Underground water level monitoring device and underground water level monitoring method

Abstract

The invention provides an underground water level monitoring device and an underground water level monitoring method. An unmanned aerial vehicle; the control device is used for acquiring a thermal infrared image which covers the target monitoring blank area and an existing data area which is located in the same hydrogeological unit as the target monitoring blank area; identifying a temperature abnormal area of the target monitoring blank area, and obtaining a first underground water interpretation water level; identifying a temperature abnormal area of the target data area, and obtaining a second underground water interpretation water level; acquiring underground water monitoring data of the target data area at the same time; based on the second underground water interpretation water level, the underground water monitoring data and the geological parameters of the target data area, constructing a fitting function mathematical model associated with the second underground water interpretation water level and the underground water monitoring water level; and acquiring the underground water level of the target monitoring blank area by using the constructed fitting function mathematical model. By adopting the monitoring device provided by the invention, the underground water level of the monitoring blank area can be monitored more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a groundwater level monitoring device and a groundwater level monitoring method. Background Art

[0002] Groundwater monitoring is for the groundwater monitoring and management department to monitor data such as groundwater level, water quality, temperature, conductivity, etc. within its jurisdiction, so as to timely grasp the dynamic changes and protect groundwater in the long term.

[0003] With the development of Internet of Things technology, currently, a groundwater monitoring system usually consists of three parts: a remote monitoring center, a communication network, and a monitoring terminal. The groundwater monitoring system relies on networks such as GPRS. Staff can view data such as groundwater level, temperature, conductivity, etc. in the monitoring center. The monitoring management software in the monitoring center can realize remote data collection and remote monitoring. All monitored data enters the database to generate various reports and curves.

[0004] In the process of implementing the present invention, the inventor found that there are at least the following problems in the prior art: Currently, groundwater monitoring is generally manual monitoring, sensor monitoring, Internet of Things and wireless communication monitoring. First, hardware needs to be deployed at monitoring points (such as groundwater monitoring wells). It is difficult to deploy equipment in uninhabited, complex terrain and remote areas (such as mountain recharge areas, gobi discharge areas), etc., which are commonly known as monitoring blank areas. Equipment maintenance is also difficult. The cost of regular manual inspections and monitoring is high. Therefore, monitoring points are mostly deployed only at key locations with good terrain conditions and convenient transportation. However, in some areas with poor signals, few people, inconvenient groundwater transportation, etc. or potential problem areas, the monitoring points are too sparse, which limits the description of the characteristics and variation laws of groundwater at different spatial positions.

[0005] Therefore, a groundwater level monitoring device and a groundwater level monitoring method are needed to at least partially solve the above technical problems. Summary of the Invention

[0006] In view of this, embodiments of the present invention provide a groundwater level monitoring device and a groundwater level monitoring method to at least solve one of the problems in the prior art.

[0007] In a first aspect, embodiments of the present invention provide a groundwater level monitoring method, and the monitoring method includes: Obtaining a thermal infrared image whose range covers a target monitoring blank area and at least one existing data area that is in the same hydrogeological unit as the target monitoring blank area from the thermal infrared image captured by a drone; wherein, the existing data area refers to a selected area that includes at least one groundwater monitoring well within the area; Identify the temperature anomaly area in the target monitoring blank area and obtain the first groundwater interpretation level; take the existing data area where the groundwater monitoring well closest to the target monitoring blank area is located as the target data area, identify the corresponding temperature anomaly area in the target data area, and obtain the second groundwater interpretation level; Based on the real-time dynamic monitoring of the groundwater monitoring wells in the target data area, obtain the groundwater monitoring data at the same time in the target data area, including the groundwater monitoring level, permeability coefficient, and water temperature; Based on the second groundwater interpretation level, groundwater monitoring data, and geological parameters of the target data area, conduct a logistic regression analysis to construct a fitting function mathematical model that correlates the second groundwater interpretation level and the groundwater monitoring level; wherein, the geological parameters include permeability, aquifer thickness, and aquifer porosity; Use the constructed fitting function mathematical model and input the first groundwater interpretation level to obtain the groundwater level in the target monitoring blank area.

[0008] In a second aspect, an embodiment of the present invention further provides a groundwater level monitoring device, and the monitoring device includes: A monitoring terminal installed in the groundwater monitoring well in the existing data area, which is used to monitor the groundwater level, permeability coefficient, and water temperature in real time; the existing data area refers to a selected area that includes at least one groundwater monitoring well within the area; An unmanned aerial vehicle (UAV), which is used to take thermal infrared images from the air that cover the target monitoring blank area and the existing data area; and A control device that is respectively communicatively connected to the monitoring terminal and the UAV. The control device includes a processor and a memory. When the processor executes the computer-executable instructions stored in the memory, the following steps are implemented: Obtain thermal infrared images that cover the target monitoring blank area and at least one existing data area that is in the same hydrogeological unit as the target monitoring blank area; Identify the temperature anomaly area in the target monitoring blank area and obtain the first groundwater interpretation level; take the existing data area where the groundwater monitoring well closest to the target monitoring blank area is located as the target data area, identify the corresponding temperature anomaly area in the target data area, and obtain the second groundwater interpretation level; Based on the real-time dynamic monitoring of the groundwater monitoring wells in the target data area, obtain the groundwater monitoring data at the same time in the target data area, including the groundwater monitoring level, permeability coefficient, and water temperature; Based on the second groundwater interpretation level, groundwater monitoring data, and geological parameters of the target data area, conduct a logistic regression analysis to construct a fitting function mathematical model that correlates the second groundwater interpretation level and the groundwater monitoring level; wherein, the geological parameters include permeability, aquifer thickness, and aquifer porosity; Using the constructed fitting function mathematical model, input the first underground water interpretation water level to obtain the underground water level of the target monitoring blank area.

[0009] Thirdly, an embodiment of the present invention further provides a groundwater level monitoring device, and the monitoring device includes: A memory for storing computer-executable instructions; A processor, when executing the computer-executable instructions stored in the memory, implements the monitoring method of the above technical solution.

[0010] Fourthly, an embodiment of the present invention further provides a storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the monitoring method of the above technical solution.

[0011] According to the monitoring method of the embodiment of the present invention, using the groundwater monitoring data (traditional measured monitoring data) of the target data area, the first underground water interpretation water level and the second underground water interpretation water level (remote sensing data), combined with the geological parameters of the target data area, a fitting function mathematical model associating the second underground water interpretation water level and the groundwater monitoring water level is constructed by fusing multi-source data, realizing the monitoring of the groundwater level in the monitoring blank area, making the inversion of the groundwater level closer to the actual situation, significantly expanding the spatial range of groundwater monitoring, and realizing large-scale, high-efficiency and low-cost dynamic monitoring of groundwater.

[0012] The additional advantages, objectives, and features of the present invention will be partially described below, and will become partially apparent to those of ordinary skill in the art after studying the following text, or can be learned from the practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the description and the drawings.

[0013] Those skilled in the art will understand that the objectives and advantages that can be achieved by the present invention are not limited to the above specifically described, and the above and other objectives that the present invention can achieve will be more clearly understood according to the following detailed description. Description of the Drawings

[0014] The drawings described herein are used to provide a further understanding of the present invention, form a part of this application, and do not limit the present invention. The components in the drawings are not drawn to scale, but are only for showing the principle of the present invention. For the convenience of showing and describing some parts of the present invention, the corresponding parts in the drawings may be enlarged, that is, may become larger relative to other components in the exemplary device actually manufactured according to the present invention. In the drawings: Figure 1 It is a flowchart of the groundwater level monitoring method according to an embodiment of the present invention; Figure 2Schematic diagram of a groundwater level monitoring device according to an embodiment of the present invention; Figure 3 Schematic diagram of a groundwater level monitoring system according to an embodiment of the present invention; Figure 4 Schematic diagram of a groundwater level monitoring device according to an embodiment of the present invention.

[0015] Description of reference numerals: 400, monitoring device; 410, monitoring terminal; 420, unmanned aerial vehicle; 430, control device; 440, solar power supply unit. Detailed implementation manners

[0016] To make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with the implementation manners and the drawings. Herein, the illustrative implementation manners of the present invention and their descriptions are used to explain the present invention, but not to limit the present invention.

[0017] Herein, it should also be noted that in order to avoid obscuring the present invention due to unnecessary details, only the structures and / or processing steps closely related to the solution according to the present invention are shown in the drawings, while other details less related to the present invention are omitted.

[0018] It should be emphasized that the term "including / comprising" when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.

[0019] Herein, it should also be noted that if not otherwise specified, the term "connection" in this document can not only refer to direct connection, but also represent indirect connection with an intermediate.

[0020] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0021] First, reference will be made to Figure 1 Describe a groundwater level monitoring method 100 according to an embodiment of the present application. As Figure 1 shown, the monitoring method 100 may include steps S110 to S150, specifically as follows: In step S110, obtain a thermal infrared image of a range covering the target monitoring blank area and at least one existing data area in the same hydrogeological unit as the target monitoring blank area; wherein, the existing data area refers to a selected area including at least one groundwater monitoring well within the area.

[0022] In step S120, identify the temperature anomaly area in the target monitoring blank area and obtain the first groundwater interpreted water level; use the existing data area where the groundwater monitoring well closest to the target monitoring blank area is located as the target data area, identify the corresponding temperature anomaly area in the target data area, and obtain the second groundwater interpreted water level.

[0023] In step S130, based on the real-time dynamic monitoring of the groundwater monitoring wells in the target data area, obtain the groundwater monitoring data of the target data area at the same time, including groundwater monitoring water level, permeability coefficient, and water temperature.

[0024] In step S140, perform a logistic regression analysis based on the second groundwater interpreted water level, groundwater monitoring data, and geological parameters of the target data area to construct a fitting function mathematical model that correlates the second groundwater interpreted water level and the groundwater monitoring water level; among them, the geological parameters include permeability, aquifer thickness, and aquifer porosity.

[0025] In step S150, use the constructed fitting function mathematical model and input the first groundwater interpreted water level to obtain the groundwater level of the target monitoring blank area.

[0026] In the embodiment of the present application, first, obtain a thermal infrared image whose range covers the target monitoring blank area and at least one existing data area in the same hydrogeological unit as the target monitoring blank area; then identify the temperature anomaly area in the target monitoring blank area in the thermal infrared image and obtain the first groundwater interpreted water level, and identify the corresponding temperature anomaly area in the target data area in the thermal infrared image and obtain the second groundwater interpreted water level; then, based on the real-time dynamic monitoring of the groundwater monitoring wells in the target data area, obtain the groundwater monitoring data of the target data area at the same time, including groundwater monitoring water level, permeability coefficient, and water temperature; perform a logistic regression analysis based on the second groundwater interpreted water level, groundwater monitoring data, and geological parameters of the target data area to construct a fitting function mathematical model that correlates the second groundwater interpreted water level and the groundwater monitoring water level; finally, use the constructed fitting function mathematical model and input the first groundwater interpreted water level to obtain the groundwater level of the target monitoring blank area.

[0027] From the description of the above process, it can be seen that according to the monitoring method 100 of the embodiment of the present application, by integrating the groundwater monitoring data (traditional measured monitoring data) of the target data area, the first groundwater interpreted water level and the second groundwater interpreted water level (remote sensing data), and the geological parameters of the target data area, a fitting function mathematical model that correlates the second groundwater interpreted water level and the groundwater monitoring water level is constructed, realizing the groundwater level monitoring of the monitoring blank area and expanding the spatial range of groundwater monitoring.

[0028] The following will combine Figure 1 Specifically describe the content of the above steps of the monitoring method 100 according to the embodiment of the present application.

[0029] In an embodiment of the present application, in step S110, a thermal infrared image is acquired that covers the target monitoring blank area and at least one existing data area in the same hydrogeological unit as the target monitoring blank area; wherein, the existing data area refers to a selected area that includes at least one groundwater monitoring well within the area. For example, one groundwater monitoring well is included in a certain existing data area. Three groundwater monitoring wells are included in a certain existing data area.

[0030] It should be noted that the groundwater monitoring wells in this embodiment should be understood to include corresponding monitoring equipment, which can monitor data such as groundwater level, water quality, temperature, and conductivity within the jurisdiction (i.e., the existing data area). Through traditional sensor monitoring, Internet of Things and wireless communication monitoring, etc., continuous and real-time dynamic monitoring data is accumulated to timely grasp the dynamic change situation.

[0031] Specifically, in order to acquire a thermal infrared image that meets the above conditions, a series of original thermal infrared images that cover the target monitoring blank area and at least one groundwater monitoring well can be acquired in advance. The area demarcated with a selected size centered on the groundwater monitoring well is the existing data area. For example, a circular area demarcated with a radius of 500 meters centered on the groundwater monitoring well, or a rectangular area with a length of 1000 meters and a width of 500 meters is the existing data area. The shape and size of the existing data area can be adjusted according to needs and are not limited.

[0032] Next, the hydrogeological unit information of the target monitoring blank area and each existing data area is respectively acquired through on-site detection or querying relevant materials, etc. If there is an existing data area in the same hydrogeological unit as the target monitoring blank area in a certain original thermal infrared image, for example, the hydrogeological unit information of one, two or more existing data areas is the same as that of the target monitoring blank area, then this original thermal infrared image is the required thermal infrared image, and the next step is carried out.

[0033] If all the existing data areas are not in the same hydrogeological unit as the target monitoring blank area, then continue to expand the coverage range of the original thermal infrared image until a thermal infrared image with an existing data area in the same hydrogeological unit as the target monitoring blank area is acquired.

[0034] Regarding the acquisition of the thermal infrared image, or rather the original thermal infrared image, an unmanned aerial vehicle equipped with a high-precision thermal imager can be used for aerial flight shooting to obtain it, and a lidar (LiDAR) can also be carried to obtain centimeter-level resolution data.

[0035] In an embodiment of the present application, in step S120, the temperature anomaly area of the target monitoring blank area is identified, and the first interpreted groundwater level is obtained; taking the existing data area where the groundwater monitoring well closest to the target monitoring blank area is located as the target data area, the corresponding temperature anomaly area of the target data area is identified, and the second interpreted groundwater level is obtained.

[0036] Specifically, before identifying the thermal infrared image obtained in step S110, preprocessing such as calibration, correction, and wavelet denoising can be performed first to convert the original gray value into the surface temperature (unit: °C), eliminate the temperature noise caused by clouds and instantaneous meteorological changes, eliminate errors, interference, and inconsistencies, and perform equalization, linear stretching, filtering processing, etc., so that the gray values of the image are more evenly distributed within the entire image range, and the visual analysis effect of the image is increased.

[0037] When the groundwater depth is relatively shallow and the water volume is abundant, it affects the surface temperature, making it show a temperature anomaly, different from the surrounding areas. By analyzing the thermal infrared image, the corresponding temperature anomaly areas of the target monitoring blank area and the target data area can be identified respectively. For example, using a classification algorithm with an SVM-random forest hybrid model to distinguish the groundwater influence area (temperature anomaly) from the background area. Example: In an image of a gobi area, the temperature in the groundwater discharge area is 2-3 °C lower than that of the surrounding area, and the classification accuracy of the model reaches 92%. Among them, SVM (Support Vector Machine): A classification algorithm based on statistical learning used to distinguish temperature anomaly areas.

[0038] Or, use a semantic segmentation network such as U-Net to directly extract the temperature anomaly area from the thermal infrared image, replacing the traditional classification algorithm.

[0039] Of course, low-power temperature and humidity sensors (such as LoRa nodes) can also be arranged in the target monitoring blank area and the target data area respectively to assist in calibrating the surface temperature, complement the remote sensing data, and form a "sky-ground" collaborative monitoring network.

[0040] Next, use the formula to calculate the water level.

[0041] Among them, is the first interpreted groundwater level or the second interpreted groundwater level. is the average value of the ground temperature anomaly.

[0042] is the permeability, reflecting the connectivity of pores or fractures, is the aquifer thickness, 、 and are the corresponding weight coefficients.

[0043] It can be understood that the above solution shows the acquisition of the first underground water interpretation level in the target monitoring blank area and the second underground water interpretation level in the target data area through thermal infrared images, and it can also be obtained through other means. For example, based on Interferometric Synthetic Aperture Radar (InSAR), the surface deformation can be monitored using the phase difference of radar satellites, or based on the Gravity Recovery and Climate Experiment Satellite (GRACE), the first underground water interpretation level and the second underground water interpretation level can be obtained by measuring the changes in the Earth's gravity field. Or, the first underground water interpretation level and the second underground water interpretation level can be determined by comprehensively considering the complementarity of the above multi-modal data.

[0044] In an embodiment of the present application, in step S130, the groundwater monitoring data in the target data area at the same time is obtained based on the real-time dynamic monitoring of the groundwater monitoring wells in the target data area, including the groundwater monitoring level, the permeability coefficient, and the water temperature.

[0045] Specifically, since the groundwater monitoring wells can accumulate continuous and real-time dynamic monitoring data through traditional sensor monitoring, Internet of Things and wireless communication monitoring, etc., the groundwater monitoring data in the target data area at the same time can be obtained from the real-time dynamic monitoring data of the groundwater monitoring wells in the target data area, including the groundwater monitoring level, the groundwater permeability coefficient, and the groundwater temperature. Among them, the same time refers to the same time period as the shooting time of the thermal infrared image obtained in step S110, which covers the target monitoring blank area and at least one existing data area in the same hydrogeological unit as the target monitoring blank area.

[0046] In an embodiment of the present application, in step S140, a fitting function mathematical model that correlates the second underground water interpretation level and the groundwater monitoring level is constructed based on the second underground water interpretation level, the groundwater monitoring data, and the geological parameters of the target data area; among them, the geological parameters include the permeability, the aquifer thickness, and the aquifer porosity.

[0047] Specifically, it may include the following steps: Step S141, establishing a mathematical model between the groundwater monitoring level and the second underground water interpretation level, wherein, is the groundwater monitoring level, is the second underground water interpretation level, is one of the groundwater monitoring level, the groundwater permeability coefficient, the groundwater temperature, and the permeability, the aquifer thickness, and the aquifer porosity of the target data area, is the corresponding weight coefficient.

[0048] Step S142: Use multiple sets of second underground hydro - interpreted water levels, groundwater monitoring water levels, groundwater permeability coefficients, groundwater water temperatures at the same time, as well as permeability, aquifer thickness, and aquifer porosity data of the target data area, implement numerical algorithms using a computer, and select a suitable solver for solution.

[0049] Then, conduct model verification: Compare and analyze the solution results with the actual monitoring data, and adjust the weight coefficients in the model until the calculated results are close to the measured data, for example, the absolute value of the error is between 2% and 5%.

[0050] It is also possible to use the Bayesian algorithm to automatically search for the optimal weight coefficients of the mathematical model , replacing manual coefficient adjustment.

[0051] Step S143: Re - select at least one set of second underground hydro - interpreted water levels, groundwater monitoring water levels, groundwater permeability coefficients, groundwater water temperatures, as well as permeability, aquifer thickness, and aquifer porosity data of the target data area to verify the model to ensure reliability.

[0052] Step S144: If the verification fails, repeat the above steps S142 and S143 until the verification passes.

[0053] In the embodiment of the present application, in step S150, using the constructed fitting - function mathematical model, input the first underground hydro - interpreted water level to obtain the groundwater level of the target monitoring blank area.

[0054] Specifically, due to the fact that the target monitoring blank area and the target data area are in the same hydro - geological unit, for the fitting - function mathematical model constructed through step S140, it is possible to input the first underground hydro - interpreted water level into the fitting - function mathematical model to obtain a relatively accurate groundwater level of the target monitoring blank area, solving the problem that it is difficult to monitor the groundwater level in the target monitoring blank area due to the difficulty of installing groundwater monitoring equipment.

[0055] It is also possible to use blockchain technology to record thermal infrared image data (remote sensing data), groundwater monitoring well sensor data (monitoring data), and model output data (groundwater level) to ensure that the data cannot be tampered with and improve the credibility of the monitoring results.

[0056] Based on the above description, according to the monitoring method of the embodiment of the present application, based on thermal infrared image remote sensing data, interpretation data, etc., integrating factors closely related to the groundwater burial relationship, enriching groundwater monitoring means, and as close as possible to truly reflect the actual situation of groundwater in the monitoring blank area, realizing the groundwater monitoring scope, and complementing the spatial characteristics and variation laws of groundwater in monitoring blank areas such as groundwater recharge areas in mountainous areas and groundwater discharge areas in gobi areas.

[0057] ReferenceFigure 2 The monitoring device 200 for implementing the monitoring method according to the embodiments of the present application includes a processor 210 and a memory 220. The monitoring device 200 may include one or more processors 210 and one or more memories 220. The memory 220 stores an executable program run by the processor 210. When the executable program is run by the processor 210, the processor 210 is caused to execute the monitoring method 100 according to the embodiments of the present application described above.

[0058] The processor 210 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities.

[0059] The memory 220 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 210 may run the program instructions to implement the client functions (implemented by the processor) and / or other desired functions in the embodiments of the present application described herein. Various application programs and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the application programs, etc.

[0060] The monitoring device 200 may further include an input device and an output device, and these components are interconnected through a bus system and / or other forms of connection mechanisms. It should be noted that Figure 2 The components and structures of the illustrated monitoring device 200 are only exemplary and not restrictive. According to needs, the monitoring device 200 may also have other components and structures.

[0061] The input device may be a device used by a user to input instructions and may include one or more of a keyboard, a mouse, a microphone, and a touch screen, etc. In addition, the input device may also be any interface for receiving information.

[0062] The output device may output various information (such as images or sounds) to the outside (such as a user) and may include one or more of a display, a speaker, etc. In addition, the output device may also be any other device with output functions.

[0063] Exemplarily, the exemplary monitoring device 200 for implementing the monitoring method 100 according to the embodiments of the present application can be applied to electronic devices such as terminal devices (such as mobile phones), tablet computers, laptop computers, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (AR), virtual reality (VR) devices, smart home devices, in-vehicle computers, etc. The embodiments of the present application do not impose any restrictions on this.

[0064] Those skilled in the art can understand the specific operations of the monitoring device 200 for implementing the monitoring method 100 according to the embodiments of the present application in combination with the content described above. For the sake of brevity, specific details are not elaborated here, and only some main operations of the processor 210 are described.

[0065] In an embodiment of the present application, when the executable program is run by the processor 210, the processor 210 is caused to perform the following steps: obtaining a thermal infrared image of a target monitoring blank area and at least one existing data area that is in the same hydrogeological unit as the target monitoring blank area; where the existing data area is a selected area that includes at least one groundwater monitoring well within the area; identifying a temperature anomaly area in the target monitoring blank area and obtaining a first interpreted groundwater level; identifying a corresponding temperature anomaly area in the target data area and obtaining a second interpreted groundwater level; obtaining groundwater monitoring data of the target data area at the same time based on the real-time dynamic monitoring of the groundwater monitoring well in the target data area, including groundwater monitoring water level, permeability coefficient, and water temperature; performing a logistic regression analysis based on the second interpreted groundwater level, groundwater monitoring data, and geological parameters of the target data area to construct a fitting function mathematical model that correlates the second interpreted groundwater level and the groundwater monitoring water level; where the geological parameters include permeability, aquifer thickness, and aquifer porosity; using the constructed fitting function mathematical model, inputting the first interpreted groundwater level to obtain the groundwater level of the target monitoring blank area.

[0066] The above exemplarily shows the monitoring method 100 according to the embodiments of the present application. The following combines Figure 3 to describe the monitoring system 300 provided in another aspect of the embodiments of the present application.

[0067] Referring to Figure 3An example monitoring system 300 for implementing the monitoring method of the embodiments of the present application will be described. The monitoring system 300 may include a thermal infrared image acquisition module 310, an interpreted water level acquisition module 320, a monitoring data acquisition module 330, a model construction module 340, and a water level acquisition module 350. Among them: The thermal infrared image acquisition module 310 is configured to: acquire thermal infrared images of a range covering the target monitoring blank area and at least one existing data area in the same hydrogeological unit as the target monitoring blank area; wherein, the existing data area refers to a selected area including at least one groundwater monitoring well within the area.

[0068] The interpreted water level acquisition module 320 is configured to: identify the temperature anomaly area in the target monitoring blank area and acquire the first interpreted groundwater level; identify the corresponding temperature anomaly area in the target data area and acquire the second interpreted groundwater level.

[0069] The monitoring data acquisition module 330 is configured to: acquire the groundwater monitoring data of the target data area at the same time based on the real-time dynamic monitoring of the groundwater monitoring wells in the target data area, including the groundwater monitoring level, the permeability coefficient, and the water temperature.

[0070] The model construction module 340 is configured to: perform a logistic regression analysis based on the second interpreted groundwater level, the groundwater monitoring data, and the geological parameters of the target data area to construct a fitting function mathematical model that correlates the second interpreted groundwater level and the groundwater monitoring level; wherein, the geological parameters include the permeability, the aquifer thickness, and the aquifer porosity.

[0071] The water level acquisition module 350 is configured to: use the constructed fitting function mathematical model and input the first interpreted groundwater level to acquire the groundwater level of the target monitoring blank area.

[0072] The monitoring system 300 proposed in the embodiments of the present invention solves the problem of difficult groundwater monitoring in the monitoring blank area through the technical route of "remote sensing data + traditional monitoring + mathematical model", realizing large-scale, high-efficiency, and low-cost groundwater dynamic monitoring, and is particularly suitable for groundwater monitoring in monitoring blank areas such as complex terrain and uninhabited areas.

[0073] Next, in combination with Figure 4 A groundwater monitoring device 400 provided in another aspect of the embodiments of the present application will be described.

[0074] Referring to Figure 4 An example monitoring device 400 for implementing the monitoring method of the embodiments of the present application will be described.

[0075] The monitoring device 400 may include a monitoring terminal 410, a drone 420, and a control device 430.

[0076] Specifically, the monitoring terminal 410 of the groundwater monitoring well installed in the existing data area, for example, uses various detection sensors to monitor the groundwater level, permeability coefficient, and water temperature of each existing data area in real time.

[0077] The unmanned aerial vehicle 420 equipped with a thermal imager is used to take thermal infrared images from the air that cover the target monitoring blank area and the existing data area, so as to obtain the required thermal infrared images that cover the target monitoring blank area and at least one existing data area in the same hydrogeological unit as the target monitoring blank area.

[0078] The control device 430 is communicatively connected to the monitoring terminal 410 and the unmanned aerial vehicle 420 respectively. The control device 430 includes a processor and a memory. When the processor executes the computer-executable instructions stored in the memory, the following steps are implemented: Obtain thermal infrared images that cover the target monitoring blank area and at least one existing data area in the same hydrogeological unit as the target monitoring blank area.

[0079] Identify the temperature anomaly area in the target monitoring blank area and obtain the first interpreted groundwater level; take the existing data area where the groundwater monitoring well closest to the target monitoring blank area is located as the target data area, identify the corresponding temperature anomaly area in the target data area, and obtain the second interpreted groundwater level.

[0080] Based on the real-time dynamic monitoring of the groundwater monitoring well in the target data area, obtain the groundwater monitoring data of the target data area at the same time, including the groundwater monitoring level, permeability coefficient, and water temperature.

[0081] Based on the second interpreted groundwater level, groundwater monitoring data, and geological parameters of the target data area, conduct a logistic regression analysis to construct a fitting function mathematical model that correlates the second interpreted groundwater level and the groundwater monitoring level; among them, the geological parameters include permeability, aquifer thickness, and aquifer porosity.

[0082] Using the constructed fitting function mathematical model, input the first interpreted groundwater level to obtain the groundwater level of the target monitoring blank area.

[0083] Regarding how to obtain the first interpreted groundwater level and the second interpreted groundwater level, and how to construct a fitting function mathematical model that correlates the second interpreted groundwater level and the groundwater monitoring level, the corresponding content is the same as that in the embodiments described above and will not be elaborated here.

[0084] Among them, the control device 430 can be installed at the groundwater monitoring well corresponding to the target data area or the remote monitoring center. For example, the control device 430 of the groundwater monitoring well in the target data area can be integrated with an AI chip (such as Jetson Nano, Kendryte K210) to complete preprocessing of thermal infrared images, water level interpretation analysis, groundwater level calculation, etc. locally, and only upload the results to the remote monitoring center or the cloud to reduce communication energy consumption.

[0085] Of course, in order to maintain the normal operation of the groundwater monitoring well end, a solar power supply unit 440 can be integrated into the monitoring terminal, for example, a flexible solar thin film battery is used. Combined with supercapacitor energy storage, it can achieve long-term maintenance-free operation, especially suitable for areas with sufficient sunlight such as deserts. When the control device is also set at the groundwater monitoring well end, the solar power supply unit 440 can also supply power to it. In areas without GPS signals, a geomagnetic sensor can be used to assist in positioning the device location.

[0086] In addition, according to the embodiments of the present application, the present application also provides a storage medium on which a computer program is stored, and when the computer program is run by a processor, it is used to execute the corresponding steps of the monitoring method 100 of the embodiments of the present application. The storage medium can include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium can be any combination of one or more computer-readable storage media.

[0087] In addition, according to the embodiments of the present application, the present application also provides a computer program product including computer instructions, and when the computer instructions are executed by a processor, the steps of the monitoring method of the embodiments of the present application are implemented.

[0088] Although example embodiments have been described herein with reference to the drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present application thereto. Those of ordinary skill in the art can make various changes and modifications therein without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as claimed in the appended claims.

[0089] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0090] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0091] In addition, those skilled in the art can understand that although some of the embodiments described herein include certain features included in other embodiments rather than other features, the combination of the features of different embodiments means that it is within the scope of this application and forms different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0092] It should be noted that the above embodiments illustrate rather than limit this application, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" before an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In the unit claims listing several devices, several of these devices can be embodied by the same hardware item. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names.

[0093] As described above, this is only the specific implementation manner of this application or the description of the specific implementation manner. The protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. The protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A groundwater level monitoring device, characterized in that: The monitoring device comprises: A monitoring terminal installed in a groundwater monitoring well in an existing data area, for real-time monitoring of groundwater level, permeability coefficient and water temperature; an existing data area refers to a selected area within the region that includes at least one groundwater monitoring well; Unmanned aerial vehicles (UAVs) are used to capture thermal infrared images from the air covering target surveillance blank areas and areas with existing data; and A control device is respectively connected to the monitoring terminal and the drone in communication, the control device comprising a processor and a memory, and when the processor executes the computer executable instructions stored in the memory, the following steps are implemented: Acquire a thermal infrared image covering the target monitoring blank area and at least one existing data area in the same hydrogeological unit as the target monitoring blank area; Identify the temperature anomaly area in the target monitoring blank area and obtain the first groundwater interpretation water level; take the existing data area where the groundwater monitoring well closest to the target monitoring blank area is located as the target data area, identify the corresponding temperature anomaly area in the target data area, and obtain the second groundwater interpretation water level; Based on the real-time dynamic monitoring of the groundwater monitoring wells in the target data area, the groundwater monitoring data of the target data area at the same time is obtained, including the groundwater monitoring water level, permeability coefficient and water temperature; A fitting function mathematical model correlating the second groundwater interpretation water level and the groundwater monitoring water level is constructed by performing logistic regression analysis based on the second groundwater interpretation water level, groundwater monitoring data and geological parameters of the target data area; wherein the geological parameters include permeability, aquifer thickness and aquifer porosity; Using the constructed fitting function mathematical model, the first groundwater interpretation water level is input to obtain the groundwater level in the target monitoring blank area.

2. The monitoring device according to claim 1, characterized in that: The control device is installed at the groundwater monitoring well corresponding to the target data area or at a remote monitoring center; and / or It also includes a solar power supply unit arranged in the existing data area, which is used to supply power to the monitoring terminal and / or the control device.

3. A method for monitoring groundwater level, characterized in that: The monitoring method comprises: Acquire a thermal infrared image covering the target monitoring blank area and at least one existing data area in the same hydrogeological unit as the target monitoring blank area from the thermal infrared image taken by the drone; wherein the existing data area refers to a selected area in the region including at least one groundwater monitoring well; Identify the temperature anomaly area in the target monitoring blank area and obtain the first groundwater interpretation water level; take the existing data area where the groundwater monitoring well closest to the target monitoring blank area is located as the target data area, identify the corresponding temperature anomaly area in the target data area, and obtain the second groundwater interpretation water level; Based on the real-time dynamic monitoring of the groundwater monitoring wells in the target data area, the groundwater monitoring data of the target data area at the same time are obtained, including the groundwater monitoring water level, permeability coefficient and water temperature; A fitting function mathematical model correlating the second groundwater interpretation water level and the groundwater monitoring water level is constructed by performing logistic regression analysis based on the second groundwater interpretation water level, groundwater monitoring data and geological parameters of the target data area; wherein the geological parameters include permeability, aquifer thickness and aquifer porosity; Using the constructed fitting function mathematical model, the first groundwater interpretation water level is input to obtain the groundwater level in the target monitoring blank area.

4. The monitoring method according to claim 3, characterized in that: Also includes: The original thermal infrared image covering the target monitoring blank area and at least one groundwater monitoring well is acquired in advance, and the area demarcated with the selected size with the groundwater monitoring well as the center is the existing data area; If there is an existing data area in the original thermal infrared image that is in the same hydrogeological unit as the target monitoring blank area, the original thermal infrared image is the required thermal infrared image.

5. The monitoring method according to claim 4, characterized in that: If all existing data areas are not in the same hydrogeological unit as the target monitoring blank area, the coverage of the original thermal infrared image is expanded until there is an existing data area in the same hydrogeological unit as the target monitoring blank area.

6. The monitoring method according to claim 3, characterized in that: The method also includes equalization, linear stretching and filtering of the acquired thermal infrared image to make the grayscale value of the image more evenly distributed within the entire image range.

7. The monitoring method according to claim 3, characterized in that: Obtaining a first groundwater interpretation water level and / or a second groundwater interpretation water level specifically includes: The SVM-random forest hybrid model was introduced using the classification algorithm, combined with wavelet denoising to eliminate the instantaneous interference of surface temperature and obtain the corresponding groundwater interpretation water level.

8. The monitoring method according to claim 3, characterized in that: The construction of the fitting function mathematical model correlating the second groundwater interpretation water level and the groundwater monitoring water level comprises: Establish a mathematical model between the groundwater monitoring level and the second groundwater interpretation level, in, To monitor the water level of groundwater, Interpret the water level for the second groundwater, It is one of the groundwater monitoring water level, groundwater permeability coefficient, groundwater temperature, and the permeability, aquifer thickness and aquifer porosity of the target data area. For the corresponding The weight coefficient of Using multiple sets of second groundwater interpretation water levels, groundwater monitoring water levels, groundwater permeability coefficients, groundwater temperatures, and permeability, aquifer thickness, and aquifer porosity data of the target data area at the same time, a computer is used to calculate and adjust the coefficients in the model until the calculation results are close to the measured data; Reselect at least one set of second groundwater interpretation water level, groundwater monitoring water level, groundwater permeability coefficient, groundwater temperature, and permeability, aquifer thickness and aquifer porosity data of the target data area to verify the model; if the verification fails, repeat the above steps.

9. The monitoring method according to claim 3, characterized in that: Based on thermal infrared images, a semantic segmentation network is used to directly extract temperature anomaly areas from thermal infrared images.

10. The monitoring method according to claim 3, characterized in that: Thermal infrared images are obtained by using drones equipped with thermal imagers.

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