Underground water pollution intelligent evaluation method and system based on Internet of Things
Through the combination of the Internet of Things and groundwater flow model, the timeliness and accuracy of groundwater pollution assessment is solved, and the identification of pollution sources and cross-regional pollution control are achieved.
Patent Information
- Application Number
- CN202510464497.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-14
- Publication Date
- 2025-07-25
AI Technical Summary
The existing technology cannot detect emergencies of groundwater pollution in a timely manner, and it is difficult to fully grasp the pollution status of the entire groundwater system, and it is impossible to accurately judge the pollution status of groundwater in each industrial plant.
Connect water quality sensors and industrial factories through the Internet of Things, divide management areas, obtain factory working status, analyze pollution impacts in combination with groundwater flow models, and set water quality pollution thresholds for real-time evaluation and decision-making support.
It has achieved timely identification and accurate assessment of the source of pollution, and can promptly detect secret discharges, give priority to environmental protection restrictions on factories with great pollution, which has improved the control of groundwater pollution.
Smart Images
Figure CN120373638A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of intelligent assessment of groundwater pollution. Specifically, it relates to an intelligent assessment method and system for groundwater pollution based on the Internet of Things. Background Art
[0002] As one of the important water resources, groundwater plays a crucial role in human life, industrial production, and the ecological environment. Monitoring personnel regularly visit these sites to collect water samples and then send them to the laboratory for analysis. Or in some large industrial enterprises, some simple water quality monitoring devices may also be installed to monitor the impact of the enterprise's own emissions on the surrounding groundwater.
[0003] At present, the frequency of regular sampling analysis and manual inspection is relatively low, and sudden situations of groundwater pollution cannot be detected in time. Once pollution problems occur, relatively large hazards may have been caused. Secondly, the monitoring scope covered by traditional methods is limited, and it is difficult to comprehensively grasp the pollution status of the entire groundwater system and accurately judge the pollution situation of each industrial factory on groundwater. Therefore, an intelligent assessment method and system for groundwater pollution based on the Internet of Things are proposed. Summary of the Invention
[0004] The purpose of the present invention is to provide an intelligent assessment method and system for groundwater pollution based on the Internet of Things to solve the problems mentioned in the above background art.
[0005] To solve the above technical problems, one of the purposes of the present invention is to provide an intelligent assessment method for groundwater pollution based on the Internet of Things, including the following steps: S1. Collect the water quality data collected by water quality sensors, and at the same time divide the management areas according to the positions of the water quality sensors; S2. Attribute industrial factories according to the divided management areas, and at the same time obtain the working status of each industrial factory; S3. Extract the historical water quality data and historical working status of the water quality sensors, and then analyze the change of the working status by combining the historical water quality data with the historical working status to obtain the in - area impact data of the working status adjustment of each industrial factory on the water quality data of the affiliated management area; S4. Obtain the flow velocity and flow direction of groundwater, and then analyze the impact on the downstream management area by combining the flow direction with the flow velocity and the in - area impact amplitude to obtain the out - of - area impact data of the working status adjustment of each industrial factory on the downstream management area; S5. Use the real - time water quality data to accurately verify the out - of - area impact data in combination with the in - area impact data. When the real - time water quality data combined with the in - area impact data is within the normal range of the out - of - area impact data, then proceed to S6; S6. Set a water quality pollution threshold, then compare the same-region impact data and cross-region impact data of the management area with the water quality pollution threshold. When the threshold is exceeded, conduct a pollution assessment on industrial factories based on the same-region impact amplitude data and cross-region impact data. Sort the industrial factories according to the pollution impact amplitude, and then send the sorted factories to the pollution management terminal for environmental protection decision-making support.
[0006] As a further improvement of this technical solution, in S1, connect the water quality sensor and industrial factories through the Internet of Things, and extract water quality data and location data from the data fed back by the water quality sensor. Obtain the monitoring range of the water quality sensor, and then divide the management area according to the monitoring range of the water quality sensor. When there is an overlap in the monitoring range areas, divide the overlapping areas to the water quality sensor closest in distance.
[0007] As a further improvement of this technical solution, in S2, extract the sewage discharge location of each industrial factory through the Internet of Things, and then assign the sewage discharge location to the management area within the same range. Extract the operation data of industrial factories through the Internet of Things, and conduct a working state analysis by combining the operation data with the total number of production machines in the industrial factory, so as to obtain the working state of each industrial factory. The more machines are working in the operation data, the higher the proportion of the working state.
[0008] As a further improvement of this technical solution, the steps of S3 are as follows: S3.1. Extract the historical water quality data and historical working state of the water quality sensor. S3.2. Intercept the adjustment period when the working state changes in the historical working state, then extract the same-period data from the historical water quality data according to the adjustment period, and then conduct a working state change analysis on the historical water quality data and historical working state in the same period to obtain the same-region impact data of the working state adjustment of each industrial factory on the water quality data of the affiliated management area.
[0009] As a further improvement of this technical solution, the steps of S4 are as follows: S4.1. Obtain the flow velocity and flow direction of groundwater. S4.2. Establish a groundwater simulation model based on the water flow velocity and flow direction, and then input the working state of industrial factories into the groundwater simulation model and combine it with the same-region impact data to conduct an impact analysis on the downstream management area, so as to obtain the cross-region impact data of the same-region impact data of different industrial factories on the water quality data of the downstream management area.
[0010] As a further improvement of this technical solution, the steps of S5 are as follows: S5.1. Combine real-time water quality data with in-region impact data to accurately verify out-region impact data, obtain water quality impact data after removing in-region impact data from the real-time water quality data, and then set the normal range threshold. S5.2. Compare the water quality impact data combined with out-region impact data with the normal range threshold. When the difference between the water quality impact data and the out-region impact data is greater than the normal range threshold, it is determined that there is illegal discharge by industrial factories in the management area. Otherwise, when the difference between the water quality impact data and the out-region impact data is within the normal range threshold, proceed to S6.
[0011] As a further improvement of this technical solution, the steps of S6 are as follows: S6.1. Set the water quality pollution threshold according to the groundwater pollution management standard. S6.2. Compare the in-region impact data combined with out-region impact data of each management area with the water quality pollution threshold. When it exceeds the water quality pollution threshold, conduct a pollution assessment of industrial factories based on the in-region impact amplitude data and the out-region impact data. Sort the industrial factories according to the pollution impact amplitude based on the assessment results, and then send them to the pollution management terminal for environmental protection decision support. Otherwise, when it does not exceed the water quality pollution threshold, continue monitoring.
[0012] As a further improvement of this technical solution, when S6.2 sorts the industrial factories according to the pollution impact amplitude based on the assessment results, the industrial factories with a greater pollution impact amplitude in the area are given higher priority in the sorting, and environmental protection restrictions on these industrial factories are considered first.
[0013] The second object of the present invention is to provide an intelligent groundwater pollution assessment system based on the Internet of Things, including an intelligent groundwater pollution assessment method according to any one of the above, including a regional management unit, an impact analysis unit, and a decision support unit. The regional management unit is used to collect water quality data collected by water quality sensors, divide the management area according to the location of the water quality sensors, attribute industrial factories to the divided management areas, and obtain the working status of each industrial factory. The impact analysis unit is used to obtain the in-region impact data of the water quality data of the attributed management area caused by the adjustment of the working status of each industrial factory, and then obtain the out-region impact data on the downstream management area caused by the adjustment of the working status of each industrial factory. The decision support unit is used to accurately verify the out-region impact data by combining the real-time water quality data with the in-region impact data, then set the water quality pollution threshold, and then compare the in-region impact data combined with the out-region impact data of the management area with the water quality pollution threshold, and then provide environmental protection decision support based on the comparison results.
[0014] Advantages of the present invention compared with the prior art: 1. An intelligent evaluation method and system for groundwater pollution based on the Internet of Things can accurately determine the pollution source by dividing the sewage discharge locations of industrial factories into attribution management areas and analyzing the working status of industrial factories. When there is a change in the working status, the system can timely analyze its impact on the water quality data of the attribution management area, providing a targeted direction for pollution control.
[0015] 2. An intelligent evaluation method and system for groundwater pollution based on the Internet of Things can comprehensively understand the impact of different industrial factories on downstream water quality by establishing a groundwater simulation model, combining the working status of industrial factories and the data of the same area's impact for downstream management area impact analysis, and obtaining the data of different area's impact, providing a basis for cross-regional pollution treatment. At the same time, through the accurate verification of real-time water quality data and the comparison with the water quality pollution threshold, it can timely detect the illegal discharge situation of industrial factories, rank the polluting enterprises, and give priority to environmental protection restrictions on industrial factories with a large pollution impact amplitude, effectively strengthening the control of groundwater pollution. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 is the overall flow block diagram of the present invention; Figure 2 is the flow block diagram for extracting the historical water quality data and historical working status of the water quality sensor of the present invention; Figure 3 is the flow block diagram for obtaining the flow velocity and flow direction of groundwater of the present invention; Figure 4 is the flow block diagram for setting the normal range threshold of the present invention; Figure 5 is the flow block diagram for setting the water quality pollution threshold according to the groundwater pollution management standard of the present invention; Figure 6 is the structural schematic diagram of the regional management unit of the present invention.
[0017] The meanings of the various reference numerals in the figure are as follows: 10. Regional management unit; 20. Impact analysis unit; 30. Decision support unit. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.
[0019] As Figure 1- Figure 6 As shown, one of the objectives of the present invention is to provide an intelligent evaluation method for groundwater pollution based on the Internet of Things, including the following steps: S1. Collect water quality data collected by water quality sensors, and at the same time divide the management area according to the positions of the water quality sensors; S1 connects the water quality sensors and industrial factories through the Internet of Things, and extracts water quality data and location data from the data fed back by the water quality sensors at the same time; Ensure a stable connection between the water quality sensors and the information systems of industrial factories through Internet of Things technology, and use wireless communication protocols, including Wi-Fi, Bluetooth, ZigBee, etc.
[0020] From the data fed back by the water quality sensors, extract water quality parameters, such as acidity and alkalinity, dissolved oxygen, heavy metal content, etc., and location information, latitude and longitude coordinates, through data parsing algorithms.
[0021] Obtain the monitoring range of the water quality sensors, and then divide the management area according to the monitoring range of the water quality sensors. When there is an overlap in the monitoring range area, divide the overlapping area to the water quality sensor closest in distance.
[0022] S2. Attribute the industrial factories according to the divided management area, and at the same time obtain the working status of each industrial factory; S2 extracts the sewage discharge positions of each industrial factory through the Internet of Things, and then attributes the sewage discharge positions to the management areas within the same range; Connect the monitoring equipment or information systems of industrial factories through the Internet of Things to obtain the sewage discharge position information of each industrial factory, represented by latitude and longitude coordinates.
[0023] Extract the operation data of industrial factories through the Internet of Things, and analyze the working status through the operation data combined with the total number of production machines in industrial factories, so as to obtain the working status of each industrial factory. The more machines working in the operation data, the higher the proportion of the working status.
[0024] Extract operation data from the equipment control system or data acquisition system of industrial factories through the Internet of Things, including information such as the number of machines in operation, the total number of production machines, production time, energy consumption, etc.; Regularly update the extracted data, and re-attribute the sewage discharge positions and analyze the working status to ensure the accuracy and timeliness of the system.
[0025] S3. Extract the historical water quality data and historical working status of the water quality sensors, and then analyze the change of the working status by combining the historical water quality data with the historical working status, so as to obtain the same-area impact data of the working status adjustment of each industrial factory on the water quality data in the attributed management area; The steps of S3 are as follows: S3.1. Extract the historical water quality data and historical working status of the water quality sensor; Extract the historical water quality data of the water quality sensor: Obtain the data of various water quality parameters recorded by the water quality sensor in a period of time from the database, including but not limited to pH value, dissolved oxygen, heavy metal content, etc., which record the water quality conditions at different time points; Extract the historical working status: For each industrial factory, obtain the working status data during the same period, including the number of running machines, the total number of production machines, and the proportion of working status, etc.
[0026] S3.2. Intercept the adjustment period when the working status changes in the historical working status, then extract the same period in the historical water quality data according to the adjustment period, and then analyze the working status change of the historical water quality data and historical working status in the same period to obtain the same-region impact data of the working status adjustment of each industrial factory on the water quality data of the affiliated management area. The specific steps are as follows: Determine the change in working status: Analyze the historical working status data of the industrial factory to find the time point when the working status changes. The change in working status is an increase or decrease in the number of running machines; Intercept the adjustment period: Take the time point of the working status change as the center and intercept a period of time before and after as the adjustment period. The length of the adjustment period can be determined according to the specific situation. A time period of one hour before and after the working status change can be intercepted, and then from the historical water quality data of the water quality sensor, extract the water quality data within the same time range as the adjustment period and establish a corresponding relationship. The calculation formula for the same-region impact data is as follows:
[0027] Among them, is the difference in water quality data before and after the adjustment, H2 is the water quality data after the working status adjustment, and H1 is the water quality data before the working status adjustment. Then, according to the adjustment amplitude of the working status, calculate the impact degree. If the number of running machines increases by 30 and the total number of production machines is 100, the working status adjustment amplitude is 0.3. Divide the difference in water quality parameters by the working status adjustment amplitude to obtain the same-region impact data ; can replace with pH value, dissolved oxygen, heavy metal content, etc., so as to calculate the same-region impact data of the working status adjustment on pH value, dissolved oxygen, and heavy metal content; Over time, continue to collect new historical data and repeat the above steps for working status change analysis. By continuously monitoring and analyzing, we can better understand the impact of the working status adjustment of industrial factories on water quality.
[0028] S4. Obtain the flow velocity and direction of groundwater, and then combine the flow direction with the flow velocity and the influence amplitude in the same area to conduct an impact analysis on the downstream management area, so as to obtain the off-site impact data of the adjustment of the working state of each industrial factory on the downstream management area; The steps of S4 are as follows: S4.1. Obtain the flow velocity and direction of groundwater. The calculation formulas are as follows:
[0029] Where J is the hydraulic gradient, h1 and h2 are the groundwater levels at two different positions, and L is the distance between the two positions; The groundwater flow velocity can be calculated by Darcy's law:
[0030] Where V is the groundwater flow velocity and K is the permeability coefficient of the aquifer.
[0031] S4.2. Establish a groundwater simulation model based on the water flow velocity and direction, and then input the working state of the industrial factory into the groundwater simulation model and combine it with the in-area impact data to conduct an impact analysis on the downstream management area, so as to obtain the off-site impact data of the in-area impact data of different industrial factories on the water quality data of the downstream management area. The specific steps are as follows: Establish a groundwater simulation model: Select MODFLOW groundwater simulation software, input relevant parameters according to the hydrogeological conditions of the research area, including the geometry, permeability coefficient, storage coefficient, boundary conditions, etc. of the aquifer, and at the same time calibrate and verify the model using the obtained groundwater flow velocity and direction data; Input the working state and in-area impact data of the industrial factory: Input the working state data of the industrial factory, such as the number of running machines, the total number of production machines, the working state proportion, etc., into the groundwater simulation model. At the same time, input the in-area impact data, that is, the impact of the adjustment of the working state of the industrial factory on the water quality data of the affiliated management area, into the model; Conduct an impact analysis on the downstream management area: Run the groundwater simulation model to simulate the flow of groundwater and the migration process of pollutants, analyze the impact of the in-area impact data of different industrial factories on the water quality data of the downstream management area, that is, the off-site impact data, and establish the groundwater simulation model formula as follows:
[0032] Where C is the pollutant concentration vector (C = (C pH , C DO , C heavymetal ), respectively representing acidity and alkalinity, dissolved oxygen, and heavy metal content), is the change rate of pollutant concentration C with time t, is the gradient operator, is the divergence operator, D is the diffusion coefficient tensor, which describes the diffusion characteristics of pollutants in all directions in groundwater due to molecular diffusion and other effects, and S is the source-sink term (including all factors that may generate or consume pollutants); Determine the coordinate range (x down , y down , z down ) of the downstream management area. For any point (x', y', z') in the downstream management area, the pollutant concentration at time can be obtained by solving the above groundwater simulation model. Take the initial water quality parameters of the downstream management area without the influence of industrial factories as C0 = (C 0，pH , C 0，DO , C 0，heavymetal ). Then, after the working state of the industrial factory is adjusted and after time t, the water quality parameters of the downstream management area become C down = (C down，pH , Cdown，DO , C down，heavymetal ). The calculation of the off-site impact data is as follows:
[0033] where I diff is the off-site impact data, and I same represents the in-site impact data, that is, the impact of the adjustment of the working state of the industrial factory on the water quality data of the affiliated management area, representing the impact of multiple water quality parameters. I same = (I pH , I DO , I heavymetal ), which respectively represent the impacts of pH, dissolved oxygen, heavy metal content, etc. Calculate the degree of change of the water quality parameters in the downstream management area relative to the in-site impact data, that is, the off-site impact data.
[0034] S5. Precisely verify the off-site impact data by combining the real-time water quality data with the in-site impact data. When the real-time water quality data combined with the in-site impact data is within the normal range of the off-site impact data, proceed to S6; The steps of S5 are as follows: S5.1. Precisely verify the off-site impact data by combining the real-time water quality data with the in-site impact data, obtain the water quality impact data after removing the in-site impact data from the real-time water quality data, and then set the normal range threshold; Determine the normal range threshold based on historical data, environmental standards, experience, etc. This threshold can be a range or a specific value, depending on the specific water quality parameters and application scenarios S5.2. Combine the water quality impact data with the off-site impact data and compare them with the normal range threshold. When the difference between the water quality impact data and the off-site impact data is greater than the normal range threshold, it is determined that there is illegal discharge by industrial factories in the management area. Conversely, when the difference between the water quality impact data and the off-site impact data is within the normal range threshold, proceed to S6.
[0035] Continuously collect real-time water quality data, continuously update the in-site impact data and the off-site impact data, repeat the above steps for real-time monitoring and judgment, and regularly adjust the normal range threshold according to the new data and actual situation to improve the accuracy of judgment.
[0036] S6. Set the water quality pollution threshold, then compare the combined in-site impact data and off-site impact data of the management area with the water quality pollution threshold. When it exceeds the water quality pollution threshold, conduct a pollution assessment of industrial factories based on the in-site impact amplitude data and the off-site impact data. Sort the industrial factories according to the pollution impact amplitude based on the assessment results, and then send them to the pollution management terminal for environmental protection decision-making support.
[0037] The steps of S6 are as follows: S6.1. Set the water quality pollution threshold according to the groundwater pollution management standard; According to the groundwater pollution management standard, determine the pollution thresholds of various water quality parameters, including the thresholds of various water quality parameters, such as the pH threshold, dissolved oxygen threshold, heavy metal content threshold, etc.
[0038] S6.2. Compare the combined in-site impact data and off-site impact data of each management area with the water quality pollution threshold. When it exceeds the water quality pollution threshold, conduct a pollution assessment of industrial factories based on the in-site impact amplitude data and the off-site impact data. Sort the industrial factories according to the pollution impact amplitude based on the assessment results, and then send them to the pollution management terminal for environmental protection decision-making support. Conversely, when it does not exceed the water quality pollution threshold, continue monitoring.
[0039] When S6.2 sorts the industrial factories according to the pollution impact amplitude based on the assessment results, the industrial factories with a greater pollution impact amplitude in the area are given higher priority in the sorting, and priority is given to imposing environmental protection restrictions on such industrial factories.
[0040] Send the sorted list of industrial factories to the pollution management terminal for environmental protection decision-making support. For industrial factories with a relatively large pollution impact amplitude, priority is given to imposing environmental protection restrictions on them, such as requiring reduced production, enhanced sewage treatment, etc.
[0041] A second object of the present invention is to provide an intelligent assessment system for groundwater pollution based on the Internet of Things, including an intelligent assessment method for groundwater pollution based on the Internet of Things as described in any one of the above, including a regional management unit 10, an impact analysis unit 20, and a decision-making support unit 30; The area management unit 10 is used to collect water quality data collected by water quality sensors, divide management areas according to the positions of the water quality sensors, assign industrial factories to the divided management areas, and obtain the working status of each industrial factory. The impact analysis unit 20 is used to obtain the in-area impact data of the adjustment of the working status of each industrial factory on the water quality data of the affiliated management area, and then obtain the out-of-area impact data of the adjustment of the working status of each industrial factory on the downstream management area. The decision support unit 30 is used to accurately verify the out-of-area impact data by combining the real-time water quality data with the in-area impact data, then set the water quality pollution threshold, and then compare the in-area impact data and the out-of-area impact data of the management area with the water quality pollution threshold, and then make environmental protection decision support according to the comparison result.
[0042] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.
Claims
1. An intelligent evaluation method for groundwater pollution based on the Internet of Things, characterized in that: It includes the following steps: S1. Collect the water quality data collected by the water quality sensors, and at the same time divide the management areas according to the locations of the water quality sensors; S2. Attribute the industrial factories to the divided management areas, and at the same time obtain the working status of each industrial factory; S3. Extract the historical water quality data and historical working status of the water quality sensors, and then combine the historical water quality data with the historical working status to conduct an analysis of the change in the working status, and obtain the in-area impact data of the adjustment of the working status of each industrial factory on the water quality data of the attributed management area; S4. Obtain the flow velocity and flow direction of the groundwater, and then combine the flow direction with the flow velocity and the in-area impact amplitude to conduct an impact analysis on the downstream management area, and obtain the out-of-area impact data of the adjustment of the working status of each industrial factory on the downstream management area; S5. Combine the real-time water quality data with the in-area impact data to accurately verify the out-of-area impact data. When the real-time water quality data combined with the in-area impact data is within the normal range of the out-of-area impact data, then proceed to S6; S6. Set the water quality pollution threshold, and then compare the in-area impact data and out-of-area impact data of the management area with the water quality pollution threshold. When it exceeds the water quality pollution threshold, conduct a pollution assessment on the industrial factories according to the in-area impact amplitude data and out-of-area impact data, sort the industrial factories according to the pollution impact amplitude, and then send them to the pollution management terminal for environmental protection decision-making support.
2. The intelligent evaluation method for groundwater pollution based on the Internet of Things according to claim 1, characterized in that: In S1, the water quality sensors and industrial factories are connected through the Internet of Things, and at the same time, the water quality data and location data are extracted from the data fed back by the water quality sensors; Obtain the monitoring range of the water quality sensors, and then divide the management areas according to the monitoring range of the water quality sensors. When there is an overlap in the monitoring range areas, divide the overlapping areas to the water quality sensor closest in distance.
3. The intelligent evaluation method for groundwater pollution based on the Internet of Things according to claim 1, characterized in that: In S2, the sewage discharge locations of each industrial factory are extracted through the Internet of Things, and then the sewage discharge locations are attributed to the management areas within the same range; Extract the operation data of the industrial factories through the Internet of Things, and conduct a working status analysis by combining the operation data with the total number of production machines in the industrial factories, so as to obtain the working status of each industrial factory. The more machines working in the operation data, the higher the proportion of the working status.
4. The intelligent evaluation method for groundwater pollution based on the Internet of Things according to claim 1, characterized in that: The steps of S3 are as follows: S3.
1. Extract the historical water quality data and historical working status of the water quality sensors; S3.
2. Intercept the adjustment period when the working status changes in the historical working status, and then extract the same period in the historical water quality data according to the adjustment period. After that, conduct a working status change analysis on the historical water quality data and historical working status of the same period, and obtain the in-area impact data of the adjustment of the working status of each industrial factory on the water quality data of the attributed management area.
5. The intelligent evaluation method for groundwater pollution based on the Internet of Things according to claim 1, characterized in that: The steps of S4 are as follows: S4.
1. Obtain the flow velocity and flow direction of the groundwater; S4.
2. Establish a groundwater simulation model according to the water flow velocity and flow direction, and then input the working status of the industrial factories into the groundwater simulation model and combine it with the in-area impact data to conduct an impact analysis on the downstream management area, so as to obtain the out-of-area impact data of the in-area impact data of different industrial factories on the water quality data of the downstream management area.
6. The intelligent evaluation method for groundwater pollution based on the Internet of Things according to claim 1, wherein: The steps of S5 are as follows: S5.
1. Combine real-time water quality data with in-region impact data to accurately verify out-region impact data, obtain water quality impact data after removing the in-region impact data from the real-time water quality data, and then set the normal range threshold. S5.
2. Compare the water quality impact data combined with the out-region impact data with the normal range threshold. When the difference between the water quality impact data and the out-region impact data is greater than the normal range threshold, it is determined that there is illegal discharge by industrial factories in the management area. Conversely, when the difference between the water quality impact data and the out-region impact data is within the normal range threshold, proceed to S6.
7. The intelligent evaluation method for groundwater pollution based on the Internet of Things according to claim 1, characterized in that: The steps of S6 are as follows: S6.
1. Set the water quality pollution threshold according to the groundwater pollution management standard. S6.
2. Compare the in-region impact data combined with the out-region impact data of each management area with the water quality pollution threshold. When it exceeds the water quality pollution threshold, conduct a pollution assessment of industrial factories based on the in-region impact amplitude data and the out-region impact data. Sort the industrial factories according to the pollution impact amplitude based on the assessment results, and then send them to the pollution management terminal for environmental protection decision support. Conversely, when it does not exceed the water quality pollution threshold, continue monitoring.
8. The intelligent evaluation method for groundwater pollution based on the Internet of Things according to claim 7, characterized in that: When sorting the industrial factories according to the pollution impact amplitude in S6.2, the industrial factories with a greater pollution impact amplitude in the area are given higher priority in the sorting, and environmental protection restrictions on these industrial factories are considered first.
9. For implementing an intelligent evaluation system for groundwater pollution based on the Internet of Things, including an intelligent evaluation method for groundwater pollution based on the Internet of Things according to any one of claims 1-8, characterized in that: It includes a regional management unit (10), an impact analysis unit (20), and a decision support unit (30). The regional management unit (10) is used to collect water quality data collected by water quality sensors, divide the management area according to the location of the water quality sensors, assign industrial factories to the divided management areas, and obtain the working status of each industrial factory. The impact analysis unit (20) is used to obtain the in-region impact data of the water quality data in the affiliated management area due to the adjustment of the working status of each industrial factory, and then obtain the out-region impact data on the downstream management area due to the adjustment of the working status of each industrial factory. The decision support unit (30) is used to accurately verify the out-region impact data by combining the real-time water quality data with the in-region impact data, then set the water quality pollution threshold, then compare the in-region impact data combined with the out-region impact data of the management area with the water quality pollution threshold, and then provide environmental protection decision support based on the comparison results.