multi-temporal simulation of the groundwater level in the study area, and the simulation results corresponding to each time are obtained, and then the multi-temporal average simulation results of the study area are obtained by mean calculation. This method aims at the time and space misalignment problem in the process of groundwater level detection, considers the unique time and space two-dimensional sparse characteristics of observation data caused by time and space misalignment, makes full use of the small number of effective observation data points, combines the high resolution and high precision characteristics of HASM, and conducts multi-phase HASM simulation by continuously rolling updating the HASM driving field and optimizing the control conditions, and finally obtains high-resolution, multi-phase, and spatially continuous simulation results, and obtains the average simulation results through mean calculation. This method can break through the limitations of the existing use of drilling technology to observe groundwater levels, and can effectively reflect the temporal and spatial changes of groundwater levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. Among them:

[0049] Figure 1 A schematic diagram of a HASM-based groundwater level modeling method according to some embodiments of the present application Figure 1 .

[0050] Figure 2 A schematic diagram of a HASM-based groundwater level modeling method according to some embodiments of the present application Figure 2 .

[0051] Figure 3 This is an example diagram of groundwater level observation records provided according to some embodiments of the present application.

[0052] Figure 4 A schematic diagram of a process for constructing an observation vector using virtual observation values ​​according to some embodiments of the present application.

[0053] Figure 5 A schematic diagram of the structure of a HASM-based groundwater level modeling system provided according to some embodiments of the present application.

[0054] Figure 6 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application.

[0055] Figure 7 The hardware structure diagram of the electronic device provided according to the embodiment of the present application. DETAILED DESCRIPTION

[0056] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. Each example is provided by way of explanation of the present application and does not limit the present application. In fact, it will be clear to those skilled in the art that modifications and variations may be made in the present application without departing from the scope or spirit of the present application. For example, a feature shown or described as a part of an embodiment may be used in another embodiment to produce yet another embodiment. Therefore, it is desired that the present application includes such modifications and variations within the scope of the appended claims and their equivalents.

[0057] In the following description, the terms "first / second / third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first / second / third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present disclosure belongs. The terms used herein are only for the purpose of describing the embodiments of the present disclosure and are not intended to limit the present disclosure.

[0059] Method Embodiment

[0060] Figure 1 , Figure 2 The flowchart of the groundwater level modeling method based on HASM provided in the embodiment of the present application is shown. In the embodiment of the present application, the execution subject of the groundwater level modeling method based on HASM can be an electronic device such as a terminal, a personal computer, and a server.

[0061] like Figure 1 , Figure 2 As shown, the groundwater level modeling method based on HASM provided in this embodiment includes:

[0062] Step S101: Obtain multi-temporal groundwater level observation data in the study area.

[0063] The study area may be any area of ​​geographical space on the earth, and this application does not limit the geographical location and size of the simulated study area.

[0064] In this embodiment, the groundwater level observation data is also called drilling data, which can be obtained by drilling (that is, first-hand drilling data), or it can be drilling data published by official channels, or drilling experimental data disclosed in other articles.

[0065] It should be noted that some existing methods also provide groundwater level simulation, but these methods mostly focus on three-dimensional simulation at the observation point location, that is, usually only simulate the three-dimensional information of groundwater at each observation point location, and it is difficult to obtain spatiotemporally continuous groundwater level simulation results. The method provided in this embodiment is dedicated to obtaining high-precision, spatiotemporally continuous simulation results in the study area to fully reveal the spatiotemporal variation law of the groundwater level in the area.

[0066] Among them, time-space continuity means that the simulation results include groundwater level values ​​at multiple continuous times. At the same time, these groundwater level values ​​are continuous in geographic space, that is, they are displayed in the form of surface data. On the surface formed by the groundwater level simulation results, each geographical location can obtain its corresponding groundwater level value. This is different from the traditional three-dimensional simulation of groundwater levels at observation points. The traditional simulation can only obtain groundwater level values ​​at the observation point. There is no simulation result at other locations outside the observation point, and it is impossible to obtain groundwater level values ​​at other locations.

[0067] In this embodiment, there are multiple observation sites (also referred to as observation points or sites) for observing groundwater levels in the study area. That is to say, multiple observation points need to be drilled in the study area to increase the accuracy of groundwater level observation. In some specific practices, usually, the location of the observation site is selected according to needs and detected by drilling. However, the depth and diameter of the observation point obtained by drilling are larger than the depth and diameter of the observation well used in other groundwater level measurement methods. In addition, the drilling cost is high, the cycle is long, the number of drillings is relatively small, and the distribution of the observation point positions is sparse. It is difficult to intuitively derive the groundwater level distribution law in the study area from the observation results of each observation point. At the same time, affected by various factors, the sparse observation points formed by drilling may also have missing or abnormal observation data, and these situations are usually difficult to be discovered in time, resulting in a further reduction in effective observation data.

[0068] In this embodiment, the groundwater level observation data obtained from multiple observation points have multi-temporal characteristics, referred to as multi-temporal observation data. Multi-temporal refers to the characteristics of the groundwater level observation data in the time series, for example, it can be groundwater level observation data of the same group of observation points obtained at different times.

[0069] In practice, multi-temporal groundwater level observation data can be obtained by measuring at preset times using sensors installed in the borehole, wherein the sensors may be, for example, instruments that directly measure water levels (such as water level gauges), instruments that measure strain (such as strain gauges), instruments that measure radar waves, etc. Furthermore, the measurement results of the above sensors may be converted into groundwater level observation data by referring to the methods of the prior art.

[0070] Step S102: Determine valid observation data points and invalid observation data points based on groundwater level observation data.

[0071] In this embodiment, the data is first classified, that is, all groundwater level observation data are divided into valid observation data points and invalid observation data points, so that different preprocessing can be used for different data, laying the foundation for carrying out simulation and obtaining high-precision simulation results.

[0072] Among them, an invalid observation data point refers to a missing observation data or an abnormal observation data, otherwise it is a valid observation data point. In other words, the standard for defining whether an observation data is valid is whether it is missing or abnormal. Of course, other standards can also be used to judge the validity of data, and this embodiment does not limit this.

[0073] It should be noted that whether it is a valid observation data point or an invalid observation data point, it corresponds to a specific observation point location and observation time. That is to say, a valid observation data point / invalid observation data point refers to an observation record at a certain observation point location at a certain observation time. If the observation record at the observation point location at the observation time is missing or abnormal, the observation record is judged to be an invalid observation data point, otherwise it is a valid observation data point.

[0074] Step S103: Based on the spatial position relationship and time relationship between the valid observation data points and the invalid observation data points, the observation values ​​of the valid observation data points are processed to obtain virtual observation values ​​corresponding to the invalid observation data points.

[0075] After obtaining the groundwater level observation data and before conducting multi-temporal simulation, the multi-temporal groundwater level observation data are preprocessed to generate virtual observation values ​​to improve the quality of the multi-temporal groundwater level observation data.

[0076] Here, the virtual observation value is relative to the actual observation value, and refers to the observation value obtained through preprocessing (proportional interpolation processing), rather than the actual observation value directly observed from the observation point.

[0077] In the process of data observation, it is difficult to avoid missing or abnormal observation values, and this kind of missing is particularly prominent in groundwater level observation data. Figure 3 shows an example of a groundwater level observation record, such as Figure 3 As shown in the figure, the multi-temporal observation data are monthly scale data (i.e., one observation value is obtained every month), the total number of observation points is N, and different observation points are represented by observation point 1, observation point 2, observation point 3...observation point N, respectively. The observation times are January, February...December, respectively. The check marks (ticks) in the figure represent observed data, and blanks represent blank record points, i.e., no records or missing measurements or abnormal values.

[0078] In previous HASM simulations, missing values ​​or outliers are usually processed by directly eliminating them. However, such processing will lead to a further reduction in observation data, resulting in a decrease in the accuracy of simulation results for the entire study area, or even the inability to obtain effective simulation results. Therefore, in this embodiment, based on the spatial position relationship and time relationship between valid observation data points and invalid observation data points, processing is performed from the two dimensions of space and time to reconstruct the virtual observation values ​​corresponding to the invalid observation data points. Such a processing method is conducive to maintaining the integrity of groundwater level observation data, reducing data loss, and avoiding data analysis and simulation deviations caused by changing the original distribution of groundwater level observation data. In addition, the processing method is based on spatial relationships and time methods, which can not only utilize the effective observation data of adjacent spatial positions, but also consider the time dimension of the effective observation data, thereby improving the accuracy and data quality of the virtual observation values.

[0079] Specifically, the observation values ​​of valid observation data points can be processed in a variety of ways. For example, Kriging interpolation, spline interpolation, triangulation interpolation and other methods can be used according to the spatial position and time relationship between the valid observation data points and the invalid observation data points. This embodiment does not limit the specific processing method.

[0080] Step S104: for each phase in the multiple phases, construct an observation vector of the phase using the observation values ​​of the valid observation data points and the virtual observation values ​​of the invalid observation data points, so as to obtain the observation vectors of all the phases.

[0081] Specifically, the aforementioned preprocessing results, i.e., the observed values ​​of the valid observed data points and the virtual observed values ​​of the invalid observed data points, can be input into the observation vector construction module (i.e. Figure 2 Module A in , generates the observation vector for each phase.

[0082] by Figure 3 Taking the observation records shown as an example, a total of 12 observation vectors are generated, one for each month. By constructing the observation vectors for each month, each observation vector can better reflect the observation data rules of that month.

[0083] Step S105: Generate the initial trend surface of the high-precision surface modeling HASM method using the observation values ​​of valid observation data points, use the observation vectors corresponding to each time as the optimization control conditions for each time phase simulation, carry out multi-time phase HASM simulation of the groundwater level in the study area, and obtain the simulation results corresponding to each time; perform mean processing on the simulation results corresponding to each time to obtain the multi-time phase average simulation results.

[0084] It should be noted that ecological and environmental factors, such as temperature, precipitation, carbon dioxide concentration, soil property values, groundwater content, etc., are the natural basis for the survival and development of human society. Scientifically understanding the basic spatial distribution and change laws of these ecological and environmental factors is the primary task of ecological and environmental informatics. Water resources are the primary ecological and environmental factors on which human beings depend for survival, and are also indispensable production resources for social and economic development. Freshwater resources in all spheres of the earth are the most important resources for humans, and the main way to obtain freshwater resources is groundwater exploitation. Therefore, how to obtain high-precision and high-spatial-resolution groundwater grid data to quantitatively evaluate existing groundwater resources is of great significance to mastering the reserves of water resources.

[0085] The HASM method is a mathematical model created by Chinese scholar Yue Tianxiang and his research team. It is mainly used to simulate the ecological environment surface of the earth, with the aim of expressing and analyzing ecological environment elements more accurately. Based on the basic theorem of surface theory, the model abstracts the grid expression of ecological environment elements into a mathematical "surface". The surface is uniquely determined by the first basic quantity and the second basic quantity. The first basic quantity can express the intrinsic quantity that is independent of the shape of the surface, which is the driving field; the second basic quantity reflects the local deformation of the surface observed outside the surface, which is the extrinsic quantity used as the optimization control condition, thereby simulating a spatially continuous ecological environment element surface, solving the error problem and multi-scale problem that have plagued the field of surface modeling for many years.

[0086] Although HASM has become an important mathematical model in the field of ecological and environmental element simulation and is widely used in climate, soil, ecology and other fields, no scholar has yet used the HASM method in the modeling process of groundwater levels. This embodiment uses the advantages of high precision and high spatial resolution of the HASM method and applies it to the spatial simulation process of groundwater levels for the first time. It fully considers the multi-phase laws of the sampling data (groundwater level observation data) and can synchronously generate groundwater level simulation results of multiple phases, thereby fully and effectively reflecting the spatiotemporal changes of the groundwater level.

[0087] Among them, the driving field of HASM, also called the trend surface, is organized in the form of raster data and can be a surface with a target resolution. The value of each grid on the surface can be a fixed value. In this embodiment, the driving field used when HASM starts running is called the initial trend surface. The initial trend surface is generated using valid observation data points to improve the accuracy of the simulation. The extrinsic quantity of HASM, also called the optimization control condition, usually uses sampling point data as the optimization control condition, for example, it can be groundwater level observation data. In this embodiment, the optimization control condition varies with time, that is, during the operation of HASM, different observation vectors are read in for different phases and used as optimization control conditions, thereby controlling the surface accuracy of the multi-phase simulation results.

[0088] Furthermore, in this embodiment, a spatial interpolation method may be used to convert point data of valid observation data points into surface data, and the generated surface data is used as the initial trend surface of the HASM method.

[0089] In summary, the method provided in this embodiment uses the observation values ​​of valid observation data points and the virtual observation values ​​of invalid observation data points to construct the observation vectors corresponding to each phase, and conducts multi-phase HASM simulation of the groundwater level in the study area (such as Figure 2 Module B) in the method uses the initial trend surface as the driving field to drive the HASM operation. During the simulation, the optimization control conditions are continuously updated, that is, the observation vectors corresponding to each time are used as the optimization control conditions. Finally, the simulation results corresponding to each time are obtained, and then the multi-temporal average simulation results of the study area are obtained by mean calculation. This method aims at the time and space misalignment problem in the process of groundwater level detection. Considering the unique spatiotemporal sparse characteristics of observation data caused by spatiotemporal misalignment, the data is first classified, and the groundwater level observation data is divided into valid observation data points and invalid observation data points. Combined with the high resolution and high precision characteristics of HASM, multi-temporal HASM simulation is carried out by continuously rolling up the driving field and optimization control conditions of HASM, and finally high-resolution and spatially continuous multi-temporal simulation results are obtained. The multi-temporal average simulation results are obtained by mean calculation. This method can break through the limitations of the existing use of drilling technology to observe groundwater levels, and can effectively reflect the spatiotemporal changes of groundwater levels.

[0090] In some embodiments of the present disclosure, outliers and missing values ​​in groundwater level observation data are detected to determine valid observation data points and invalid observation data points.

[0091] Among them, the missing values ​​of the groundwater level observation data are detected, for example, the traversal method can be used, that is, the observation records of each observation point in each time phase are traversed. If the observation record exists, it means that it is not missing. If the observation record is empty, it means that the record is missing.

[0092] In some optional embodiments, abnormal values ​​of groundwater level observation data are detected, including the following steps: for any observation site of the groundwater level, subtract the minimum value from the maximum value of all valid observation data of the observation site to obtain the variation range of the observation data; determine the effective observation range based on the variation range of the observation data; and mark the observation values ​​that are not within the effective observation range among all the observation data of the observation site as abnormal values.

[0093] Specifically, for each observation site, we first counted the maximum and minimum values ​​of the valid observation data (also called valid observation records) obtained at all observation times at the site, and then used the maximum value of the valid observation record (X max ) minus the minimum value (X min ), get the variation range of the observed data Δ, and then define the effective observation range as (X min -Δ,X max +Δ), and mark the observation values ​​that are not in the valid observation range among all the observation data of the observation site as outliers. By determining the effective observation range, it is possible to ensure that the outliers are within a reasonable range while retaining the relationship between the original data.

[0094] Furthermore, after marking the outliers, it can also include: deleting the observation data marked as outliers (i.e., exceeding the valid observation range) and converting them into empty records. Such processing makes the outliers and missing values ​​have a unified data record format, which is convenient for subsequent processing.

[0095] In some embodiments of the present disclosure, step S103: based on the spatial position relationship and time relationship between the valid observation data points and the invalid observation data points, the observation values ​​of the valid observation data points are processed to obtain the virtual observation values ​​corresponding to the invalid observation data points, which may specifically include the following steps: arbitrarily select an invalid observation data point as the current invalid observation data point; calculate the spatial component of the virtual observation value of the current invalid observation data point based on the spatial position relationship between the current invalid observation data point and each observation site of the first valid observation data point set; wherein the first valid observation data point set is a set of valid observation data points with the same observation time as the current invalid observation data point but different observation sites; calculate the time component of the virtual observation value based on the observation time of the current invalid observation data point and the observation time of the second valid observation data point set; wherein the second valid observation data point set is a set of valid observation data points with the same observation site as the current invalid observation data point but different observation times; perform weighted averaging on the spatial component and the time component to obtain the virtual observation value of the current invalid observation data point.

[0096] In this embodiment, in order to calculate the virtual observation value of each invalid observation data point, any invalid observation data point is used as the current invalid observation data point, and the first valid observation data point set and the second valid observation data point set related to the current invalid observation data point are first found, wherein the first valid observation data point set is composed of valid observation data points with the same observation time and different observation sites as the current invalid observation data point, that is, observation records of different sites at the same time point; the second valid observation data point set is composed of valid observation data points with the same observation site and different observation times as the current invalid observation data point, that is, observation records of the same site at different times. Then, the spatial component of the virtual observation value of the current invalid observation data point is calculated based on the first valid observation data point set, and the time component of the virtual observation value is calculated based on the second valid observation data point set, and the two are weighted averaged to finally obtain the virtual observation value of the current invalid observation data point. This processing method allows different invalid observation data points to use different spatial and temporal components, fully considering the spatial information and time information related to the current invalid observation data point, and improving the accuracy of the virtual observation value of the invalid observation data point.

[0097] In some embodiments, the spatial component of the virtual observation value of the current invalid observation data point is calculated based on the spatial position relationship between the current invalid observation data point and each observation site in the first valid observation data point set, including: calculating the spatial distance between the current invalid observation data point and each observation site in the first valid observation data point set to obtain a spatial distance vector; calculating a spatial distance weight vector based on the spatial distance vector; and weightedly summing the spatial distance weight vector and the observation value of the first valid observation data point set to obtain the spatial component of the virtual observation value of the current invalid observation data point.

[0098] Among them, the spatial distance between the current invalid observation data point and each observation site in the first valid observation data point set can be expressed by Euclidean distance. The calculation formula of Euclidean distance can refer to the existing technology and will not be repeated here.

[0099] by Figure 3 Taking the observation records shown as an example, for the observation records in January, the observation records of observation point 2, observation point 3, observation point 5, observation point 6, observation point 7, and observation point 9 are blank record points, that is, invalid observation data points. Take any invalid observation data point, such as observation point 2, as the current invalid observation data point, then the first valid observation data point set includes the observation records of observation point 1, observation point 4, observation point 8, etc. in January. The position coordinates of each observation point are known, so the spatial distance (Euclidean distance) from each observation point in the first valid observation data point set to the current invalid observation data point (observation point 2) can be calculated, where the spatial distance from observation point 1 to observation point 2 is recorded as d 1; The spatial distance from observation point 4 to observation point 2 is denoted as d 2 , and so on, we get the spatial distance vector (d 1 ,d 2 ,…d n ), n is the number of valid observation data in the first valid observation data point set.

[0100] Get the spatial distance vector (d 1 ,d 2 ,…d n ), the spatial distance weight (abbreviated as: distance weight) can be calculated according to the following formula:

[0101]

[0102] In the formula, w i represents the spatial distance weight of the i-th valid observation data in the first valid observation data point set, d i represents the i-th spatial distance in the spatial distance vector, n is the number of valid observation data in the first valid observation data point set, and n is a positive integer.

[0103] The (w 1 ,w 2, …,w i …w n ) is called the spatial distance weight vector. According to the following formula (2), the spatial distance weight vector and the observation value of the first valid observation data point set are weighted and summed to obtain the spatial component O of the virtual observation value of the current invalid observation data point 1 (Also known as: Virtual Observation 1):

[0104]

[0105] Where, obs i Represents the i-th valid observation data in the first valid observation data point set.

[0106] In formula (2), the weight of each valid observation data is the ratio of the spatial distance weight of the corresponding position to the sum of the spatial distance weights. This can not only maintain the sensitivity of the spatial distance weight to the change of local values, but also avoid the influence of extreme values ​​and enhance the spatial component O. 1 Stability of calculations.

[0107] In some embodiments, the time component of the virtual observation value is calculated based on the observation time of the current invalid observation data point and the observation time of the second valid observation data point set, including: calculating the time distance between the observation time of the current invalid observation data point and the different observation times of the second valid observation data point set to obtain a time distance vector; calculating a time distance weight vector based on the time distance vector; and performing a weighted summation of the time distance weight vector and the observation value of the second valid observation data point set to obtain the time component of the virtual observation value of the current invalid observation data point.

[0108] Among them, the time distance weight can be calculated according to the following formula:

[0109]

[0110] In the formula, w tj represents the time distance weight of the jth valid observation data in the second valid observation data point set, m is the number of valid observation data in the second valid observation data point set, m is a positive integer, M is the observation time of the current invalid observation data point, t j represents the observation time of the jth valid observation data in the second valid observation data point set, t j -M represents the time distance between the observation time of the current invalid observation data point and the different observation time of the second valid observation data point set.

[0111] The w calculated by formula (3) tj The value of constitutes the time distance weight. On this basis, the time component O of the virtual observation value of the current invalid observation data point is calculated according to the following formula (4): 2 (Also known as: Virtual Observation 2):

[0112]

[0113] Where, obs tj Represents the jth valid observation data in the second valid observation data point set.

[0114] Then, the spatial component and the temporal component are weighted averaged according to the following formula (5) to obtain the virtual observation value O′ of the current invalid observation data point:

[0115]

[0116] In formula (5), the spatial component O of the virtual observation value is 1 and the time component O 2The weights are determined by the proportion of the number n of valid observation data in the first valid observation data point set and the number m of valid observation data in the second valid observation data point set to the total number of valid observation data in the two sets. This setting makes the calculation of weights objective and flexible, which can not only avoid the interference of subjective factors, but also balance the adjacent spatiotemporal dimensional data of different invalid observation data, thereby improving the accuracy of virtual observation values.

[0117] by Figure 3 Taking observation point 2 in as an example, according to the calculation steps above, the spatial component O is finally calculated by formula (5): 1 and the time component O 2 The weighted average is regarded as the virtual observation value O′ of observation point 2 in January. The above calculation process is repeated for other invalid observation data points, such as observation point 3, observation point 5, observation point 6, observation point 7, observation point 9, etc., until all blank values ​​in the observation record table have corresponding virtual observation values ​​calculated, and then step S104 is executed to input the observation values ​​of the valid observation data points and the virtual observation values ​​of the invalid observation data points into the observation vector construction module to construct the observation vectors of each phase, and then use the observation vectors of each phase to carry out multi-phase HASM simulation.

[0118] In some embodiments of the present disclosure, before carrying out the multi-phase HASM simulation, the following steps are used to construct the initial trend surface of HASM: for each observation site, the observation values ​​of the valid observation data points of the observation site are averaged to obtain the average value of the observation data corresponding to the observation site; the average values ​​of the observation data corresponding to each observation site are combined to generate a mean value sequence; based on the mean value sequence, the Kriging interpolation method is used to perform interpolation processing to obtain the grid surface of the study area, and the grid surface is used as the initial trend surface of the HASM method.

[0119] Still Figure 3 Take the observation record in as an example, which is monthly data, including 12 months of observation data. For each observation site, take the average value of each site in different months, build the average value sequence, and then use the Kriging interpolation method to obtain the grid surface of the area as the initial surface (initial trend surface) of the HASM method.

[0120] In some embodiments of the present disclosure, after obtaining the initial trend surface, the multi-phase HASM simulation may include the following steps: sorting the multiple phases in chronological order, and starting from the starting phase, executing the following steps in sequence according to the sorting results to carry out HASM simulation: judging the position of the current phase in the sorting results; if the current phase is the starting phase, taking the observation vector corresponding to the starting time as the optimization control condition, and the initial trend surface as the driving field, the HASM simulation is carried out on the groundwater level in the study area to obtain the simulation result of the starting phase; if the current phase is a phase other than the starting phase, taking the simulation result of the previous phase of the current phase in the sorting results as the driving field, and taking the observation vector corresponding to the current time as the optimization control condition, the HASM simulation is carried out on the groundwater level in the study area to obtain the simulation result of the current phase; repeating the above steps until the simulation results of the groundwater levels of all phases are obtained.

[0121] For example, Figure 3 For example, the observation record includes 12 months of data in total. Starting from January in monthly order, the observation vector of January is used as the optimization control condition, and the initial trend surface is used as the driving field (initial field). HASM is run to carry out simulation to obtain the simulation result of January; then the groundwater level simulation of February is carried out. Since February is not the starting month, the simulation result of the previous month, that is, January, is used as the driving field, and the observation vector of the current phase, that is, February, is used as the optimization control condition. HASM is run to obtain the simulation result of February... Repeat the above process in monthly order until the HASM simulation of 12 months is completed, and the groundwater level simulation results of each phase (i.e. 12 months) are obtained.

[0122] On the basis of obtaining the multi-temporal simulation results, the multi-temporal average simulation results are obtained by averaging the simulation results of each temporal phase. Since the simulation results output by the HASM method are gridded surfaces, averaging the simulation results of 12 months is actually to first calculate the sum of the grid values ​​at the same position in the simulation results of each month, and then divide it by 12 (the number of temporal phases) to obtain the multi-temporal average simulation results, which are used to reflect the annual groundwater level in the study area.

[0123] To summarize, the technical solution of this embodiment provides a spatial modeling method for groundwater level information based on the high-precision surface modeling method (HASM). This method takes into account the sparse characteristics of groundwater level observation records, and provides optimization control conditions for the HASM method by calculating virtual observation values ​​and constructing observation vectors of each time phase. By updating the driving field and optimizing the control conditions in the HASM iterative simulation, the sampling information at different time points is fully utilized to simultaneously obtain high-resolution, spatiotemporally continuous, and multi-phase groundwater level simulation results.

[0124] The method provided in this embodiment applies the HASM method to the groundwater level modeling process for the first time, utilizing the high precision and high spatial resolution characteristics of the HASM method, and making full use of multi-temporal drilling information in the modeling process. It can simultaneously obtain the groundwater level simulation results and the average state of the groundwater level for multiple months, laying a foundation for analyzing the spatiotemporal distribution of the groundwater level in the study area.

[0125] System Example

[0126] To implement the method of the embodiment of the present application, the embodiment of the present application also provides a groundwater level modeling system based on HASM, which runs on electronic devices such as terminals or servers, such as Figure 5 As shown, the system includes:

[0127] The acquisition unit 501 is configured to acquire groundwater level observation data of multiple time phases in a study area; wherein, in the study area, there are multiple observation stations for observing groundwater levels;

[0128] The determination unit 502 is configured to determine valid observation data points and invalid observation data points according to the groundwater level observation data; wherein the invalid observation data points refer to observation data missing or observation data abnormal, otherwise they are valid observation data points;

[0129] The processing unit 503 is configured to process the observation value of the valid observation data point according to the spatial position relationship and the time relationship between the valid observation data point and the invalid observation data point to obtain the virtual observation value corresponding to the invalid observation data point;

[0130] A construction unit 504 is configured to construct, for each phase in the multiple phases, an observation vector of the phase using the observation value of the valid observation data point and the virtual observation value of the invalid observation data point, so as to obtain respective observation vectors of all phases;

[0131] The simulation unit 505 is configured to generate an initial trend surface of the high-precision surface modeling HASM method using the observation values ​​of the effective observation data points, and use the observation vectors corresponding to each time as the optimization control conditions for each time phase simulation to carry out multi-time phase HASM simulation of the groundwater level in the study area to obtain simulation results corresponding to each time; and perform mean processing on the simulation results corresponding to each time to obtain a multi-time phase average simulation result.

[0132] In actual application, each unit included in the groundwater level modeling system can be implemented by a processor in the groundwater level modeling system. Of course, the processor needs to run the program stored in the memory to implement the functions of the above program modules.

[0133] It should be noted that: the groundwater level modeling system provided in the above embodiment only uses the division of the above program modules as an example when carrying out groundwater level modeling. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the groundwater level modeling system is divided into different program modules to complete all or part of the processing described above. In addition, the groundwater level modeling system provided in the above embodiment and the groundwater level modeling method embodiment belong to the same concept. The specific implementation process and the technical effects achieved are detailed in the method embodiment, which will not be repeated here.

[0134] Device Embodiment

[0135] Figure 6 Schematic diagram of the structure of an electronic device provided according to some embodiments of the present application; Figure 6 As shown, the electronic device includes:

[0136] One or more processors 601;

[0137] The computer-readable storage medium may be configured to store one or more programs 602. When one or more processors 601 execute one or more programs 602, the following steps are implemented:

[0138] Obtaining multi-temporal groundwater level observation data in the study area; in the study area, there are multiple observation stations for observing groundwater levels;

[0139] Determine valid observation data points and invalid observation data points according to the groundwater level observation data; wherein the invalid observation data points refer to observation data missing or observation data abnormal, otherwise they are valid observation data points;

[0140] According to the spatial position relationship and the time relationship between the valid observation data point and the invalid observation data point, the observation value of the valid observation data point is processed to obtain the virtual observation value corresponding to the invalid observation data point;

[0141] For each phase in the multiple phases, construct an observation vector of the phase using the observation value of the valid observation data point and the virtual observation value of the invalid observation data point to obtain the observation vectors of all phases;

[0142] The observation values ​​of the effective observation data points are used to generate the initial trend surface of the high-precision surface modeling HASM method, and the observation vectors corresponding to each time are used as the optimization control conditions for each time phase simulation. Multi-time phase HASM simulation of the groundwater level in the study area is carried out to obtain the simulation results corresponding to each time; the simulation results corresponding to each time are averaged to obtain the multi-time phase average simulation results.

[0143] Figure 7 The hardware structure of the electronic device provided according to some embodiments of the present application; Figure 7 As shown, the hardware structure of the electronic device may include: a processor 701 , a communication interface 702 , a computer-readable storage medium (also called a memory) 703 and a communication bus 704 .

[0144] The processor 701 , the communication interface 702 , and the computer-readable storage medium 703 communicate with each other via a communication bus 704 .

[0145] The computer-readable storage medium 703 may be configured to store one or more programs.

[0146] Optionally, the communication interface 702 may be an interface of a communication module, such as an interface of a GSM module.

[0147] The processor 701 executes one or more programs, which implement the following steps:

[0148] Obtaining multi-temporal groundwater level observation data in the study area; in the study area, there are multiple observation stations for observing groundwater levels;

[0149] Determine valid observation data points and invalid observation data points according to the groundwater level observation data; wherein the invalid observation data points refer to observation data missing or observation data abnormal, otherwise they are valid observation data points;

[0150] According to the spatial position relationship and the time relationship between the valid observation data point and the invalid observation data point, the observation value of the valid observation data point is processed to obtain the virtual observation value corresponding to the invalid observation data point;

[0151] For each phase in the multiple phases, construct an observation vector of the phase using the observation value of the valid observation data point and the virtual observation value of the invalid observation data point to obtain the observation vectors of all phases;

[0152] The observation values ​​of the effective observation data points are used to generate the initial trend surface of the high-precision surface modeling HASM method, and the observation vectors corresponding to each time are used as the optimization control conditions for each time phase simulation. Multi-time phase HASM simulation of the groundwater level in the study area is carried out to obtain the simulation results corresponding to each time; the simulation results corresponding to each time are averaged to obtain the multi-time phase average simulation results.

[0153] The processor 701 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0154] The electronic device of the embodiment of the present application exists in various forms, including but not limited to:

[0155] (1) Mobile communication devices: These devices are characterized by their mobile communication functions and their main purpose is to provide voice and data communications. These terminals include: smart phones (e.g., iPhone), multimedia phones, functional phones, and low-end phones.

[0156] (2) Ultra-mobile personal computer devices: These devices fall into the category of personal computers, have computing and processing capabilities, and generally also have mobile Internet access features. These terminals include: PDA, MID and UMPC devices, such as iPad.

[0157] (3) Portable entertainment devices: These devices can display and play multimedia content. They include audio and video players (such as iPods), handheld game consoles, e-books, smart toys, and portable car navigation devices.

[0158] (4) Server: A device that provides computing services. The server consists of a processor, hard disk, memory, system bus, etc. The server is similar to a general computer architecture, but because it needs to provide highly reliable services, it has higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0159] (5) Other electronic devices with data interaction functions.

[0160] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0161] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or implemented as computer code originally stored in a remote recording medium or a non-temporary machine storage medium downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the HASM-based groundwater level modeling method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0162] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and constraints involved in the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present application.

[0163] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0164] The device and system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components described as units may or may not be physical units, i.e., they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0165] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A groundwater level modeling method based on HASM, characterized in that: include: Obtaining multi-temporal groundwater level observation data in the study area; in the study area, there are multiple observation stations for observing groundwater levels; Determine valid observation data points and invalid observation data points according to the groundwater level observation data; wherein the invalid observation data points refer to observation data missing or observation data abnormal, otherwise they are valid observation data points; According to the spatial position relationship and the time relationship between the valid observation data point and the invalid observation data point, the observation value of the valid observation data point is processed to obtain the virtual observation value corresponding to the invalid observation data point; For each phase in the multiple phases, construct an observation vector of the phase using the observation value of the valid observation data point and the virtual observation value of the invalid observation data point to obtain the observation vectors of all phases; The observation values ​​of the effective observation data points are used to generate the initial trend surface of the high-precision surface modeling HASM method, and the observation vectors corresponding to each time are used as the optimization control conditions for each time phase simulation. Multi-time phase HASM simulation of the groundwater level in the study area is carried out to obtain the simulation results corresponding to each time; the simulation results corresponding to each time are averaged to obtain the multi-time phase average simulation results.

2. The method according to claim 1, characterized in that The outliers and missing values ​​in the groundwater level observation data are detected to determine the valid observation data points and the invalid observation data points, wherein the outliers of the groundwater level observation data are detected, including the following steps: For any observation station of groundwater level, the maximum value of all valid observation data of the observation station is subtracted from the minimum value to obtain the variation range of the observation data; Determine the effective observation range according to the variation range of the observation data; Observation values ​​that are not within the valid observation range among all observation data of the observation site are marked as abnormal values.

3. The method according to claim 1, characterized in that According to the spatial position relationship and time relationship between the valid observation data points and the invalid observation data points, the observation values ​​of the valid observation data points are processed to obtain virtual observation values ​​corresponding to the invalid observation data points, including the following steps: Take any invalid observation data point as the current invalid observation data point; According to the spatial position relationship between the current invalid observation data point and each observation site of the first valid observation data point set, the spatial component of the virtual observation value of the current invalid observation data point is calculated; wherein the first valid observation data point set is a set of valid observation data points with the same observation time as the current invalid observation data point but with different observation sites; Calculate the time component of the virtual observation value according to the observation time of the current invalid observation data point and the observation time of the second valid observation data point set; wherein the second valid observation data point set is a set of valid observation data points with the same observation site as the current invalid observation data point but with different observation time; The spatial component and the temporal component are weighted averaged to obtain a virtual observation value of the current invalid observation data point.

4. The method according to claim 3, characterized in that: According to the spatial position relationship between the current invalid observation data point and each observation site of the first valid observation data point set, the spatial component of the virtual observation value of the current invalid observation data point is calculated, including: Calculate the spatial distance between the current invalid observation data point and each observation site in the first valid observation data point set to obtain a spatial distance vector; Calculate a spatial distance weight vector based on the spatial distance vector; The spatial distance weight vector and the observation value of the first valid observation data point set are weightedly summed to obtain the spatial component of the virtual observation value of the current invalid observation data point.

5. The method according to claim 3, characterized in that: According to the observation time of the current invalid observation data point and the observation time of the second valid observation data point set, the time component of the virtual observation value is calculated, including: Calculate the time distance between the observation time of the current invalid observation data point and different observation times of the second set of valid observation data points to obtain a time distance vector; According to the time distance vector, a time distance weight vector is calculated; The time distance weight vector is weightedly summed with the observation value of the second valid observation data point set to obtain the time component of the virtual observation value of the current invalid observation data point.

6. The method according to claim 1, characterized in that The initial trend surface of the high-precision surface modeling HASM method is generated by using the observed values ​​of the effective observed data points, including: For each observation site, the observation values ​​of the valid observation data points of the observation site are averaged to obtain the average value of the observation data corresponding to the observation site; Combine the average values ​​of the observation data corresponding to each observation station to generate an average value sequence; Based on the mean value sequence, the Kriging interpolation method is used to perform interpolation processing to obtain the grid surface of the study area, and the grid surface is used as the initial trend surface of the HASM method.

7. The method according to claim 1, characterized in that The observation vectors corresponding to each time are used as the optimization control conditions for each time phase simulation, and multi-time phase HASM simulation is carried out on the groundwater level in the study area to obtain the simulation results corresponding to each time phase, including: The multiple phases are sorted in chronological order, and the following steps are performed in sequence from the start phase according to the sorting result to carry out HASM simulation: Determine the position of the current phase in the sorting result. If the current phase is the starting phase, use the observation vector corresponding to the starting phase as the optimization control condition, use the initial trend surface as the driving field, conduct HASM simulation on the groundwater level in the study area, and obtain the simulation result of the starting phase. If the current phase is other than the starting phase, the simulation result of the previous phase of the current phase in the sorting result is used as the driving field, and the observation vector corresponding to the current phase is used as the optimization control condition to carry out HASM simulation on the groundwater level of the study area to obtain the simulation result of the current phase; Repeat the above steps until the simulation results of groundwater levels in all phases are obtained.

8. A groundwater level modeling system based on HASM, characterized in that: include: An acquisition unit is configured to acquire groundwater level observation data of multiple phases in a study area; wherein, in the study area, there are multiple observation sites for observing groundwater levels; A determination unit, configured to determine valid observation data points and invalid observation data points according to the groundwater level observation data; wherein the invalid observation data points refer to observation data missing or observation data abnormal, otherwise they are valid observation data points; a processing unit configured to process the observation values ​​of the valid observation data points according to the spatial position relationship and the time relationship between the valid observation data points and the invalid observation data points, so as to obtain virtual observation values ​​corresponding to the invalid observation data points; A construction unit is configured to construct, for each phase in the multiple phases, an observation vector of the phase using the observation value of the valid observation data point and the virtual observation value of the invalid observation data point, so as to obtain respective observation vectors of all phases; The simulation unit is configured to generate an initial trend surface of a high-precision surface modeling HASM method using the observation values ​​of the effective observation data points, and use the observation vectors corresponding to each time as the optimization control conditions for each time phase simulation to carry out multi-time phase HASM simulation of the groundwater level in the study area to obtain simulation results corresponding to each time; and perform mean processing on the simulation results corresponding to each time to obtain a multi-time phase average simulation result.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: A memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program.

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