Groundwater environment monitoring well whole process data management method and system

Abstract

The invention discloses a groundwater environment monitoring well full-process data management method and system, and relates to the technical field of groundwater environment monitoring well investigation, and the method comprises the steps: obtaining multi-source investigation data of a groundwater environment monitoring well, carrying out the preprocessing, extracting defect semantic features according to the multi-source investigation data of a defective groundwater environment monitoring well, and obtaining the defect semantic features; performing multi-label identification on the groundwater environment monitoring well according to the defect semantic features, realizing comprehensive state evaluation and generating corresponding operation and maintenance information; and selecting the groundwater environment monitoring well meeting the requirements to read water quality detection data, extracting the spatial-temporal characteristics of the water quality detection data to predict the groundwater environment quality in the region, and generating a monitoring early warning according to the groundwater environment quality prediction result in the region. According to the invention, efficient implementation of groundwater monitoring well investigation and evaluation projects is ensured, the daily management capability of the groundwater monitoring well is improved, and the groundwater environment supervision capability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of groundwater environment monitoring well investigation, and more specifically, to a full-process data management method and system for groundwater environment monitoring wells. Background Art

[0002] As an important support for groundwater environmental monitoring work, the "health status" of groundwater environmental monitoring wells directly affects the accuracy of monitoring results. Therefore, orderly carrying out groundwater monitoring well investigation → groundwater monitoring well assessment → groundwater monitoring well management and maintenance → groundwater monitoring network construction → groundwater monitoring data long sequence analysis and display → groundwater monitoring early warning work can solidly promote groundwater monitoring work and enhance groundwater environmental supervision capabilities.

[0003] Monitoring well surveys are characterized by many categories, large numbers, rich survey content (including information entry, photography, signatures), and a large number of investigators and reviewers. When using traditional questionnaire surveys, there may be problems such as long field reporting time, large digitization workload, high error rate in survey information entry, and complex management. In order to avoid the above problems, a full-process intelligent data management system that includes multi-source data such as groundwater environmental monitoring well survey, assessment, management, maintenance, and automatic evaluation of groundwater quality data is needed. It can efficiently carry out the implementation of groundwater monitoring well survey and assessment projects, greatly improve work efficiency, and ensure project quality. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a full-process data management method and system for groundwater environment monitoring wells, which realizes full-process intelligent data management of multi-source data such as groundwater environment monitoring well investigation, assessment, management, maintenance and automatic evaluation of groundwater quality data.

[0005] A first aspect of the present invention provides a method for managing the full-process data of a groundwater environment monitoring well, comprising the following steps: Acquire multi-source survey data of groundwater environment monitoring wells and perform preprocessing, and conduct preliminary status assessment of the groundwater environment monitoring wells based on the preprocessed multi-source survey data; Read the substandard groundwater environment monitoring wells through the preliminary status assessment, extract the defect semantic features based on the multi-source survey data of the substandard groundwater environment monitoring wells, build a comprehensive status assessment model, perform multi-label recognition of the groundwater environment monitoring wells based on the defect semantic features, and perform comprehensive status assessment based on the defect category labels; Generate operation and maintenance information according to the evaluation results of the comprehensive status evaluation, select groundwater environment monitoring wells that meet the requirements to read water quality detection data, and extract the spatiotemporal characteristics of the water quality detection data; The groundwater environment quality in the region is predicted based on the temporal and spatial characteristics, and monitoring and early warning are generated based on the groundwater environment quality prediction results in the region.

[0006] In this scheme, multi-source survey data of groundwater environment monitoring wells are obtained and pre-processed, and a preliminary status assessment of the groundwater environment monitoring wells is performed based on the pre-processed multi-source survey data, specifically: Obtaining basic information, foundation conditions, structural conditions, and connectivity conditions of groundwater environment monitoring wells in the region as multi-source survey data of groundwater monitoring wells, merging the multi-source survey data, and matching them using unique identifiers; Perform data cleaning on the matched multi-source survey data, process duplicate data, outliers and missing values, and perform dimensionality reduction and standardization on the cleaned multi-source survey data to obtain pre-processed multi-source survey data; Read historical multi-source survey data according to the unique identifier corresponding to the groundwater environment monitoring well, obtain the status evaluation label corresponding to the historical multi-source survey data, and select the historical multi-source survey data that meets the monitoring function standard based on the status evaluation label; A generative adversarial network is trained according to the acquired historical multi-source survey data to obtain a generator model and a discriminator model, the acquired generator model is used as a decoder, and an autoencoder network is trained with the acquired historical multi-source survey data to obtain an encoder to map the pre-processed multi-source survey data to a latent space; Acquire latent feature samples corresponding to historical multi-source survey data closest to the pre-processed multi-source survey data in the latent space, and decode through a decoder to acquire reconstructed data that is similar to but not identical to the input multi-source survey data; The residuals of the preprocessed multi-source survey data and the reconstructed data are obtained, and the presence of defects in the groundwater environment monitoring well is determined based on the comparison results of the residuals with a preset difference threshold, so as to achieve a preliminary status assessment.

[0007] In this solution, the substandard groundwater environment monitoring wells are read through the preliminary status assessment, and the defect semantic features are extracted based on the multi-source survey data of the substandard groundwater environment monitoring wells, specifically: Obtain preliminary status estimation results of groundwater environment monitoring wells in the region, screen defective groundwater environment monitoring wells according to the preliminary status estimation results, and generate non-compliance labels; Extract multi-source survey data corresponding to groundwater environment monitoring wells with substandard labels, and use a preset shallow convolutional neural network to extract and compress defect features of the multi-source survey data. The shallow convolutional neural network consists of three convolutional layers, three maximum pooling layers and two fully connected layers. The one-dimensional feature vector of the substandard groundwater environment monitoring wells is obtained in the fully connected layer; Acquire monitoring well damage instances with severity labels based on historical multi-source survey data, perform cluster analysis on the monitoring well damage instances, obtain cluster clusters corresponding to the last clustering result after iterative clustering, generate defect data subsets, and determine the defect type corresponding to each defect data subset; In each defect data subset, defect attributes are extracted according to the monitoring well damage instance samples, and the defect attribute vectors are integrated to construct a defect attribute set. The one-dimensional feature vector is used for similarity calculation to select the n attribute vectors with the highest similarity in the defect attribute set, and the one-dimensional feature vector is concatenated with the selected attribute vector to generate defect semantic features.

[0008] In this solution, a comprehensive status assessment model is constructed to perform multi-label identification of groundwater environment monitoring wells based on the defect semantic features, and to perform comprehensive status assessment based on the defect category labels, specifically: A comprehensive state assessment model is constructed based on a defect semantic feature extraction module and a relationship network module. The relationship network module is composed of two fully connected layers. A training sample is constructed using monitoring well damage instance samples with severity annotations in each defect data subset. A relationship score of each training sample is obtained. The minimum mean square error of the unique hot encoding of the training sample is calculated based on the relationship score to perform model training. In the first fully connected layer, the defect semantic features obtained by the defect semantic feature extraction module are activated and output using the ReLU activation function, and in the second fully connected layer, the output of the first fully connected layer is mapped to the (0,1) interval using the Sigmoid function to generate a relationship score; Multi-label identification of defects corresponding to groundwater environment monitoring wells is performed based on the relationship scores, the defect category labels and corresponding severity of substandard groundwater environment monitoring wells are determined, and a comprehensive status assessment result is generated based on the defect category labels and severity based on a preset assessment threshold interval.

[0009] In this solution, operation and maintenance information is generated according to the evaluation results of the comprehensive status evaluation, specifically: Obtain comprehensive status evaluation results of substandard groundwater environment monitoring wells in the region, and select groundwater environment monitoring wells with poor monitoring functions and general monitoring functions for suspension based on the comprehensive status evaluation results; Obtain the defect category label of the highest severity corresponding to each underground environmental monitoring well in the suspended groundwater environmental monitoring wells with general monitoring functions, and obtain the severity trend change corresponding to the defect category label based on historical multi-source survey data; The severity trend change is compared with the trend change threshold, and the groundwater environment monitoring with general monitoring function that is less than the trend change threshold is unsuspended. Finally, operation and maintenance information is generated based on the unique identifiers corresponding to the suspended groundwater environment monitoring wells with poor monitoring functions and general monitoring functions, and sent and displayed according to a preset method.

[0010] In this scheme, groundwater environment monitoring wells that meet the requirements are selected to read water quality test data, and the spatiotemporal characteristics of the water quality test data are extracted, specifically: Obtain groundwater environment monitoring wells with good monitoring functions as shown in the comprehensive status assessment results in the region, extract water quality detection data sequences within a preset time, and calculate the Pearson correlation coefficient based on the water quality detection data sequences to obtain the correlation of monitoring data between groundwater environment monitoring wells; Determine the connection relationship between groundwater monitoring wells according to the distance between them and the correlation of monitoring data, generate the corresponding topological structure and represent it in a graph, take the groundwater monitoring wells with good monitoring functions in the area as graph nodes, and set the edge structure between nodes according to the connection relationship; An adjacency matrix is ​​constructed based on the edge structure, and the graph representation is learned using a graph attention network. The adjacency matrix is ​​used as input, and the attention weight between any two nodes in the neighborhood is obtained according to a multi-head self-attention mechanism. The adjacency matrix is ​​weightedly aggregated using the attention weight, and the feature representation is updated to obtain the spatial characteristics of the groundwater environmental quality in the region. The updated feature representation is imported into the gated recurrent unit, and the hidden state is used to update the temporal characteristics of the groundwater environmental quality in the region. The temporal attention mechanism is introduced to weight the attention of the temporal features at different moments. The weighted temporal features are fused with the spatial features after pooling operation to obtain the spatiotemporal characteristics of water quality detection data in the region.

[0011] In this scheme, the groundwater environmental quality in the region is predicted based on the temporal and spatial characteristics, and monitoring and early warning are generated based on the groundwater environmental quality prediction results in the region, specifically: A groundwater environmental quality prediction model is constructed based on a graph attention network and a gated recurrent unit, and the spatiotemporal characteristics of the number of water quality detections in the region are obtained through the graph attention network and the gated recurrent unit, and the spatiotemporal characteristics are passed through a fully connected layer to obtain the groundwater environmental quality prediction results in the region; Dynamic analysis is performed based on the groundwater environmental quality prediction results within the preset time period to obtain trend changes, set absolute thresholds and trend change thresholds, and when the obtained groundwater environmental quality prediction results or trend changes in the area are greater than the corresponding absolute thresholds or trend change thresholds, a monitoring warning is generated and sent and displayed in the preset manner.

[0012] The second aspect of the present invention provides a full-process data management system for groundwater environment monitoring wells, the system comprising: a multi-source survey data integration module, a monitoring well status assessment module, a monitoring well operation and maintenance early warning module, a groundwater environment quality analysis module and a groundwater monitoring early warning module; The multi-source survey data integration module is responsible for acquiring multi-source survey data of groundwater environment monitoring wells and performing pre-processing; The monitoring well status assessment module is responsible for judging whether the groundwater environment monitoring well has defects based on the preprocessed multi-source survey data, realizing preliminary status assessment, extracting defect semantic features based on the multi-source survey data of the groundwater environment monitoring well that does not meet the preliminary status assessment standard, building a comprehensive status assessment model, performing multi-label recognition of the groundwater environment monitoring well based on the defect semantic features, and performing comprehensive status assessment based on the defect category labels; The monitoring well operation and maintenance early warning module is responsible for generating operation and maintenance information according to the evaluation results of the comprehensive status evaluation, and sending and displaying it in a preset manner; The groundwater environment quality analysis module is responsible for selecting groundwater environment monitoring wells that meet the requirements in the region to read water quality detection data, extracting the spatiotemporal characteristics of the water quality detection data to predict the groundwater environment quality in the region; The groundwater monitoring and early warning module is responsible for generating water environment monitoring and early warning through the groundwater environment quality prediction results in the region, and sending and displaying them in a preset manner.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention realizes the full-process data management and monitoring and early warning of groundwater environment monitoring wells by integrating, preprocessing and evaluating the state of multi-source survey data corresponding to groundwater environment monitoring wells, and combining the extraction and prediction of spatiotemporal features of water quality detection data. Through the extraction and prediction of spatiotemporal features of water quality detection data, the trend of groundwater environment quality changes can be accurately predicted, and finally, through the monitoring and early warning mechanism, groundwater environmental problems can be discovered and handled in a timely manner to ensure the safety of the groundwater environment.

[0014] The present invention provides a full-process intelligent data management system for multi-source data such as groundwater environment monitoring well investigation, assessment, management, maintenance and automatic evaluation of groundwater quality data, which can efficiently carry out the implementation of groundwater monitoring well investigation and assessment projects, greatly improving work efficiency and work quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments or exemplary embodiments of the present invention, the drawings required for use in the embodiments or exemplary descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained according to the drawings without paying creative work.

[0016] Figure 1 A flow chart showing the full process data management method for groundwater environment monitoring wells; Figure 2 A flowchart for extracting semantic features of defects in substandard groundwater environment monitoring wells is shown; Figure 3 A flow chart showing the spatiotemporal characteristics of water quality test data extracted within a region; Figure 4 A block diagram of the full-process data management system for groundwater environment monitoring wells is shown. DETAILED DESCRIPTION

[0017] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0019] Figure 1 A flow chart showing the full-process data management method for groundwater environment monitoring wells.

[0020] like Figure 1 As shown, the first embodiment of the present invention provides a full-process data management method for groundwater environment monitoring wells, including: S102, obtaining multi-source survey data of groundwater environment monitoring wells and preprocessing them, and performing a preliminary status assessment on the groundwater environment monitoring wells based on the preprocessed multi-source survey data; S104, reading substandard groundwater environment monitoring wells through the preliminary status assessment, extracting defect semantic features based on multi-source survey data of substandard groundwater environment monitoring wells, building a comprehensive status assessment model, performing multi-label recognition of groundwater environment monitoring wells based on the defect semantic features, and performing comprehensive status assessment based on defect category labels; S106, generating operation and maintenance information according to the evaluation result of the comprehensive status evaluation, selecting groundwater environment monitoring wells that meet the requirements to read water quality detection data, and extracting the spatiotemporal characteristics of the water quality detection data; S108, predicting the groundwater environment quality in the region according to the spatiotemporal characteristics, and generating monitoring and early warning based on the groundwater environment quality prediction results in the region.

[0021] It should be noted that the basic information, basic conditions, structural conditions and connectivity conditions of groundwater environment monitoring wells in the region are obtained as multi-source survey data of groundwater monitoring wells. The basic information includes the name and number, monitoring well type, monitoring well address, the river basin to which it belongs, and the location relationship with the site. The basic conditions include the monitoring well type, wellhead protection device, well pipe type, upper well pipe preservation condition, well pipe outer diameter and wall thickness, well depth, etc. The structural conditions include the number of water intake sections, well wall pipe length, water filter pipe length, sedimentation pipe length, monitoring layer, etc. The connectivity conditions include the data of water permeability sensitivity experiment, oscillator details, sampling frequency, excitation head, water level recovery, etc. The multi-source survey data are merged and matched using a unique identifier (such as the groundwater environment monitoring well number) to ensure that the data of each monitoring well can be accurately associated; the matched multi-source survey data are cleaned, duplicate data, outliers and missing values ​​are processed, and the cleaned multi-source survey data are reduced in dimension. According to the status assessment requirements of the groundwater environment monitoring wells, key features related to the status of the monitoring wells are selected, redundant or irrelevant features are removed, and the data dimension is reduced. The format and unit of the data are unified through standardization processing to facilitate subsequent analysis. After the processing is completed, the pre-processed multi-source survey data is obtained.

[0022] The historical multi-source survey data are read according to the unique identifier corresponding to the groundwater environment monitoring well, and the status evaluation labels corresponding to the historical multi-source survey data are obtained, which are divided into good monitoring function, general monitoring function, and poor monitoring function; the historical multi-source survey data corresponding to the labels of good monitoring function and general monitoring function are extracted as the historical multi-source survey data with the monitoring function meeting the standard; a generative adversarial network is trained according to the acquired historical multi-source survey data, and the generative adversarial network can model the latent space corresponding to the multi-source survey data under the monitoring function meeting the standard, obtain the generator model and the discriminator model, use the generator model to learn the latent distribution of the multi-source survey data, and use the discriminator module to measure the fit of the latent distribution of the multi-source survey data. The obtained generator model is used as a decoder, and the autoencoder network is trained through the obtained historical multi-source survey data, with the goal of minimizing the reconstruction error, and the encoder is obtained to map the preprocessed multi-source survey data to a latent space; the vector in the latent space can represent important information of the sample and can reduce the influence of randomness, and the latent feature samples corresponding to the historical multi-source survey data closest to the preprocessed multi-source survey data are obtained in the latent space, and decoded through the decoder to obtain reconstructed data that is similar to but not the same as the input multi-source survey data; the residual between the preprocessed multi-source survey data and the reconstructed data is obtained, and whether there are defects in the groundwater environment monitoring well is determined based on the comparison result of the residual with a preset difference threshold, so as to achieve a preliminary status assessment.

[0023] Figure 2 A flow chart for extracting semantic features of defects in substandard groundwater environment monitoring wells is shown.

[0024] According to an embodiment of the present invention, the substandard groundwater environment monitoring wells are read through the preliminary status assessment, and the defect semantic features are extracted based on the multi-source survey data of the substandard groundwater environment monitoring wells, specifically: S202, obtaining preliminary status estimation results of groundwater environment monitoring wells in the region, screening defective groundwater environment monitoring wells according to the preliminary status estimation results, and generating non-compliance labels; S204, extracting multi-source survey data corresponding to groundwater environment monitoring wells with substandard labels, extracting and compressing defect features of the multi-source survey data using a preset shallow convolutional neural network, wherein the shallow convolutional neural network is composed of three convolutional layers, three maximum pooling layers, and two fully connected layers, and obtaining a one-dimensional feature vector of the substandard groundwater environment monitoring wells in the fully connected layer; S206, obtaining monitoring well damage instances with severity labels based on historical multi-source survey data, performing cluster analysis on the monitoring well damage instances, obtaining cluster clusters corresponding to the last clustering result after iterative clustering, generating defect data subsets, and determining defect types corresponding to each defect data subset; S208, extracting defect attributes according to monitoring well damage instance samples in each defect data subset, integrating defect attribute vectors to construct a defect attribute set, using similarity calculation to select n attribute vectors with the highest similarity in the defect attribute set using the one-dimensional feature vector, splicing the one-dimensional feature vector with the selected attribute vector to generate defect semantic features.

[0025] It should be noted that the shallow convolutional neural network is preset to learn task-specific information, and the defect features corresponding to the groundwater environment monitoring wells with substandard labels are extracted. The shallow convolutional neural network consists of three convolutional layers, three maximum pooling layers, and two fully connected layers. Each convolutional layer is followed by a maximum pooling layer to prevent the shallow convolutional neural network from overfitting and reduce the computational cost. The fully connected layer of the shallow convolutional neural network is used to output a one-dimensional feature vector as a defect feature. Each type of defect in the groundwater environment monitoring well can be described by using multiple attributes, and the defect attributes, such as defect location, defect size, and defect structure, are obtained according to the monitoring well damage instance with severity annotations. By describing the defect semantic attributes through attribute vectors, the internal connection of the defect semantic attributes can be enhanced, and the occurrence process of the defect can be better reflected. The one-dimensional feature vector is used to select the n attribute vectors with the highest similarity in the defect attribute set using similarity calculation to reveal the association between the features, and the obtained one-dimensional feature vector is spliced ​​with the attribute vector to generate the defect semantic feature.

[0026] A comprehensive status assessment model is constructed according to the defect semantic feature extraction module and the relationship network module. The relationship network module consists of two fully connected layers. The monitoring well damage instance samples with severity annotations in each defect data subset are used to construct training samples, and the relationship score of each training sample is obtained. The minimum mean square error between the training sample and the unique hot encoding is calculated according to the relationship score to perform model training. In the first fully connected layer, the defect semantic features obtained by the defect semantic feature extraction module are activated and output using the ReLU activation function. In the second fully connected layer, the output of the first fully connected layer is mapped to the (0,1) interval using the Sigmoid function to generate a relationship score. Multi-label identification of defects corresponding to groundwater environment monitoring wells is performed according to the relationship score, and the defect category labels and corresponding severity of substandard groundwater environment monitoring wells are determined. According to the defect category labels and severity, a comprehensive status assessment result is generated based on a preset assessment threshold interval.

[0027] It should be noted that the comprehensive status assessment results of substandard groundwater environment monitoring wells in the area are obtained, and groundwater environment monitoring wells with poor monitoring functions and general monitoring functions are selected for suspending according to the comprehensive status assessment results; the defect category label with the highest severity corresponding to each underground environment monitoring well in the suspended groundwater environment monitoring wells with general monitoring functions is obtained, and the severity trend change corresponding to the defect category label is obtained according to historical multi-source survey data; the severity trend change is compared with the trend change threshold, and the groundwater environment monitoring with general monitoring functions that is less than the trend change threshold is unsuspended, and finally, operation and maintenance information is generated according to the unique identifiers corresponding to the suspended groundwater environment monitoring wells with poor monitoring functions and general monitoring functions, and sent and displayed according to a preset method.

[0028] Figure 3 A flow chart is shown for extracting the spatiotemporal characteristics of water quality detection data within a region.

[0029] According to an embodiment of the present invention, groundwater environment monitoring wells that meet the requirements are selected to read water quality detection data, and the spatiotemporal characteristics of the water quality detection data are extracted, specifically: S302, obtaining groundwater environment monitoring wells with good monitoring functions as determined by comprehensive status assessment results in the region, extracting a water quality detection data sequence within a preset time, and calculating a Pearson correlation coefficient based on the water quality detection data sequence to obtain the correlation of monitoring data between the groundwater environment monitoring wells; S304, determining the connection relationship between the groundwater environment monitoring wells according to the distance between the groundwater environment monitoring wells and the correlation of the monitoring data, generating a corresponding topological structure and performing a graph representation, taking the groundwater environment monitoring wells with good monitoring functions in the region as graph nodes, and setting the edge structure between the nodes according to the connection relationship; S306, constructing an adjacency matrix based on the edge structure, using a graph attention network to learn the graph representation, taking the adjacency matrix as input, obtaining the attention weights between any two nodes in the neighborhood according to a multi-head self-attention mechanism, performing weighted aggregation on the adjacency matrix using the attention weights, updating the feature representation, and obtaining the spatial characteristics of the groundwater environmental quality in the region; S308, import the updated feature representation into the gated recurrent unit, use the hidden state update to obtain the time characteristics of the groundwater environmental quality in the region, introduce the time attention mechanism to weight the time characteristics at different times, merge the weighted time characteristics with the spatial characteristics after the pooling operation, and obtain the spatiotemporal characteristics of the water quality detection data in the region.

[0030] It should be noted that the attention weight between any two nodes in the neighborhood is obtained according to the multi-head self-attention mechanism. The multi-head self-attention mechanism accelerates the calculation speed of the model through parallel computing. The multi-head self-attention mechanism is used to scale the features corresponding to each monitoring well node according to the attention score and aggregate the relevant information of the neighborhood monitoring well nodes. In the multi-layer structure, the concatenation and integration of the last layer uses the average operation to obtain the feature representation corresponding to the monitoring well node. , specifically: , in is the activation function, is the total number of attention heads, is the number of attention heads, is the parameter vector between nodes in the attention head, is the weight matrix between nodes in the attention head, It is the characteristic representation of the neighborhood monitoring well node.

[0031] Add a gated recurrent unit after the graph attention network. The gated recurrent unit overcomes the problem of gradient vanishing and gradient exploding in complex networks and represents the feature corresponding to the monitoring well node. By updating the hidden state, capturing the temporal dependency, introducing the temporal attention mechanism in the hidden state update to dynamically adjust the weights between the hidden states, weighting the attention for the temporal features at different moments, and highlighting the more important temporal features. The weighted temporal features are fused with the spatial features after the pooling operation to obtain the spatiotemporal features of the water quality detection data in the area.

[0032] Based on the graph attention network and gated recurrent unit, a groundwater environmental quality prediction model is constructed. The Groundwater Quality Standard (GB / T 14848-2017) and other relevant standards are flexibly written into the groundwater environmental quality prediction model, and the quality prediction of water quality test data is carried out. The groundwater environmental quality prediction level is Class I, Class II, Class III, Class IV, and Class V, and the quality prediction results automatically form corresponding evaluation result charts. The spatiotemporal characteristics of the number of water quality tests in the region are obtained through the graph attention network and gated recurrent unit, and the spatiotemporal characteristics are used to obtain the groundwater environmental quality prediction results in the region through the fully connected layer; according to the groundwater environmental quality prediction results in the preset time period, dynamic analysis is performed to obtain trend changes, set absolute thresholds and trend change thresholds, and when the obtained groundwater environmental quality prediction results or trend changes in the region are greater than the corresponding absolute threshold or trend change threshold, a monitoring warning is generated, and it is sent and displayed in a preset manner.

[0033] The water quality detection data and groundwater environment quality prediction results of each groundwater environment monitoring well in the region are obtained, and based on the water quality detection data, it is determined whether the corresponding groundwater environment is polluted, and the groundwater environment monitoring wells with pollution are obtained. The pollution portrait of the monitoring well is generated according to the groundwater environment quality prediction results, pollutant categories and pollutant concentrations. The groundwater environment monitoring wells with pollution in the region are clustered based on the pollution portrait, and similar pollution areas are divided according to the clustering results. The divided pollution areas are sorted using the average groundwater environment quality prediction results, and the area with the highest pollution level is selected based on the sorting results. The pollution source is traced in the selected area in combination with the pollutant category. After tracing the pollution source, a dynamic analysis of groundwater pollution is performed according to the distribution of pollution areas. According to the pollutant category and the geographical and hydrological characteristics in the region, historical pollution monitoring information is retrieved, and the groundwater environment quality prediction model is supplemented with the historical pollution monitoring information. A predicted pollution path is generated according to the groundwater environment quality prediction results of each groundwater environment monitoring well, and the next pollution area is obtained through the predicted pollution path, and a groundwater environment pollution warning is generated.

[0034] Figure 4 A block diagram of the full-process data management system for groundwater environment monitoring wells is shown.

[0035] The second aspect of the present invention provides a groundwater environment monitoring well full-process data management system 4, which includes: a multi-source survey data integration module 401, a monitoring well status assessment module 402, a monitoring well operation and maintenance early warning module 403, a groundwater environment quality analysis module 404 and a groundwater monitoring early warning module 405; The multi-source survey data integration module 401 is responsible for acquiring multi-source survey data of groundwater environment monitoring wells and performing pre-processing; The monitoring well status assessment module 402 is responsible for judging whether the groundwater environment monitoring well has defects based on the preprocessed multi-source survey data, realizing preliminary status assessment, extracting defect semantic features based on the multi-source survey data of the groundwater environment monitoring well that does not meet the preliminary status assessment standard, building a comprehensive status assessment model, performing multi-label recognition of the groundwater environment monitoring well based on the defect semantic features, and performing comprehensive status assessment based on the defect category labels; The monitoring well operation and maintenance early warning module 403 is responsible for generating operation and maintenance information according to the evaluation results of the comprehensive status evaluation, and sending and displaying it in a preset manner; The groundwater environment quality analysis module 404 is responsible for selecting groundwater environment monitoring wells that meet the requirements in the region to read water quality detection data, extracting the spatiotemporal characteristics of the water quality detection data to predict the groundwater environment quality in the region; The groundwater monitoring and early warning module 405 is responsible for generating water environment monitoring and early warning through the prediction results of groundwater environment quality in the region, and sending and displaying them in a preset manner.

[0036] Preferably, the full-process data management system for groundwater environment monitoring wells also includes a data visualization module, which is used to visualize multi-source survey data, status assessment results, operation and maintenance information, water quality detection data and monitoring and early warning results, so as to facilitate users to intuitively understand the status of groundwater environment monitoring wells and water quality change trends.

[0037] The third embodiment of the present invention provides a computer-readable storage medium, which includes a full-process data management method program for groundwater environment monitoring wells. When the full-process data management method program for groundwater environment monitoring wells is executed by a processor, the steps of the full-process data management method for groundwater environment monitoring wells are implemented.

[0038] In the several embodiments provided in the present application, it should be understood that the disclosed method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as: multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0039] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0040] Alternatively, if the above-mentioned integrated module of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0041] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for managing the whole process data of groundwater environment monitoring wells, characterized in that: The following steps are involved: Acquire multi-source survey data of groundwater environment monitoring wells and perform preprocessing, and perform preliminary status assessment on the groundwater environment monitoring wells based on the preprocessed multi-source survey data; Read the substandard groundwater environment monitoring wells through the preliminary status assessment, extract the defect semantic features based on the multi-source survey data of the substandard groundwater environment monitoring wells, build a comprehensive status assessment model, perform multi-label recognition of the groundwater environment monitoring wells based on the defect semantic features, and perform comprehensive status assessment based on the defect category labels; Generate operation and maintenance information according to the evaluation results of the comprehensive status evaluation, select groundwater environment monitoring wells that meet the requirements to read water quality detection data, and extract the spatiotemporal characteristics of the water quality detection data; The groundwater environment quality in the region is predicted based on the temporal and spatial characteristics, and monitoring and early warning are generated based on the groundwater environment quality prediction results in the region.

2. A method for managing the full process data of groundwater environment monitoring wells according to claim 1, characterized in that: Obtain multi-source survey data of groundwater environment monitoring wells and perform preprocessing, and conduct preliminary status assessment of the groundwater environment monitoring wells based on the preprocessed multi-source survey data, specifically: Obtaining basic information, foundation conditions, structural conditions, and connectivity conditions of groundwater environment monitoring wells in the region as multi-source survey data of groundwater monitoring wells, merging the multi-source survey data, and matching them using unique identifiers; Perform data cleaning on the matched multi-source survey data, process duplicate data, outliers and missing values, and perform dimensionality reduction and standardization on the cleaned multi-source survey data to obtain pre-processed multi-source survey data; Read historical multi-source survey data according to the unique identifier corresponding to the groundwater environment monitoring well, obtain the status evaluation label corresponding to the historical multi-source survey data, and select the historical multi-source survey data that meets the monitoring function standard based on the status evaluation label; A generative adversarial network is trained according to the acquired historical multi-source survey data to obtain a generator model and a discriminator model, the acquired generator model is used as a decoder, and an autoencoder network is trained with the acquired historical multi-source survey data to obtain an encoder to map the pre-processed multi-source survey data to a latent space; Acquire latent feature samples corresponding to historical multi-source survey data closest to the pre-processed multi-source survey data in the latent space, and decode through a decoder to acquire reconstructed data that is similar to but not identical to the input multi-source survey data; The residuals of the preprocessed multi-source survey data and the reconstructed data are obtained, and the presence of defects in the groundwater environment monitoring well is determined based on the comparison results of the residuals with a preset difference threshold, so as to achieve a preliminary status assessment.

3. A method for managing the full process data of groundwater environment monitoring wells according to claim 1, characterized in that: The substandard groundwater environment monitoring wells are read through the preliminary status assessment, and the defect semantic features are extracted based on the multi-source survey data of the substandard groundwater environment monitoring wells, specifically: Obtain preliminary status estimation results of groundwater environment monitoring wells in the region, screen defective groundwater environment monitoring wells according to the preliminary status estimation results, and generate non-compliance labels; Extract multi-source survey data corresponding to groundwater environment monitoring wells with substandard labels, and use a preset shallow convolutional neural network to extract and compress defect features of the multi-source survey data. The shallow convolutional neural network consists of three convolutional layers, three maximum pooling layers and two fully connected layers. The one-dimensional feature vector of the substandard groundwater environment monitoring wells is obtained in the fully connected layer; Acquire monitoring well damage instances with severity labels based on historical multi-source survey data, perform cluster analysis on the monitoring well damage instances, obtain cluster clusters corresponding to the last clustering result after iterative clustering, generate defect data subsets, and determine the defect type corresponding to each defect data subset; In each defect data subset, defect attributes are extracted according to the monitoring well damage instance samples, and the defect attribute vectors are integrated to construct a defect attribute set. The one-dimensional feature vector is used for similarity calculation to select the n attribute vectors with the highest similarity in the defect attribute set, and the one-dimensional feature vector is concatenated with the selected attribute vector to generate defect semantic features.

4. A method for managing the full process data of groundwater environment monitoring wells according to claim 1, characterized in that: A comprehensive status assessment model is constructed to perform multi-label identification of groundwater environment monitoring wells based on the defect semantic features, and to perform comprehensive status assessment based on the defect category labels, specifically: A comprehensive state assessment model is constructed based on a defect semantic feature extraction module and a relationship network module. The relationship network module is composed of two fully connected layers. A training sample is constructed using monitoring well damage instance samples with severity annotations in each defect data subset. A relationship score of each training sample is obtained. The minimum mean square error of the unique hot encoding of the training sample is calculated based on the relationship score to perform model training. In the first fully connected layer, the defect semantic features obtained by the defect semantic feature extraction module are activated and output using the ReLU activation function, and in the second fully connected layer, the output of the first fully connected layer is mapped to the (0,1) interval using the Sigmoid function to generate a relationship score; Multi-label identification of defects corresponding to groundwater environment monitoring wells is performed based on the relationship scores, the defect category labels and corresponding severity of substandard groundwater environment monitoring wells are determined, and a comprehensive status assessment result is generated based on the defect category labels and severity based on a preset assessment threshold interval.

5. A method for managing the full process data of groundwater environment monitoring wells according to claim 1, characterized in that: Generate operation and maintenance information according to the evaluation result of the comprehensive status evaluation, specifically: Obtain comprehensive status evaluation results of substandard groundwater environment monitoring wells in the region, and select groundwater environment monitoring wells with poor monitoring functions and general monitoring functions for suspension based on the comprehensive status evaluation results; Obtain the defect category label of the highest severity corresponding to each underground environmental monitoring well in the suspended groundwater environmental monitoring wells with general monitoring functions, and obtain the severity trend change corresponding to the defect category label based on historical multi-source survey data; The severity trend change is compared with the trend change threshold, and the groundwater environment monitoring with general monitoring function that is less than the trend change threshold is unsuspended. Finally, operation and maintenance information is generated based on the unique identifiers corresponding to the suspended groundwater environment monitoring wells with poor monitoring functions and general monitoring functions, and sent and displayed according to a preset method.

6. A method for managing the full process data of groundwater environment monitoring wells according to claim 1, characterized in that: Select groundwater environment monitoring wells that meet the requirements to read water quality test data and extract the spatiotemporal characteristics of the water quality test data, specifically: Obtain groundwater environment monitoring wells with good monitoring functions as shown in the comprehensive status assessment results in the region, extract water quality detection data sequences within a preset time, and calculate the Pearson correlation coefficient based on the water quality detection data sequences to obtain the correlation of monitoring data between groundwater environment monitoring wells; Determine the connection relationship between groundwater monitoring wells according to the distance between them and the correlation of monitoring data, generate the corresponding topological structure and represent it in a graph, take the groundwater monitoring wells with good monitoring functions in the area as graph nodes, and set the edge structure between nodes according to the connection relationship; An adjacency matrix is ​​constructed based on the edge structure, and the graph representation is learned using a graph attention network. The adjacency matrix is ​​used as input, and the attention weight between any two nodes in the neighborhood is obtained according to a multi-head self-attention mechanism. The adjacency matrix is ​​weightedly aggregated using the attention weight, and the feature representation is updated to obtain the spatial characteristics of the groundwater environmental quality in the region. The updated feature representation is imported into the gated recurrent unit, and the hidden state is used to update the temporal characteristics of the groundwater environmental quality in the region. The temporal attention mechanism is introduced to weight the attention of the temporal features at different moments. The weighted temporal features are fused with the spatial features after pooling operation to obtain the spatiotemporal characteristics of water quality detection data in the region.

7. A method for managing the full process data of groundwater environment monitoring wells according to claim 1, characterized in that: The groundwater environment quality in the region is predicted based on the spatiotemporal characteristics, and monitoring and early warning are generated based on the groundwater environment quality prediction results in the region, specifically: A groundwater environmental quality prediction model is constructed based on a graph attention network and a gated recurrent unit, and the spatiotemporal characteristics of the number of water quality detections in the region are obtained through the graph attention network and the gated recurrent unit, and the spatiotemporal characteristics are passed through a fully connected layer to obtain the groundwater environmental quality prediction results in the region; Dynamic analysis is performed based on the groundwater environmental quality prediction results within the preset time period to obtain trend changes, set absolute thresholds and trend change thresholds, and when the obtained groundwater environmental quality prediction results or trend changes in the area are greater than the corresponding absolute thresholds or trend change thresholds, a monitoring warning is generated and sent and displayed in the preset manner.

8. A full-process data management system for groundwater environment monitoring wells, characterized in that: A method for managing the full-process data of a groundwater environment monitoring well as described in any one of claims 1 to 7 is implemented, wherein the system comprises: a multi-source survey data integration module, a monitoring well status assessment module, a monitoring well operation and maintenance early warning module, a groundwater environment quality analysis module, and a groundwater monitoring early warning module; The multi-source survey data integration module is responsible for acquiring multi-source survey data of groundwater environment monitoring wells and performing pre-processing; The monitoring well status assessment module is responsible for judging whether the groundwater environment monitoring well has defects based on the preprocessed multi-source survey data, realizing preliminary status assessment, extracting defect semantic features based on the multi-source survey data of the groundwater environment monitoring well that does not meet the preliminary status assessment standard, building a comprehensive status assessment model, performing multi-label recognition of the groundwater environment monitoring well based on the defect semantic features, and performing comprehensive status assessment based on the defect category labels; The monitoring well operation and maintenance early warning module is responsible for generating operation and maintenance information according to the evaluation results of the comprehensive status evaluation, and sending and displaying it in a preset manner; The groundwater environment quality analysis module is responsible for selecting groundwater environment monitoring wells that meet the requirements in the region to read water quality detection data, extracting the spatiotemporal characteristics of the water quality detection data to predict the groundwater environment quality in the region; The groundwater monitoring and early warning module is responsible for generating water environment monitoring and early warning through the groundwater environment quality prediction results in the region, and sending and displaying them in a preset manner.

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