CN115545678A - Water quality monitoring method based on water environment portrait and pollutant traceability - Google Patents
Water quality monitoring method based on water environment portrait and pollutant traceability Download PDFInfo
- Publication number
- CN115545678A CN115545678A CN202211502936.5A CN202211502936A CN115545678A CN 115545678 A CN115545678 A CN 115545678A CN 202211502936 A CN202211502936 A CN 202211502936A CN 115545678 A CN115545678 A CN 115545678A
- Authority
- CN
- China
- Prior art keywords
- monitoring
- water quality
- tracing
- water
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 184
- 238000012544 monitoring process Methods 0.000 title claims abstract description 168
- 239000003344 environmental pollutant Substances 0.000 title claims abstract description 69
- 231100000719 pollutant Toxicity 0.000 title claims abstract description 69
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000011144 upstream manufacturing Methods 0.000 claims abstract description 34
- 238000005192 partition Methods 0.000 claims abstract description 18
- 238000009826 distribution Methods 0.000 claims abstract description 12
- 230000007613 environmental effect Effects 0.000 claims abstract description 12
- 238000012806 monitoring device Methods 0.000 claims abstract description 11
- 238000013136 deep learning model Methods 0.000 claims description 16
- 230000008859 change Effects 0.000 claims description 11
- 238000005516 engineering process Methods 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 10
- 230000006870 function Effects 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 8
- 239000010865 sewage Substances 0.000 claims description 7
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 5
- 238000013526 transfer learning Methods 0.000 claims description 4
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000000513 principal component analysis Methods 0.000 claims description 3
- 238000010206 sensitivity analysis Methods 0.000 claims description 3
- 238000011835 investigation Methods 0.000 abstract description 12
- 239000000356 contaminant Substances 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 230000015654 memory Effects 0.000 description 3
- 238000012821 model calculation Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000004941 influx Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000011109 contamination Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000010842 industrial wastewater Substances 0.000 description 1
- 230000008595 infiltration Effects 0.000 description 1
- 238000001764 infiltration Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 230000007787 long-term memory Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003012 network analysis Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000005086 pumping Methods 0.000 description 1
- 238000001303 quality assessment method Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000002352 surface water Substances 0.000 description 1
- 238000003911 water pollution Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/018—Certifying business or products
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Educational Administration (AREA)
- Biophysics (AREA)
- Game Theory and Decision Science (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Molecular Biology (AREA)
- Computer Hardware Design (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Finance (AREA)
- Geometry (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
- Accounting & Taxation (AREA)
Abstract
The invention discloses a water quality monitoring method based on water environment portrait and pollutant tracing, which overcomes the problems of difficult water quality monitoring, high pollutant tracing cost and low efficiency in the prior art and comprises the following steps: s1: acquiring pipe network information, dividing drainage partitions and establishing a drainage model; s2: distributing water quality monitoring points, and installing a monitoring device for monitoring the water quality; s3: establishing a water environment portrait according to the water quality monitoring data and the environmental data; s4: and tracing the upstream pollutants and predicting the downstream pollutants according to the water quality monitoring data. The water quality of a monitoring area is analyzed by utilizing the water environment image, pollution tracing is carried out aiming at single-point alarming, the emission pollution source is reduced to a certain area range through a pipe network model and the regional distribution monitoring data of the water quality monitoring equipment, the final pollution source is locked according to the pollution emission characteristics of the regional drainage unit, most of manual investigation time is saved, and the working efficiency is improved.
Description
Technical Field
The invention relates to the technical field of water source pollution monitoring, in particular to a water quality monitoring method based on water environment portrait and pollutant tracing.
Background
With the continuous development and expansion of cities, urban infrastructures bear larger and larger workload, particularly to urban riverways and underground pipe network systems, and the borne landscape, drainage and flood control, and water supply and drainage pressure of life production are gradually increased. Meanwhile, the improvement of the living standard also correspondingly puts higher requirements on urban management, which includes the guarantee of urban water environment quality and the prevention and treatment of water body pollution. However, water pollution events have great treatment difficulty and difficulty in forecasting and early warning.
At present, the drainage pipe network has the main problem that the pollution source is easy to be misplaced, namely, the pollution source is identified at the position where the pollution source does not exist or the pollution source is identified at the position where the pollution source should exist. The premise of efficiently identifying the dislocation pollution source is to trace the source of the pollutant, namely, the cause-effect corresponding relation of the pollutant and the source in the pipe network is judged by qualitatively or quantitatively researching the source of the pollutant. The traditional pollution source investigation work can only preliminarily judge the type and the pollution production load of the pollution source, and the specific contribution rate of different pollution sources to the pipe network water body is difficult to reflect; in addition, the number of drainage households of a drainage pipe network is large, the variety is large, the sewage components are complex, the main pollution sources comprise domestic sewage, industrial wastewater, surface runoff, infiltration underground water, surface water flowing backwards and the like, and the pollution source investigation work needs high cost. And the searching of the standard-exceeding discharge unit can only be checked one by one according to the experience of related workers and the knowledge of regional sewage enterprises, so that the working efficiency is lower and the accuracy is not high.
Disclosure of Invention
The invention aims to solve the problems of difficulty in water quality monitoring, high pollutant tracing cost and low efficiency in the prior art, and provides a water quality monitoring method based on water environment images and pollutant tracing.
In order to achieve the purpose, the invention adopts the following technical scheme: a water quality monitoring method based on water environment portrait and pollutant tracing comprises the following steps:
s1: acquiring pipe network information, dividing drainage partitions and establishing a drainage model;
s2: distributing water quality monitoring points, and installing a monitoring device to monitor the water quality;
s3: establishing a water environment portrait according to the water quality monitoring data and the environmental data;
s4: and tracing the upstream pollutants and predicting the downstream pollutants according to the water quality monitoring data.
The drainage subareas refer to the work of dividing one area into a plurality of drainage areas with different drainage modes by considering the factors of the terrain, the water system, the hydrogeology, the water level of a drainage area, the administrative division and the like of the drainage area, wherein the division of the drainage subareas is realized by dividing high water and low water, dividing internal water and external water, dividing main water and guest water, draining nearby water, mainly by self-drainage and taking pumping drainage as assistance, and the requirements of water conservancy and administrative division management are properly considered.
The tall and erect aspect of the invention has outstanding capability of tracing water environment images and pollutants. According to the invention, the water environment portrait of a certain period can be deduced through the existing data (data information such as river water quality, flow data, weather and temperature during discharge) according to a specific model. The invention can further produce data such as a predicted trend curve, a trend curve comparison, a multi-dimensional data comparison and the like based on the obtained water environment image, and can provide corresponding help for water environment treatment according to the data. On the other hand, the invention reduces the emission pollution source to a certain area range through the pipe network model and the water quality monitoring device area distribution monitoring data, and locks the final pollution source according to the pollution emission characteristics of the drainage unit in the area. Most of manual investigation time is saved, and the working efficiency is improved.
Preferably, the step S1 further includes:
s1.1: combing a pipe network model of a monitoring area based on basic information of a sewage system, and dividing a main pipe, a main pipe and branch pipes;
s1.2: dividing a monitoring area into a plurality of drainage sheet areas according to the type of a pipe network and the connection relation of the pipe network;
s1.3: and aiming at the drainage subarea, monitoring points are distributed at the middle key node or the branch pipe confluence part according to the trend of the pipe network.
The basic information of the sewage system comprises the pipe network trend, topological relation, pipe diameter, gradient and the like; usually, each drainage subarea is provided with a corresponding drainage outlet, and the drainage condition of the current whole drainage subarea can be monitored in real time at a monitoring point arranged at the drainage outlet. And aiming at each drainage subarea, monitoring points are distributed at middle key nodes or branch pipe confluence positions according to the trend of the pipe network.
Preferably, the step S2 is further expressed as:
s2.1: water quality monitoring points and flow monitoring points are distributed at the access part of the pipe network and along the line of the primary pipe network;
s2.2: laying video monitoring points at a water flow convergence point;
s2.3: and a rotation monitoring or manual handheld monitoring method is adopted, the monitoring range is narrowed, and the monitoring distribution is iterated.
The premise of tracing the source of the pollutants is that corresponding monitoring devices are required to be installed according to site selection rules, and relevant data are collected. Appoint in the monitoring range of dividing and supervise the row of mouthful and the appointed position of supervision district, the monitoring devices installation of being convenient for, the surveillance camera head can cover and require the supervision area. The pipe network access place comprises a second-level pipe network access place, a first-level pipe network access place, a special user access municipal pipe network access port and the like.
Preferably, the step S3 further includes:
s3.1: acquiring historical water quality data and environmental data of a river network and a pipe network, preprocessing the acquired data, and establishing a data set;
s3.2: analyzing factors influencing the environmental water quality through principal component analysis and special sensitivity analysis;
s3.3: marking a water quality data label, and establishing a water environment deep learning model;
s3.4: and (3) outputting a prediction result by using the water environment deep learning model, and establishing a water environment image curve chart and a real-time database table by combining real-time monitoring data to realize water quality monitoring of the river network and the pipe network.
The water environment portrait can make the object more focused, the portrait can also improve the decision efficiency, the water environment portrait is divided into different types according to the data conditions of the water quality and the flow rate of the target drainage unit, and the difference of factors such as weather, temperature, date type and the like during drainage, the different types are quickly organized together, and then the obtained types are extracted to form the water environment portrait of one type. According to the invention, the water environment portrait of a certain period can be deduced through the existing data (data information such as river water quality, flow data, weather and temperature during discharge) according to the deep learning model. The invention can further produce data such as a predicted trend curve, a trend curve comparison, a multi-dimensional data comparison and the like based on the obtained water environment image, and can provide corresponding help for water environment treatment according to the data.
Preferably, the step S3.3 further comprises:
s3.3.1: dividing a data set into a training set and a testing set;
s3.3.2: building a deep learning model adopting an LSTM network for each monitoring point;
s3.3.3: training the model by using a training set, evaluating the model by using a test set, and storing the model when the model meets the requirements of actual deployment accuracy and stability;
s3.3.4: and loading a deep learning model, and realizing water quality and flow forecast of the section or the node without actual measurement data by adopting a transfer learning technology to realize comprehensive monitoring of the river network and the pipe network.
According to the scheme, an intelligent water environment portrait is built by combining a newly developed deep learning technology, a transfer learning technology and an Internet of things technology, and urban water environment management with efficient modeling, rapid forecasting and global perception is realized. The LSTM model, namely a long-short term memory model (long-short term memory), is a special RNN model and is proposed for solving the problem of gradient diffusion of the RNN model; in the conventional RNN, the training algorithm uses the BPTT, when the time is long, the residual error that needs to be returned decreases exponentially, which results in slow update of network weight, and the effect of long-term memory of RNN cannot be reflected, and a storage unit is needed to store memory, so the LSTM model is proposed.
Preferably, the step S4 further includes:
s4.1: acquiring an alarm list, selecting alarm points, and tracing the pollution source;
s4.2: analyzing the alarm condition of the monitoring points by tracing, determining the occurrence range of the pollution source, and displaying the secondary monitoring points and the enterprise information in the range of the pollution source;
s4.3: and re-detecting the water quality condition of the secondary monitoring points in the pollution source generation range, continuously tracing according to new monitoring data, reducing the pollution source range, and displaying information of n meters of enterprises in the pollution range.
And tracing the source of the pollutants according to the selected alarm point. The secondary monitoring points are monitoring points set by the user, and a monitoring device is not actually set. And all enterprises within the range of n meters are suspicious enterprises of pollution by taking the finally defined pollution source pipe section as the center.
Preferably, said step S4.2 is further expressed as:
s4.2.1: associating the pipe network and the monitoring station in each drainage subarea with an enterprise by utilizing a space analysis function in the GIS;
s4.2.2: if the pollutant at a certain point exceeds the standard, taking the certain point as a pollution source, tracing the pollutant upstream, primarily determining a pollution range, tracing the pollutant downstream, and determining an influence range, the alarm time of a downstream monitoring point and the change of the pollutant concentration.
Tracing upstream, determining suspicious pipe sections, delineating suspicious secondary monitoring points, arranging suspicious drainage users, and determining affected tertiary drainage partitions; tracing the source downstream, and determining the quantity of the affected pipe sections, the quantity of the affected drainage partitions, the alarm time of the downstream monitoring points and the change of the pollutant concentration. After the drainage subareas which should be checked are determined, the enterprises in the specific drainage subareas can be checked one by one, and finally the enterprises with the highest pollution source suspicion degree are locked.
Preferably, the upstream tracing specifically includes:
judging the state of the monitoring point within t hours of the upstream monitoring point, and recording the alarm time, the alarm index and the alarm index value if the state is abnormal;
and searching two adjacent monitoring results at the upstream as a normal and abnormal combination, and if a plurality of combination types simultaneously appear, tracing the combination at the most upstream to determine the suspicious position of the pollution source.
And according to the first tracing, after a suspicious range is defined, acquiring secondary monitoring points in the range, monitoring the water quality data of the secondary monitoring points offline, after monitoring values are input online, continuing tracing, searching the most upstream one-positive one-different combination, and reducing the pollution source range again.
Preferably, said step S4.3 is further expressed as:
s4.3.1: monitoring whether the water quality data of the secondary monitoring points in the pollution range is normal, if so, reducing the pollution range, and if not, judging whether other secondary monitoring points exist in the pollution range until all suspicious secondary monitoring points in the pollution range are confirmed;
s4.3.2: upstream tracing, determining a final pollution range, downstream tracing, determining a final influence range, warning time of a downstream monitoring point and pollutant concentration change.
Personnel cost is reduced and pollutant tracing efficiency is promoted to a great extent under the traditional pollutant tracing mode, and personnel cost is reduced to a great extent through a mode of combining model calculation and manual investigation.
Therefore, the invention has the following beneficial effects: 1. the method has the advantages that the emission pollution source is reduced to a certain area range through the pipe network model and the regional distribution monitoring data of the water quality monitoring device, the final pollution source is locked according to the pollution emission characteristics of the drainage unit in the region, the personnel cost is greatly reduced and the pollutant tracing efficiency is improved under the traditional pollutant tracing mode, and the personnel cost is greatly reduced through the mode of combining model calculation and manual investigation; 2. through the water environment portrait, data such as a predicted trend curve, a trend curve comparison, a multi-dimensional data comparison and the like can be further produced, so that corresponding help is provided for water environment management.
Drawings
FIG. 1 is a flow chart of the operation of the method of the present invention.
FIG. 2 is a flow chart of the water environment image function of the present invention.
Fig. 3 is a flow chart of tracing the source of the contaminant according to the present invention.
FIG. 4 is a diagram illustrating a pollution tracing log according to the present invention.
Fig. 5 is a tracing principle diagram of upstream pollutants of the invention.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
in the embodiment shown in fig. 1, a water quality monitoring method based on water environment portrait and pollutant tracing can be seen, and the operation flow is as follows: acquiring pipe network information, dividing drainage partitions and establishing a drainage model; step two, laying water quality monitoring points, installing a monitoring device and monitoring the water quality; step three, establishing a water environment portrait according to the water quality monitoring data and the environmental data; and fourthly, tracing the upstream pollutants and predicting the downstream pollutants according to the water quality monitoring data.
The tall and erect aspect of the invention has outstanding capability of tracing water environment images and pollutants. According to the method, the water environment image of a certain period can be deduced according to a specific model through the existing data (the data information such as the water quality and the flow rate of a river channel, the weather and the temperature during discharge and the like). The invention can further produce data such as a predicted trend curve, trend curve comparison, multi-dimensional data comparison and the like based on the obtained water environment image, and can provide corresponding help for water environment treatment according to the data. On the other hand, the invention is based on a drainage model and combines with a real-time monitoring data network, and the embodiment also provides the functions of a tracker, upstream and downstream pipeline network analysis, node water depth, pipeline fullness, pipeline flow rate and the like, gradually reduces the investigation range by using the space analysis function of the GIS, and can also deduce the position of the pollutant by selecting the alarm point position by combining with a manual investigation mode. Pollution tracing is performed aiming at single-point alarming, most of manual investigation time is saved, and the working efficiency is improved.
The technical solution of the present application is further explained by specific examples below.
The first step is as follows: acquiring pipe network information, dividing drainage partitions and establishing a drainage model.
Combing a pipe network model of a monitoring area based on basic information (pipe network trend, topological relation, pipe diameter, gradient and the like) of a sewage system, and dividing a main pipe, a main pipe and branch pipes; dividing a monitoring area into a plurality of drainage sheet areas according to the type of a pipe network and the connection relation of the pipe network (generally, each drainage partition area is provided with a corresponding drainage outlet, and a monitoring point arranged at the drainage outlet can monitor the drainage condition of the current whole drainage partition area in real time); and aiming at the drainage subarea, monitoring points are distributed at the middle key node or the branch pipe confluence part according to the trend of the pipe network.
The second step is that: and (4) laying water quality monitoring points, and installing a monitoring device to monitor the water quality.
The premise of tracing the source of the pollutant is that a corresponding monitoring device needs to be installed according to the site selection rule to obtain relevant data.
The specific layout rule is as follows:
the river course layout rule is as follows:
1. river water quality monitoring and stationing principle: the section is manually monitored in national control, provincial control and city control, so that the continuity of monitoring data is ensured and the requirement of water quality assessment is met; the entry and exit section is used for judging the source and the destination of pollutants according to monitoring data and is arranged at built-up areas of cities and towns, industrial areas, rural population gathering points and important water flow merging positions as appropriate; in order to solve the water quality change caused by water distribution, monitoring sections are arranged on the upstream and downstream of a water distribution gate pump station, and if the water quality is obviously different, the sections are arranged on the upper and lower parts of the gate pump station; if the water quality has no obvious difference, only a monitoring section can be arranged on the gate pump station; for important rivers with longer flows, monitoring sections are arranged after a proper distance in order to solve the change conditions of water quality and water quantity; monitoring sections are arranged at the river mouth before the major branch of the water system is converged, and the inlet and the outlet of lakes, reservoirs and main rivers.
2. The video monitoring point distribution principle of the river channel: the distance between the point locations is not more than 500 meters in a built-up area and an industrial area of a town, and the point locations are arranged at population gathering points, important water flow influx points, important traffic points and other important point locations in rural areas; covering a river channel monitoring blind spot of the whole area; and the two banks of the river are crossed every 300-800 meters.
3. The distribution principle of the river water level water speed flow station is as follows: the number of the river channels is 1 at each end, and important water flow influx positions can be arranged according to the circumstances; areas where sudden water increase may be caused after river branch confluence; for important rivers with longer flows, in order to solve the water quantity change situation, a monitoring section is arranged after a proper distance; in order to solve the water quantity change caused by water distribution, monitoring sections are arranged on the upstream and downstream of a water distribution gate pump station according to the upstream and downstream water quantity conditions.
The pipe network layout rule is as follows:
1. the online monitoring point positions of the water quality and the flow of the pipe network are as follows: water quality monitoring points and flow monitoring points are arranged at pipe network access positions (a second-level pipe network access position is connected to a first-level pipe network, a special user is connected to a municipal pipe network access port), main road intersections and the lines of the first-level pipe network.
2. And laying a video monitoring point at the water flow convergence point.
3. The rainwater pipe network layout principle: a key rainwater drainage outlet of the river channel; the main pipe network and the secondary pipe network are connected at the junction.
And a rotation monitoring or manual handheld monitoring method is adopted, the monitoring range is narrowed, and the monitoring distribution is iterated.
The third step: and establishing a water environment portrait according to the water quality monitoring data and the environmental data.
The water environment portrait can make the object more focused, the portrait can also improve the decision efficiency, the water environment portrait is divided into different types according to the data conditions of the water quality and the flow rate of the target drainage unit, and the difference of factors such as weather, temperature, date type and the like during drainage, the different types are quickly organized together, and then the obtained types are extracted to form the water environment portrait of one type.
According to the method, an intelligent water environment portrait is built by combining a deep learning technology, a transfer learning technology and an Internet of things technology, and urban water environment management with efficient modeling, rapid forecasting and global perception is realized. Specifically, the method comprises the following steps:
1. the method comprises the steps of obtaining water quality data and environmental data (rainfall, temperature, flow and the like) of a river network and a pipe network in the past year through the Internet of things technology, preprocessing (data cleaning, characteristic analysis and normalization) the obtained data, and sorting the actually measured data into a data set meeting the quality requirement of deep learning data.
2. And analyzing various factors influencing the water quality of the urban water environment through principal component analysis and characteristic sensitivity analysis.
3. Marking a water quality data label, and establishing a water environment deep learning model: dividing the sorted data set into a training set and a testing set; then, establishing a deep learning model adopting an LSTM (long-short time model) network for each monitoring point, wherein parameters of the LSTM model mainly comprise flow, liquid level, COD (chemical oxygen demand), current day weather temperature date and the like in the pipeline network; training the model by using a training set, evaluating the model by using a test set, and storing the model when the model meets the requirements of actual deployment accuracy and stability; and loading the trained deep learning model of the water quality and flow of the river network sections/pipe network nodes, and realizing the water quality and flow forecast of the sections or the nodes without actual measurement data by adopting a migration learning technology, thereby realizing the comprehensive supervision of the urban river network and the pipe network system.
4. And (3) outputting a prediction result by using the water environment deep learning model, and establishing a water environment image curve chart and a real-time database table by combining real-time monitoring data to realize water quality monitoring of the river network and the pipe network.
The specific functions of the water environment deep learning model are as shown in FIG. 2: firstly, acquiring basic data configuration, including selecting monitoring points and monitoring parameters, and setting prediction cut-off time; then, configuring environmental data, including setting a temperature range of a prediction time period, predicting a time rainfall condition and predicting a time flow condition; and then inputting the basic data and the environmental data into a water environment deep learning model, outputting a prediction curve, and combining the prediction curve with real-time data collected by monitoring point equipment to obtain a water environment image curve graph and a real-time database table.
The fourth step: and tracing the upstream pollutants and predicting the downstream pollutants according to the water quality monitoring data.
The tracing process of the pollutants is shown in figure 3:
1. and acquiring an alarm list, selecting an alarm point, and tracing the pollution source.
2. And analyzing the alarm condition of the monitoring points by tracing, determining the occurrence range of the pollution source, and displaying the secondary monitoring points and the enterprise information in the pollution source range.
Associating the pipe network and the monitoring station in each drainage subarea with an enterprise by utilizing a space analysis function in the GIS; if the pollutant at a certain point exceeds the standard, the certain point is taken as a pollution source, the pollutant is traced upstream, the pollution range is preliminarily determined (including determining a suspicious pipe section, delineating suspicious secondary monitoring points, arranging suspicious users and determining affected tertiary drainage partitions), meanwhile, the source is traced downstream, and the influence range, the warning time of the downstream monitoring points and the change of the pollutant concentration are determined.
3. The water quality condition of a secondary monitoring point in the pollution source occurrence range is re-detected, the tracing is continued according to new monitoring data, the pollution source range is reduced, and n (set according to actual conditions) meters of enterprises in the pollution range are subjected to information display: monitoring whether the water quality data of the secondary monitoring points in the pollution range is normal, if so, reducing the pollution range, if not, judging whether other secondary monitoring points exist in the pollution range, if so, continuously judging whether the water quality data of the secondary monitoring points are normal until all suspicious secondary monitoring points in the pollution range are confirmed; tracing upstream, determining a final pollution range (comprising suspicious even drainage households and affected tertiary partitions), tracing downstream, and determining final affected pipe section data, affected drainage partitions, warning time of downstream monitoring points and pollutant concentration change.
Specifically, the method comprises the following steps:
tracing the contaminants upstream, the preliminary determination of the contamination range includes:
the upstream tracing is divided into two steps: (1) tracing and analyzing an upstream first-level monitoring point; (2) and performing source tracing analysis on the upstream secondary virtual monitoring points.
(1) Judging the state of the monitoring point within t hours of the upstream monitoring point, and recording the alarm time, the alarm index and the alarm index value if the state is abnormal; and if the state is normal, recording that the monitoring point is normal, and establishing a pollution tracing log.
In the present embodiment, the language is as follows:
[ NORMAL ] the XXX monitoring point monitors that XXX is normal from XXX to XXX (t hours before the alarm time occurs-alarm time);
[ ABNORMALITY ] XXX monitoring points monitor that XXX exceeds standard at XXXX time, and the monitoring value is XXX.
A specific example is shown in fig. 4.
(2) And searching a normal and abnormal combination of two adjacent monitoring results at the upstream, and if a plurality of combination types simultaneously appear, tracing the most upstream combination to determine the suspicious position of the pollution source.
The specific tracing process, i.e. the model principle, is shown in fig. 5, and the triangles in fig. 5 represent enterprises:
in this embodiment, the whole plot is divided into 4 drainage partitions, on the basis of the divided drainage partitions, a pipe network and a monitoring station in each drainage partition are associated with an enterprise by using a space analysis function in gis, and an arrow represents the flow direction of pipe water flow.
At this time, if the pollutant detected at the point A exceeds the standard, that is, if the pollutant is detected at the point A beyond the standard
(wherein the content of a certain index in the water at the pipeline monitoring point A,
the content threshold value of one index in water at a pipeline monitoring point A), then tracing back pollutants upstream, and checking the content of the corresponding one index at a position B and a position C of a downstream pipeline.
If no related personnel of the secondary monitoring station exist in the current drainage subarea, the monitoring point position can be manually set and the monitoring index measurement is carried out. As in fig. 4, the staff may choose a location such as C for index measurement if there is no secondary monitoring site within the drainage partition (2). Then the current contaminant can be specifically classified into 4 types of cases:
After the drainage partitions which should be checked are determined, the enterprises in the specific drainage partitions can be checked one by one, and finally the enterprises with the highest pollution source suspicion degree (the enterprises with the highest suspicion degree may not be unique) are locked.
The scheme of the invention has the following advantages:
1. the water environment portrait can be used for deducing a water environment portrait of a certain period according to a deep learning model through the existing data (data information such as river channel water quality, flow data, weather and temperature during discharge and the like); the invention can further produce data such as a predicted trend curve, a trend curve comparison, a multi-dimensional data comparison and the like based on the obtained water environment image, and can provide corresponding help for water environment treatment according to the data.
2. The drainage model is used as a basis, a real-time monitoring data network is combined, the investigation range is gradually reduced by the space analysis function of gis, the drainage model is combined with a manual investigation mode, the alarm point position can be selected automatically, the position of a pollutant is deduced, the personnel cost is reduced, the tracing efficiency of the pollutant is improved, and the personnel cost is reduced to a great extent by the combination of model calculation and manual investigation.
3. The system can further improve the information standard system, and improve the standard and standard system in the aspects of information data organization format standard, data updating maintenance mode, data interface standard, universal system interface service standard, information exchange and sharing mechanism, information system operation and maintenance management standard and the like. By applying new technologies such as cloud computing, big data, mobile interconnection, the Internet of things and a 5G network, and through continuous technological innovation, inexhaustible power is provided for the development of digital water treatment.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.
Claims (9)
1. A water quality monitoring method based on water environment portrait and pollutant tracing is characterized by comprising the following steps:
s1: acquiring pipe network information, dividing drainage partitions and establishing a drainage model;
s2: distributing water quality monitoring points, and installing a monitoring device for monitoring the water quality;
s3: establishing a water environment portrait according to the water quality monitoring data and the environmental data;
s4: and tracing the upstream pollutants and predicting the downstream pollutants according to the water quality monitoring data.
2. The water quality monitoring method based on the water environment portrait and the pollutant tracing is characterized in that the step S1 further comprises the following steps:
s1.1: combing a pipe network model of a monitoring area based on basic information of a sewage system, and dividing a main pipe, a main pipe and branch pipes;
s1.2: dividing a monitoring area into a plurality of drainage sheet areas according to the type of a pipe network and the connection relation of the pipe network;
s1.3: and aiming at the drainage subarea, monitoring points are distributed at the middle key node or the branch pipe confluence part according to the trend of the pipe network.
3. The water quality monitoring method based on the water environment portrait and the pollutant tracing as claimed in claim 1 or 2, wherein the step S2 is further represented as:
s2.1: water quality monitoring points and flow monitoring points are distributed at the access part of the pipe network and along the line of the primary pipe network;
s2.2: laying video monitoring points at a water flow convergence point;
s2.3: and a rotation monitoring or manual handheld monitoring method is adopted, the monitoring range is reduced, and the monitoring distribution is iterated.
4. The water quality monitoring method based on the water environment portrait and the pollutant tracing is characterized in that the step S3 further comprises the following steps:
s3.1: acquiring historical water quality data and environmental data of a river network and a pipe network, preprocessing the acquired data, and establishing a data set;
s3.2: analyzing factors influencing the environmental water quality through principal component analysis and special sensitivity analysis;
s3.3: marking a water quality data label, and establishing a water environment deep learning model;
s3.4: and (3) outputting a prediction result by using the water environment deep learning model, and establishing a water environment image curve chart and a real-time database table by combining real-time monitoring data to realize water quality monitoring of the river network and the pipe network.
5. The water quality monitoring method based on the water environment portrait and the pollutant tracing is characterized in that the step S3.3 further comprises the following steps:
s3.3.1: dividing the data set into a training set and a testing set;
s3.3.2: building a deep learning model adopting an LSTM network for each monitoring point;
s3.3.3: training the model by using a training set, evaluating the model by using a test set, and storing the model when the model meets the requirements of actual deployment accuracy and stability;
s3.3.4: and loading a deep learning model, and realizing water quality and flow forecast of the section or the node without actual measurement data by adopting a transfer learning technology to realize comprehensive monitoring of the river network and the pipe network.
6. The water quality monitoring method based on the water environment portrait and the pollutant tracing is characterized in that the step S4 further comprises the following steps:
s4.1: acquiring an alarm list, selecting an alarm point, and tracing the source of the pollutant;
s4.2: analyzing the alarm condition of the monitoring points by tracing, determining the occurrence range of the pollution source, and displaying the secondary monitoring points and the enterprise information in the range of the pollution source;
s4.3: and re-detecting the water quality condition of the secondary monitoring points in the pollution source generation range, continuously tracing according to new monitoring data, reducing the pollution source range, and displaying information of n meters of enterprises in the pollution range.
7. The water quality monitoring method based on water environment portrait and pollutant traceability as claimed in claim 6, wherein the step S4.2 is further represented as:
s4.2.1: associating the pipe network and the monitoring station in each drainage subarea with an enterprise by utilizing a space analysis function in the GIS;
s4.2.2: if the pollutant at a certain point exceeds the standard, taking the certain point as a pollution source, tracing the pollutant upstream, primarily determining a pollution range, tracing the pollutant downstream, and determining an influence range, the alarm time of a downstream monitoring point and the change of the pollutant concentration.
8. The water quality monitoring method based on the water environment portrait and the pollutant traceability as claimed in claim 7, wherein the upstream traceability specifically comprises:
judging the state of the monitoring point within t hours of the upstream monitoring point, and recording the alarm time, the alarm index and the alarm index value if the state is abnormal;
and searching two adjacent monitoring results at the upstream as a normal and abnormal combination, and if a plurality of combination types simultaneously appear, tracing the combination at the most upstream to determine the suspicious position of the pollution source.
9. A water quality monitoring method based on water environment portrait and pollutant source tracing according to claim 6, 7 or 8, characterized in that said step S4.3 is further expressed as:
s4.3.1: monitoring whether the water quality data of the secondary monitoring points in the pollution range is normal, if so, reducing the pollution range, and if not, judging whether other secondary monitoring points exist in the pollution range until all suspicious secondary monitoring points in the pollution range are confirmed;
s4.3.2: upstream tracing, determining a final pollution range, downstream tracing, determining a final influence range, warning time of a downstream monitoring point and pollutant concentration change.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211502936.5A CN115545678A (en) | 2022-11-29 | 2022-11-29 | Water quality monitoring method based on water environment portrait and pollutant traceability |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211502936.5A CN115545678A (en) | 2022-11-29 | 2022-11-29 | Water quality monitoring method based on water environment portrait and pollutant traceability |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115545678A true CN115545678A (en) | 2022-12-30 |
Family
ID=84722533
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211502936.5A Pending CN115545678A (en) | 2022-11-29 | 2022-11-29 | Water quality monitoring method based on water environment portrait and pollutant traceability |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115545678A (en) |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115859068A (en) * | 2023-03-03 | 2023-03-28 | 四川三思德科技有限公司 | Environmental information sensing method and system for intelligent water conservancy architecture |
| CN115979348A (en) * | 2023-03-17 | 2023-04-18 | 近通物联网(苏州)有限公司 | Method, device and equipment for investigating mixed flow of rain and sewage of pipe network and medium |
| CN116029884A (en) * | 2023-03-29 | 2023-04-28 | 北京云庐科技有限公司 | River pollution tracing method based on river channel water pollution tracing model |
| CN116105804A (en) * | 2023-04-13 | 2023-05-12 | 江苏海峡环保科技发展有限公司 | Intelligent water quality safety detection and early warning system |
| CN116128144A (en) * | 2023-02-15 | 2023-05-16 | 北京清环智慧水务科技有限公司 | Urban drainage pipe network partition linkage management method and system |
| CN116168771A (en) * | 2023-04-26 | 2023-05-26 | 北京建工环境修复股份有限公司 | Method and system for monitoring pollution of perfluorinated compounds in river based on Internet of things |
| CN116205592A (en) * | 2023-02-07 | 2023-06-02 | 广东慧航天唯科技有限公司 | Pollution anomaly tracing method for drainage pipe network of intelligent Internet of things |
| CN116298159A (en) * | 2023-03-14 | 2023-06-23 | 南京高科环境科技有限公司 | Industrial sewage plant pollutant tracing method and device, storage medium and computer equipment |
| CN116757707A (en) * | 2023-06-19 | 2023-09-15 | 深圳市同富信息技术有限公司 | Crop fruit growth tracing method and system |
| CN117007762A (en) * | 2023-09-21 | 2023-11-07 | 南通香境生物医药科技有限公司 | Multi-water-area water quality monitoring method based on data visualization |
| CN117151391A (en) * | 2023-08-30 | 2023-12-01 | 深圳市水务工程检测有限公司 | Environment pollution monitoring system and method based on Internet of things |
| CN117167671A (en) * | 2023-09-06 | 2023-12-05 | 深圳市科荣软件股份有限公司 | Pipe network operation monitoring control system based on big data |
| CN117269443A (en) * | 2023-09-11 | 2023-12-22 | 杭州智驳科技有限公司 | Intelligent digital rural environment monitoring system based on big data |
| CN117408440A (en) * | 2023-12-15 | 2024-01-16 | 湖南蒙拓环境科技有限公司 | River drain sewage intelligent treatment method and system based on multidimensional sensor |
| CN117420283A (en) * | 2023-12-19 | 2024-01-19 | 上海科泽智慧环境科技有限公司 | Integrated multi-channel water quality monitoring method and system |
| CN117473134A (en) * | 2023-12-28 | 2024-01-30 | 河南省新乡水文水资源测报分中心 | Water resource monitoring and management method and system for tracing pollutants |
| CN117607379A (en) * | 2023-11-27 | 2024-02-27 | 中电建生态环境集团有限公司 | Pollution source investigation method applicable to culvert drainage under different working conditions |
| CN117933572A (en) * | 2024-03-20 | 2024-04-26 | 四川飞洁科技发展有限公司 | Water quality prediction method and related device |
| CN117949046A (en) * | 2024-03-22 | 2024-04-30 | 山东省科学院海洋仪器仪表研究所 | Cluster networking type marine environment tracking and monitoring method and system for abnormal water clusters |
| CN118130745A (en) * | 2024-05-08 | 2024-06-04 | 芯视界（北京）科技有限公司 | Drainage pipe network monitoring method and device, electronic equipment and storage medium |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105278492A (en) * | 2014-06-26 | 2016-01-27 | 广东柯内特环境科技有限公司 | Intelligent monitoring system and method for area pollution discharge |
| CN110222372A (en) * | 2019-05-08 | 2019-09-10 | 中国水利水电科学研究院 | A kind of Flow of River water quality real-time predicting method and device based on data assimilation |
| CN111524030A (en) * | 2020-04-22 | 2020-08-11 | 常州市环境科学研究院 | Plain river network area water environment monitoring early warning and safety guarantee management system |
| CN112101789A (en) * | 2020-09-16 | 2020-12-18 | 清华大学合肥公共安全研究院 | Water pollution alarm grade identification method based on artificial intelligence |
| CN112163481A (en) * | 2020-09-16 | 2021-01-01 | 清华大学合肥公共安全研究院 | Water environment pollution analysis method based on video recognition |
| CN113128884A (en) * | 2021-04-25 | 2021-07-16 | 南京大学 | Method and system for tracing regional water environment based on river network mathematical model |
| CN113159456A (en) * | 2020-09-30 | 2021-07-23 | 中科三清科技有限公司 | Water quality prediction method, device, electronic device, and storage medium |
| US20210303998A1 (en) * | 2020-03-28 | 2021-09-30 | Tata Consultancy Services Limited | Multi-chiller scheduling using reinforcement learning with transfer learning for power consumption prediction |
| CN114386686A (en) * | 2021-12-30 | 2022-04-22 | 北京师范大学 | Improved LSTM-based watershed water quality short-term prediction method |
| CN114444259A (en) * | 2021-12-20 | 2022-05-06 | 浙江仁欣环科院有限责任公司 | Rain and sewage pipe network tracing and tracking system and method |
| CN114841601A (en) * | 2022-05-24 | 2022-08-02 | 保定金迪地下管线探测工程有限公司 | Dynamic tracing analysis method and system for drainage water pollution |
-
2022
- 2022-11-29 CN CN202211502936.5A patent/CN115545678A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105278492A (en) * | 2014-06-26 | 2016-01-27 | 广东柯内特环境科技有限公司 | Intelligent monitoring system and method for area pollution discharge |
| CN110222372A (en) * | 2019-05-08 | 2019-09-10 | 中国水利水电科学研究院 | A kind of Flow of River water quality real-time predicting method and device based on data assimilation |
| US20210303998A1 (en) * | 2020-03-28 | 2021-09-30 | Tata Consultancy Services Limited | Multi-chiller scheduling using reinforcement learning with transfer learning for power consumption prediction |
| CN111524030A (en) * | 2020-04-22 | 2020-08-11 | 常州市环境科学研究院 | Plain river network area water environment monitoring early warning and safety guarantee management system |
| CN112101789A (en) * | 2020-09-16 | 2020-12-18 | 清华大学合肥公共安全研究院 | Water pollution alarm grade identification method based on artificial intelligence |
| CN112163481A (en) * | 2020-09-16 | 2021-01-01 | 清华大学合肥公共安全研究院 | Water environment pollution analysis method based on video recognition |
| CN113159456A (en) * | 2020-09-30 | 2021-07-23 | 中科三清科技有限公司 | Water quality prediction method, device, electronic device, and storage medium |
| CN113128884A (en) * | 2021-04-25 | 2021-07-16 | 南京大学 | Method and system for tracing regional water environment based on river network mathematical model |
| CN114444259A (en) * | 2021-12-20 | 2022-05-06 | 浙江仁欣环科院有限责任公司 | Rain and sewage pipe network tracing and tracking system and method |
| CN114386686A (en) * | 2021-12-30 | 2022-04-22 | 北京师范大学 | Improved LSTM-based watershed water quality short-term prediction method |
| CN114841601A (en) * | 2022-05-24 | 2022-08-02 | 保定金迪地下管线探测工程有限公司 | Dynamic tracing analysis method and system for drainage water pollution |
Non-Patent Citations (3)
| Title |
|---|
| 李青等: "天津市水环境在线监测监控系统的建设", 《中国给水排水》 * |
| 程雨涵等: "基于在线监测数据诊断管网异常排放", 《给水排水》 * |
| 邵世保等: "慈湖河流域水质监测预警管理体系的构建与应用", 《环境与发展》 * |
Cited By (33)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116205592A (en) * | 2023-02-07 | 2023-06-02 | 广东慧航天唯科技有限公司 | Pollution anomaly tracing method for drainage pipe network of intelligent Internet of things |
| CN116128144A (en) * | 2023-02-15 | 2023-05-16 | 北京清环智慧水务科技有限公司 | Urban drainage pipe network partition linkage management method and system |
| CN116128144B (en) * | 2023-02-15 | 2024-02-13 | 北京清环智慧水务科技有限公司 | Urban drainage pipe network partition linkage management method and system |
| CN115859068A (en) * | 2023-03-03 | 2023-03-28 | 四川三思德科技有限公司 | Environmental information sensing method and system for intelligent water conservancy architecture |
| CN115859068B (en) * | 2023-03-03 | 2023-05-16 | 四川三思德科技有限公司 | Environment information sensing method and system for intelligent water conservancy architecture |
| CN116298159A (en) * | 2023-03-14 | 2023-06-23 | 南京高科环境科技有限公司 | Industrial sewage plant pollutant tracing method and device, storage medium and computer equipment |
| CN116298159B (en) * | 2023-03-14 | 2024-01-23 | 南京高科环境科技有限公司 | Industrial sewage plant pollutant tracing method and device, storage medium and computer equipment |
| CN115979348A (en) * | 2023-03-17 | 2023-04-18 | 近通物联网(苏州)有限公司 | Method, device and equipment for investigating mixed flow of rain and sewage of pipe network and medium |
| CN116029884A (en) * | 2023-03-29 | 2023-04-28 | 北京云庐科技有限公司 | River pollution tracing method based on river channel water pollution tracing model |
| CN116029884B (en) * | 2023-03-29 | 2023-05-30 | 北京云庐科技有限公司 | River pollution tracing method based on river channel water pollution tracing model |
| CN116105804A (en) * | 2023-04-13 | 2023-05-12 | 江苏海峡环保科技发展有限公司 | Intelligent water quality safety detection and early warning system |
| CN116168771A (en) * | 2023-04-26 | 2023-05-26 | 北京建工环境修复股份有限公司 | Method and system for monitoring pollution of perfluorinated compounds in river based on Internet of things |
| CN116757707A (en) * | 2023-06-19 | 2023-09-15 | 深圳市同富信息技术有限公司 | Crop fruit growth tracing method and system |
| CN116757707B (en) * | 2023-06-19 | 2024-05-24 | 深圳市同富信息技术有限公司 | Crop fruit growth tracing method and system |
| CN117151391A (en) * | 2023-08-30 | 2023-12-01 | 深圳市水务工程检测有限公司 | Environment pollution monitoring system and method based on Internet of things |
| CN117167671A (en) * | 2023-09-06 | 2023-12-05 | 深圳市科荣软件股份有限公司 | Pipe network operation monitoring control system based on big data |
| CN117167671B (en) * | 2023-09-06 | 2024-05-14 | 深圳市科荣软件股份有限公司 | Pipe network operation monitoring control system based on big data |
| CN117269443A (en) * | 2023-09-11 | 2023-12-22 | 杭州智驳科技有限公司 | Intelligent digital rural environment monitoring system based on big data |
| CN117269443B (en) * | 2023-09-11 | 2024-05-03 | 杭州智驳科技有限公司 | Intelligent digital rural environment monitoring system based on big data |
| CN117007762B (en) * | 2023-09-21 | 2023-12-29 | 南通香境生物医药科技有限公司 | Multi-water-area water quality monitoring method based on data visualization |
| CN117007762A (en) * | 2023-09-21 | 2023-11-07 | 南通香境生物医药科技有限公司 | Multi-water-area water quality monitoring method based on data visualization |
| CN117607379A (en) * | 2023-11-27 | 2024-02-27 | 中电建生态环境集团有限公司 | Pollution source investigation method applicable to culvert drainage under different working conditions |
| CN117408440A (en) * | 2023-12-15 | 2024-01-16 | 湖南蒙拓环境科技有限公司 | River drain sewage intelligent treatment method and system based on multidimensional sensor |
| CN117408440B (en) * | 2023-12-15 | 2024-03-08 | 湖南蒙拓环境科技有限公司 | River drain sewage intelligent treatment method and system based on multidimensional sensor |
| CN117420283B (en) * | 2023-12-19 | 2024-03-08 | 上海科泽智慧环境科技有限公司 | Integrated multi-channel water quality monitoring method and system |
| CN117420283A (en) * | 2023-12-19 | 2024-01-19 | 上海科泽智慧环境科技有限公司 | Integrated multi-channel water quality monitoring method and system |
| CN117473134B (en) * | 2023-12-28 | 2024-03-26 | 河南省新乡水文水资源测报分中心 | Water resource monitoring and management method and system for tracing pollutants |
| CN117473134A (en) * | 2023-12-28 | 2024-01-30 | 河南省新乡水文水资源测报分中心 | Water resource monitoring and management method and system for tracing pollutants |
| CN117933572A (en) * | 2024-03-20 | 2024-04-26 | 四川飞洁科技发展有限公司 | Water quality prediction method and related device |
| CN117933572B (en) * | 2024-03-20 | 2024-06-11 | 四川飞洁科技发展有限公司 | Water quality prediction method and related device |
| CN117949046A (en) * | 2024-03-22 | 2024-04-30 | 山东省科学院海洋仪器仪表研究所 | Cluster networking type marine environment tracking and monitoring method and system for abnormal water clusters |
| CN117949046B (en) * | 2024-03-22 | 2024-06-04 | 山东省科学院海洋仪器仪表研究所 | Cluster networking type marine environment tracking and monitoring method and system for abnormal water clusters |
| CN118130745A (en) * | 2024-05-08 | 2024-06-04 | 芯视界（北京）科技有限公司 | Drainage pipe network monitoring method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115545678A (en) | Water quality monitoring method based on water environment portrait and pollutant traceability | |
| KR102101625B1 (en) | ICT integrated monitoring water management system for smart water city construction, and integrated control server for the same | |
| CN102884407B (en) | System and method for monitoring resources in a water utility network | |
| WO2021259009A1 (en) | Operation scheduling method and apparatus for deep sewage drainage tunnel, and computer device | |
| CN110196083A (en) | Monitoring recognition methods, device and the electronic equipment in drainage pipeline networks pollution path | |
| Fenner et al. | A decision support model for the rehablitation of “non-critical” sewers | |
| CN102235575A (en) | Data processing method and system for checking pipeline leakage | |
| CN111077864A (en) | Intelligent drainage scheduling and analyzing method and system | |
| CN113011903A (en) | Water pollution accurate tracing method based on GIS and hydraulic model | |
| CN111501953A (en) | Exception analysis method, system, medium and equipment for drainage pipe network | |
| CN113125663A (en) | Water quality monitoring and early warning system and method | |
| CN107516167A (en) | Municipal drainage integration operation intelligent distribution and management system | |
| CN113902172A (en) | Sewage treatment method, system, device and medium | |
| CN115127037B (en) | Water supply pipe network leakage positioning method and system | |
| CN115577506A (en) | Method for pre-diagnosing and accurately troubleshooting problems of sewage pipe network | |
| CN109271465A (en) | A kind of Hydrological Data Analysis and methods of exhibiting based on cloud computing | |
| CN112581077A (en) | Intelligent water affair management platform | |
| CN109840870A (en) | A kind of drainage system grid-based management pattern analysis method and the medium for storing it | |
| GB2507184A (en) | Anomaly event classification in a network of pipes for resource distribution | |
| CN112985713A (en) | Pipe network leakage monitoring method and system based on edge calculation | |
| KR20080020745A (en) | A method of water resources integration management based usn | |
| Feng et al. | Visual analytic hierarchical process for in situ identification of leakage risk in urban water distribution network | |
| Liu et al. | City pipe network intelligent service based on GIS and internet of things | |
| KR20230064858A (en) | Decision support management system for determining priority of water quality critical management area and optimal locations of water quality sensor for water supply pipe network, and method for the same | |
| CN117113038B (en) | Urban water and soil loss Huang Nishui event tracing method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20221230 |