CN117668508A - Investigation and point distribution method for determining groundwater pollution range - Google Patents
Investigation and point distribution method for determining groundwater pollution range Download PDFInfo
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Abstract
The invention discloses a investigation and distribution method for determining an underground water pollution range, which belongs to the field of water pollution detection and comprises the following specific steps: (1) Collecting and sorting groundwater data to construct a groundwater model; (2) Arranging sensors by utilizing an intelligent sensor technology and collecting data in real time; (3) Establishing a groundwater quality database and detecting anomalies and trends; (4) Simulating groundwater flow in a pollution source area to carry out pollution tracing; (5) Detecting high-resolution groundwater to determine a pollution diffusion range; (6) Storing the collected ground data for subsequent viewing by researchers; the method can provide more accurate results, can be applied to various types of data, can effectively process high-dimensional data, is beneficial to quickly identifying potential pollution source areas, improves data safety and reliability, can automate data processing flow, improves efficiency and reduces human errors.
Description
Technical Field
The invention relates to the field of water pollution detection, in particular to a investigation and distribution method for determining a groundwater pollution range.
Background
Groundwater is a valuable natural resource stored in underground rock, providing essential support for urban water supply, agricultural irrigation and ecosystems. However, groundwater quality is gradually impaired due to human activity and natural factors. Groundwater pollution has become an urgent problem in the field of environmental protection today, and constitutes a great threat to human health and the ecosystem. Conventional groundwater pollution investigation methods typically rely on limited monitoring well points and sampling frequencies, which lead to insufficient investigation range and omission of pollution sources. Moreover, conventional methods require a significant amount of manpower and time, are costly, and often fail to provide the necessary data to support emergency decisions on a real-time or near real-time basis. Therefore, an intelligent and innovative method for investigating and distributing the pollution of the underground water is needed, and the accuracy and efficiency of investigation can be improved, so that the underground water resource and the ecological environment are better protected.
The existing investigation point distribution method for determining the pollution range of the underground water cannot be applied to various types of data, and the efficiency of processing high-dimensional data is low, so that potential pollution source areas cannot be identified quickly; in addition, the existing investigation point distribution method for determining the groundwater pollution range has low data security and reliability and low data processing efficiency; therefore, we propose a investigation and distribution method for determining the pollution range of underground water.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a investigation and distribution method for determining the pollution range of underground water.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the investigation and distribution method for determining the pollution range of the underground water comprises the following specific steps:
(1) Collecting and sorting groundwater data to construct a groundwater model;
(2) Arranging sensors by utilizing an intelligent sensor technology and collecting data in real time;
(3) Establishing a groundwater quality database and detecting anomalies and trends;
(4) Simulating groundwater flow in a pollution source area to carry out pollution tracing;
(5) Detecting high-resolution groundwater to determine a pollution diffusion range;
(6) The collected ground data is stored for subsequent review by the researcher.
As a further scheme of the invention, the underground water data collection and arrangement in the step (1) comprises the following specific steps:
step one: removing noise in each group of groundwater data through Gaussian filtering and smoothing the data, calculating standard deviation of the groundwater data, detecting and screening abnormal data according to the calculated standard deviation, unifying data formats, detecting whether repeated data records exist or not, and deleting the repeated data if the repeated data exist;
step two: detecting missing values in each group of data, marking the positions of the missing values in the corresponding data, carrying out statistics and visual analysis on the missing values in each group of data to obtain the distribution condition and the influence range of the missing values, and replacing the abnormal values or the missing values by the average value or the median of the corresponding K groups of data points found by the KNN algorithm.
As a further scheme of the invention, the groundwater model construction in the step (1) comprises the following specific steps:
step 1: acquiring information of the flow speed and the direction of groundwater according to collected groundwater flow data, describing groundwater flow through Darcy's law, and then establishing a mass transfer equation by utilizing a principle of conservation of mass of pollutants;
step 2: obtaining a diffusion coefficient through laboratory test or literature data, obtaining a water head difference through water well data or ground water level monitoring, obtaining a source item according to the property and the strength of a pollution source, and digitizing a mass transmission equation through a finite difference method or a finite element method;
step 3: simulating groundwater flow and pollutant transmission processes in different time steps in a computer model to obtain a groundwater model, predicting the distribution of pollutant concentration in groundwater along with time and space according to the obtained diffusion coefficient, water head difference, source item and boundary condition, comparing the model predicted value with groundwater monitoring data, evaluating the model performance through a statistical analysis method, and adjusting the model diffusion coefficient, water head difference and source item according to the comparison result.
As a further scheme of the present invention, the specific calculation formula of darcy's law in step 1 is as follows:
v＝dh/dl (1)
wherein v represents the groundwater flow speed; dh represents the head difference; dl represents the distance of fluid through the groundwater layer;
the specific calculation formula of the mass transfer equation in the step 1 is as follows:
wherein C represents a contaminant concentration; t represents time; d represents a diffusion coefficient;representing a Laplace operator; s represents the source term.
As a further scheme of the invention, the sensor layout in the step (2) comprises the following specific steps:
step I: determining parameters to be monitored, selecting sensors according to monitoring requirements, determining the mounting positions of the sensors according to a groundwater model, collecting and calibrating each group of sensors, and adjusting the sampling interval and the data transmission frequency of the sensors;
step II: the sensor is placed at a determined installation position in the groundwater through a wellhead or a borehole, and then periodically records groundwater quality data according to a predetermined sampling interval, and processes the sensor data through an automated algorithm to generate a real-time monitoring report.
As a further aspect of the present invention, the specific step of detecting abnormal trends in the step (3) is as follows:
step (1): extracting past groundwater data from a groundwater quality database, integrating and summarizing each group of data into a sample data set, dividing the sample data set into two groups of feature subsets according to a preset threshold value, randomly selecting one group of feature subsets, repeating feature selection and data set segmentation until the depth of a decision tree reaches a preset value, and determining the label of a leaf node as the category with the maximum number of samples in the node;
step (2): constructing a complete decision tree through recursion splitting and leaf node label determination, forming a random forest model by using the generated multiple groups of decision trees, selecting any subset as a test set for each group of data, using the rest subsets as training sets to train the random forest model, and detecting the trained random forest model through the test sets;
step (3): counting the loss value of the detection result, replacing the test set with another subset, taking the rest subset as a training set, calculating the loss value again until all data are predicted once, and selecting the corresponding combined parameter with the minimum loss value as the optimal parameter in the data interval and replacing the original parameter of the random forest model;
step (4): the random forest model receives the underground water data of each group collected by the periodic receiving sensor, starts from the root node of the random forest model, traverses the branches of the tree step by step according to the characteristic conditions of the underground water data of each group until the branches reach the leaf nodes, and takes the labels of the leaf nodes as detection results and outputs the detection results.
As a further aspect of the present invention, the specific data storage step in step (6) is as follows
The first step: dividing each group of underground water data, detection data and tracing data according to a preset time interval to obtain a plurality of groups of data blocks, generating the identification of each group of data blocks through a hash algorithm, collecting the link point information of each group of blocks, and obtaining the load condition of each group of nodes;
and a second step of: selecting corresponding blockchain nodes to store each group of data blocks through a load balancing algorithm, after the data blocks are stored, performing configuration copying on a specified number of data blocks to a plurality of groups of blockchain nodes according to the requirements of a system and available resources, and when the data stored by the nodes are changed, transmitting data update from one node to other nodes through a data synchronization algorithm;
and a third step of: after a new set of data blocks is constructed, the new set of data blocks are broadcast to the blockchain network and are transmitted to a plurality of groups of blockchain nodes, each group of blockchain nodes performs consensus verification, the validity and legality of the data blocks are confirmed, and the data blocks are added into the blockchain network for storage.
Compared with the prior art, the invention has the beneficial effects that:
1. the investigation and distribution method for determining the groundwater pollution range comprises the steps of extracting past groundwater data from a groundwater quality database, integrating and inducing each group of data into a sample data set, dividing the sample data set into two groups of feature subsets according to a preset threshold value, randomly selecting one group of feature subsets, repeatedly carrying out feature selection and data set segmentation until the depth of a decision tree reaches a preset value, determining the label of a leaf node as the category with the largest sample number in the node, constructing a complete decision tree through recursion splitting and leaf node label determination, forming a random forest model by the generated multiple groups of decision trees, receiving each group of groundwater data collected by a periodic receiving sensor by the random forest model, traversing branches of the tree gradually according to the feature condition of each group of groundwater data until the leaf node is reached, taking the label of the leaf node as a detection result and outputting the detection result, providing more accurate result, simultaneously being applicable to various types of data, effectively processing high-dimensional data, and being beneficial to quickly identifying potential pollution source areas.
2. Dividing each group of groundwater data, detection data and tracing data according to a preset time interval to obtain a plurality of groups of data blocks, generating identifications of each group of data blocks through a hash algorithm, collecting link point information of each group of blocks, acquiring load conditions of each group of nodes, selecting corresponding block chain nodes through a load balancing algorithm to store each group of data blocks, configuring and copying a specified number of data blocks to the plurality of groups of block chain nodes according to requirements and available resources of a system after the data block storage is completed, transmitting data update from one node to other nodes through a data synchronization algorithm when the data stored by the nodes changes, broadcasting the data update to a plurality of groups of block chain nodes after a new data block is constructed, transmitting the new data block to the plurality of groups of block chain nodes, performing consensus verification on each group of block chain nodes, confirming validity and legality of the data block, adding the data blocks into the block chain network, improving data safety and reliability, and enabling automatic data processing flow, improving efficiency and reducing human errors.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Fig. 1 is a flow chart of a survey and distribution method for determining the pollution range of underground water.
Detailed Description
Example 1
Referring to fig. 1, a method for investigation and distribution of groundwater pollution range is provided, which comprises the following specific steps:
groundwater data is collected and consolidated to construct a groundwater model.
Specifically, noise in each group of groundwater data is removed through Gaussian filtering, the data is smoothed, standard deviation of the groundwater data is calculated, abnormal data is detected and screened according to the calculated standard deviation, data formats are unified, whether repeated data records exist is detected, if the repeated data exist, missing values in each group of data are deleted, positions of the missing values in the corresponding data are marked, statistics and visual analysis are carried out on the missing values in each group of data to obtain distribution conditions and influence ranges of the missing values, and average values or median values of corresponding K groups of data points found through a KNN algorithm replace the abnormal values or the missing values.
Specifically, according to the collected groundwater flow speed and direction information, groundwater flow is described through Darcy's law, then a mass transfer equation is established by utilizing a pollutant mass conservation principle, a diffusion coefficient is obtained through laboratory test or literature data, then a water head difference is obtained through well data or groundwater level monitoring, a source item is obtained according to the property and strength of a pollution source, then the mass transfer equation is digitalized through a finite difference method or a finite element method, groundwater flow and pollutant transfer processes in different time steps are simulated in a computer model to obtain a groundwater model, the distribution of pollutant concentration in groundwater along with time and space is predicted according to the obtained diffusion coefficient, water head difference, source item and boundary condition, model performance is evaluated through a statistical analysis method, and model diffusion coefficient, water head difference and source item are adjusted according to the comparison result.
In this embodiment, the specific calculation formula of darcy's law is as follows:
v＝dh/dl (1)
wherein v represents the groundwater flow speed; dh represents the head difference; dl represents the distance of fluid through the groundwater layer;
the specific calculation formula of the mass transfer equation is as follows:
wherein C represents a contaminant concentration; t represents time; d represents a diffusion coefficient;representing a Laplace operator; s represents the source term.
And arranging sensors by utilizing an intelligent sensor technology and collecting data in real time.
Specifically, the parameters to be monitored are determined to select the sensors according to the monitoring requirements, then the installation positions of the sensors are determined according to the groundwater model, the sensors of each group are collected and calibrated, the sampling interval and the data transmission frequency of the sensors are adjusted, the sensors are placed at the installation positions determined in the groundwater through a wellhead or a drill hole, then the sensors regularly record groundwater quality data according to the preset sampling interval, and the sensor data are processed through an automatic algorithm to generate a real-time monitoring report.
And (5) establishing a groundwater quality database and detecting anomalies and trends.
Specifically, the past groundwater data is extracted from a groundwater quality database, each group of data is integrated and generalized into a sample data set, then the sample data set is divided into two groups of feature subsets according to a preset threshold, a group of feature subsets is randomly selected, feature selection and data set segmentation are repeatedly carried out until the depth of a decision tree reaches a preset value, the label of a leaf node is determined to be the category with the largest sample number in the node, a complete decision tree is established through recursive splitting and leaf node label determination, the generated multiple groups of decision trees form a random forest model, for each group of data, any subset is selected as a test set, the rest subsets are used as training sets to train the random forest model, the trained random forest model is detected through the test set, the loss value of the detection result is counted, the test set is replaced with another subset, the rest subsets are used as training sets, the loss value is calculated again until all data are predicted once, the corresponding combination parameters are selected as optimal parameters in a data interval and replace the original parameters of the random forest model, the random forest model is received by the random forest model, each group of data is received, the leaf nodes are traversed from the random forest tree, the leaf nodes are traversed to the feature tree, the feature tree is detected, the leaf nodes are reached, the condition of the underground water is reached, and the condition is reached, the condition is reached.
Example 2
Referring to fig. 1, a method for investigation and distribution of groundwater pollution range is provided, which comprises the following specific steps:
and simulating groundwater flow in a pollution source area to trace pollution.
And (5) carrying out high-resolution groundwater detection to determine the pollution diffusion range.
The collected ground data is stored for subsequent review by the researcher.
Specifically, each group of underground water data, detection data and tracing data are segmented according to a preset time interval to obtain a plurality of groups of data blocks, then identifiers of each group of data blocks are generated through a hash algorithm, link point information of each group of blocks is collected, load conditions of each group of nodes are obtained, corresponding block chain nodes are selected through a load balancing algorithm to store each group of data blocks, after the data blocks are stored, a specified number of data blocks are configured and copied to the plurality of groups of block chain nodes according to requirements of a system and available resources, when the data stored by the nodes are changed, data update is transmitted from one node to other nodes through a data synchronization algorithm, after a new group of data blocks is constructed, the new data blocks are broadcast to the plurality of groups of block chain nodes, the block chain nodes are transmitted to carry out consensus verification, validity and legality of the data blocks are confirmed, and the data blocks are stored in the block chain network.
Claims (7)
1. The investigation and distribution method for determining the pollution range of the underground water is characterized by comprising the following specific steps of:
(1) Collecting and sorting groundwater data to construct a groundwater model;
(2) Arranging sensors by utilizing an intelligent sensor technology and collecting data in real time;
(3) Establishing a groundwater quality database and detecting anomalies and trends;
(4) Simulating groundwater flow in a pollution source area to carry out pollution tracing;
(5) Detecting high-resolution groundwater to determine a pollution diffusion range;
(6) The collected ground data is stored for subsequent review by the researcher.
2. The investigation and distribution method for determining the pollution range of underground water according to claim 1, wherein the underground water data collection and arrangement in the step (1) specifically comprises the following steps:
step one: removing noise in each group of groundwater data through Gaussian filtering and smoothing the data, calculating standard deviation of the groundwater data, detecting and screening abnormal data according to the calculated standard deviation, unifying data formats, detecting whether repeated data records exist or not, and deleting the repeated data if the repeated data exist;
step two: detecting missing values in each group of data, marking the positions of the missing values in the corresponding data, carrying out statistics and visual analysis on the missing values in each group of data to obtain the distribution condition and the influence range of the missing values, and replacing the abnormal values or the missing values by the average value or the median of the corresponding K groups of data points found by the KNN algorithm.
3. The investigation and distribution method for determining the pollution range of groundwater according to claim 2, wherein the specific steps of the groundwater model construction in the step (1) are as follows:
step 1: acquiring information of the flow speed and the direction of groundwater according to collected groundwater flow data, describing groundwater flow through Darcy's law, and then establishing a mass transfer equation by utilizing a principle of conservation of mass of pollutants;
step 2: obtaining a diffusion coefficient through laboratory test or literature data, obtaining a water head difference through water well data or ground water level monitoring, obtaining a source item according to the property and the strength of a pollution source, and digitizing a mass transmission equation through a finite difference method or a finite element method;
step 3: simulating groundwater flow and pollutant transmission processes in different time steps in a computer model to obtain a groundwater model, predicting the distribution of pollutant concentration in groundwater along with time and space according to the obtained diffusion coefficient, water head difference, source item and boundary condition, comparing the model predicted value with groundwater monitoring data, evaluating the model performance through a statistical analysis method, and adjusting the model diffusion coefficient, water head difference and source item according to the comparison result.
4. A survey and distribution method for determining a pollution range of groundwater according to claim 3, wherein the specific calculation formula of darcy's law in step 1 is as follows:
v＝dh/dl (1)
wherein v represents the groundwater flow speed; dh represents the head difference; dl represents the distance of fluid through the groundwater layer;
the specific calculation formula of the mass transfer equation in the step 1 is as follows:
wherein C represents a contaminant concentration; t represents time; d represents a diffusion coefficient;representing a Laplace operator; s represents the source term.
5. A survey and settlement method for determining a pollution range of groundwater according to claim 3, wherein the sensor layout in step (2) comprises the following specific steps:
step I: determining parameters to be monitored, selecting sensors according to monitoring requirements, determining the mounting positions of the sensors according to a groundwater model, collecting and calibrating each group of sensors, and adjusting the sampling interval and the data transmission frequency of the sensors;
step II: the sensor is placed at a determined installation position in the groundwater through a wellhead or a borehole, and then periodically records groundwater quality data according to a predetermined sampling interval, and processes the sensor data through an automated algorithm to generate a real-time monitoring report.
6. The investigation and distribution method for determining a groundwater pollution range according to claim 5, wherein the abnormal trend detection in the step (3) specifically comprises the following steps:
step (1): extracting past groundwater data from a groundwater quality database, integrating and summarizing each group of data into a sample data set, dividing the sample data set into two groups of feature subsets according to a preset threshold value, randomly selecting one group of feature subsets, repeating feature selection and data set segmentation until the depth of a decision tree reaches a preset value, and determining the label of a leaf node as the category with the maximum number of samples in the node;
step (2): constructing a complete decision tree through recursion splitting and leaf node label determination, forming a random forest model by using the generated multiple groups of decision trees, selecting any subset as a test set for each group of data, using the rest subsets as training sets to train the random forest model, and detecting the trained random forest model through the test sets;
step (3): counting the loss value of the detection result, replacing the test set with another subset, taking the rest subset as a training set, calculating the loss value again until all data are predicted once, and selecting the corresponding combined parameter with the minimum loss value as the optimal parameter in the data interval and replacing the original parameter of the random forest model;
step (4): the random forest model receives the underground water data of each group collected by the periodic receiving sensor, starts from the root node of the random forest model, traverses the branches of the tree step by step according to the characteristic conditions of the underground water data of each group until the branches reach the leaf nodes, and takes the labels of the leaf nodes as detection results and outputs the detection results.
7. The investigation and distribution method for determining underground water pollution range according to claim 1, wherein said data storage in step (6) is specifically performed as follows
The first step: dividing each group of underground water data, detection data and tracing data according to a preset time interval to obtain a plurality of groups of data blocks, generating the identification of each group of data blocks through a hash algorithm, collecting the link point information of each group of blocks, and obtaining the load condition of each group of nodes;
and a second step of: selecting corresponding blockchain nodes to store each group of data blocks through a load balancing algorithm, after the data blocks are stored, performing configuration copying on a specified number of data blocks to a plurality of groups of blockchain nodes according to the requirements of a system and available resources, and when the data stored by the nodes are changed, transmitting data update from one node to other nodes through a data synchronization algorithm;
and a third step of: after a new set of data blocks is constructed, the new set of data blocks are broadcast to the blockchain network and are transmitted to a plurality of groups of blockchain nodes, each group of blockchain nodes performs consensus verification, the validity and legality of the data blocks are confirmed, and the data blocks are added into the blockchain network for storage.
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