CN117214398A - Deep underground water body pollutant detection method and system - Google Patents
Deep underground water body pollutant detection method and system Download PDFInfo
- Publication number
- CN117214398A CN117214398A CN202311131742.3A CN202311131742A CN117214398A CN 117214398 A CN117214398 A CN 117214398A CN 202311131742 A CN202311131742 A CN 202311131742A CN 117214398 A CN117214398 A CN 117214398A
- Authority
- CN
- China
- Prior art keywords
- data
- underground water
- pollutants
- deep underground
- water body
- 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.)
- Granted
Links
- 239000003344 environmental pollutant Substances 0.000 title claims abstract description 47
- 231100000719 pollutant Toxicity 0.000 title claims abstract description 47
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 37
- 238000001514 detection method Methods 0.000 title claims abstract description 36
- 238000000034 method Methods 0.000 claims abstract description 44
- 239000000126 substance Substances 0.000 claims abstract description 23
- 238000004458 analytical method Methods 0.000 claims abstract description 22
- 238000009792 diffusion process Methods 0.000 claims abstract description 18
- 238000005516 engineering process Methods 0.000 claims abstract description 10
- 238000012544 monitoring process Methods 0.000 claims abstract description 8
- 238000003911 water pollution Methods 0.000 claims abstract description 8
- 230000004927 fusion Effects 0.000 claims description 16
- 239000003673 groundwater Substances 0.000 claims description 11
- 238000007405 data analysis Methods 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 6
- 239000000356 contaminant Substances 0.000 claims description 5
- 238000010801 machine learning Methods 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 abstract description 11
- 238000013473 artificial intelligence Methods 0.000 abstract description 2
- 230000007613 environmental effect Effects 0.000 abstract description 2
- 239000003403 water pollutant Substances 0.000 abstract 1
- 230000008569 process Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000033116 oxidation-reduction process Effects 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
Landscapes
- Geophysics And Detection Of Objects (AREA)
Abstract
The application belongs to the technical field of environmental monitoring, and particularly relates to a method and a system for detecting pollutants in a deep underground water body, wherein the method uses a plurality of data acquisition methods for geophysical detection, chemical analysis and remote sensing images to acquire multi-mode data of the pollution of the deep underground water body; the method comprises the steps of fusing data of geophysical detection, chemical analysis and remote sensing images, acquiring deep underground water pollution information from multiple dimensions, establishing a pollutant diffusion prediction model by using an artificial intelligence algorithm, analyzing historical data and real-time monitoring data, predicting trend and speed of pollutants, adopting a non-invasive detection technology, sampling operation without damaging underground environment, greatly saving time and labor cost, improving detection efficiency of the deep underground water pollutants, establishing an accurate pollutant diffusion prediction model, and providing accurate trend and speed prediction for a decision maker.
Description
Technical Field
The application belongs to the technical field of environmental monitoring, and particularly relates to a method and a system for detecting pollutants in a deep underground water body.
Background
Groundwater is an important component of water resources and has a close relationship with human society. Groundwater is often the main water supply source of local, and groundwater detection sampling is in order to be able to know the quality of groundwater in time.
Currently, the detection method of the pollutants in the deep underground water body mainly depends on the traditional chemical analysis detection method, sampling equipment comprises a drilling sampler, a tubular sampler and the like, and if the comprehensive understanding of the water quality characteristics of the deep underground water is required, the traditional detection equipment and method have limitations. Firstly, the sampling operation is difficult and the operation is complicated, and a large amount of field work and sampling equipment are needed; secondly, the detection speed is slow, and accurate results can be obtained usually only after a long time, so that urgent pollution detection requirements cannot be met, and huge detection data cannot be efficiently processed and analyzed.
Disclosure of Invention
The application provides a method and a system for detecting pollutants in a deep underground water body, and aims to improve the detection accuracy and reliability of the pollutants in the deep underground water body.
In order to achieve the above purpose, the technical scheme adopted by the application is as follows: a method for detecting pollutants in deep underground water body comprises the following steps:
(1) And (3) data acquisition: using a plurality of data acquisition methods to acquire multi-mode data of deep subsurface water pollution for geophysical exploration, chemical analysis and remote sensing images respectively;
(2) Multimodal data fusion: the data of geophysical exploration, chemical analysis and remote sensing images are fused, deep underground water pollution information is obtained from multiple dimensions, and then the data indexes are expressed as data characteristics: t geophysical data, T chemical analysis data, T remote sensing image data;
(3) Data analysis: and (3) establishing a pollutant diffusion prediction model by using a machine learning algorithm and a big data analysis technology, analyzing historical data and real-time monitoring data, and predicting the trend and the speed of pollutants.
Further, the geophysical exploration and chemical analysis adopts a non-invasive detection technology to detect the deep underground water body efficiently under the condition of not damaging the underground environment.
Further, the remote sensing image is obtained through a satellite or an aviation platform.
Further, the non-invasive detection technique employs sonic detection, where first, a suitable sound source device and receiving means are selected and installed in the appropriate location in the deep body of groundwater.
Further, the sound source device is an ultrasonic generator, and the receiving device is an ultrasonic sensor.
Further, chemical information of the pollutants is obtained through the T chemical analysis data; t geophysical data and T remote sensing image data are realized through multi-mode data fusion, detection and tracking methods.
Further, the detection tracking method comprises a HIST tracking module and a CFT tracking module, the subtask results of the two modules are fused through a decision-level fusion part, the result of the whole algorithm is finally output, and the model parameters of the two tracking modules are updated according to the result.
Further, the pollutant diffusion prediction model is a radial basis function neural network diffusion tracking model.
The system comprises an ultrasonic generator, an ultrasonic sensor, a cloud processor and a remote sensing image.
Further, the cloud processor is connected with the ultrasonic generator, the ultrasonic sensor and the remote sensing image through a wireless network, so that data can be stored and analyzed, and detection and diffusion of pollutants are realized.
Compared with the prior art, the application has the following beneficial effects:
1. according to the application, through the analysis of fusion multi-mode data, more comprehensive pollution information can be obtained from different dimensions, and the detection accuracy and reliability of the deep underground water body pollutants are greatly improved.
2. The application adopts a non-invasive detection technology, does not need to destroy the underground environment to carry out sampling operation, greatly saves time and labor cost, and improves the detection efficiency of the pollutants in the deep underground water body.
3. The application establishes an accurate pollutant diffusion prediction model, provides accurate trend and speed prediction for decision makers, and establishes a deep underground water pollution treatment scheme.
Drawings
FIG. 1 is a flow chart of a method for detecting pollutants in a deep underground water body,
FIG. 2 is a flow chart of a radial basis function neural network diffusion tracking model of the present application.
Detailed Description
The present application will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present application, but are not intended to limit the application in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present application.
As shown in fig. 1, a method for detecting pollutants in a deep underground water body comprises the following steps:
(1) And (3) data acquisition: using a plurality of data acquisition methods to acquire multi-mode data of deep subsurface water pollution for geophysical exploration, chemical analysis and remote sensing images respectively;
multimodal data may contain several types of data:
geophysical data: the system comprises a resistivity image, an electromagnetic image and a seismic section, and can provide information on groundwater distribution, pollutant diffusion and the like.
Chemical analysis data: the total suspended matter concentration, the total dissolved solids, the pH value and the oxidation-reduction potential in the groundwater sample provide specific information of pollutants.
Remote sensing image data: the remote sensing image obtained by the satellite or the aviation platform can be utilized, and factors which can influence underground water, such as soil type, vegetation coverage and the like, can be known through analysis of the earth surface coverage.
(2) Multimodal data fusion: the data of geophysical exploration, chemical analysis and remote sensing images are fused, deep underground water pollution information is obtained from multiple dimensions, and then the data indexes are expressed as data characteristics: t geophysical data, T chemical analysis data, T remote sensing image data;
geophysical exploration and chemical analysis employ non-invasive detection techniques to efficiently detect deep groundwater bodies without damaging the subsurface environment.
First, a suitable sound source device (e.g., an ultrasonic generator) and receiving device (e.g., an ultrasonic sensor) are selected and installed in place near the groundwater body.
The ultrasonic generator is used for transmitting the sound wave signals within a certain frequency range, the propagation speed and the propagation path of the sound wave signals in the underground water body and the underground medium can be influenced by a plurality of factors, the information on the aspects of underground water distribution, pollutant diffusion and the like can be provided, wherein the information comprises the total suspended matter concentration, the total dissolved solids, the pH value, the oxidation-reduction potential and the like in the underground water sample, and the specific information on pollutants can be provided.
The ultrasonic sensor receives the reflected acoustic wave signal and determines the propagation time and characteristics of the acoustic wave signal through signal processing and analysis.
By utilizing the propagation time and characteristics of sound waves and combining the physical properties and chemical properties of the underground water body, some important parameter characteristics of the underground water body can be deduced; obtaining chemical information of the pollutants through T chemical analysis data; the method is realized by multi-mode data fusion, detection and tracking for the T geophysical data and the T remote sensing image data: namely a his tracking module and a CFT tracking module.
The detection tracking is composed of three parts, including two tracking modules and a decision-level fusion part of the tracking modules; the HIST tracking module and the CFT tracking module respectively belong to a generating type-discriminant hybrid tracking method and a discriminant tracking method, each of which independently completes the tracking subtasks, and the fusion of the subtask results of the two modules is completed through a decision-level fusion part, and finally the result of the whole algorithm is output, and the model parameters of the two tracking modules are updated according to the result.
And the HIST tracking module utilizes RGB color histogram characteristics extracted by the visible light image sequence to complete a tracking subtask based on global characteristics, can process challenges such as target deformation, rapid movement and the like, and has no boundary effect which can bring noise interference.
And the CFT tracking module extracts features such as HOG, CN, image intensity and the like by utilizing image sequences of infrared and visible light modes to complete a tracking subtask comprising local features and global features, and can process interference such as illumination change, shielding, background disorder and the like. In addition, the module performs tracking by utilizing a plurality of characteristics and is used for self-adaptive fusion among results obtained by multi-characteristic tracking.
And the decision-stage fusion part is used for solving the self-adaptive weight of the tracking module through a reliability measurement rule, then finishing the optimal position of the decision-stage fusion output target, and updating the corresponding parameters of the tracking module according to the result.
(3) Data analysis: and (3) establishing a pollutant diffusion prediction model by using a machine learning algorithm and a big data analysis technology, analyzing historical data and real-time monitoring data, and predicting the trend and the speed of pollutants.
And (3) data processing: during the data sampling process, the sensor equipment is aged or has reduced performance, and the line fault or interference during the data transmission process can cause the loss or distortion of the data; if this data is used directly for contaminant monitoring, the reliability of the data will be severely affected, leading to erroneous decisions and false positives. Therefore, in the process of initializing the sampling data, noise reduction and repair processing are carried out on the data, and the elimination of interference data is very important. For missing data, the missing data with smaller time interval can be repaired by adopting a linear interpolation method:
wherein: x is x t And x t+j The pollutant index parameters are sampled at the time t and the time t+j respectively; x is x t+1 Is the missing parameter at t+i.
For distorted data, since the contaminant index data is continuously sampled and sequentially returned, the sampled data of adjacent time periods does not undergo abrupt changes. If the variation range of the sampling data at a certain moment is more or less than 10% of the monitoring value before and after the sampling data, the sampling data is considered to be distorted. The average smoothing method can be adopted for processing:
in the formula (I), the total number of the components,and->The thresholds of adjacent sample data errors, respectively.
A model of contaminant diffusion prediction is shown in fig. 2:
let the network input x be M dimensions: x= [ x ] 1 ,x 2 ,...,x M ] T The hidden layer has K nodes in total, the output y is L dimension, the length of the input and output sample pair is N, and the function of the hidden layer node of the radial base network is taken as Gaussian base function:
the input collected data vector is mapped to an hidden layer, and the hidden layer node j outputs as follows:
wherein: delta is the normalized constant of the hidden layer node and c is the Gaussian function of the hidden layer node
Heart vector, c j ＝[c j1 ,c j2 ,...,c jM ] T
The linear mapping of conversion dimension is realized from an implicit layer to an output layer of the RBF network, namely, the output layer node k outputs as follows:
wherein: omega jk Adjusting weights from an implicit layer to an output layer; θ k Is the bias of the output layer node k. y is k As a response to the respective input signals, output to the workspace as important regulatory variables according to different contexts.
The system for realizing the method for detecting the pollutants in the deep underground water body comprises an ultrasonic generator, an ultrasonic sensor, a cloud processor and a remote sensing image.
The cloud processor is connected with the ultrasonic generator, the ultrasonic sensor and the remote sensing image through a wireless network, can store and analyze data, and can detect and predict the diffusion of pollutants.
The accurate detection of the pollutants in the deep underground water body is realized by combining an artificial intelligence technology and multi-mode data analysis; the non-invasive detection technology is utilized to realize the rapid detection of the pollutants in the deep underground water body; through machine learning and big data analysis, a prediction and optimization scheme for pollutant diffusion is provided.
The foregoing describes specific embodiments of the present application. It is to be understood that the application is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the application. The embodiments of the application and the features of the embodiments may be combined with each other arbitrarily without conflict.
Claims (10)
1. The method for detecting the pollutants in the deep underground water body is characterized by comprising the following steps of:
(1) And (3) data acquisition: using a plurality of data acquisition methods to acquire multi-mode data of deep subsurface water pollution for geophysical exploration, chemical analysis and remote sensing images respectively;
(2) Multimodal data fusion: the data of geophysical exploration, chemical analysis and remote sensing images are fused, deep underground water pollution information is obtained from multiple dimensions, and then the data indexes are expressed as data characteristics: t geophysical data, T chemical analysis data, T remote sensing image data;
(3) Data analysis: and (3) establishing a pollutant diffusion prediction model by using a machine learning algorithm and a big data analysis technology, analyzing historical data and real-time monitoring data, and predicting the trend and the speed of pollutants.
2. The method for detecting the pollutants in the deep underground water body according to claim 1, wherein the method comprises the following steps: the geophysical exploration and chemical analysis adopts a non-invasive detection technology to carry out high-efficiency detection on the deep underground water body under the condition of not damaging the underground environment.
3. The method for detecting the pollutants in the deep underground water body according to claim 1, wherein the method comprises the following steps: the remote sensing image is obtained through a satellite or an aviation platform.
4. The method for detecting the pollutants in the deep underground water body according to claim 2, wherein the method comprises the following steps: the non-invasive detection technology adopts acoustic detection, selects proper sound source equipment and a receiving device, and installs the sound source equipment and the receiving device at proper positions of deep underground water bodies.
5. The method for detecting the pollutants in the deep underground water body according to claim 4, wherein the method comprises the following steps: the sound source device is an ultrasonic generator, and the receiving device is an ultrasonic sensor.
6. The method for detecting the pollutants in the deep underground water body according to claim 1, wherein the method comprises the following steps: obtaining chemical information of the pollutants through the T chemical analysis data; t geophysical data and T remote sensing image data are realized through multi-mode data fusion, detection and tracking methods.
7. The method for detecting the pollutants in the deep underground water body according to claim 6, wherein the method comprises the following steps: the detection tracking method comprises an H IST tracking module and a CFT tracking module, the fusion of the subtask results of the two modules is completed through a decision-level fusion part, the result of the whole algorithm is finally output, and the model parameters of the two tracking modules are updated according to the result.
8. The method for detecting the pollutants in the deep underground water body according to claim 1, wherein the method comprises the following steps: the pollutant diffusion prediction model is a radial basis function neural network diffusion tracking model.
9. A system comprising a method for detecting a contaminant in a body of deep groundwater according to any one of claims 1 to 8, wherein: the system comprises an ultrasonic generator, an ultrasonic sensor, a cloud processor and a remote sensing image.
10. The system of the method for detecting contaminants in a body of deep groundwater according to claim 9, wherein: the cloud processor is connected with the ultrasonic generator, the ultrasonic sensor and the remote sensing image through a wireless network, and can store and analyze data to realize detection and diffusion of pollutants.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311131742.3A CN117214398B (en) | 2023-09-04 | 2023-09-04 | Deep underground water body pollutant detection method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311131742.3A CN117214398B (en) | 2023-09-04 | 2023-09-04 | Deep underground water body pollutant detection method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117214398A true CN117214398A (en) | 2023-12-12 |
| CN117214398B CN117214398B (en) | 2024-05-14 |
Family
ID=89036303
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311131742.3A Active CN117214398B (en) | 2023-09-04 | 2023-09-04 | Deep underground water body pollutant detection method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117214398B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118090977A (en) * | 2024-03-14 | 2024-05-28 | 江苏省连云港环境监测中心 | Water quality monitoring system based on wireless sensor network |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101295027A (en) * | 2008-06-18 | 2008-10-29 | 上海大学 | Synthetic nondestructive detecting method for hidden dangers of levee |
| CN101680295A (en) * | 2007-02-15 | 2010-03-24 | 高真工程公司 | Method and apparatus for fluid migration profiling |
| CN101782567A (en) * | 2010-02-11 | 2010-07-21 | 中国农业大学 | Intelligent ammonia nitrogen transmitting system and ammonia nitrogen in situ high-frequency detection method |
| US20130197891A1 (en) * | 2012-01-31 | 2013-08-01 | Michael L. Jessop | Subsurface hydrogeologic system modeling |
| CN103575928A (en) * | 2013-11-11 | 2014-02-12 | 中国地质大学（武汉） | Reservoir leakage Doppler detecting instrument |
| CN106646661A (en) * | 2016-09-22 | 2017-05-10 | 张春志 | Comprehensive survey system for hydrogeology of mineral deposit |
| CN110033472A (en) * | 2019-03-15 | 2019-07-19 | 电子科技大学 | A kind of stable objects tracking under the infrared ground environment of complexity |
| CN110186820A (en) * | 2018-12-19 | 2019-08-30 | 河北中科遥感信息技术有限公司 | Multisource data fusion and environomental pollution source and pollutant distribution analysis method |
| CN111640481A (en) * | 2020-05-22 | 2020-09-08 | 南京大学 | Multi-source data fusion-based Parkinson's disease medicine recommendation model |
| CN111796331A (en) * | 2020-08-24 | 2020-10-20 | 吉林大学 | Ground magnetic resonance detection device and method for shallow groundwater and hydrocarbon substances |
| CN112883564A (en) * | 2021-02-01 | 2021-06-01 | 中国海洋大学 | Water body temperature prediction method and prediction system based on random forest |
| CN113379261A (en) * | 2021-06-17 | 2021-09-10 | 上海市环境科学研究院 | Technical method for managing and controlling pollution risk of soil and underground water in polluted site |
| EP3971857A1 (en) * | 2020-09-18 | 2022-03-23 | Guident Ltd | System & method for remote monitoring of a vehicle, robot or drone |
| CN114384015A (en) * | 2022-01-12 | 2022-04-22 | 中国环境科学研究院 | Water environment monitoring method based on multi-source remote sensing and machine learning |
| CN114417740A (en) * | 2022-03-30 | 2022-04-29 | 青岛杰瑞工控技术有限公司 | Deep sea breeding situation sensing method |
| CN114926749A (en) * | 2022-07-22 | 2022-08-19 | 山东大学 | Near-surface atmospheric pollutant inversion method and system based on remote sensing image |
| CN116339337A (en) * | 2023-03-29 | 2023-06-27 | 上海无线电设备研究所 | Target intelligent positioning control system and method based on infrared imaging, laser radar and sound directional detection |
-
2023
- 2023-09-04 CN CN202311131742.3A patent/CN117214398B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101680295A (en) * | 2007-02-15 | 2010-03-24 | 高真工程公司 | Method and apparatus for fluid migration profiling |
| CN101295027A (en) * | 2008-06-18 | 2008-10-29 | 上海大学 | Synthetic nondestructive detecting method for hidden dangers of levee |
| CN101782567A (en) * | 2010-02-11 | 2010-07-21 | 中国农业大学 | Intelligent ammonia nitrogen transmitting system and ammonia nitrogen in situ high-frequency detection method |
| US20130197891A1 (en) * | 2012-01-31 | 2013-08-01 | Michael L. Jessop | Subsurface hydrogeologic system modeling |
| CN103575928A (en) * | 2013-11-11 | 2014-02-12 | 中国地质大学（武汉） | Reservoir leakage Doppler detecting instrument |
| CN106646661A (en) * | 2016-09-22 | 2017-05-10 | 张春志 | Comprehensive survey system for hydrogeology of mineral deposit |
| CN110186820A (en) * | 2018-12-19 | 2019-08-30 | 河北中科遥感信息技术有限公司 | Multisource data fusion and environomental pollution source and pollutant distribution analysis method |
| CN110033472A (en) * | 2019-03-15 | 2019-07-19 | 电子科技大学 | A kind of stable objects tracking under the infrared ground environment of complexity |
| CN111640481A (en) * | 2020-05-22 | 2020-09-08 | 南京大学 | Multi-source data fusion-based Parkinson's disease medicine recommendation model |
| CN111796331A (en) * | 2020-08-24 | 2020-10-20 | 吉林大学 | Ground magnetic resonance detection device and method for shallow groundwater and hydrocarbon substances |
| EP3971857A1 (en) * | 2020-09-18 | 2022-03-23 | Guident Ltd | System & method for remote monitoring of a vehicle, robot or drone |
| CN112883564A (en) * | 2021-02-01 | 2021-06-01 | 中国海洋大学 | Water body temperature prediction method and prediction system based on random forest |
| CN113379261A (en) * | 2021-06-17 | 2021-09-10 | 上海市环境科学研究院 | Technical method for managing and controlling pollution risk of soil and underground water in polluted site |
| CN114384015A (en) * | 2022-01-12 | 2022-04-22 | 中国环境科学研究院 | Water environment monitoring method based on multi-source remote sensing and machine learning |
| CN114417740A (en) * | 2022-03-30 | 2022-04-29 | 青岛杰瑞工控技术有限公司 | Deep sea breeding situation sensing method |
| CN114926749A (en) * | 2022-07-22 | 2022-08-19 | 山东大学 | Near-surface atmospheric pollutant inversion method and system based on remote sensing image |
| CN116339337A (en) * | 2023-03-29 | 2023-06-27 | 上海无线电设备研究所 | Target intelligent positioning control system and method based on infrared imaging, laser radar and sound directional detection |
Non-Patent Citations (7)
| Title |
|---|
| WANG, BB ET AL: "Weight Kullback-Leibler Average Interactive multiple model Probabilistic Data Association Filter", 《2020 5TH INTERNATIONAL CONFERENCE ON MECHANICAL, CONTROL AND COMPUTER ENGINEERING (ICMCCE 2020)》, 28 August 2021 (2021-08-28), pages 1220 - 1224 * |
| ZHU, QD ET AL: "Multimodal Time Series Data Fusion Based on SSAE and LSTM", 《2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE 》, 29 October 2021 (2021-10-29), pages 1 - 10 * |
| 吴青华等: "《三维激光扫描数据处理技术及其工程应用》", 30 November 2020, 山东大学出版社, pages: 47 - 49 * |
| 唐建华: "《管道漏磁内检测数据预处理方法》", 30 April 2019, 东北大学出版社, pages: 104 - 105 * |
| 李丽等: "对水环境监测及水污染防治问题的相关探讨", 《资源节约与环保》, no. 2, 25 February 2021 (2021-02-25), pages 58 - 59 * |
| 王伟等: "《竞赛机器人设计与制作》", vol. 1, 30 November 2022, 武汉：武汉大学出版社, pages: 135 - 137 * |
| 罗成伟: "基于多模态数据融合的视觉目标跟踪算法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》, no. 1, pages 138 - 1871 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118090977A (en) * | 2024-03-14 | 2024-05-28 | 江苏省连云港环境监测中心 | Water quality monitoring system based on wireless sensor network |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117214398B (en) | 2024-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109685011A (en) | A kind of underground utilities detection recognition method based on deep learning | |
| CN112130132A (en) | Underground pipeline detection method and system based on ground penetrating radar and deep learning | |
| CN112232392A (en) | Data interpretation and identification method for three-dimensional ground penetrating radar | |
| CN113359212B (en) | Reservoir characteristic prediction method and model based on deep learning | |
| CN113822201B (en) | Deep learning method for underwater object shape recognition based on flow field velocity component time course | |
| CN112883564A (en) | Water body temperature prediction method and prediction system based on random forest | |
| Zhang et al. | Defect identification for oil and gas pipeline safety based on autonomous deep learning network | |
| CN117198330A (en) | Sound source identification method and system and electronic equipment | |
| CN115114949A (en) | Intelligent ship target identification method and system based on underwater acoustic signals | |
| Belghith et al. | Acoustic diversity classifier for automated marine big data analysis | |
| Luo et al. | A space-frequency joint detection and tracking method for line-spectrum components of underwater acoustic signals | |
| Yuksel et al. | A novel cascaded deep learning model for the detection and quantification of defects in pipelines via magnetic flux leakage signals | |
| CN116660996B (en) | Drifting type shallow sea local earth sound parameter prediction method based on deep learning | |
| CN107765259B (en) | Power transmission line laser ranging signal denoising method based on lifting wavelet improved threshold | |
| CN116626753B (en) | Microseism event identification method and system based on multi-modal neural network | |
| CN117214398B (en) | Deep underground water body pollutant detection method and system | |
| CN117451055A (en) | Underwater sensor positioning method and system based on basis tracking noise reduction | |
| Hu et al. | Defects identification and location of underground space for ground penetrating radar based on deep learning | |
| Liu et al. | Advances in automatic identification of road subsurface distress using ground penetrating radar: State of the art and future trends | |
| Lei et al. | Multi-feature fusion sonar image target detection evaluation based on particle swarm optimization algorithm | |
| Guo et al. | Research on tunnel lining image target recognition method based on YOLOv3 | |
| CN113221651A (en) | Seafloor sediment classification method using acoustic propagation data and unsupervised machine learning | |
| Yuksel et al. | Defect detection and quantification from magnetic flux leakage signals based on deep learning | |
| Qian et al. | Deep Learning-Augmented Stand-off Radar Scheme for Rapidly Detecting Tree Defects | |
| Srinivas et al. | Buried pipes detection through ground penetrating radar images |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |