CN210983490U - Water quality sudden pollution dynamic early warning information monitoring system - Google Patents
Water quality sudden pollution dynamic early warning information monitoring system Download PDFInfo
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- CN210983490U CN210983490U CN201921749239.3U CN201921749239U CN210983490U CN 210983490 U CN210983490 U CN 210983490U CN 201921749239 U CN201921749239 U CN 201921749239U CN 210983490 U CN210983490 U CN 210983490U
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Abstract
The utility model discloses a dynamic early warning information monitoring system for sudden water pollution, which comprises a pollution source management system, a prediction early warning system, a water quality information release system and a data transmission system; the pollution source management system is used for collecting and storing pollution source data in a flow domain and forming a database, the prediction early warning module comprises a monitoring point water environment monitoring module and a pollution source waste water monitoring module and is used for collecting water at the monitoring point water environment and a pollution source at fixed time and carrying out water quality early warning through a wavelet denoising analysis and neural network prediction subsystem, the water quality information issuing system is used for transmitting and issuing a detection result in real time, and the data transmission system is used for data interaction among the pollution source management system, the prediction early warning system and the water quality information issuing system. The utility model has the advantages of degree of automation is high, have sample detection and numerical analysis prediction concurrently.
Description
Technical Field
The utility model relates to a sewage detection field especially relates to a quality of water sudden pollution developments early warning information monitored control system.
Background
The problem of illegal steal and drainage of the overproof sewage is serious in China, and serious pollution and damage are caused to a water system and the ecological environment. Strengthening effective supervision on the phenomenon of excessive sewage stealing and draining, tracing the source of pollutants and tracing the responsibility are very difficult tasks.
Due to the characteristics of emergencies and short periods of sudden pollution events, continuous monitoring of water quality at short time intervals (less than 1h) is required, and water quality parameters closely related to the sudden pollution cannot realize high-frequency monitoring based on an online sensor due to measurement principles or monitoring time and the like, so that continuous real-time detection cannot be performed in a short time due to the existence of detection time, and results are published, so that the difficulty in troubleshooting of the sudden pollution is high.
SUMMERY OF THE UTILITY MODEL
To the deficiency of the prior art, the technical problem to be solved by the present patent application is: how to provide a degree of automation height, have concurrently sampling test and numerical analysis forecast quality of water sudden pollution developments early warning information monitored control system.
In order to achieve the above purpose, the utility model adopts the following technical scheme:
a water quality sudden pollution dynamic early warning information monitoring system comprises a pollution source management system, a prediction early warning system, a water quality information release system and a data transmission system; the pollution source management system is used for collecting and storing pollution source data in a flow domain and forming a database, the prediction early warning module comprises a monitoring point water environment monitoring module and a pollution source waste water monitoring module and is used for collecting water at the monitoring point water environment and a pollution source at fixed time and carrying out water quality early warning through a wavelet denoising analysis and neural network prediction subsystem, the water quality information issuing system is used for transmitting and issuing a detection result in real time, and the data transmission system is used for data interaction among the pollution source management system, the prediction early warning system and the water quality information issuing system.
When the system is used, time is needed for water quality detection, a database is established through a pollution source management system after polluted waste water and standard water are sampled, timing sampling is carried out at a pollution source discharge position and an environmental water source position after sampling is carried out regularly during actual detection, the data of the pollution source management system are called through a wavelet denoising analysis subsystem and a neural network prediction subsystem to be trained to form time series pollution residual values, comparison is carried out through predicted data and real-time data, if the difference value exceeds the threshold values of the wavelet denoising analysis subsystem and the neural network prediction subsystem, an alarm is triggered, and the data are issued through a water quality information issuing system.
And as optimization, the wavelet de-noising analysis and neural network prediction subsystem comprises a sampling sub-module, a sample analysis sub-module and a prediction sub-module, wherein the sample analysis sub-module has a de-noising function.
Therefore, the wavelet denoising analysis method has good time-frequency localization performance and is a time-frequency conversion tool with wide application.
Preferably, the sampling submodule is a sewage sampler arranged at the environment water and the pollution source.
Like this, can carry out sample detection to two water sources simultaneously, ensure the accuracy that detects.
As an optimization, the sample analysis submodule is a three-dimensional fluorescence spectrometer.
As optimization, the prediction submodule is based on a time series prediction model which is established by MAT L AB and is established according to standard water quality, polluted water quality and real-time water quality detection.
Therefore, intelligent detection and analysis of the chip can be realized through the data model, and the automation degree is improved.
And as optimization, the pollution source management system is used for performing integrated storage and called comparison on the data of the influence of a single pollution source and the combination of at least two pollution sources on the water quality.
Therefore, the pollution source full coverage can be realized, the accuracy of the reference data of the detection result is guaranteed, the large error of the result is avoided, and the situation of no contrast is caused.
Preferably, the data transmission system is a wireless transmission technology.
Therefore, stable data transmission can be guaranteed, and transmission efficiency is improved.
And as optimization, sampling detection of the water environment monitoring module and the pollution source wastewater monitoring module at the monitoring point is carried out simultaneously.
Thus, the two are detected simultaneously, and the sampling time range can be expanded.
Drawings
Fig. 1 is a frame diagram of a dynamic early warning information monitoring system for sudden water quality pollution.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments.
Referring to fig. 1, a water quality sudden pollution dynamic early warning information monitoring system comprises a pollution source management system, a prediction early warning system, a water quality information release system and a data transmission system; the pollution source management system is used for collecting and storing pollution source data in a flow domain and forming a database, the prediction early warning module comprises a monitoring point water environment monitoring module and a pollution source waste water monitoring module and is used for collecting water at the monitoring point water environment and a pollution source at fixed time and carrying out water quality early warning through a wavelet denoising analysis and neural network prediction subsystem, the water quality information issuing system is used for transmitting and issuing a detection result in real time, and the data transmission system is used for data interaction among the pollution source management system, the prediction early warning system and the water quality information issuing system.
When the system is used, time is needed for water quality detection, a database is established through a pollution source management system after polluted waste water and standard water are sampled, timing sampling is carried out at a pollution source discharge position and an environmental water source position after sampling is carried out regularly during actual detection, the data of the pollution source management system are called through a wavelet denoising analysis subsystem and a neural network prediction subsystem to be trained to form time series pollution residual values, comparison is carried out through predicted data and real-time data, if the difference value exceeds the threshold values of the wavelet denoising analysis subsystem and the neural network prediction subsystem, an alarm is triggered, and the data are issued through a water quality information issuing system.
Specifically, the pollution source wastewater monitoring module is a monitoring point which is set up at a pollution discharge position and is used for monitoring sudden discharge at the pollution source position; the monitoring point water environment monitoring module is arranged at the downstream of a pollution source and used for monitoring real-time water quality data, and the monitoring point water environment monitoring module realize independent monitoring, so that the monitoring time point range is enlarged.
In this embodiment, the wavelet denoising analysis and neural network prediction subsystem includes a sampling sub-module, a sample analysis sub-module and a prediction sub-module, and the sample analysis sub-module has a denoising function.
Therefore, the wavelet denoising analysis method has good time-frequency localization performance and is a time-frequency conversion tool with wide application.
In this embodiment, the sampling sub-module is a sewage sampler disposed at the environmental water and the pollution source.
Like this, can carry out sample detection to two water sources simultaneously, ensure the accuracy that detects.
In this embodiment, the sample analysis submodule is a three-dimensional fluorescence spectrometer.
In this embodiment, the prediction submodule is a time-series prediction model established based on MAT L AB and based on standard water quality, polluted water quality, and real-time water quality detection.
Therefore, intelligent detection and analysis of the chip can be realized through the data model, and the automation degree is improved.
In this embodiment, the pollution source management system is used for performing integrated storage and called comparison on data of the influence of a single pollution source and a combination of at least two pollution sources on water quality.
Therefore, the pollution source full coverage can be realized, the accuracy of the reference data of the detection result is guaranteed, the large error of the result is avoided, and the situation of no contrast is caused.
In this embodiment, the data transmission system is a wireless transmission technology.
Therefore, stable data transmission can be guaranteed, and transmission efficiency is improved.
In this embodiment, sampling and detecting of the water environment monitoring module and the pollution source wastewater monitoring module at the monitoring point are performed simultaneously.
Thus, the two are detected simultaneously, and the sampling time range can be expanded.
In the specific implementation: the working process of the wavelet denoising analysis and neural network prediction subsystem is as follows: denoising the standardized baseline data by adopting a wavelet denoising method, removing high-frequency noise in the baseline data, and calculating an abnormal threshold range based on the standardized baseline data; training the denoised water quality time sequence by adopting a Back Propagation Neural Network (BPNN), and calculating a prediction residual by combining actual water quality monitoring data to obtain better prediction precision; predicting a current water quality time sequence by adopting the trained wavelet-neural network model, and calculating a predicted value and an actual monitoring value residual sequence by combining current actual monitoring data; and comparing the residual sequence obtained by the calculation with a residual sequence calculated in a standard, when the residual is within a threshold interval, indicating that the water quality time sequence is normal, and when the residual exceeds the threshold interval, indicating that an alarm is possibly given to an abnormal event, and detailed monitoring is required or even an emergency plan is started.
The above, only be the concrete implementation of the preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art is in the technical scope of the present invention, according to the technical solution of the present invention and the utility model, the concept of which is equivalent to replace or change, should be covered within the protection scope of the present invention.
Claims (8)
1. A water quality sudden pollution dynamic early warning information monitoring system is characterized by comprising a pollution source management system, a prediction early warning system, a water quality information release system and a data transmission system; the pollution source management system is used for collecting and storing pollution source data in a flow domain and forming a database, the prediction early warning module comprises a monitoring point water environment monitoring module and a pollution source waste water monitoring module and is used for collecting water at the monitoring point water environment and a pollution source at fixed time and carrying out water quality early warning through a wavelet denoising analysis and neural network prediction subsystem, the water quality information issuing system is used for transmitting and issuing a detection result in real time, and the data transmission system is used for data interaction among the pollution source management system, the prediction early warning system and the water quality information issuing system.
2. The system for monitoring the dynamic early warning information of the sudden water quality pollution according to claim 1, wherein the wavelet denoising analysis and neural network prediction subsystem comprises a sampling submodule, a sample analysis submodule and a prediction submodule, and the sample analysis submodule has a denoising function.
3. The system for monitoring the dynamic early warning information of the sudden water quality pollution according to claim 2, wherein the sampling submodule is a sewage sampler arranged at the environment water and pollution source.
4. The system for monitoring the dynamic early warning information on the sudden water pollution according to claim 3, wherein the sample analysis submodule is a three-dimensional fluorescence spectrometer.
5. The system for monitoring the dynamic early warning information of the sudden water quality pollution as claimed in claim 4, wherein the prediction submodule is a time series prediction model established based on MAT L AB and based on standard water quality, polluted water quality and real-time water quality detection.
6. The system for monitoring the dynamic early warning information of the sudden water quality pollution according to claim 5, wherein the pollution source management system is used for performing integrated storage and calling comparison on data of the influence of a single pollution source and the combination of at least two pollution sources on the water quality.
7. The system for monitoring the water quality sudden pollution dynamic early warning information as claimed in claim 6, wherein the data transmission system is a wireless transmission technology.
8. The system for monitoring the dynamic early warning information on the sudden water quality pollution according to claim 7, wherein the sampling detection of the water environment monitoring module and the pollution source wastewater monitoring module at the monitoring point is carried out simultaneously.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111811580A (en) * | 2020-07-24 | 2020-10-23 | 重庆华悦生态环境工程研究院有限公司 | Water quantity/water quality monitoring and point distribution method and early warning response system |
| CN112084382A (en) * | 2020-09-04 | 2020-12-15 | 安徽思环科技有限公司 | Pretreatment method for three-dimensional fluorescence data of water quality of industrial park pollution source |
| CN112215495A (en) * | 2020-10-13 | 2021-01-12 | 北京工业大学 | Pollution source contribution calculation method based on long-time memory neural network |
| CN112785193A (en) * | 2021-02-04 | 2021-05-11 | 广东鹏裕建设有限公司 | Reservoir omnibearing monitoring method and system, computer equipment and storage medium |
| CN113159130A (en) * | 2021-03-25 | 2021-07-23 | 中电建电力检修工程有限公司 | Construction sewage treatment method |
| CN118095843A (en) * | 2024-02-20 | 2024-05-28 | 深圳衡伟环境技术有限公司 | Remote supervision system and method based on Internet of things technology |
-
2019
- 2019-10-18 CN CN201921749239.3U patent/CN210983490U/en active Active
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111811580A (en) * | 2020-07-24 | 2020-10-23 | 重庆华悦生态环境工程研究院有限公司 | Water quantity/water quality monitoring and point distribution method and early warning response system |
| CN112084382A (en) * | 2020-09-04 | 2020-12-15 | 安徽思环科技有限公司 | Pretreatment method for three-dimensional fluorescence data of water quality of industrial park pollution source |
| CN112215495A (en) * | 2020-10-13 | 2021-01-12 | 北京工业大学 | Pollution source contribution calculation method based on long-time memory neural network |
| CN112215495B (en) * | 2020-10-13 | 2022-05-24 | 北京工业大学 | Pollution source contribution calculation method based on long-time and short-time memory neural network |
| CN112785193A (en) * | 2021-02-04 | 2021-05-11 | 广东鹏裕建设有限公司 | Reservoir omnibearing monitoring method and system, computer equipment and storage medium |
| CN113159130A (en) * | 2021-03-25 | 2021-07-23 | 中电建电力检修工程有限公司 | Construction sewage treatment method |
| CN118095843A (en) * | 2024-02-20 | 2024-05-28 | 深圳衡伟环境技术有限公司 | Remote supervision system and method based on Internet of things technology |
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