Automatic monitoring and early warning method and device based on groundwater numerical simulation

Abstract

Embodiments of the present application relate to the technical field of groundwater environment monitoring, and particularly relate to an automatic monitoring and early warning method and device based on groundwater numerical simulation. A specific embodiment of the method comprises: performing data preprocessing on pre-acquired groundwater sensing data to obtain groundwater time series data; constructing a groundwater numerical model; inputting into the groundwater numerical model to obtain groundwater quality prediction information; performing rendering processing to obtain a groundwater pollution spatiotemporal feature map; determining risk index information corresponding to the groundwater quality prediction information; generating early warning information based on the risk index information and pre-set early warning threshold information, and sending the groundwater pollution spatiotemporal feature map and the early warning information to a monitoring center device for alarm processing. The embodiment can realize high-precision, forward-looking and fully-automatic groundwater risk early warning.

Description

Technical Field

[0001] The embodiments of this application relate to the field of groundwater environment monitoring technology, specifically to an automatic monitoring and early warning method and device based on groundwater numerical simulation. Background Technology

[0002] Groundwater monitoring is a technical means for management departments to implement long-term protection by dynamically tracking data such as groundwater levels and quality, aiming to understand the dynamic changes in groundwater. Currently, groundwater environmental monitoring typically employs methods such as manual monitoring and offline analysis, relying on threshold comparisons to monitor groundwater pollution.

[0003] However, in practice, the following technical problems often arise when using the above methods for groundwater environmental monitoring: (1) It mainly relies on manual monitoring and offline analysis, resulting in data lag and inability to respond in real time; (2) It relies heavily on threshold comparison, lacks physical process simulation, and has low prediction accuracy and high false alarm and false negative rates; (3) The numerical model is disconnected from the monitoring data, the parameters and boundaries cannot be updated automatically, and the model is inaccurate for a long time; (4) There is no closed-loop process, and there is no automatic response and feedback correction after the warning, which makes it difficult to support emergency decision-making. Summary of the Invention

[0004] The summary section of this application is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description section below. This summary section is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0005] Some embodiments of this application propose an automatic monitoring and early warning method, apparatus, computer equipment, and computer-readable storage medium based on groundwater numerical simulation to solve one or more of the technical problems mentioned in the background section above.

[0006] In a first aspect, some embodiments of this application provide an automatic monitoring and early warning method based on groundwater numerical simulation. The method includes: preprocessing pre-acquired groundwater sensor data to obtain groundwater time-series data; constructing a groundwater numerical model based on a pre-acquired sample groundwater sensor data sequence set; inputting the groundwater time-series data into the groundwater numerical model to obtain groundwater quality prediction information; rendering the groundwater quality prediction information to obtain a groundwater pollution spatiotemporal feature map; determining the risk index information corresponding to the groundwater quality prediction information; generating early warning information based on the risk index information and a pre-set early warning threshold; and sending the groundwater pollution spatiotemporal feature map and the early warning information to a monitoring center device for alarm processing.

[0007] Secondly, some embodiments of this application provide an automatic monitoring and early warning device based on groundwater numerical simulation. The device includes: a data preprocessing unit configured to preprocess pre-acquired groundwater sensor data to obtain groundwater time-series data; a construction unit configured to construct a groundwater numerical model based on a pre-acquired sample groundwater sensor data sequence set; an input unit configured to input the groundwater time-series data into the groundwater numerical model to obtain groundwater quality prediction information; a rendering unit configured to render the groundwater quality prediction information to obtain a groundwater pollution spatiotemporal feature map; a determination unit configured to determine the risk index information corresponding to the groundwater quality prediction information; and a generation unit configured to generate early warning information based on the risk index information and a pre-set early warning threshold information, and to send the groundwater pollution spatiotemporal feature map and the early warning information to a monitoring center device for alarm processing.

[0008] Thirdly, some embodiments of this application provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0009] Fourthly, some embodiments of this application provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0010] The above-described embodiments of this application have the following beneficial effects: The automatic monitoring and early warning method based on groundwater numerical simulation, as described in some embodiments of this application, can achieve automatic data acquisition and real-time transmission by deploying multi-source monitoring equipment for water level, water quality, etc.; it couples real-time monitoring data with a groundwater numerical simulation model, dynamically correcting model parameters and boundary conditions through a data assimilation algorithm to ensure the model remains consistent with the actual hydrogeological state; it uses the updated model to dynamically extrapolate and assess future changes in water level, water quality, and flow field; it automatically issues graded early warnings based on preset thresholds, and links control equipment and emergency platforms to form a fully automatic closed-loop early warning system of "monitoring—simulation—prediction—early warning—feedback," achieving accurate, early, and intelligent early warning of groundwater risks. This aims to solve the problems of poor real-time performance, inaccurate prediction, low automation, and lack of dynamic closed-loop in existing technologies, providing an integrated automatic monitoring and early warning method to achieve high-precision, forward-looking, and fully automatic groundwater risk early warning. It links multi-source automatic monitoring, groundwater numerical simulation, data assimilation, dynamic prediction, and graded early warning to form a closed-loop system with real-time drive, model self-updating, early warning, and automatic response. Therefore, we can: (1) integrate monitoring, simulation and assimilation: real-time data automatically drives model updates, rather than offline calibration. (2) physical model + data-driven fusion: more stable and more interpretable than pure statistical prediction. (3) fully automated closed loop: from data collection to early warning and then to feedback, no manual intervention is required. (4) dynamic early warning threshold: adaptively adjusted according to the model and trend, reducing the false alarm rate. Attached Figure Description

[0011] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0012] Figure 1 This is a flowchart of some embodiments of the automatic monitoring and early warning method based on groundwater numerical simulation according to this application; Figure 2 This is a schematic diagram of the system structure of some embodiments of the automatic monitoring and early warning method based on groundwater numerical simulation according to this application; Figure 3 These are design diagrams of a receiving module according to some embodiments of the automatic monitoring and early warning method based on groundwater numerical simulation of this application; Figure 4 This is a schematic diagram of the groundwater pollution simulation and prediction workflow according to some embodiments of the automatic monitoring and early warning method based on groundwater numerical simulation of this application. Figure 5This is a schematic diagram of the early warning configuration of some embodiments of the automatic monitoring and early warning method based on groundwater numerical simulation according to this application; Figure 6 These are schematic diagrams of some embodiments of the automatic monitoring and early warning device based on groundwater numerical simulation according to this application; Figure 7 This is a schematic diagram of the structure of a computer device suitable for implementing some embodiments of this application. Detailed Implementation

[0013] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0014] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0015] It should be noted that the concepts of "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0016] It should be noted that the terms "a" and "a plurality of" used in this application are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0017] The names of the messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0018] The present application will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] Figure 1 A flowchart 100 of some embodiments of the automatic monitoring and early warning method based on groundwater numerical simulation according to this application is shown. The automatic monitoring and early warning method based on groundwater numerical simulation includes the following steps: Step 101: Perform data preprocessing on the pre-acquired groundwater sensing data to obtain groundwater time series data.

[0020] In some embodiments, the implementing entity of the automatic monitoring and early warning method based on groundwater numerical simulation can preprocess pre-acquired groundwater sensor data to obtain groundwater time-series data. Groundwater sensor data can be acquired from a groundwater database via wired or wireless connections. The groundwater database can be a database used to store groundwater sensor data. The groundwater sensor data can be data collected by groundwater monitoring sensors. The groundwater sensor data includes a set of groundwater quality index values. The groundwater quality index values ​​in the set of groundwater quality index values ​​can characterize a groundwater quality index.

[0021] Specifically, the aforementioned implementing entities can be referenced. Figure 2 The diagram shows a system structure schematic of some embodiments of the automatic monitoring and early warning method based on groundwater numerical simulation according to this application. The method for obtaining groundwater sensor data from the groundwater database described above can be found in [reference needed]. Figure 2 The diagram shows a design of a receiving module according to some embodiments of the automatic monitoring and early warning method based on groundwater numerical simulation according to this application. Figure 2 As shown, an unattended management model can be adopted to achieve automatic collection and transmission of well water level and water quality information. The automatic groundwater monitoring station employs a combination of self-reporting and query-response telemetry methods, and a working mode compatible with timed self-reporting, event-based reporting, and public monitoring. Sensing terminals can include different types and functions of real-time remote monitoring equipment. Through a unified IoT receiving module, information such as water level, water quality, and equipment operating conditions from different monitoring devices can be collected and received uniformly, achieving unified aggregation, unified filtering, unified calculation, and unified management.

[0022] Specifically, the detailed steps for obtaining groundwater sensor data from the groundwater database can be found in [reference needed]. Figure 3 The diagram shows a design of a receiving module according to some embodiments of the automatic monitoring and early warning method based on groundwater numerical simulation according to this application.

[0023] As an example, groundwater monitoring sensors may include, but are not limited to, water level sensors and water quality sensors. The aforementioned groundwater quality indicators may be, but are not limited to, at least one of the following: pH value, dissolved oxygen, trace element concentration, or organic pollutant concentration.

[0024] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future wireless connection methods.

[0025] In some optional implementations of certain embodiments, the execution entity performs data preprocessing on the pre-acquired groundwater sensing data to obtain groundwater time-series data, which may include the following steps: The first step is to acquire historical groundwater sensor data. This historical groundwater sensor data includes a set of historical groundwater quality index values. This historical groundwater sensor data can be obtained from the aforementioned groundwater database. This historical groundwater sensor data represents data collected by groundwater monitoring sensors at historical times. The historical groundwater quality index values ​​in the aforementioned historical groundwater quality index value set represent a specific groundwater quality index at that historical time.

[0026] As an example, the aforementioned historical moment could be a moment prior to the current moment by a preset duration. The preset duration could be, but is not limited to, at least one of the following: one hour, five hours, or one day.

[0027] The second step involves determining the anomaly range of the groundwater tank based on the historical groundwater quality index values ​​included in the aforementioned historical groundwater sensor data. Specifically, the anomaly range of the groundwater tank can be determined using a pre-defined anomaly range determination algorithm based on the historical groundwater quality index values ​​included in the aforementioned historical groundwater sensor data.

[0028] As an example, the above-mentioned preset abnormal interval determination algorithm can be a box plot algorithm.

[0029] The third step is to remove all groundwater quality index values ​​outside the abnormal interval of the groundwater tank from the groundwater quality index value set, which are included in the groundwater sensing data, and to determine the set of groundwater quality index values ​​after deletion as the set of groundwater quality time series index values ​​included in the groundwater time series data.

[0030] Optionally, the aforementioned implementing entity may also perform the following steps: The first step is to add the above groundwater sensing data to the historical groundwater sensing data to obtain the updated historical groundwater sensing data.

[0031] The second step is to identify the updated historical groundwater sensor data as historical groundwater sensor data so that the above steps for determining the abnormal range of the groundwater tank can be performed again.

[0032] Therefore, the built-in boxplot algorithm can automatically remove outliers and impute missing data to form a standardized time-series dataset. The normal range of the boxplot can be dynamically updated based on recent historical data, making the identification of outliers clear and straightforward.

[0033] Step 102: Construct a groundwater numerical model based on the pre-acquired sample groundwater sensor data sequence set.

[0034] In some embodiments, the aforementioned executing entity can construct a groundwater numerical model based on a pre-acquired sample groundwater sensor data sequence set. This sample groundwater sensor data sequence set can be obtained from a groundwater database. The sample groundwater sensor data in the aforementioned sample groundwater sensor data sequence set can characterize data collected by groundwater monitoring sensors at historical moments. The aforementioned groundwater numerical model can be a pre-trained model that takes groundwater time-series data as input and groundwater quality prediction information as output. The aforementioned groundwater numerical model can include: a groundwater flow finite element model, a groundwater solute migration finite element model, and a water quality anomaly detection model. The aforementioned groundwater flow finite element model can be a pre-constructed numerical model that takes groundwater time-series data as input and groundwater flow simulation data as output. The aforementioned groundwater solute migration finite element model can be a pre-constructed numerical model that takes groundwater time-series data as input and groundwater solute simulation data as output. The aforementioned water quality anomaly detection model can be a pre-built neural network model that takes groundwater flow simulation data and groundwater solute simulation data as input and groundwater pollution source tracing information as output.

[0035] As an example, the aforementioned historical time of the sample can be a time prior to the current time within a preset sample duration. The preset sample duration can be, but is not limited to, at least one of the following: one month, one year, or two years. The aforementioned finite element model of groundwater flow can be a CAE (Computer-Aided Engineering) simulation model. The aforementioned finite element model of groundwater solute migration can be a CAE simulation model. The aforementioned water quality anomaly detection model can be, but is not limited to, at least one of the following: a GNN (Graph Neural Network) model, a GCN (Graph Convolutional Network) model, or a KNN (K-Nearest Neighbors) model.

[0036] Therefore, finite element models of groundwater flow and groundwater solute migration can be established. By accessing online monitoring data, such as monitoring well water level and water quality data, online real-time simulation of groundwater quality diffusion can be achieved. The simulation results of water quality diffusion can be used as training datasets, and pollution source tracing can be carried out through deep machine learning methods.

[0037] Optionally, the implementing entity can also establish a spatial three-dimensional model based on a pre-acquired target area map using three-dimensional modeling methods. The target area map can be a map of the area where automatic groundwater monitoring and early warning are desired.

[0038] Therefore, a three-dimensional model of the surface and underground space can be established (based on data such as borehole profiles), enabling a three-dimensional display of the target area both above and below ground. Here, the aforementioned target area can be the area where groundwater monitoring is desired.

[0039] In some optional implementations of certain embodiments, the execution entity constructs a groundwater numerical model based on a pre-acquired set of sample groundwater sensor data sequences, which may include the following steps: The first step is to construct a finite element model of groundwater flow based on the above-mentioned sample groundwater sensor data sequence set.

[0040] The second step involves constructing a finite element model of groundwater solute migration based on the aforementioned sample groundwater sensor data sequence set. The specific implementation method for generating this finite element model and its resulting technical effects can be found in step 102 of the above embodiments, and will not be repeated here.

[0041] The third step is to construct a water quality anomaly detection model based on the aforementioned sample groundwater sensor data sequence set. The specific implementation method for generating the water quality anomaly detection model and its resulting technical effects can be found in step 102 of the above embodiments, and will not be repeated here.

[0042] The fourth step involves fusing the groundwater flow finite element model, the groundwater solute migration finite element model, and the water quality anomaly detection model to obtain a groundwater numerical model. Specifically, fusing these three models can be defined as the groundwater flow finite element model, the groundwater solute migration finite element model, and the water quality anomaly detection model included in the groundwater numerical model.

[0043] In some optional implementations of certain embodiments, the execution entity constructs a groundwater flow finite element model based on the aforementioned sample groundwater sensor data sequence set. It can select a target sample groundwater sensor data sequence from the sample groundwater sensor data sequence set and perform the following construction steps: The first step involves inputting all target sample groundwater sensor data (excluding the last target sample) from the target sample groundwater sensor data sequence into the initial groundwater flow finite element model to obtain the initial groundwater flow simulation data. Specifically, a sample groundwater sensor data sequence can be randomly selected from the aforementioned set of sample groundwater sensor data sequences as the target sample groundwater sensor data sequence. The initial groundwater flow finite element model can be an unadjusted finite element numerical model that uses all target sample groundwater sensor data (excluding the last target sample) as input and the initial groundwater flow simulation data as output.

[0044] The second step involves comparing the initial groundwater flow simulation data with the groundwater sensing data of the last target sample in the target sample groundwater sensing data sequence to obtain the simulation comparison results. Specifically, a preset comparison algorithm can be used to compare the initial groundwater flow simulation data with the groundwater sensing data of the last target sample in the target sample groundwater sensing data sequence to obtain the simulation comparison results.

[0045] As an example, the aforementioned preset comparison algorithm may be, but is not limited to, at least one of the following: root mean square error algorithm, Nash efficiency coefficient algorithm, coefficient of determination algorithm, or visual comparison algorithm.

[0046] The second step involves adjusting the initial groundwater flow finite element model based on the simulation comparison results, resulting in an adjusted initial groundwater flow finite element model. This adjustment can be achieved using a pre-defined algorithm based on the simulation comparison results.

[0047] As an example, the aforementioned preset adjustment algorithm may be, but is not limited to, at least one of the following: PEST (Parameter Estimation), genetic algorithm, or simulated annealing algorithm.

[0048] The third step is to remove the target sample groundwater sensing data sequence from the above sample groundwater sensing data sequence set to obtain the deleted sample groundwater sensing data sequence set.

[0049] The fourth step involves determining that the deleted sample groundwater sensor data sequence set meets the preset adjustment conditions, and then defining the adjusted initial groundwater flow finite element model as the groundwater flow finite element model. The preset adjustment conditions can include the deleted sample groundwater sensor data sequence set being empty.

[0050] Optionally, the aforementioned execution entity may also, in response to determining that the deleted sample groundwater sensor data sequence set does not meet the preset adjustment conditions, determine the adjusted initial groundwater flow finite element model as the initial groundwater flow finite element model, and select the target sample groundwater sensor data sequence from the aforementioned sample groundwater sensor data sequence set, from each unselected sample groundwater sensor data sequence, for re-execution of the aforementioned construction steps.

[0051] Specifically, groundwater pollution simulation and prediction assessment can be based on the results of groundwater environmental investigation and evaluation, and can involve the construction of a conceptual model of groundwater pollution, simulation of the current state of groundwater pollution, and prediction of groundwater pollution trends. The main steps of groundwater pollution simulation and prediction assessment include the construction of a conceptual model of groundwater pollution, simulation of the current state of groundwater pollution, prediction of groundwater pollution trends, and preparation of a technical report. For a detailed workflow, please refer to [reference needed]. Figure 4 The diagram illustrates a groundwater pollution simulation and prediction workflow according to some embodiments of the automatic monitoring and early warning method based on groundwater numerical simulation of this application.

[0052] The purpose of constructing a conceptual model for groundwater pollution is to collect relevant data, analyze the results of groundwater environmental surveys and assessments, generalize the hydrogeological conditions of the assessment area, clarify the aquifer media and groundwater flow characteristics of the assessment area, generalize the groundwater pollution characteristics of the assessment area, clarify the relationship between pollution sources, pollution pathways, and pollution receptors, identify the processes and representative factors involved in pollutant migration and transformation, and form a conceptual model of groundwater pollution. (Based on a thorough analysis of hydrogeological conditions and pollution characteristics, this study constructed a conceptual model of groundwater pollution, which generalizes the aquifer structure, groundwater flow field, pollution source strength, and pollutant migration and transformation mechanisms, providing a basis for numerical simulation). Based on the conceptual model of groundwater pollution, the focus of simulation prediction and assessment work and quantitative analysis methods were determined. Following the principle of incremental work, appropriate mathematical modeling tools were selected to simulate groundwater flow characteristics and pollutant migration and transformation processes, and the reliability of the model was verified. Specific work included: model tool selection, application of analytical methods, numerical model creation, and calibration verification. Based on the assessment objectives, a reasonable simulation scenario is designed, and a validated and reliable model is used to predict groundwater pollution trends, quantitatively expressing the development trend of groundwater pollution, and further assessing the rate and scope of pollution diffusion, the impact on pollution receptors, and the environmental benefits of different pollution control measures. Specifically, based on groundwater flow models and groundwater solute migration models (CAE simulation calculation models), the migration and transformation process of pollutants is dynamically simulated according to information such as the location of groundwater pollution accidents, pollutant types, leakage volume, leakage mode (instantaneous or continuous), and hydrogeological conditions at the time. The concentration distribution of pollutants and the time it takes for pollutants to reach sensitive points (such as groundwater sources) are predicted. The duration and spatial distribution of pollution zones exceeding specified thresholds are calculated. Combined with changes in water quality trends, water quality standard indices, and the number of consecutive uplifts, the impact area and degree of impact are comprehensively assessed, providing support for emergency response measures and risk management.

[0053] Step 103: Input the groundwater time series data into the above groundwater numerical model to obtain groundwater quality prediction information.

[0054] In some embodiments, the executing entity may input the aforementioned groundwater time-series data into the aforementioned groundwater numerical model to obtain groundwater quality prediction information. The aforementioned groundwater quality prediction information may include: groundwater pollutant concentration values.

[0055] Step 104: Render the groundwater quality prediction information to obtain a spatiotemporal feature map of groundwater pollution.

[0056] In some embodiments, the aforementioned executing entity can render the aforementioned groundwater quality prediction information to obtain a spatiotemporal feature map of groundwater pollution. Specifically, an image rendering algorithm can be used to render the aforementioned groundwater quality prediction information onto a map to obtain the spatiotemporal feature map of groundwater pollution.

[0057] Specifically, the spatiotemporal variations of pollutant diffusion are presented on a map. Concentration field data of pollutants at different times are obtained through numerical simulation, and GIS (Geographical Information System) spatial rendering technology is used to visualize the pollutant diffusion process on the map. Spatially, the concentration distribution is rendered with hierarchical colors, clearly showing the spatial characteristics of the pollution plume's migration along the flow direction, lateral diffusion, and decreasing concentration gradient. Temporally, by comparing renderings from multiple time periods, the variation patterns of pollutant diffusion range, peak concentration, and affected area over time are depicted. The model calculation server enables control over the location and concentration of potential pollution sources, achieving the function of predicting pollutant transport under specific pollution scenarios.

[0058] The system can construct a daily management system for risk sources, targeting key enterprises, landfills, gas stations, golf courses, and centralized groundwater drinking water sources. It can also collect various environmental risk source information data, including enterprise information, basic information on risk units, accident handling facilities, environmental emergency response and rescue resources, and risk prevention measures.

[0059] Step 105: Determine the risk index information corresponding to the groundwater quality prediction information.

[0060] In some embodiments, the aforementioned implementing entity determines the risk index information corresponding to the aforementioned groundwater quality prediction information.

[0061] For example, determining the risk index information corresponding to groundwater quality prediction information can be done by, in response to the determination that the groundwater pollutant concentration value included in the groundwater quality prediction information is greater than the water quality standard, identifying the information characterizing "pollution exceeding the standard" as risk index information.

[0062] Step 106: Based on the risk index information and the pre-set early warning threshold information, generate early warning information, and send the groundwater pollution spatiotemporal feature map and early warning information to the monitoring center equipment for alarm processing.

[0063] In some embodiments, the aforementioned executing entity may generate early warning information based on the aforementioned risk index information and the pre-set early warning threshold information, and send the aforementioned groundwater pollution spatiotemporal feature map and the aforementioned early warning information to the monitoring center equipment for alarm processing.

[0064] Specifically, the aforementioned implementing entities determine the risk index information corresponding to the aforementioned groundwater quality prediction information, which can be referenced. Figure 5 The diagram illustrates the early warning configuration of some embodiments of the automatic monitoring and early warning method based on groundwater numerical simulation according to this application. (See attached diagram.) Figure 5As shown, yellow or red alerts can be generated based on the changing trends of monitoring values ​​included in the groundwater quality prediction information. The aforementioned monitoring center equipment can be a device used to display warning text or issue alert sounds in response to receiving the aforementioned alert information.

[0065] As an example, the aforementioned warning threshold information can be information characterizing a monitoring threshold. This monitoring threshold could be the average of monitoring values ​​over the previous 15 days.

[0066] Optionally, the aforementioned implementing entity may also perform the following steps: The first step is to obtain the accuracy rate of early warnings and the results of early warning processing from the monitoring center equipment.

[0067] As an example, the accuracy rate of the aforementioned early warning could be 90%. The result of the aforementioned early warning processing could be information indicating that "pollutant treatment is complete".

[0068] The second step involves adjusting the warning threshold information and relevant parameters in the groundwater numerical model based on the aforementioned warning accuracy and processing results. This adjustment can be achieved using the preset adjustment algorithm described above.

[0069] The third step is to determine the adjusted groundwater numerical model and early warning threshold information as groundwater numerical model and early warning threshold information, respectively, so that the above input steps can be executed again.

[0070] Therefore, the above-mentioned automatic monitoring and early warning method based on groundwater numerical simulation can achieve the following beneficial effects: (1) Improved real-time performance: minute-level response, with efficiency improved by more than 80% compared to traditional methods; (2) Improved prediction accuracy: water level prediction error ≤0.15m, risk identification accuracy ≥95%; (3) Reduced false alarm rate: adaptive threshold and data assimilation, false alarm rate ≤3%; (4) Full automation: reduced manpower input, can operate stably without human intervention for a long time; (5) Forward-looking early warning: predicts risks 72 hours in advance, reserving time for emergency response.

[0071] Further reference Figure 6 As an implementation of the methods shown in the above figures, this application provides some embodiments of an automatic monitoring and early warning device based on groundwater numerical simulation. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this automatic monitoring and early warning device based on groundwater numerical simulation can be specifically applied to various electronic devices.

[0072] like Figure 6As shown, an automatic monitoring and early warning device 600 based on groundwater numerical simulation in some embodiments includes: a data preprocessing unit 601, a construction unit 602, an input unit 603, a rendering unit 604, a determination unit 605, and a generation unit 606. The system includes a data preprocessing unit 601, configured to preprocess pre-acquired groundwater sensor data to obtain groundwater time-series data; a construction unit 602, configured to construct a groundwater numerical model based on a pre-acquired sample groundwater sensor data sequence set; an input unit 603, configured to input the groundwater time-series data into the groundwater numerical model to obtain groundwater quality prediction information; a rendering unit 604, configured to render the groundwater quality prediction information to obtain a groundwater pollution spatiotemporal feature map; a determination unit 605, configured to determine the risk index information corresponding to the groundwater quality prediction information; and a generation unit 606, configured to generate early warning information based on the risk index information and a pre-set early warning threshold information, and to send the groundwater pollution spatiotemporal feature map and the early warning information to the monitoring center equipment for alarm processing.

[0073] It is understandable that the units and references described in the automatic monitoring and early warning device 600 based on groundwater numerical simulation are... Figure 1 The steps described in the method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the automatic monitoring and early warning device 600 based on groundwater numerical simulation and the units contained therein, and will not be repeated here.

[0074] The following is for reference. Figure 7 It shows a schematic diagram of the structure of an electronic device (e.g., a computing device) 700 suitable for implementing some embodiments of this application. Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this application.

[0075] like Figure 7 As shown, the electronic device 700 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory 702 or a program loaded from a storage device 708 into a random access memory 703. The random access memory 703 also stores various programs and data required for the operation of the electronic device 700. The processing unit 701, the read-only memory 702, and the random access memory 703 are interconnected via a bus 704. An input / output interface 705 is also connected to the bus 704.

[0076] Typically, the following devices can be connected to the input / output interface 705: input devices 706 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 707 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 708 including, for example, magnetic tape, hard disk, etc.; and communication devices 709. Communication device 709 allows electronic device 700 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 7 An electronic device 700 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 7 Each box shown can represent a device or multiple devices as needed.

[0077] In particular, according to some embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 709, or installed from storage device 708, or installed from read-only memory 702. When the computer program is executed by processing device 701, it performs the functions defined above in the methods of some embodiments of this application.

[0078] It should be noted that, in some embodiments of this application, the computer-readable medium described may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0079] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0080] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: preprocess pre-acquired groundwater sensor data to obtain groundwater time-series data; construct a groundwater numerical model based on a pre-acquired sample groundwater sensor data sequence set; input the aforementioned groundwater time-series data into the aforementioned groundwater numerical model to obtain groundwater quality prediction information; render the aforementioned groundwater quality prediction information to obtain a groundwater pollution spatiotemporal feature map; determine the risk index information corresponding to the aforementioned groundwater quality prediction information; generate early warning information based on the aforementioned risk index information and pre-set early warning threshold information; and send the aforementioned groundwater pollution spatiotemporal feature map and the aforementioned early warning information to the monitoring center equipment for alarm processing.

[0081] Computer program code for performing operations of some embodiments of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0082] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0083] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0084] The above description is merely a selection of preferred embodiments of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this application.

Claims

1. An automatic monitoring and early warning method based on groundwater numerical simulation, comprising: The previously acquired groundwater sensor data is preprocessed to obtain groundwater time-series data; A groundwater numerical model is constructed based on a pre-acquired set of sample groundwater sensor data sequences. The groundwater time series data is input into the groundwater numerical model to obtain groundwater quality prediction information; The groundwater quality prediction information is rendered to obtain a spatiotemporal feature map of groundwater pollution. Determine the risk index information corresponding to the groundwater quality prediction information; Based on the risk index information and the pre-set early warning threshold information, an early warning message is generated, and the spatiotemporal feature map of groundwater pollution and the early warning message are sent to the monitoring center equipment for alarm processing.

2. The automatic monitoring and early warning method based on groundwater numerical simulation according to claim 1, characterized in that, The method further includes: Obtain early warning accuracy and early warning processing results from the monitoring center equipment; Based on the early warning accuracy and the early warning processing results, adjust the early warning threshold information and relevant parameters in the groundwater numerical model; The adjusted groundwater numerical model and early warning threshold information are determined as groundwater numerical model and early warning threshold information, respectively, for re-execution of the input step.

3. The automatic monitoring and early warning method based on groundwater numerical simulation according to claim 1, characterized in that, The groundwater sensing data includes: a set of groundwater quality index values; and the step of preprocessing the pre-acquired groundwater sensing data to obtain groundwater time-series data includes: Acquire historical groundwater sensor data, wherein the historical groundwater sensor data includes: a set of historical groundwater quality index values; Based on the historical groundwater quality index value set included in the historical groundwater sensor data, the abnormal interval of the groundwater tank is determined. The groundwater quality index values ​​included in the groundwater sensing data that are concentrated outside the abnormal interval of the groundwater tank are deleted from the groundwater quality index value set, and the deleted groundwater quality index value set is determined as the groundwater quality time series index value set included in the groundwater time series data.

4. The automatic monitoring and early warning method based on groundwater numerical simulation according to claim 3, characterized in that, The method further includes: The groundwater sensing data is added to the historical groundwater sensing data to obtain updated historical groundwater sensing data; The updated historical groundwater sensor data is identified as historical groundwater sensor data for re-execution of the step of determining the abnormal range of the groundwater tank.

5. The automatic monitoring and early warning method based on groundwater numerical simulation according to claim 1, characterized in that, The construction of a groundwater numerical model based on a pre-acquired set of sample groundwater sensor data includes: Based on the sample groundwater sensor data sequence set, a finite element model of groundwater flow is constructed; Based on the sample groundwater sensor data sequence set, a finite element model of groundwater solute migration is constructed. Based on the groundwater sensor data sequence set of the sample, a water quality anomaly detection model is constructed; The finite element model of groundwater flow, the finite element model of groundwater solute migration, and the water quality anomaly detection model are fused together to obtain a groundwater numerical model.

6. The automatic monitoring and early warning method based on groundwater numerical simulation according to claim 5, characterized in that, The construction of a finite element model of groundwater flow based on the sample groundwater sensor data sequence set includes: Select the target sample groundwater sensing data sequence from the sample groundwater sensing data sequence set, and perform the following construction steps: The groundwater sensing data of each target sample, except for the last target sample groundwater sensing data, are input into the initial groundwater flow finite element model to obtain the initial groundwater flow simulation data. The simulation data of the initial groundwater flow is compared with the groundwater sensing data of the last target sample in the target sample groundwater sensing data sequence to obtain the simulation comparison results. Based on the simulation comparison results, the initial groundwater flow finite element model was adjusted to obtain the adjusted initial groundwater flow finite element model. The target sample groundwater sensing data sequence is deleted from the sample groundwater sensing data sequence set to obtain the deleted sample groundwater sensing data sequence set. In response to the determination that the deleted sample groundwater sensor data sequence set meets the preset adjustment conditions, the adjusted initial groundwater flow finite element model is determined as the groundwater flow finite element model.

7. The automatic monitoring and early warning method based on groundwater numerical simulation according to claim 6, characterized in that, The method further includes: In response to the determination that the deleted sample groundwater sensor data sequence set does not meet the preset adjustment conditions, the adjusted initial groundwater flow finite element model is determined as the initial groundwater flow finite element model, and the target sample groundwater sensor data sequence is selected from the sample groundwater sensor data sequence set and from each unselected sample groundwater sensor data sequence for the construction step to be executed again.

8. An automatic monitoring and early warning device based on groundwater numerical simulation, characterized in that, include: The data preprocessing unit is configured to preprocess the pre-acquired groundwater sensing data to obtain groundwater time-series data. The building unit is configured to construct a groundwater numerical model based on a pre-acquired set of sample groundwater sensor data sequences. The input unit is configured to input the groundwater time series data into the groundwater numerical model to obtain groundwater quality prediction information. The rendering unit is configured to render the groundwater quality prediction information to obtain a spatiotemporal feature map of groundwater pollution. The determining unit is configured to determine the risk index information corresponding to the groundwater quality prediction information; The generation unit is configured to generate early warning information based on the risk index information and the pre-set early warning threshold information, and to send the groundwater pollution spatiotemporal feature map and the early warning information to the monitoring center equipment for alarm processing.

9. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 7.

10. A computer-readable medium, characterized in that, It stores a computer program, characterized in that the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 7.

Images