Intelligent monitoring method and system for groundwater remediation process based on multi-modal monitoring

Abstract

The application discloses a groundwater remediation process intelligent supervision method and system based on multi-modal monitoring, and belongs to the technical field of groundwater supervision. According to the actual situation of the remediation area, a probe well is laid out, a sensor is set, and a remediation agent is put, and a data acquisition node is initialized; water quality data clusters are constructed based on the acquisition nodes, remediation stages are divided, and the degradation efficiency of different water quality indexes in each stage is evaluated to realize dynamic tracking of the remediation effect; the degradation efficiency is calculated to obtain the average efficiency, a degradation state feedback matrix is constructed, and the degradation state is marked as normal or abnormal; the correlation degree of water quality remediation anomalies between the probe wells is evaluated based on the feedback matrix, the probe wells with correlation are identified, and are output to the staff port. Through multi-modal monitoring and dynamic efficiency evaluation, the application realizes accurate supervision and abnormal tracing of the groundwater remediation process, the water quality change is affected by the same factor, the problem area can be quickly located, and the remediation efficiency and decision scientificity are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of groundwater monitoring, in particular to a groundwater remediation process intelligent monitoring method and system based on multi-modal monitoring. BACKGROUND

[0002] As an important water resource, the pollution remediation of groundwater has become a key issue in the field of environmental protection. Traditional groundwater remediation processes rely heavily on manual sampling and laboratory analysis, which has problems such as long monitoring period, data lag, limited coverage, etc., making it difficult to reflect the dynamic changes in the remediation process in a timely manner. In addition, existing monitoring methods focus on single-point or single-index data collection, lacking integrated analysis and correlation judgment of multi-location, multi-period and multi-index data, resulting in an inability to accurately assess the overall trend and local anomalies of remediation effectiveness.

[0003] During the remediation process, the degradation efficiency of the remediation agent is affected by various factors, such as hydrogeological conditions, uneven distribution of pollutants, and differences in remediation agent concentration. Traditional methods are difficult to quickly identify areas with low remediation efficiency, and are unable to determine whether the degradation state between different locations has spatial correlation, making it difficult to provide accurate basis for dynamic adjustment of remediation strategies. SUMMARY

[0004] The purpose of the present application is to provide a groundwater remediation process intelligent monitoring method and system based on multi-modal monitoring to solve the problems raised in the background.

[0005] To solve the above technical problems, the present application provides the following technical solutions:

[0006] The groundwater remediation process intelligent monitoring system based on multi-modal monitoring comprises a monitoring preparation module, a data processing and degradation efficiency calculation module, a degradation state feedback module, and an abnormal correlation degree evaluation and output module.

[0007] The monitoring preparation module is used to complete monitoring preparation according to the actual situation of the groundwater remediation area, including laying exploratory wells, setting water quality index monitoring sensors, injecting groundwater remediation agents, and initializing the data acquisition time nodes of the sensors.

[0008] The data processing and degradation efficiency calculation module is used to construct water quality index data clusters based on the acquisition time nodes, divide the groundwater remediation stages, and evaluate the degradation efficiency of different types of water quality indexes in each exploratory well in each remediation stage.

[0009] The degradation state feedback module is used to quantify the degradation efficiency mean value of the corresponding water quality index based on the degradation efficiency of each exploratory well, construct a degradation state feedback matrix, and mark the degradation state as normal or abnormal based on the comparison between the degradation efficiency and the mean value.

[0010] The anomaly correlation degree evaluation and output module is configured to evaluate water quality restoration anomaly correlation degrees among different exploratory wells based on the degradation state feedback matrix, determine whether there is degradation state correlation among the exploratory wells, and output exploratory well information with correlation to a staff terminal.

[0011] As a preferred scheme of the present application, the monitoring preparation module comprises an exploratory well layout unit, a sensor configuration unit, a restoration agent injection control unit, and a collection time initialization unit.

[0012] The exploratory well layout unit is configured to plan and determine exploratory well positions in the restoration area according to the groundwater restoration area and a specified exploratory well layout density requirement.

[0013] The sensor configuration unit is configured to install water quality index monitoring sensors in each exploratory well and complete sensor debugging.

[0014] The restoration agent injection control unit is configured to inject a specified amount of groundwater restoration agent into each exploratory well according to restoration requirements, so as to achieve degradation of water pollutants.

[0015] The collection time initialization unit is configured to set a data collection time node of the water quality index monitoring sensor, and trigger a collection time node coding mechanism after completion of restoration agent injection, so as to ensure that the sensor collects data at the set time.

[0016] As a preferred scheme of the present application, the data processing and degradation efficiency calculation module comprises a data cluster construction unit, a restoration stage division unit, and a degradation efficiency calculation unit.

[0017] The data cluster construction unit is configured to receive data collected by the water quality index monitoring sensor, and form water quality index category data clusters based on collection time nodes.

[0018] The restoration stage division unit is configured to divide the groundwater restoration process into continuous restoration stages with adjacent two collection time nodes as boundaries, and mark each restoration stage.

[0019] The degradation efficiency calculation unit is configured to extract values of different categories of water quality indexes in each exploratory well in each restoration stage, calculate degradation efficiency of the corresponding indexes in the stage, so as to reflect pollutant degradation effects of the indexes in the stage.

[0020] As a preferred scheme of the present application, the degradation state feedback module comprises an efficiency mean value calculation unit, a feedback matrix construction unit, and a state marking unit.

[0021] The efficiency average calculation unit is used to summarize the degradation efficiency of the same type of water quality index in all exploration wells within the same repair stage, and calculate the average degradation efficiency of that type of index.

[0022] The feedback matrix construction unit is used to construct a degradation state feedback matrix with the category of water quality index as the row index and the remediation stage as the column index.

[0023] The status marking unit is used to compare the degradation efficiency of a certain type of index in each exploration well with the average degradation efficiency of that type of index. If the degradation efficiency is greater than or equal to the average, it is marked as normal degradation status at the corresponding position in the feedback matrix; if it is less than the average, it is marked as abnormal degradation status.

[0024] As a preferred embodiment of the present invention, the abnormal correlation evaluation and output module includes a correlation calculation unit, a threshold determination unit, and a result output unit;

[0025] The correlation calculation unit is used to extract the degradation state feedback matrix of any two exploration wells, and calculate the ratio of the overlap of abnormal states between the matrices to the total number of abnormal states through logical operations to obtain the correlation degree of water quality remediation between the two exploration wells.

[0026] The threshold determination unit is used to preset a water quality remediation abnormality correlation threshold. If the correlation between two exploration wells is greater than or equal to the threshold, it is determined that there is a correlation between the two in the degradation state; if it is less than the threshold, it is determined that there is no correlation.

[0027] The result output unit is used to compile the exploration well information that is determined to have a correlation with degradation status into a report, output it to the staff's monitoring port, and support the staff to adjust the repair strategy accordingly.

[0028] A smart monitoring method for groundwater remediation processes based on multimodal monitoring, comprising the following steps:

[0029] Step S1: Complete monitoring preparations based on the actual conditions of the groundwater remediation area, including deploying exploratory wells, setting up water quality indicator monitoring sensors, dispensing groundwater remediation agents, and initializing the data acquisition time nodes of the sensors;

[0030] Step S2: Construct water quality index data clusters based on the collection time nodes, divide the groundwater remediation stages, and evaluate the degradation efficiency of different categories of water quality indicators in each exploration well within each remediation stage;

[0031] Step S3: Based on the degradation efficiency of each exploration well, quantify the average degradation efficiency of the corresponding water quality indicators, construct a degradation status feedback matrix, and mark the degradation status as normal or abnormal modes according to the comparison between degradation efficiency and average value.

[0032] Step S4: Based on the degradation state feedback matrix, assess the correlation of water quality remediation anomalies among different exploration wells to determine whether there is a correlation of degradation state among exploration wells, and output the information of exploration wells with correlation to the staff port.

[0033] As a preferred embodiment of the present invention, the specific implementation process of step S1 includes:

[0034] Based on the area of ​​the groundwater remediation zone, and in accordance with the specified well deployment density requirements, wells are deployed in the groundwater remediation zone. Water quality monitoring sensors are installed in the wells, and groundwater remediation agents are introduced through the wells to degrade water pollutants.

[0035] The water quality indicator monitoring sensor is initialized with a collection time node. After the groundwater remediation agent is added, the encoding mechanism of the collection time node is triggered, and the water quality indicator monitoring sensor is instructed to collect water quality indicator data at the collection time node.

[0036] As a preferred embodiment of the present invention, the specific implementation process of step S2 includes:

[0037] Based on the data collection time points, a data cluster of water quality indicators was constructed, denoted as... ,in, This represents the a-th exploration well. This represents the i-th data collection time point. Let represent the value of the water quality indicator for category x, and y represent the category number of the water quality indicator. Indicates exploration well At the time point of data collection The corresponding water quality indicator data clusters generated at that time;

[0038] Based on the data acquisition time nodes, a repair phase is formed by two adjacent data acquisition time nodes. The i-th repair phase is denoted as . And assess during the repair phase Internal exploration well Degradation efficiency of Class x water quality indicators in China In the formula, and These represent data clusters derived from water quality indicator categories. Water quality index data cluster The value of the xth category of water quality index, Indicates the duration of the repair phase.

[0039] As a preferred embodiment of the present invention, the specific implementation process of step S3 includes:

[0040] Based on degradation efficiency, quantification is performed at the same remediation stage. Average degradation efficiency of Class x water quality indicators In the formula, A represents the total number of exploratory wells within the groundwater remediation area;

[0041] Based on the average degradation efficiency, a degradation status feedback matrix is ​​constructed, wherein the row index of the degradation status feedback matrix is ​​the class number of the water quality index, and the column index is the number of the remediation stage.

[0042] If degradation efficiency Greater than or equal to the average degradation efficiency Then, setting the element in the x-th row and i-th column of the degradation state feedback matrix to 0 indicates that the degradation state is normal. If the degradation efficiency... Less than the average degradation efficiency If we set the element in the x-th row and i-th column of the degradation state feedback matrix to 1, it indicates an abnormal degradation state; then we obtain the well data. The degradation state feedback matrix, denoted as .

[0043] As a preferred embodiment of the present invention, the specific implementation process of step S4 includes:

[0044] Based on the degradation state feedback matrix, the correlation of water quality remediation anomalies between exploration wells was assessed. In the formula, Let a represent the b-th exploratory well, and a ≠ b. Indicates exploration well The degradation state feedback matrix, Represents the degradation state feedback matrix With degradation state feedback matrix The number of 1s included in the logical AND operation. Represents the degradation state feedback matrix With degradation state feedback matrix The number of 1s included in the logical OR operation;

[0045] A preset correlation threshold is set; if the water quality remediation is abnormally correlated... If the correlation is greater than or equal to the correlation threshold, then it is considered an exploration well. with exploration well If there is a correlation between the degradation states, then the well is considered an exploration well. with exploration well There is no correlation between degradation states, and each well with a correlation between degradation states is output to the staff port.

[0046] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0047] By deploying multiple exploratory wells and installing water quality monitoring sensors in the remediation area, a multi-dimensional data acquisition network is constructed to obtain water quality data at different locations and time points, breaking the limitations of single-point monitoring. Remediation stages are divided based on sensor acquisition time points, breaking down the continuous remediation process into multiple quantifiable stages. Degradation efficiency is calculated within each stage, enabling dynamic tracking of the remediation effect. By calculating the average degradation efficiency of the same water quality indicators within the same remediation stage, an objective anomaly judgment standard is established. The degradation efficiency of each exploratory well is compared with the average, presenting the degradation status intuitively in matrix form, avoiding subjective judgment errors. Based on the degradation status feedback matrix, the anomaly correlation between exploratory wells is calculated. If two exploratory wells have a high anomaly correlation, it indicates that their water quality changes are affected by the same factor (such as pollution diffusion or remediation agent concentration), allowing for rapid location of problem areas and providing precise basis for adjusting remediation strategies. Attached Figure Description

[0048] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0049] Figure 1 This is a schematic diagram illustrating the steps of the intelligent monitoring method for groundwater remediation based on multimodal monitoring according to the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] In this first embodiment: a smart monitoring system for groundwater remediation process based on multimodal monitoring is provided. The system includes: a monitoring preparation module, a data processing and degradation efficiency calculation module, a degradation status feedback module, and an anomaly correlation assessment and output module.

[0052] The monitoring preparation module is used to complete the monitoring preparation according to the actual situation of the groundwater remediation area, including deploying exploratory wells, setting up water quality index monitoring sensors, dispensing groundwater remediation agents, and initializing the data acquisition time nodes of the sensors.

[0053] The monitoring preparation module includes a well deployment unit, a sensor configuration unit, a repair agent dosing control unit, and a data acquisition time initialization unit.

[0054] The well placement unit is used to plan and determine the location of wells within the groundwater remediation area based on the area of ​​the groundwater remediation zone and the specified well placement density requirements.

[0055] The sensor configuration unit is used to install water quality index monitoring sensors in each exploration well and to complete the sensor debugging.

[0056] The remediation agent delivery control unit is used to deliver a fixed amount of groundwater remediation agent to each exploration well according to the remediation requirements, so as to achieve the degradation of water pollutants;

[0057] The data acquisition time initialization unit is used to set the data acquisition time nodes of the water quality index monitoring sensor, and to trigger the encoding mechanism of the data acquisition time nodes after the remediation agent is added, so as to ensure that the sensor acquires data at the set time.

[0058] The data processing and degradation efficiency calculation module is used to construct water quality index data clusters based on the collection time nodes, divide the groundwater remediation stages, and evaluate the degradation efficiency of different categories of water quality indicators in each exploration well within each remediation stage.

[0059] The data processing and degradation efficiency calculation module includes a data cluster construction unit, a remediation stage division unit, and a degradation efficiency calculation unit.

[0060] The data cluster construction unit is used to receive data collected by water quality indicator monitoring sensors and classify it into water quality indicator data clusters based on the collection time nodes.

[0061] The remediation phase division unit is used to divide the groundwater remediation process into continuous remediation phases based on two adjacent data collection time nodes, and to mark each remediation phase.

[0062] The degradation efficiency calculation unit is used to extract the values ​​of different types of water quality indicators in each exploration well within each remediation stage, and calculate the degradation efficiency of the corresponding indicators within that stage to reflect the pollutant degradation effect of the indicators in that stage.

[0063] The degradation status feedback module is used to quantify the average degradation efficiency of corresponding water quality indicators based on the degradation efficiency of each exploration well, construct a degradation status feedback matrix, and mark the degradation status as normal or abnormal modes based on the comparison between degradation efficiency and the average value.

[0064] The degradation state feedback module includes an efficiency mean calculation unit, a feedback matrix construction unit, and a state marking unit.

[0065] The efficiency average calculation unit is used to summarize the degradation efficiency of the same category of water quality indicators in all exploration wells within the same repair stage, and calculate the average degradation efficiency of that category of indicators;

[0066] The feedback matrix construction unit is used to construct a degradation status feedback matrix with the category of water quality index as the row index and the remediation stage as the column index.

[0067] The status marking unit is used to compare the degradation efficiency of a certain type of index in each exploration well with the average degradation efficiency of that type of index. If the degradation efficiency is greater than or equal to the average, it is marked as normal degradation status at the corresponding position in the feedback matrix; if it is less than the average, it is marked as abnormal degradation status.

[0068] The anomaly correlation assessment and output module is used to assess the anomaly correlation of water quality remediation among different exploration wells based on the degradation state feedback matrix, in order to determine whether there is a degradation state correlation among exploration wells, and output the information of exploration wells with correlation to the staff port.

[0069] The abnormal correlation assessment and output module includes a correlation calculation unit, a threshold determination unit, and a result output unit.

[0070] The correlation calculation unit is used to extract the degradation state feedback matrix of any two exploration wells, and calculate the ratio of the overlap of abnormal states between the matrices to the total number of abnormal states through logical operations to obtain the correlation of water quality remediation anomalies between the two exploration wells.

[0071] The threshold determination unit is used to preset the correlation threshold of water quality remediation anomalies. If the correlation between two exploration wells is greater than or equal to the threshold, it is determined that there is a correlation between the two in the degradation state; if it is less than the threshold, it is determined that there is no correlation.

[0072] The results output unit is used to compile the information of exploration wells that are determined to have a correlation with degradation status into a report, output it to the staff's monitoring port, and support the staff to adjust the repair strategy accordingly.

[0073] Please see Figure 1 In this second embodiment, a smart monitoring method for groundwater remediation based on multimodal monitoring is provided to be applicable to the first embodiment. In this embodiment, a groundwater remediation project at a chemical waste site is used as the application scenario. The site is contaminated with benzene compounds due to historical chemical production. The remediation area is 5000㎡, and the contamination depth is 3-8m. The goal is to reduce the concentration of benzene compounds from the initial 8mg / L to below 0.5mg / L (meeting the Class III standard of the Groundwater Quality Standard GB / T14848-2017).

[0074] The method includes the following steps:

[0075] Step S1: Complete monitoring preparations based on the actual conditions of the groundwater remediation area, including deploying exploratory wells, setting up water quality indicator monitoring sensors, dispensing groundwater remediation agents, and initializing the data acquisition time nodes of the sensors;

[0076] For example, based on the area of ​​the groundwater remediation area, according to the specified well deployment density requirements, wells are deployed in the groundwater remediation area, water quality index monitoring sensors are installed in the wells, and groundwater remediation agents are introduced through the wells to degrade water pollutants;

[0077] The data acquisition time nodes of the water quality index monitoring sensor are initialized. After the groundwater remediation agent is added, the encoding mechanism of the data acquisition time node is triggered, and the water quality index monitoring sensor is instructed to collect water quality index data at the data acquisition time node.

[0078] For example, based on the area to be remediated, five exploratory wells (numbered K1-K5) were deployed according to the density requirement of "one exploratory well per 1000㎡", with a depth of 10m each, to ensure coverage of the pollution depth range. Water quality monitoring sensors were installed in each well, monitoring indicators including benzene series compound concentration, pH value, and dissolved oxygen concentration, with sensor accuracies of 0.01mg / L, 0.01pH, and 0.1mg / L, respectively. Data was automatically uploaded to the monitoring platform. A bioremediation agent (active ingredient: Pseudomonas aeruginosa) was added to each well at a dosage of 5kg per well. The agent penetrated into the groundwater layer through the wells to degrade the benzene series compounds. Data collection was set at five time points: 0h (initial value), 24h, 48h, 72h, and 96h after agent application. Once the agent application was completed, a coding mechanism was triggered, and the sensors automatically collected data at the set times.

[0079] Step S2: Construct water quality index data clusters based on the collection time nodes, divide the groundwater remediation stages, and evaluate the degradation efficiency of different categories of water quality indicators in each exploration well within each remediation stage;

[0080] For example, based on the data collection time points, a data cluster of water quality indicators is constructed, denoted as... ,in, This represents the a-th exploration well. This represents the i-th data collection time point. Let represent the value of the water quality indicator for category x, and y represent the category number of the water quality indicator. Indicates exploration well At the time point of data collection The corresponding water quality indicator data clusters generated at that time;

[0081] Based on the data acquisition time nodes, a repair phase is formed by two adjacent data acquisition time nodes. The i-th repair phase is denoted as . And assess during the repair phase Internal exploration well Degradation efficiency of Class x water quality indicators in China In the formula, and These represent data clusters derived from water quality indicator categories. Water quality index data cluster The value of the xth category of water quality index, Indicates the duration of the repair phase;

[0082] Sensor data from five acquisition time points are received and categorized into water quality index data clusters according to "exploration well - time point". For example, the data cluster of exploration well K1 at the 24-hour node includes benzene series concentration of 6.2 mg / L, pH value of 7.3, and dissolved oxygen concentration of 2.5 mg / L. Using adjacent acquisition time points as boundaries, four remediation stages are divided (T1: 0h→24h, T2: 24h→48h, T3: 48h→72h, T4: 72h→96h), with each stage lasting 24 hours. Taking benzene series concentration as an example, the degradation efficiency of each exploration well in each stage is calculated (degradation efficiency = difference between initial and final concentration of stage / stage duration). For example, the degradation efficiency of benzene series compounds in exploration well K1 during the T1 stage is (8.0-6.2) mg / L ÷ 24h = 0.075 mg / (L·h), and during the T2 stage it is (6.2-4.5) ÷ 24≈0.0708 mg / (L·h).

[0083] Step S3: Based on the degradation efficiency of each exploration well, quantify the average degradation efficiency of the corresponding water quality indicators, construct a degradation status feedback matrix, and mark the degradation status as normal or abnormal modes according to the comparison between degradation efficiency and average value.

[0084] For example, based on degradation efficiency, quantification is performed in the same remediation stage. Average degradation efficiency of Class x water quality indicators In the formula, A represents the total number of exploratory wells within the groundwater remediation area;

[0085] Based on the average degradation efficiency, a degradation status feedback matrix is ​​constructed, where the row index of the degradation status feedback matrix is ​​the class number of the water quality index, and the column index is the number of the remediation stage.

[0086] If degradation efficiency Greater than or equal to the average degradation efficiency Then, setting the element in the x-th row and i-th column of the degradation state feedback matrix to 0 indicates that the degradation state is normal. If the degradation efficiency... Less than the average degradation efficiency If we set the element in the x-th row and i-th column of the degradation state feedback matrix to 1, it indicates an abnormal degradation state; then we obtain the well data. The degradation state feedback matrix, denoted as ;

[0087] Taking stage T1 as an example, the benzene series degradation efficiency of the five exploration wells were 0.075, 0.072, 0.068, 0.071, and 0.069 mg / (L·h), respectively. The mean was calculated as (0.075 + 0.072 + 0.068 + 0.071 + 0.069) ÷ 5 = 0.071 mg / (L·h). A 3-row, 4-column degradation status feedback matrix was constructed using water quality indicators (benzene series, pH, dissolved oxygen) as row indexes and the remediation stage (T1-T4) as column indexes. The degradation efficiency of each exploration well was compared with the mean. For example, the benzene series degradation efficiency of exploration well K1 in stage T1 was 0.075 ≥ 0.071, and the corresponding position in the matrix was marked as "0" (normal); the efficiency of exploration well K3 in stage T1 was 0.068 < 0.071, and it was marked as "1" (abnormal).

[0088] Step S4: Based on the degradation state feedback matrix, assess the correlation of water quality remediation anomalies among different exploration wells to determine whether there is a correlation of degradation state among exploration wells, and output the information of exploration wells with correlation to the staff port;

[0089] For example, the correlation between water quality remediation anomalies among exploration wells is assessed based on the degradation state feedback matrix. In the formula, Let a represent the b-th exploratory well, and a ≠ b. Indicates exploration well The degradation state feedback matrix, Represents the degradation state feedback matrix With degradation state feedback matrix The number of 1s included in the logical AND operation. Represents the degradation state feedback matrix With degradation state feedback matrix The number of 1s included in the logical OR operation;

[0090] A preset correlation threshold is set; if the water quality remediation is abnormally correlated... If the correlation is greater than or equal to the correlation threshold, then it is considered an exploration well. with exploration well If there is a correlation between the degradation states, then the well is considered an exploration well. with exploration well There was no correlation between the degradation states, and the wells with correlation between degradation states were output to the staff port. The well pairs with correlation (K3-K4, K2-K5) were compiled into a report, which included the correlation value, abnormal stage and indicators. Based on the report, the staff judged that the concentration of remediation agent in the K3-K4 area may be insufficient, and 2 kg / well of bioremediation agent was added in a targeted manner. After 72 hours, the concentration of benzene series compounds in the area dropped to 2.1 mg / L, and the degradation efficiency returned to normal.

[0091] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0092] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent monitoring of groundwater remediation processes based on multimodal monitoring, characterized in that, The method includes the following steps: Step S1: Complete monitoring preparations based on the actual conditions of the groundwater remediation area, including deploying exploratory wells, setting up water quality indicator monitoring sensors, dispensing groundwater remediation agents, and initializing the data acquisition time nodes of the sensors; Step S2: Construct water quality index data clusters based on the collection time nodes, divide the groundwater remediation stages, and evaluate the degradation efficiency of different categories of water quality indicators in each exploration well within each remediation stage; Step S3: Based on the degradation efficiency of each exploration well, quantify the average degradation efficiency of the corresponding water quality indicators, construct a degradation status feedback matrix, and mark the degradation status as normal or abnormal modes according to the comparison between degradation efficiency and average value. Step S4: Based on the degradation state feedback matrix, assess the correlation of water quality remediation anomalies among different exploration wells to determine whether there is a correlation of degradation state among exploration wells, and output the information of exploration wells with correlation to the staff port; The specific implementation process of step S1 includes: Based on the area of ​​the groundwater remediation zone, and in accordance with the specified well deployment density requirements, wells are deployed in the groundwater remediation zone. Water quality monitoring sensors are installed in the wells, and groundwater remediation agents are introduced through the wells to degrade water pollutants. The water quality index monitoring sensor is initialized with a collection time node. After the groundwater remediation agent is added, the encoding mechanism of the collection time node is triggered, and the water quality index monitoring sensor is instructed to collect water quality index data at the collection time node. The specific implementation process of step S2 includes: Based on the data collection time points, a data cluster of water quality indicators was constructed, denoted as... ,in, This represents the a-th exploration well. This represents the i-th data collection time point. Let represent the value of the water quality indicator for category x, and y represent the category number of the water quality indicator. Indicates exploration well At the time point of data collection The corresponding water quality indicator data clusters generated at that time; Based on the data acquisition time nodes, a repair phase is formed by two adjacent data acquisition time nodes. The i-th repair phase is denoted as . And assess during the repair phase Internal exploration well Degradation efficiency of Class x water quality indicators in China In the formula, and These represent data clusters derived from water quality indicator categories. Water quality index data cluster The value of the xth category of water quality index, Indicates the duration of the repair phase; The specific implementation process of step S3 includes: Based on degradation efficiency, quantification is performed at the same remediation stage. Average degradation efficiency of Class x water quality indicators In the formula, A represents the total number of exploratory wells within the groundwater remediation area; Based on the average degradation efficiency, a degradation status feedback matrix is ​​constructed, wherein the row index of the degradation status feedback matrix is ​​the class number of the water quality index, and the column index is the number of the remediation stage. If degradation efficiency Greater than or equal to the average degradation efficiency Then, setting the element in the x-th row and i-th column of the degradation state feedback matrix to 0 indicates that the degradation state is normal. If the degradation efficiency... Less than the average degradation efficiency If we set the element in the x-th row and i-th column of the degradation state feedback matrix to 1, it indicates an abnormal degradation state; then we obtain the well data. The degradation state feedback matrix, denoted as .

2. The intelligent monitoring method for groundwater remediation process based on multimodal monitoring according to claim 1, characterized in that, The specific implementation process of step S4 includes: Based on the degradation state feedback matrix, the correlation of water quality remediation anomalies between exploration wells was assessed. In the formula, Let a represent the b-th exploratory well, and a ≠ b. Indicates exploration well The degradation state feedback matrix, Represents the degradation state feedback matrix With degradation state feedback matrix The number of 1s included in the logical AND operation. Represents the degradation state feedback matrix With degradation state feedback matrix The number of 1s included in the logical OR operation; A preset correlation threshold is set; if the water quality remediation is abnormally correlated... If the correlation is greater than or equal to the correlation threshold, then it is considered an exploration well. with exploration well If there is a correlation between the degradation states, then the well is considered an exploration well. with exploration well There is no correlation between degradation states, and each well with a correlation between degradation states is output to the staff port.

3. A groundwater remediation process intelligent monitoring system based on multimodal monitoring, executing the groundwater remediation process intelligent monitoring method based on multimodal monitoring as described in any one of claims 1-2, characterized in that, The system includes: a monitoring preparation module, a data processing and degradation efficiency calculation module, a degradation status feedback module, and an anomaly correlation assessment and output module. The monitoring preparation module is used to complete monitoring preparation according to the actual situation of the groundwater remediation area, including setting up exploratory wells, setting up water quality index monitoring sensors, adding groundwater remediation agents, and initializing the data acquisition time nodes of the sensors. The data processing and degradation efficiency calculation module is used to construct water quality index data clusters based on the collection time nodes, divide the groundwater remediation stages, and evaluate the degradation efficiency of different categories of water quality indicators in each exploration well within each remediation stage. The degradation status feedback module is used to quantify the average degradation efficiency of corresponding water quality indicators based on the degradation efficiency of each exploration well, construct a degradation status feedback matrix, and mark the degradation status as normal or abnormal modes according to the comparison between degradation efficiency and average value. The abnormal correlation assessment and output module is used to assess the abnormal correlation of water quality remediation among different exploration wells based on the degradation state feedback matrix, so as to determine whether there is a degradation state correlation among exploration wells, and output the information of exploration wells with correlation to the staff port.

4. The intelligent monitoring system for groundwater remediation process based on multimodal monitoring according to claim 3, characterized in that, The monitoring preparation module includes a well deployment unit, a sensor configuration unit, a repair agent dosing control unit, and a data acquisition time initialization unit. The well deployment unit is used to plan and determine the location of wells within the groundwater remediation area based on the area of ​​the groundwater remediation area and the specified well deployment density requirements. The sensor configuration unit is used to install water quality index monitoring sensors in each exploration well and to complete the sensor debugging. The remediation agent delivery control unit is used to deliver a fixed amount of groundwater remediation agent to each exploration well according to the remediation requirements, so as to achieve the degradation of water pollutants; The data acquisition time initialization unit is used to set the data acquisition time node of the water quality index monitoring sensor, and to trigger the encoding mechanism of the acquisition time node after the remediation agent is added, so as to ensure that the sensor acquires data at the set time.

5. The intelligent monitoring system for groundwater remediation process based on multimodal monitoring according to claim 3, characterized in that, The data processing and degradation efficiency calculation module includes a data cluster construction unit, a repair stage division unit, and a degradation efficiency calculation unit. The data cluster construction unit is used to receive data collected by water quality index monitoring sensors and classify it into water quality index data clusters based on the collection time nodes. The remediation stage division unit is used to divide the groundwater remediation process into continuous remediation stages based on two adjacent collection time nodes, and to mark each remediation stage. The degradation efficiency calculation unit is used to extract the values ​​of different types of water quality indicators in each exploration well within each remediation stage, and calculate the degradation efficiency of the corresponding indicators within that stage to reflect the pollutant degradation effect of the indicators in that stage.

6. The intelligent monitoring system for groundwater remediation process based on multimodal monitoring according to claim 3, characterized in that, The degradation state feedback module includes an average efficiency calculation unit, a feedback matrix construction unit, and a state marking unit. The efficiency average calculation unit is used to summarize the degradation efficiency of the same type of water quality index in all exploration wells within the same repair stage, and calculate the average degradation efficiency of that type of index. The feedback matrix construction unit is used to construct a degradation state feedback matrix with the category of water quality index as the row index and the remediation stage as the column index. The status marking unit is used to compare the degradation efficiency of a certain type of index in each exploration well with the average degradation efficiency of that type of index. If the degradation efficiency is greater than or equal to the average, it is marked as normal degradation status at the corresponding position in the feedback matrix; if it is less than the average, it is marked as abnormal degradation status.

7. The intelligent monitoring system for groundwater remediation process based on multimodal monitoring according to claim 3, characterized in that, The abnormal correlation assessment and output module includes a correlation calculation unit, a threshold determination unit, and a result output unit; The correlation calculation unit is used to extract the degradation state feedback matrix of any two exploration wells, and calculate the ratio of the overlap of abnormal states between the matrices to the total number of abnormal states through logical operations to obtain the correlation degree of water quality remediation between the two exploration wells. The threshold determination unit is used to preset the correlation threshold of water quality remediation abnormality. If the correlation between two exploration wells is greater than or equal to the threshold, it is determined that there is a correlation between the two in the degradation state. If it is less than the threshold, it is determined that there is no correlation; The result output unit is used to compile the exploration well information that is determined to have a correlation with degradation status into a report, output it to the staff's monitoring port, and support the staff to adjust the repair strategy accordingly.

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