CN104657912A - Method and system for detecting abnormal user based on water-electricity ratio and support vector clustering - Google Patents
Method and system for detecting abnormal user based on water-electricity ratio and support vector clustering Download PDFInfo
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Abstract
The invention discloses a method and a system for detecting an abnormal user based on a water-electricity ratio and support vector clustering. The method comprises the following steps: firstly, acquiring water and electricity data of the user; secondly, obtaining non-zero consumption data in the water and electricity data; thirdly, calculating the water-electricity ratio of the non-zero consumption data; fourthly, inputting the water-electricity ratio to a support vector classifier for performing classification to obtain classification data; finally, judging whether the classification data is a noise cluster or not, and if the classification data is the noise cluster, the classification data is abnormal data; if the classification data is not the noise cluster, the classification data is normal data. The electricity consumption and the water consumption are effectively integrated with a water-electricity ratio or electricity-water ratio method and serve as input vectors for support vector clustering analysis in sequence for performing detection, so that the false detection rate and the missing detection rate of the water consumption and the electricity consumption during separate detection can be effectively avoided. A support vector clustering design can obtain relatively high classification accuracy by utilizing relatively good distinguishing performance. With a method for dynamically setting a support vector kernel function and a maximum number of iterations, the sensitivity of the detection method can be improved.
Description
Technical field
The present invention relates to water, multiplexing electric abnormality user detection technique field, especially relate to a kind of based on water electricity ratio and support vector clustering abnormal user detection method and system.
Background technology
Along with the development of infotech, collecting terminal application is more and more extensive, the workload that greatly reducing manually visits checks meter.Major part city can realize the data acquisition of water, electric consumption at present substantially by the mode of automatic centralized reading, for follow-up management, charging provide solid foundation.But due to network, acquisition terminal stability, user's stealing, steal a variety of causes such as water, inevitably there will be the problem of image data exception, if do not detect timely for the data of exception and process, can bring economic loss to enterprise, serious is even related to by water, Electrical Safety.Therefore, the abnormal user of utility requirements detects and seems particularly important.
Traditional abnormal deviation data examination method has a variety of, as based on the Forecasting Methodology of mechanism model, statistical method, detection method, neural net method and support vector machine method etc. based on distance, often kind of detection method has oneself the scope of application and condition, and it affects the validity and reliability of detection method.Therefore, select suitable detection method most important for anomaly data detection.Because Running-water Company and Utilities Electric Co. adhere to different enterprise separately, user's use water, electricity consumption data are not shared, and traditional electricity consumption, use water abnormality detection, can only carry out in enterprises, usually, in Electric Power Marketing System, use water marketing system, detection is carried out to user's consumption and history consumption and analyzes.Owing to being separately detect by water, electricity consumption data, do not combine both data, comprehensively analyze, therefore easily there is local convergence in analysis result, causes the inaccurate problem of testing result.
Existing utility requirements abnormal user detection method, is therefore necessary to be improved.
Summary of the invention
For the deficiency that above-mentioned prior art exists, the object of this invention is to provide a kind of based on water electricity ratio and support vector clustering abnormal user detection method and system.
To achieve these goals, the technical solution adopted in the present invention is:
An object of the present invention proposes one based on water electricity ratio and support vector clustering abnormal user detection method; Two of object of the present invention proposes one based on water electricity ratio and support vector clustering abnormal user detection system.
An object of the present invention is achieved through the following technical solutions:
Provided by the invention based on water electricity ratio and support vector clustering abnormal user detection method, comprise the following steps:
S1: the water yield electric quantity data gathering user;
S2: obtain the non-zero usage data in water yield electric quantity data;
S3: the water power ratio calculating non-zero usage data;
S4: water power ratio is input to support vector sorter and carries out classification and obtain grouped data;
S5: judge whether grouped data is noise bunch if so, is then abnormal data;
S6: is if not then normal data.
Further, further comprising the steps of:
S21: obtain zero usage data in water yield electric quantity data;
S22: judge whether zero usage data is small incidental expenses amount entirely if not, is then abnormal user;
S23: if be then normal users.
Further, the user's water yield electric quantity data acquisition in described step S1, comprises and gathers the original water consumption of resident, power consumption, water family number, electric family number and date data.
Further, the sampling feature vectors of the support vector sorter in described step S4 is water consumption/power consumption w/e and power consumption/water consumption e/w; Kernel function adopts Gaussian function
Two of object of the present invention is achieved through the following technical solutions:
Provided by the invention based on water electricity ratio and support vector clustering abnormal user detection system, comprise data sampling module, data screening module, water power than computing module, support vector clustering module and data outputting module;
Described data sampling module, for gathering the original water consumption of resident, power consumption, water family number, electric family number and date data;
Described data screening module, for screening the raw data of sampling, exports the non-zero usage data filtered out;
Described water power, than computing module, compares and electric water ratio, successively as the input of support vector clustering analyzer for the water power calculating non-zero usage data;
Described support vector clustering module, for water power that water power is exported than computing module than and electric water carry out support vector clustering analysis than respectively as two initial vectors, by bunch output of the noise in analysis result;
Described data outputting module, for receiving the Output rusults of support vector clustering module and data screening module, and abnormal water power user data as a whole.
Further, the user's water yield electric quantity data acquisition in described data sampling module, comprises and gathers the original water consumption of resident, power consumption, water family number, electric family number and date data.
Further, the sampling feature vectors of the support vector sorter in described support vector clustering module is water consumption/power consumption w/e and power consumption/water consumption e/w; Kernel function adopts Gaussian function
Adopt after said structure, the present invention compared to the prior art advantageously:
The present invention adopts water power ratio, the method for electric water ratio has carried out effective integration to the power consumption of user and water consumption, input vector successively as support vector clustering analysis detects, and effectively can avoid false drop rate and the loss of water consumption, water power amount separate detection.Support vector clustering designs, and it can be utilized to distinguish performance preferably and obtain higher classification accuracy.By dynamically arranging the method for support vector kernel function and maximum iteration time, the sensitivity of detection method can be improved.Usage data screening modular design, the vector being zero by water consumption or power consumption screens, and directly exports as net result, no longer carries out follow-up cluster analysis, avoids because noise individuality participates in training and affects the problem of detection efficiency.
Accompanying drawing explanation
Below in conjunction with drawings and Examples, the present invention is further described:
Fig. 1 is one-piece construction schematic diagram of the present invention;
Fig. 2 is the water power abnormal user detection method process flow diagram based on utility requirements ratio and support vector clustering of the present invention.
Embodiment
The following stated is only preferred embodiment of the present invention, does not therefore limit protection scope of the present invention.
Embodiment 1
As shown in the figure, the present embodiment provide based on water electricity ratio and support vector clustering abnormal user detection method, comprise the following steps:
S1: the water yield electric quantity data gathering user;
S2: obtain the non-zero usage data in water yield electric quantity data;
S3: the water power ratio calculating non-zero usage data;
S4: water power ratio is input to support vector sorter and carries out classification and obtain grouped data;
S5: judge whether grouped data is noise bunch if so, is then abnormal data;
S6: is if not then normal data.
Further comprising the steps of:
S21: obtain zero usage data in water yield electric quantity data;
S22: judge whether zero usage data is small incidental expenses amount entirely if not, is then abnormal user;
S23: if be then normal users.
User's water yield electric quantity data acquisition in described step S1, comprises and gathers the original water consumption of resident, power consumption, water family number, electric family number and date data.
The sampling feature vectors of the support vector sorter in described step S4 is water consumption/power consumption w/e and power consumption/water consumption e/w; Kernel function adopts Gaussian function
The present embodiment additionally provides the abnormal user detection system based on utility requirements ratio and support vector clustering, comprises data sampling module, data screening module, water power than computing module, support vector clustering module and data outputting module;
Described data sampling module, for gathering the original water consumption of resident, power consumption, water family number, electric family number and date data;
Described data screening module, for screening the raw data of sampling, exports the non-zero usage data filtered out;
Described water power, than computing module, compares and electric water ratio, successively as the input of support vector clustering analyzer for the water power calculating non-zero usage data;
Described support vector clustering module, for water power that water power is exported than computing module than and electric water carry out support vector clustering analysis than respectively as two initial vectors, by bunch output of the noise in analysis result;
Described data outputting module, for receiving the Output rusults of support vector clustering module and data screening module, and abnormal water power user data as a whole.
User's water yield electric quantity data acquisition in described data sampling module, comprises and gathers the original water consumption of resident, power consumption, water family number, electric family number and date data.
The sampling feature vectors of the support vector sorter in described support vector clustering module is water consumption/power consumption w/e and power consumption/water consumption e/w; Kernel function adopts Gaussian function
Embodiment 2
The present embodiment carries out the abnormality detection based on utility requirements, do not need to make exception or normal labeled to data object, but based on 2 hypothesis: (1), at whole Electric Power Marketing System or with in water marketing system, the quantity of normal water power user will much larger than the quantity of abnormal user; (2) utility requirements of abnormal user and normal users also exist the difference of essence.Because the water power consumption of normal water power user consumes different with the water power of abnormal user, and abnormal user quantity is relatively less, therefore carry out based on support vector clustering analysis to utility requirements data, excavate abnormal water power data wherein, and be regarded as abnormal water power user.
Fig. 1 shows the abnormal user detection method one-piece construction schematic diagram based on support vector clustering, in figure, data sampling module is sampled from raw data, the data of sampling output to data screening module and filter, then pass to water power to calculate than computing module, finally pass to support vector clustering module and carry out analyzing and exporting.
Fig. 2 shows the abnormal user detection method schematic flow sheet based on support vector clustering.
Be that sample carries out data sampling from the water consumption of the water power user of certain Vico-provincial Cities three core spaces and power consumption data, time range is year May in October, 2013 to 2014, data total amount is 1266007, sampled data sample is 10000, and it is as shown in table 1 that the data sample that sampling obtains comprises field:
Table 1 water power user data sample field
| Water family number | Water name in an account book | Water address | Electricity family number | Electricity name in an account book | Electricity address | Check meter the time | Water consumption | Power consumption |
Water yield electricity screening module filters the data that obtain of sampling, and outputs to water power carry out computation and analysis than computing module for the water yield and all non-vanishing user data of electricity.Otherwise according to the assessment of actual water power business department and on-site land survey, the water yield and electricity are zero entirely, and this type of is normal data, and the water yield is zero, electricity is not zero, is judged to be abnormal data, and the water yield is not zero, electricity is zero, is judged to be abnormal data yet.
Water power to the data after filtration, calculates w/e (water consumption/power consumption) and e/w (power consumption/water consumption) than computing module respectively.
For w/e and e/w after calculating respectively as two proper vectors of this sample, then whole sample delivery is analyzed to support vector clustering module.Wherein,
shown Gaussian function is as the kernel function of algorithm, and the maximum iteration time of algorithm is set to 100000 times, is exported by the noise individuality in analysis result as abnormal user data.
Finally the null vector user data of the analysis result of support vector clustering module and water yield electricity screening module is carried out merging and obtains final abnormal water power user data.
Water power abnormal user detection method widely uses at present, and other principle is same as the prior art, repeats no more here.
Claims (7)
1., based on water electricity ratio and support vector clustering abnormal user detection method, it is characterized in that: comprise the following steps:
S1: the water yield electric quantity data gathering user;
S2: obtain the non-zero usage data in water yield electric quantity data;
S3: the water power ratio calculating non-zero usage data;
S4: water power ratio is input to support vector sorter and carries out classification and obtain grouped data;
S5: judge whether grouped data is noise bunch if so, is then abnormal data;
S6: is if not then normal data.
2. according to claim 1 based on water electricity ratio and support vector clustering abnormal user detection method, it is characterized in that: further comprising the steps of:
S21: obtain zero usage data in water yield electric quantity data;
S22: judge whether zero usage data is small incidental expenses amount entirely if not, is then abnormal user;
S23: if be then normal users.
3. according to claim 1 based on water electricity ratio and support vector clustering abnormal user detection method, it is characterized in that: the user's water yield electric quantity data acquisition in described step S1, comprise and gather the original water consumption of resident, power consumption, water family number, electric family number and date data.
4. according to claim 1 based on water electricity ratio and support vector clustering abnormal user detection method, it is characterized in that: the sampling feature vectors of the support vector sorter in described step S4 is water consumption/power consumption w/e and power consumption/water consumption e/w; Kernel function adopts Gaussian function
5. based on water electricity ratio and support vector clustering abnormal user detection system, it is characterized in that: comprise data sampling module, data screening module, water power than computing module, support vector clustering module and data outputting module;
Described data sampling module, for gathering the original water consumption of resident, power consumption, water family number, electric family number and date data;
Described data screening module, for screening the raw data of sampling, exports the non-zero usage data filtered out;
Described water power, than computing module, compares and electric water ratio, successively as the input of support vector clustering analyzer for the water power calculating non-zero usage data;
Described support vector clustering module, for water power that water power is exported than computing module than and electric water carry out support vector clustering analysis than respectively as two initial vectors, by bunch output of the noise in analysis result;
Described data outputting module, for receiving the Output rusults of support vector clustering module and data screening module, and abnormal water power user data as a whole.
6. according to claim 5 based on water electricity ratio and support vector clustering abnormal user detection system, it is characterized in that: the user's water yield electric quantity data acquisition in described data sampling module, comprise and gather the original water consumption of resident, power consumption, water family number, electric family number and date data.
7. according to claim 5 based on water electricity ratio and support vector clustering abnormal user detection system, it is characterized in that: the sampling feature vectors of the support vector sorter in described support vector clustering module is water consumption/power consumption w/e and power consumption/water consumption e/w; Kernel function adopts Gaussian function
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| CN109977984A (en) * | 2018-11-06 | 2019-07-05 | 国网新疆电力有限公司电力科学研究院 | Stealing user's judgment method based on support vector machines |
| CN109977984B (en) * | 2018-11-06 | 2023-06-20 | 国网新疆电力有限公司营销服务中心(资金集约中心、计量中心) | Power stealing user judging method based on support vector machine |
| CN113011997A (en) * | 2021-02-20 | 2021-06-22 | 上海电机学院 | Power grid user electricity utilization abnormal behavior detection method |
| CN114154999A (en) * | 2021-10-27 | 2022-03-08 | 国网河北省电力有限公司营销服务中心 | Electricity stealing prevention method, device, terminal and storage medium |
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