CN104598514A - Algorithm for finding rare environmental monitoring data combination mode based on clustering analysis - Google Patents
Algorithm for finding rare environmental monitoring data combination mode based on clustering analysis Download PDFInfo
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- CN104598514A CN104598514A CN201410685956.XA CN201410685956A CN104598514A CN 104598514 A CN104598514 A CN 104598514A CN 201410685956 A CN201410685956 A CN 201410685956A CN 104598514 A CN104598514 A CN 104598514A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
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Abstract
The invention relates to an algorithm for finding a rare environmental monitoring data combination mode based on clustering analysis. According to the algorithm, the clustering analysis principle is adopted for analyzing the data mode of the multi-parameter data combination of the environmental monitoring data and finding the combination mode of the rare monitoring data hidden therein, so that environmental monitoring staff are reminded. The environmental monitoring data considered by the algorithm are not confined to a specific monitoring parameter, but are a monitoring data combination in the overall consideration of multiple parameters at the same time. According to the algorithm for finding the rare environmental monitoring data combination mode based on clustering analysis, the clustering analysis principle is adopted, the multi-parameter monitoring data combination is in the overall consideration and compared with historical data, and a data combination mode will be judged to be a rare data combination mode in history if the number of the occurrence times of the current data combination mode is less than the corresponding threshold value.
Description
Art
The present invention relates to a kind of algorithm finding the rare data assemblies pattern of environmental monitoring based on cluster analysis.This algorithm can utilize cluster analysis principle, analyzes the data pattern that the Multi-parameter data of environmental monitoring data combines, and finds the Monitoring Data integrated mode that wherein hidden history is rare, thus can remind the attention of environmental monitoring personnel.
Background technology
In environmental monitoring, the ANOMALOUS VARIATIONS of environmental monitoring data may be caused by Monitoring Data quality problems, also may be the change of hint environment.Therefore, for the discovery of abnormal data in environmental monitoring data, environmental monitoring department can be assisted to find environmental monitoring data quality problems, and environmental change is given warning in advance.As a kind of form of abnormal data, when the rare data assemblies pattern of history appears in environmental monitoring data, just need to remind environmental monitoring personnel may there is data quality problem and maybe may occur environmental change.
Algorithm described in the invention can be analyzed environmental monitoring data by cluster analysis, to find the rare data assemblies pattern of history occurred in environmental monitoring data.
Summary of the invention
The environmental monitoring data that algorithm described in the invention is considered is not limited to certain specific monitoring parameter, but considers the Monitoring Data combination of multiple parameters of synchronization.Algorithm described in the invention utilizes cluster analysis principle, consider the Monitoring Data combination of multiparameter, and compare with historical data, if when finding that the number of times that current data assemblies pattern occurs in history is less than respective threshold, this data assemblies pattern will be judged as the rare data assemblies pattern of history.
Ultimate principle of the present invention is that the environmental monitoring data with ground is periodically variable, if there is the data assemblies that history is rare, then can think that this data assemblies is abnormal conditions, the tendency change of Monitoring Data quality problems or environment may be reflected, therefore should cause the attention of environmental monitoring personnel.In algorithm, the present invention adopts gridding method to multi-parameter monitoring data construct history feature database, and compare with Real-time Monitoring Data and history feature database, if current Real-time Monitoring Data is combined in institute's occurrence number in history feature database and lower than setting threshold value, is then judged to be that this data assemblies is the rare data assemblies of history.
Arthmetic statement of the present invention as shown in Figure 1, this algorithm is realized by software development, can first generate history feature database, then the combination of current environment Monitoring Data is compared with history feature database, if current Monitoring Data combination is lower than set threshold value, be then judged to be the rare data assemblies pattern of environmental monitoring.
The invention has the beneficial effects as follows: by the analytical approach of cluster analysis, the rare data assemblies data exception of history in environmental monitoring data can be found, thus environmental monitoring personnel inspection Monitoring Data quality can be reminded or pay attention to Trend of Environmental Change.
Accompanying drawing explanation
Below in conjunction with accompanying drawing, the present invention is further described.
Fig. 1 finds the algorithm flow chart of the rare data assemblies pattern of environmental monitoring based on cluster analysis
Embodiment
In FIG, the algorithm flow finding the rare data assemblies pattern of environmental monitoring based on cluster analysis is described.Key step comprises:
1, statistical study is carried out to different monitoring parameter combination (P parameter), with each monitoring parameter for dimension, each dimension is divided into D decile, thus all values of this parameter combinations form are divided into (D^P) individual multi-dimensional grid
2, according to historical data, the number of times (N) that the historical data calculating this parameter combinations occurs in each grid
3, by data occurrence number history of forming property data base in grid composition and each grid
4, the data of this monitoring parameter data assemblies current and history feature database are contrasted
5, the judgement of rare data assemblies pattern: grid corresponding historical data combination occurrence number (N) < sets threshold value L belonging to current Monitoring Data combination, then judge that this Monitoring Data is combined as the rare data assemblies pattern of environmental monitoring, otherwise be not judged to be the rare data assemblies pattern of environmental monitoring.
Claims (3)
1. one kind finds the algorithm of the rare data assemblies pattern of environmental monitoring based on cluster analysis, it is characterized in that: the present invention adopts gridding method to multi-parameter monitoring data construct history feature database, and compare with Real-time Monitoring Data and history feature database, if current Real-time Monitoring Data is combined in institute's occurrence number in history feature database and lower than setting threshold value, is then judged to be that this data assemblies is the rare data assemblies of history.
2. a kind of algorithm finding the rare data assemblies pattern of environmental monitoring based on cluster analysis according to claim 1, its algorithm characteristics is: the environmental monitoring data that algorithm is considered is not limited to certain specific monitoring parameter, but considers the Monitoring Data combination of multiple parameters of synchronization.
3. a kind of algorithm finding the rare data assemblies pattern of environmental monitoring based on cluster analysis according to claim 1, its algorithm steps feature is:
1) statistical study is carried out to different monitoring parameter combination (P parameter), with each monitoring parameter for dimension, each dimension is divided into D decile, thus all values of this parameter combinations form are divided into (D^P) individual multi-dimensional grid
2) according to historical data, the number of times (N) that the historical data calculating this parameter combinations occurs in each grid
3) by data occurrence number history of forming property data base in grid composition and each grid
4) data of this monitoring parameter data assemblies current and history feature database are contrasted
5) judgement of rare data assemblies pattern: grid corresponding historical data combination occurrence number (N) < sets threshold value L belonging to current Monitoring Data combination, then judge that this Monitoring Data is combined as the rare data assemblies pattern of environmental monitoring, otherwise be not judged to be the rare data assemblies pattern of environmental monitoring.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201410685956.XA CN104598514A (en) | 2014-11-26 | 2014-11-26 | Algorithm for finding rare environmental monitoring data combination mode based on clustering analysis |
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| CN201410685956.XA CN104598514A (en) | 2014-11-26 | 2014-11-26 | Algorithm for finding rare environmental monitoring data combination mode based on clustering analysis |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113376327A (en) * | 2021-07-08 | 2021-09-10 | 海南绿能环境工程有限公司 | Environmental monitoring information management method and system based on big data |
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| CN103020642A (en) * | 2012-10-08 | 2013-04-03 | 江苏省环境监测中心 | Water environment monitoring and quality-control data analysis method |
| CN103176221A (en) * | 2013-03-07 | 2013-06-26 | 李春华 | Mineralizing abnormal feature identification method based on different geological unit background values and contrast values |
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2014
- 2014-11-26 CN CN201410685956.XA patent/CN104598514A/en active Pending
Patent Citations (2)
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|---|---|---|---|---|
| CN103020642A (en) * | 2012-10-08 | 2013-04-03 | 江苏省环境监测中心 | Water environment monitoring and quality-control data analysis method |
| CN103176221A (en) * | 2013-03-07 | 2013-06-26 | 李春华 | Mineralizing abnormal feature identification method based on different geological unit background values and contrast values |
Non-Patent Citations (2)
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| 田启明等: "基于网格距离的聚类算法的设计、实现和应用", 《计算机应用》 * |
| 舒红平等: "网格聚类在多雷达数据融合算法中的应用", 《电子科大学学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113376327A (en) * | 2021-07-08 | 2021-09-10 | 海南绿能环境工程有限公司 | Environmental monitoring information management method and system based on big data |
| CN113376327B (en) * | 2021-07-08 | 2023-01-17 | 海南海笙信息科技有限公司 | Environmental monitoring information management method and system based on big data |
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Application publication date: 20150506 |