CN114022305A - Underground water dynamic monitoring method and device - Google Patents
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Abstract
The invention provides a method and a device for dynamically monitoring underground water, wherein the method comprises the following steps: acquiring monitoring data of the underground water body, wherein the monitoring data comprises water level monitoring data or water quality monitoring data; determining feature data based on the monitoring data; inputting the characteristic data into a preset neural network model, and outputting an abnormal result of the monitoring data; and the neural network model is obtained after training according to characteristic data corresponding to the abnormal monitoring data as a sample. According to the method, the abnormal data is judged through the neural network model, the error problem caused by long-time analysis of the abnormal data manually is avoided, and the accurate positioning of the abnormal data is realized. Meanwhile, based on the current neural network processing speed, abnormal data can be found in time, time is strived for the prediction and analysis of water level or water quality data, and timeliness, stability, continuity and high efficiency of water level or water quality monitoring are guaranteed.
Description
Technical Field
The invention relates to the field of water quality monitoring, in particular to a method and a device for dynamically monitoring underground water.
Background
And underground water monitoring, which is to monitor the data of underground water level, water quality and the like in the district for an underground water monitoring management department so as to master the dynamic change condition in time and protect the underground water for a long time. With the continuous development of society, the continuous acceleration of industrialization and urbanization process, environmental pollution is more serious, and particularly, the phenomena of deterioration of the water quality condition of underground water, water area atrophy and the like occur. Therefore, the method has very important significance for detecting the abnormal condition of the underground water body and making correct countermeasures.
However, due to the influence of factors such as environment and collection strategy, the collected water quality monitoring data is prone to have deviation from the actual condition, which affects the precision of water quality detection, and thus affects the accuracy of water quality detection.
At present, in the process of monitoring underground water, abnormal data is mainly searched manually. However, continuous manual analysis of abnormal data is prone to errors, and stability of the monitored data cannot be ensured. The time for manually searching the abnormal data is long, and the timeliness of the data cannot be guaranteed.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a device for dynamically monitoring underground water.
The invention provides a method for dynamically monitoring underground water, which comprises the following steps: acquiring monitoring data of the underground water body, wherein the monitoring data comprises water level monitoring data or water quality monitoring data; determining feature data based on the monitoring data; inputting the characteristic data into a preset neural network model, and outputting an abnormal result of the monitoring data; and the neural network model is obtained after training according to characteristic data corresponding to the abnormal monitoring data as a sample.
According to the groundwater dynamic monitoring method, after the abnormal result of the monitoring data is output, the abnormal data is sent to a human-computer interface to be displayed; and receiving the elimination result or the filling result of the abnormal data to obtain the corrected monitoring data.
According to an embodiment of the invention, after obtaining the corrected monitoring data, the method further includes: determining second characteristic data based on the corrected monitoring data and combined with meteorological precipitation data and geological data; inputting the second characteristic data into a preset second neural network model, and outputting a water level or water quality prediction result; and the second neural network model is obtained after training according to the determined water quality monitoring data result as a label.
According to an embodiment of the invention, the determining the second characteristic data based on the corrected monitoring data and the meteorological precipitation data and geological data includes:
and performing autocorrelation analysis or cross-correlation analysis on the corrected underground water monitoring data, the meteorological precipitation data and the geological data, and determining second characteristic data according to the result of the autocorrelation analysis or the cross-correlation analysis.
According to an embodiment of the invention, the performing autocorrelation analysis or cross-correlation analysis on the corrected groundwater monitoring data, the meteorological precipitation data and the geological data includes: determining autocorrelation coefficients or cross-correlation coefficients of all parameters in the corrected monitoring data, the meteorological precipitation data and the geological data by using spectral analysis and a spectral analysis method; and determining second characteristic data according to the autocorrelation coefficient or the cross-correlation coefficient.
The groundwater dynamic monitoring method according to one embodiment of the invention further comprises the following steps: and setting various judgment conditions according to the running state of the equipment, forming different types of early warning strategies, and implementing early warning analysis of dynamic monitoring data.
According to the groundwater dynamic monitoring method, the operation condition of the equipment comprises the online rate of the equipment, the data receiving rate, the troubleshooting progress or the battery power.
The invention also provides a device for dynamically monitoring underground water, which comprises: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring monitoring data of the underground water body, and the monitoring data comprises water level monitoring data or water quality monitoring data; the characteristic extraction module is used for determining characteristic data based on the monitoring data; the data processing module is used for inputting the characteristic data into a preset neural network model and outputting an abnormal result of the monitoring data; and the neural network model is obtained after training according to characteristic data corresponding to the abnormal monitoring data as a sample.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of any one of the above-mentioned groundwater dynamic monitoring methods.
The invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the method for dynamic groundwater monitoring as described in any one of the above.
According to the groundwater dynamic monitoring method and device provided by the invention, the abnormal data is judged through the neural network model, the error problem caused by long-time artificial analysis of the abnormal data is avoided, and the accurate positioning of the abnormal data is realized. Meanwhile, based on the current neural network processing speed, abnormal data can be found in time, time is strived for the prediction and analysis of water level or water quality data, and timeliness, stability, continuity and high efficiency of water level or water quality monitoring are guaranteed.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a groundwater dynamic monitoring method provided by the present invention;
FIG. 2 is a schematic structural diagram of a groundwater dynamic monitoring device provided by the invention;
fig. 3 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The groundwater dynamic monitoring method and apparatus of the present invention will be described with reference to fig. 1-3. Fig. 1 is a schematic flow chart of a groundwater dynamic monitoring method provided by the present invention, and as shown in fig. 1, the present invention provides a groundwater dynamic monitoring method, including:
101. acquiring monitoring data of the underground water body, wherein the monitoring data comprises water level monitoring data or water quality monitoring data.
The monitoring data comprises water level monitoring data and water quality monitoring data, and can be acquired through monitoring stations.
102. Based on the monitoring data, characteristic data is determined.
And (4) carrying out characteristic extraction on the collected water quality monitoring data to obtain characteristic data.
103. And inputting the characteristic data into a preset neural network model, and outputting an abnormal result of the monitoring data.
The invention applies the artificial intelligent machine learning technology to realize the automatic identification of the abnormal data of the underground water level, calculate the data and locate the abnormality, and realize the rapid screening of the abnormal data. The preset neural network model is a model which is trained in advance, and the neural network model is trained by taking known abnormal data as a label and taking the whole data as a sample. And obtaining a neural network model meeting the precision requirement through a large number of samples, and using the neural network model as the preset neural network model. For example, a current convolutional neural network or a time series neural network may be employed. Accordingly, the step 102 is also to perform feature extraction according to the corresponding neural network type.
According to the underground water dynamic monitoring method, abnormal data is judged through the neural network model, the error problem caused by long-time analysis of the abnormal data manually is avoided, and the accurate positioning of the abnormal data is realized. Meanwhile, based on the current neural network processing speed, abnormal data can be found in time, time is strived for the prediction and analysis of water level or water quality data, and timeliness, stability, continuity and high efficiency of water level or water quality monitoring are guaranteed.
In one embodiment, after the outputting of the abnormal result of the monitoring data, the method further comprises sending the abnormal data to a human-computer interface for display; and receiving the elimination result or the filling result of the abnormal data to obtain the corrected monitoring data.
Mobile phone APP software based on an Android system can be developed and designed. The basic information, the operation maintenance information, the water quality sampling information, the hydrogeological environment information, the monitoring equipment information and the like of the monitoring well can be filled and inquired in real time.
The underground water monitoring operation and maintenance management APP integrates functions of dynamic early warning of equipment, dynamic checking of data receiving, dynamic checking of monitoring operation, data reporting, operation and maintenance follow-up, task transmission in the APP and the like, improves supervision efficiency and effect of engineering, and effectively ensures realization of quality, progress, safety and other targets.
And when the neural network model outputs abnormal data, the abnormal data is sent to a human-computer interface through the APP and marked. If the monitoring data is displayed for a period of time, the abnormal data is marked and displayed for the user.
And checking the abnormal value of the marker by the staff of the whole editing process, and removing the abnormal data after confirming the marked data, or manually filling the abnormal data after the abnormal data is provided, so as to obtain the corrected monitoring data. In addition, a complete log can be established for the operation of each numerical value, and the content comprises an operator, operation time and operation content, so that the tracing and the query are facilitated.
In an embodiment, after obtaining the corrected monitoring data, the method further includes: determining second characteristic data based on the corrected monitoring data and combined with meteorological precipitation data and geological data; inputting the second characteristic data into a preset second neural network model, and outputting a water level or water quality prediction result; and the second neural network model is obtained after training according to the determined water level or water quality monitoring data result as a label.
The invention also establishes another neural network model for predicting the water level or the water quality. Meanwhile, the invention considers that the prediction is only carried out through water quality or digital data, the data source is single, and the result accuracy is not high. In particular, the present invention determines feature data (feature data for distinguishing the above anomaly detection, hereinafter referred to as second feature data) based on the corrected monitoring data, in combination with the meteorological precipitation data and the geological data. And inputting the second characteristic data into a preset second neural network model, and outputting a water level or water quality prediction result. And the second water quality model is obtained after training according to the determined monitoring data result as a label. For example, monitoring data is collected once per minute for 60 minutes continuously to determine second characteristic data, the second characteristic data corresponding to the monitoring data of the first 59 groups is used as the input of the second neural network model, the data of the 60 minutes is used as a label, and the second neural network model is trained. Through a second neural network model which is trained for multiple times, water level (or water quality) data of the 60 th minute can be predicted according to the characteristics of the data of the 59 minutes.
In one embodiment, the determining second characteristic data based on the modified monitoring data in combination with the meteorological precipitation data and the geological data comprises: and performing autocorrelation analysis or cross-correlation analysis on the corrected underground water monitoring data, the meteorological precipitation data and the geological data, and determining second characteristic data according to the result of the autocorrelation analysis or the cross-correlation analysis.
In order to further improve the accuracy of data prediction, the invention carries out autocorrelation analysis or cross-correlation analysis according to the corrected underground water monitoring data, the meteorological precipitation data and the geological data, finally determines second characteristic data, and carries out dynamic prediction on water level or water quality data according to the second characteristic data.
In one embodiment, the performing an autocorrelation analysis or a cross-correlation analysis on the corrected groundwater monitoring data, the meteorological precipitation data and the geological data includes: determining autocorrelation coefficients or cross-correlation coefficients of all parameters in the corrected monitoring data, the meteorological precipitation data and the geological data by using spectral analysis and a spectral analysis method; and determining second characteristic data according to the autocorrelation coefficient or the cross-correlation coefficient.
In one embodiment, further comprising: and setting various judgment conditions according to the running state of the equipment, forming different types of early warning strategies, and implementing early warning analysis of dynamic monitoring data.
In one embodiment, the device operating condition includes a device line rate, a data receiving rate, a troubleshooting progress or a battery level.
In the process of dynamically monitoring water quality or water level data, multiple judgment conditions can be preliminarily set according to equipment running conditions such as the on-line rate of the equipment, the data receiving rate, the troubleshooting progress and the battery power, different types of early warning strategies are formed, and early warning analysis of the dynamically monitored data is implemented.
For example, the ratio of the on-line equipment to the total equipment is taken as the on-line rate of the equipment. And the proportion of the received monitoring data to the total data is used as the data receiving rate. And when a fault occurs, determining the troubleshooting progress according to the time used for eliminating the fault. The electric quantity of the equipment is collected, early warning can be carried out, and the phenomena of shutdown of the equipment and the like are avoided.
By the method, the device for acquiring the monitoring data is early warned, the stability of data acquisition can be effectively ensured, and the accuracy of a prediction result is improved.
The groundwater dynamic monitoring device provided by the invention is described below, and the groundwater dynamic monitoring device described below and the groundwater dynamic monitoring method described above can be referred to correspondingly.
Fig. 2 is a schematic structural diagram of a groundwater dynamic monitoring device provided by the present invention, and as shown in fig. 2, the groundwater dynamic monitoring device includes: a data acquisition module 201, a feature extraction module 202 and a data processing module 203. The data acquisition module 201 is configured to acquire monitoring data of an underground water body, where the monitoring data includes water level monitoring data or water quality monitoring data; the feature extraction module 202 is configured to determine feature data based on the monitoring data; the data processing module 203 is used for inputting the characteristic data into a preset neural network model and outputting an abnormal result of the monitoring data; and the neural network model is obtained after training according to characteristic data corresponding to the abnormal monitoring data as a sample.
The device embodiment provided in the embodiments of the present invention is for implementing the above method embodiments, and for details of the process and the details, reference is made to the above method embodiments, which are not described herein again.
According to the groundwater dynamic monitoring device provided by the embodiment of the invention, abnormal data is judged through the neural network model, so that the error problem caused by long-time artificial analysis of the abnormal data is avoided, and the accurate positioning of the abnormal data is realized. Meanwhile, based on the current neural network processing speed, abnormal data can be found in time, time is strived for the prediction and analysis of water level or water quality data, and timeliness, stability, continuity and high efficiency of water level or water quality monitoring are guaranteed.
Fig. 3 is a schematic structural diagram of an electronic device provided in the present invention, and as shown in fig. 3, the electronic device may include: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a communication bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the communication bus 304. Processor 301 may invoke logic instructions in memory 303 to perform a groundwater dynamics monitoring method comprising: acquiring monitoring data of the underground water body, wherein the monitoring data comprises water level monitoring data or water quality monitoring data; determining feature data based on the monitoring data; inputting the characteristic data into a preset neural network model, and outputting an abnormal result of the monitoring data; and the neural network model is obtained after training according to characteristic data corresponding to the abnormal monitoring data as a sample.
In addition, the logic instructions in the memory 303 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the groundwater dynamics monitoring method provided by the above methods, the method comprising: acquiring monitoring data of the underground water body, wherein the monitoring data comprises water level monitoring data or water quality monitoring data; determining feature data based on the monitoring data; inputting the characteristic data into a preset neural network model, and outputting an abnormal result of the monitoring data; and the neural network model is obtained after training according to characteristic data corresponding to the abnormal monitoring data as a sample.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to perform the groundwater dynamic monitoring method provided in the above embodiments, the method including: acquiring monitoring data of the underground water body, wherein the monitoring data comprises water level monitoring data or water quality monitoring data; determining feature data based on the monitoring data; inputting the characteristic data into a preset neural network model, and outputting an abnormal result of the monitoring data; and the neural network model is obtained after training according to characteristic data corresponding to the abnormal monitoring data as a sample.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for dynamically monitoring groundwater, comprising:
acquiring monitoring data of the underground water body, wherein the monitoring data comprises water level monitoring data or water quality monitoring data;
determining feature data based on the monitoring data;
inputting the characteristic data into a preset neural network model, and outputting an abnormal result of the monitoring data;
and the neural network model is obtained after training according to characteristic data corresponding to the abnormal monitoring data as a sample.
2. A groundwater dynamic monitoring method according to claim 1, wherein after the abnormal result of the monitoring data is output, the method further comprises sending the abnormal data to a human-computer interface for displaying;
and receiving the elimination result or the filling result of the abnormal data to obtain the corrected monitoring data.
3. A groundwater dynamic monitoring method according to claim 2, wherein after obtaining the corrected monitoring data, further comprising:
determining second characteristic data based on the corrected monitoring data and combined with meteorological precipitation data and geological data;
inputting the second characteristic data into a preset second neural network model, and outputting a water level or water quality prediction result;
and the second neural network model is obtained after training according to the determined water level or water quality monitoring data result as a label.
4. A groundwater dynamics monitoring method according to claim 3, wherein the determining second characteristic data based on the modified monitoring data in combination with meteorological precipitation data and geological data comprises:
and performing autocorrelation analysis or cross-correlation analysis on the corrected underground water monitoring data, the meteorological precipitation data and the geological data, and determining second characteristic data according to the result of the autocorrelation analysis or the cross-correlation analysis.
5. A groundwater dynamics monitoring method according to claim 4, wherein the performing of autocorrelation or cross-correlation analysis on the modified groundwater monitoring data, the meteorological precipitation data and the geological data comprises:
determining autocorrelation coefficients or cross-correlation coefficients of all parameters in the corrected monitoring data, the meteorological precipitation data and the geological data by using spectral analysis and a spectral analysis method;
and determining second characteristic data according to the autocorrelation coefficient or the cross-correlation coefficient.
6. A groundwater dynamics monitoring method according to claim 1, characterized in that the method further comprises:
and setting various judgment conditions according to the running state of the equipment, forming different types of early warning strategies, and implementing early warning analysis of dynamic monitoring data.
7. A groundwater dynamic monitoring method according to claim 6, wherein the device operating conditions include a device on-line rate, a data reception rate, a troubleshooting progress or a battery level.
8. The utility model provides an underground water dynamic monitoring device which characterized in that includes:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring monitoring data of the underground water body, and the monitoring data comprises water level monitoring data or water quality monitoring data;
the characteristic extraction module is used for determining characteristic data based on the monitoring data;
the data processing module is used for inputting the characteristic data into a preset neural network model and outputting an abnormal result of the monitoring data;
and the neural network model is obtained after training according to characteristic data corresponding to the abnormal monitoring data as a sample.
9. An electronic device comprising a memory, a processor and a computer program stored on said memory and executable on said processor, characterized in that said processor, when executing said program, carries out the steps of the groundwater dynamics monitoring method according to any of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the groundwater dynamics monitoring method according to any one of claims 1 to 7.
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| CN117073806A (en) * | 2023-10-16 | 2023-11-17 | 深圳市利源水务设计咨询有限公司 | Sewage treatment ectopic monitoring method and system based on artificial intelligence |
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