CN114352946A - Drainage engineering pipeline blocks up early warning system - Google Patents
Drainage engineering pipeline blocks up early warning system Download PDFInfo
- Publication number
- CN114352946A CN114352946A CN202210250911.4A CN202210250911A CN114352946A CN 114352946 A CN114352946 A CN 114352946A CN 202210250911 A CN202210250911 A CN 202210250911A CN 114352946 A CN114352946 A CN 114352946A
- Authority
- CN
- China
- Prior art keywords
- blockage
- pipeline
- early warning
- time
- real
- 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
Images
Abstract
The invention relates to the technical field of artificial intelligence, in particular to a drainage engineering pipeline blockage early warning system. The system comprises a data acquisition unit: acquiring the water flow, the residue weight, the unblocked rate and the water level height in the well of the pipeline at each moment to form a characteristic vector at the moment; a data processing unit: acquiring a clustering distance for clustering the characteristic vectors of each moment into various pipeline blockage degrees according to a vector sequence consisting of the characteristic vectors of the historical time period at each moment; a blockage early warning unit: and acquiring a real-time vector sequence in a time period, obtaining the pipeline blockage degree to which the real-time vector sequence belongs by utilizing the clustering distance, and analyzing and early warning according to the pipeline blockage degree. The blocking condition of the real-time pipeline data is analyzed according to the blocking condition of the historical data, and early warning analysis of different blocking degrees is carried out according to the blocking condition, so that the early warning result is prevented from being influenced by analysis errors of the blocking condition.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a drainage engineering pipeline blockage early warning system.
Background
The drainage pipe blocks up the back and easily causes the problem such as anti-water, ponding because of the drainage is not unblocked, if can not in time monitor, influences normal drainage on the one hand, and on the other hand will appear crack scheduling problem when the pipeline blocks up seriously around the pipeline, causes bigger loss. The current common technical means is to judge the blockage condition of the pipeline according to the water flow in the pipeline and the water level in the well, however, the weather also has an influence on the blockage condition of the pipeline, so that the confirmation of the blockage of the pipeline according to the indexes is inaccurate, and the error is large.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a drainage engineering pipeline blockage early warning system, which adopts the following technical scheme:
the data acquisition unit is used for acquiring water flow, residue weight and water level height in a well in a pipeline according to a set time interval, calculating the unblocked rate of the pipeline at a corresponding moment according to the water flow and the residue weight, and forming a feature vector by the water flow, the residue weight, the water level height and the unblocked rate at each moment;
the data processing unit is used for forming a vector sequence by a plurality of characteristic vectors in a set time period, and acquiring clustering distances for clustering the characteristic vectors into various pipeline blockage degrees according to the difference degrees among the vector sequences corresponding to a plurality of historical time periods;
and the blockage early warning unit is used for acquiring a real-time vector sequence in a time period in real time, obtaining the pipeline blockage degree to which the real-time vector sequence belongs based on the clustering distance, and early warning the pipeline blockage degree in real time according to the pipeline blockage degree.
Further, the plurality of degrees of pipe blockage in the data processing unit includes: high clogging, medium clogging and low clogging.
Further, the method for calculating the difference degree in the data processing unit includes:
calculating the clear rate change of the corresponding time period according to the clear rate of each moment in the vector sequence, and calculating the difference value of the two historical time periods corresponding to the clear rate change;
calculating the similarity of water level height change between two historical time periods according to all the water level heights in the vector sequence;
and combining the difference value and the similarity to obtain the difference between the two vector sequences corresponding to the historical time periods.
Further, the method for real-time early warning in the blockage early warning unit comprises the following steps:
when the real-time vector sequence belongs to the high blockage, further analyzing the blockage situation in the time period, and early warning according to the analysis result;
when the real-time vector sequence belongs to the medium blockage or the low blockage, workers need to be immediately informed to clean the pipeline.
Further, the method for analyzing the blockage situation under the high blockage in the blockage early warning unit comprises the following steps:
and respectively calculating the difference between the water level height and the standard water level height at each moment in the real-time vector sequence, acquiring the standard deviation between all the differences, obtaining the water level change characteristics in the corresponding time period of the real-time vector sequence according to the standard deviation, confirming the reason of high blockage according to the water level change characteristics, and carrying out early warning based on the reason of high blockage.
Further, the method for performing early warning based on the high blockage reason in the blockage early warning unit comprises the following steps:
setting a characteristic threshold value, and when the water level change characteristic is greater than or equal to the characteristic threshold value, determining that the blockage caused by rain and snow weather is judged incorrectly, and not needing to clean the pipeline; otherwise, when the water level change characteristic is smaller than the characteristic threshold value, the pipeline is confirmed to be blocked, and early warning is timely carried out.
Further, the difference degree and the difference value are in a positive correlation relationship, and the difference degree and the similarity are in a negative correlation relationship.
Further, the unblocking rate and the water flow rate in the data acquisition unit are in a positive correlation relationship, and the unblocking rate and the residue weight are in a negative correlation relationship.
Further, the method for obtaining the clustering distance in the data processing unit includes:
calculating an average difference between a plurality of differences, obtaining the clustering radius according to the average difference, and enabling the average difference and the clustering radius to have a negative correlation relationship.
The embodiment of the invention at least has the following beneficial effects: the blocking condition of the real-time pipeline data is analyzed according to the blocking condition of the historical data, and early warning analysis of different blocking degrees is carried out according to the blocking condition, so that the early warning result is prevented from being influenced by analysis errors of the blocking condition.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a block diagram of a drainage engineering pipe blockage warning system according to an embodiment of the present invention.
Detailed Description
In order to further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects of the system for early warning of pipe blockage in drainage engineering according to the present invention will be made with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The concrete scheme of the drainage engineering pipeline blockage early warning system provided by the invention is specifically described below by combining the attached drawings.
Referring to fig. 1, a block diagram of a drainage engineering pipe blockage warning system according to an embodiment of the present invention is shown, where the system includes:
the data acquisition unit 10 is configured to acquire water flow, residue weight, and water level height in the well in the pipeline according to a set time interval, calculate a smooth rate of the pipeline at a corresponding time according to the water flow and the residue weight, and form a feature vector by the water flow, the residue weight, the water level height, and the smooth rate at each time.
Specifically, because the flow size of water can respond to whether blocking up in the pipeline, if the flow of water diminishes, explain then to have the risk of blockking up in the pipeline, need send the people in time to clear up, consequently use water flow monitoring instrument to acquire the water flow change of pipeline in real time: and placing a water flow detector at the tail end of the pipeline, and collecting the water flow V of the pipeline for 1min by using the water flow detector.
The higher the water level in the well is, the higher the risk that the pipeline is blocked is, so that the water level height feedback instrument is placed in the well and packaged by a waterproof material, and the water level height H in the well is collected for 1min once by using the water level height feedback instrument.
Because the water flow of the pipeline and the water level height in the well can be influenced by extreme weather conditions such as rain and snow weather, the blockage condition of the pipeline cannot be accurately reflected only by collecting the water level height H and the water flow V, so the embodiment of the invention detects the weight of residues in the pipeline and analyzes the blockage condition of the pipeline according to the weight change of the residues: the strain type weighing sensor is arranged in the pipeline, and the reading device is arranged at a reserved position of the pipeline extending out of a wellhead. And detecting the weight G of the residue in the pipeline by using a strain type weighing sensor, and acquiring data once in 1 min.
Further, the unblocked rate of the pipeline at each moment is calculated according to the water flow and the weight of the residues collected at each moment, the unblocked rate and the water flow are in a positive correlation relationship, and the unblocked rate and the weight of the residues are in a negative correlation relationship, so that the unblocked rate is calculated according to the following formula:
wherein the content of the first and second substances,
is as follows
The smooth rate of the pipeline at any moment;
is as follows
The weight of the residue in the pipeline at that time;
is as follows
The water flow of the pipeline at all times.
The water level height H, the water flow V, the residue weight G and the unblocked rate U of the pipeline at each moment form a characteristic vector corresponding to the moment
Each time instant corresponds to a feature vector.
And the data processing unit 20 is configured to combine the plurality of feature vectors in the set time period into a vector sequence, and obtain a clustering distance for clustering the plurality of feature vectors into a plurality of pipeline blockage degrees according to the difference degree between the plurality of vector sequences corresponding to the plurality of historical time periods.
Specifically, in the embodiment of the present invention, an hour is used as a set time period, the feature vectors at each time in the hour are combined into a vector sequence in the time period, so as to obtain a plurality of vector sequences corresponding to a plurality of historical time periods, and a difference between the vector sequences is calculated, then the calculation method of the difference is as follows: calculating the clear rate change of the corresponding time period according to the clear rate of each moment in the vector sequence, and calculating the difference value of the corresponding clear rate change between the two historical time periods; calculating the similarity of water level height changes between two historical time periods according to all the water level heights in the vector sequence; and combining the difference and the similarity to obtain the difference between the corresponding vector sequences corresponding to the two historical time periods, wherein the difference and the difference are in positive correlation and the difference and the similarity are in negative correlation.
As an example, the calculation formula of the difference degree in the embodiment of the present invention is:
wherein the content of the first and second substances,
the difference degree between the corresponding vector sequences of the time period A and the time period B is shown;
respectively representing the maximum clear rate and the minimum clear rate in the vector sequence of the time period A;
the average value of the smooth rate in the vector sequence corresponding to the time period A is obtained;
respectively representing the maximum clear rate and the minimum clear rate in the vector sequence of the time period B;
the average value of the smooth rate in the vector sequence corresponding to the time period B is obtained;
the time period a and the time period B correspond to the similarity between the vector sequences.
In order to classify the vector sequences into classes, and each class corresponds to a blockage degree, three pipeline blockage degrees, namely high blockage, medium blockage and low blockage, are taken as examples in the embodiment of the invention. And setting the clustering radius in the DBSCAN clustering according to the difference degree to cluster the vector sequences into three types, wherein the method for acquiring the clustering radius comprises the following steps: calculate moreAverage degree of difference between individual degrees of difference, from which the cluster radius is calculated, i.e.
。
And the blockage early warning unit 30 is used for acquiring the real-time vector sequence in a time period in real time, obtaining the blockage degree of the pipeline to which the real-time vector sequence belongs based on the clustering distance, and early warning the blockage degree of the pipeline in real time according to the blockage degree of the pipeline.
Specifically, a real-time vector sequence in a time period is obtained in real time by using the data acquisition unit 10, the real-time vector sequence and a plurality of historical vector sequences are used as analysis data, the difference degrees between the vector sequences are respectively calculated, density clustering is performed on the difference degrees by using the clustering radius determined in the data processing unit 20, the blocking degree of a pipeline to which the real-time vector sequence belongs is obtained according to the clustering result, and then blocking early warning is performed according to the confirmed blocking degree of the pipeline, the method is as follows: when the real-time vector sequence belongs to high blockage, further analyzing the blockage condition in the time period, and early warning according to the analysis result; when the real-time vector sequence is in medium blockage or low blockage, workers need to be immediately informed to clean the pipeline.
Further, when the high blockage exists, respectively calculating the difference between the water level height at each moment in the real-time vector sequence and the standard water level height, acquiring the standard difference between all the differences, obtaining the water level change characteristics in the corresponding time period of the real-time vector sequence according to the standard difference, confirming the reason of the high blockage according to the water level change characteristics, carrying out early warning based on the reason of the high blockage, namely setting a characteristic threshold value, and when the water level change characteristics are greater than or equal to the characteristic threshold value, confirming that the blockage caused by rain and snow weather is misjudged, so that the pipeline cleaning is not needed; and otherwise, when the water level change characteristic is smaller than the characteristic threshold value, confirming that the pipeline is blocked, and timely carrying out early warning.
As an example, the calculation formula of the water level variation characteristic is as follows:
wherein, in the step (A),
in order to be a characteristic of the change of the water level,
is the standard deviation.
The characteristic threshold value is set by a user according to the execution environment of the implementer.
In summary, the embodiment of the present invention provides a drainage engineering pipeline blockage early warning system, in which a data acquisition unit 10 acquires water flow, residue weight, and water level height in a well in a pipeline according to a set time interval, calculates the smooth rate of the pipeline at a corresponding time according to the water flow and the residue weight, and forms a feature vector with the water flow, the residue weight, the water level height, and the smooth rate at each time; forming a vector sequence by a plurality of characteristic vectors in a set time period in the data processing unit 20, and acquiring clustering distances for clustering the characteristic vectors into various pipeline blockage degrees according to the difference degrees among a plurality of vector sequences corresponding to a plurality of historical time periods; the real-time vector sequence in a time period is obtained in real time in the blockage early warning process 30, the blockage degree of the pipeline to which the real-time vector sequence belongs is obtained based on the clustering distance, and real-time early warning is carried out according to the blockage degree of the pipeline. The blocking condition of the real-time pipeline data is analyzed according to the blocking condition of the historical data, and early warning analysis of different blocking degrees is carried out according to the blocking condition, so that the early warning result is prevented from being influenced by analysis errors of the blocking condition.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. The utility model provides a drainage engineering pipe blockage early warning system which characterized in that, this system includes:
the data acquisition unit is used for acquiring water flow, residue weight and water level height in a well in a pipeline according to a set time interval, calculating the unblocked rate of the pipeline at a corresponding moment according to the water flow and the residue weight, and forming a feature vector by the water flow, the residue weight, the water level height and the unblocked rate at each moment;
the data processing unit is used for forming a vector sequence by a plurality of characteristic vectors in a set time period, and acquiring clustering distances for clustering the characteristic vectors into various pipeline blockage degrees according to the difference degrees among the vector sequences corresponding to a plurality of historical time periods;
and the blockage early warning unit is used for acquiring a real-time vector sequence in a time period in real time, obtaining the pipeline blockage degree to which the real-time vector sequence belongs based on the clustering distance, and early warning the pipeline blockage degree in real time according to the pipeline blockage degree.
2. The system of claim 1, wherein the plurality of pipe blockage levels in the data processing unit comprises: high clogging, medium clogging and low clogging.
3. The system of claim 1, wherein the method of calculating the degree of difference in the data processing unit comprises:
calculating the clear rate change of the corresponding time period according to the clear rate of each moment in the vector sequence, and calculating the difference value of the two historical time periods corresponding to the clear rate change;
calculating the similarity of water level height change between two historical time periods according to all the water level heights in the vector sequence;
and combining the difference value and the similarity to obtain the difference between the two vector sequences corresponding to the historical time periods.
4. The system of claim 2, wherein the method of real-time early warning in the blockage early warning unit comprises:
when the real-time vector sequence belongs to the high blockage, further analyzing the blockage situation in the time period, and early warning according to the analysis result;
when the real-time vector sequence belongs to the medium blockage or the low blockage, workers need to be immediately informed to clean the pipeline.
5. The system of claim 4, wherein the method of analyzing the blockage situation under the high blockage in the blockage warning unit comprises:
and respectively calculating the difference between the water level height and the standard water level height at each moment in the real-time vector sequence, acquiring the standard deviation between all the differences, obtaining the water level change characteristics in the corresponding time period of the real-time vector sequence according to the standard deviation, confirming the reason of high blockage according to the water level change characteristics, and carrying out early warning based on the reason of high blockage.
6. The system of claim 5, wherein the method for performing early warning based on the high congestion cause in the congestion early warning unit comprises:
setting a characteristic threshold value, and when the water level change characteristic is greater than or equal to the characteristic threshold value, determining that the blockage caused by rain and snow weather is judged incorrectly, and not needing to clean the pipeline; otherwise, when the water level change characteristic is smaller than the characteristic threshold value, the pipeline is confirmed to be blocked, and early warning is timely carried out.
7. The system of claim 3, wherein the degree of difference is positively correlated with the difference and the degree of difference is negatively correlated with the degree of similarity.
8. The system of claim 1, wherein the rate of patency is positively correlated with the flow rate of water and negatively correlated with the residue weight in the data acquisition unit.
9. The system of claim 1, wherein the method for obtaining the clustering distance in the data processing unit comprises:
calculating an average difference degree among a plurality of difference degrees, obtaining the clustering distance according to the average difference degree, wherein the average difference degree and the clustering distance have a negative correlation relationship.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210250911.4A CN114352946B (en) | 2022-03-15 | 2022-03-15 | Drainage engineering pipeline blocks up early warning system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210250911.4A CN114352946B (en) | 2022-03-15 | 2022-03-15 | Drainage engineering pipeline blocks up early warning system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114352946A true CN114352946A (en) | 2022-04-15 |
| CN114352946B CN114352946B (en) | 2022-06-03 |
Family
ID=81094568
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210250911.4A Active CN114352946B (en) | 2022-03-15 | 2022-03-15 | Drainage engineering pipeline blocks up early warning system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114352946B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115268362A (en) * | 2022-09-01 | 2022-11-01 | 江苏新晖测控科技有限公司 | Tunnel fire-fighting pool liquid level monitoring and early warning system and method based on Internet |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008196889A (en) * | 2007-02-09 | 2008-08-28 | Toshiba Corp | Water quality measuring system |
| CN101509604A (en) * | 2009-03-20 | 2009-08-19 | 武汉大学 | Method and device for detecting and assessing deposit in metal pipe |
| CN105242267A (en) * | 2015-09-29 | 2016-01-13 | 合肥工业大学 | Method for positioning blockage point in non-metal pipeline by means of ground penetrating radar |
| CN105930653A (en) * | 2016-04-19 | 2016-09-07 | 清华大学 | Pipe explosion pre-warning method based on metering zone flow monitoring data |
| CN106322121A (en) * | 2016-08-26 | 2017-01-11 | 中国石油大学(华东) | Early monitoring device and method for hydrate blockage of deep water gas well production pipeline |
| CN109577449A (en) * | 2018-12-03 | 2019-04-05 | 肖修军 | A kind of municipal drainage network monitoring control system based on big data |
| CN109827081A (en) * | 2019-02-28 | 2019-05-31 | 昆明理工大学 | A kind of buried drain pipe road plugging fault and branch pipe tee connection part diagnostic method based on acoustics active detecting |
| CN110533890A (en) * | 2019-09-11 | 2019-12-03 | 苏州千层茧农业科技有限公司 | Simple sewer blockage emergency advance warning delivery system |
| CN110990995A (en) * | 2019-10-23 | 2020-04-10 | 万翼科技有限公司 | Method and device for positioning water flow abnormity of drainage pipeline |
| CN113701802A (en) * | 2021-07-13 | 2021-11-26 | 中国南方电网有限责任公司超高压输电公司广州局 | Anomaly monitoring method and anomaly monitoring system for pipeline system |
-
2022
- 2022-03-15 CN CN202210250911.4A patent/CN114352946B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008196889A (en) * | 2007-02-09 | 2008-08-28 | Toshiba Corp | Water quality measuring system |
| CN101509604A (en) * | 2009-03-20 | 2009-08-19 | 武汉大学 | Method and device for detecting and assessing deposit in metal pipe |
| CN105242267A (en) * | 2015-09-29 | 2016-01-13 | 合肥工业大学 | Method for positioning blockage point in non-metal pipeline by means of ground penetrating radar |
| CN105930653A (en) * | 2016-04-19 | 2016-09-07 | 清华大学 | Pipe explosion pre-warning method based on metering zone flow monitoring data |
| CN106322121A (en) * | 2016-08-26 | 2017-01-11 | 中国石油大学(华东) | Early monitoring device and method for hydrate blockage of deep water gas well production pipeline |
| CN109577449A (en) * | 2018-12-03 | 2019-04-05 | 肖修军 | A kind of municipal drainage network monitoring control system based on big data |
| CN109827081A (en) * | 2019-02-28 | 2019-05-31 | 昆明理工大学 | A kind of buried drain pipe road plugging fault and branch pipe tee connection part diagnostic method based on acoustics active detecting |
| CN110533890A (en) * | 2019-09-11 | 2019-12-03 | 苏州千层茧农业科技有限公司 | Simple sewer blockage emergency advance warning delivery system |
| CN110990995A (en) * | 2019-10-23 | 2020-04-10 | 万翼科技有限公司 | Method and device for positioning water flow abnormity of drainage pipeline |
| CN113701802A (en) * | 2021-07-13 | 2021-11-26 | 中国南方电网有限责任公司超高压输电公司广州局 | Anomaly monitoring method and anomaly monitoring system for pipeline system |
Non-Patent Citations (1)
| Title |
|---|
| 闫菁等: "排水管道堵塞故障的声诊断方法研究", 《云南大学学报(自然科学版)》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115268362A (en) * | 2022-09-01 | 2022-11-01 | 江苏新晖测控科技有限公司 | Tunnel fire-fighting pool liquid level monitoring and early warning system and method based on Internet |
| CN115268362B (en) * | 2022-09-01 | 2023-08-04 | 江苏新晖测控科技有限公司 | Tunnel fire-fighting pool liquid level monitoring and early warning system and method based on Internet |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114352946B (en) | 2022-06-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109783903B (en) | Industrial water pipeline fault diagnosis method and system based on time sequence | |
| WO2022068417A1 (en) | Vessel loitering detection method based on ais data | |
| CN105242534B (en) | Based on telemetry parameter and it is associated with the satellitosis monitoring method to satellite controlling behavior | |
| CN114352946B (en) | Drainage engineering pipeline blocks up early warning system | |
| CN110648480B (en) | Single variable alarm system and method based on change rate | |
| CN111709465B (en) | Intelligent identification method for rough difference of dam safety monitoring data | |
| US11174624B2 (en) | Method for identifying number of shoveling-and-loading processes of loader and device for identifying the number of the shoveling-and-loading processes of the loader | |
| CN110930357A (en) | In-service steel wire rope surface defect detection method and system based on deep learning | |
| CN112966856A (en) | Mountain torrent risk prediction method and prediction system | |
| CN111222526A (en) | Fishing boat real-time fishing behavior recognition method, device, equipment and storage medium | |
| CN108920429A (en) | A kind of abnormal data analysis method of Water level trend monitoring | |
| CN117057616B (en) | Water conservancy monitoring method and system based on digital twin | |
| CN116522270B (en) | Data processing system for smart sponge city | |
| CN110992415B (en) | Water surface floater pollution evaluation system and method based on big data | |
| CN114580260B (en) | Landslide interval prediction method based on machine learning and probability theory | |
| CN110084169A (en) | A kind of architecture against regulations object recognition methods based on K-Means cluster and profile topological constraints | |
| Alferes et al. | Efficient automated quality assessment: Dealing with faulty on-line water quality sensors | |
| CN106599456A (en) | Method for constructing geomorphologic unit hydrograph distinguishing confluence speed differences of slope surface and channel | |
| CN114662895A (en) | Pipe network comprehensive risk assessment method and device | |
| CN113095694A (en) | Method for constructing rainfall sand transportation model suitable for multi-landform type area | |
| CN113723716A (en) | Passenger flow classification early warning abnormity warning method, device and storage medium | |
| CN112504357A (en) | Dynamic analysis method and system for river channel flow capacity | |
| CN117113236B (en) | Smart city monitoring system and data processing method | |
| CN117423213A (en) | Landslide hazard monitoring system and method | |
| CN104637062A (en) | Target tracking method based on particle filter integrating color and SURF (speeded up robust feature) |
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 |