CN111553550A - Power big data quality assessment method aiming at user behavior analysis - Google Patents
Power big data quality assessment method aiming at user behavior analysis Download PDFInfo
- Publication number
- CN111553550A CN111553550A CN201911255343.1A CN201911255343A CN111553550A CN 111553550 A CN111553550 A CN 111553550A CN 201911255343 A CN201911255343 A CN 201911255343A CN 111553550 A CN111553550 A CN 111553550A
- Authority
- CN
- China
- Prior art keywords
- data
- accuracy
- evaluating
- user behavior
- behavior analysis
- 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.)
- Pending
Links
- 238000001303 quality assessment method Methods 0.000 title description 2
- 238000011156 evaluation Methods 0.000 claims abstract description 41
- 238000005070 sampling Methods 0.000 claims abstract description 24
- 238000000034 method Methods 0.000 claims abstract description 23
- 230000010354 integration Effects 0.000 claims abstract description 10
- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000011157 data evaluation Methods 0.000 claims abstract description 8
- 238000013480 data collection Methods 0.000 claims abstract description 4
- 238000012800 visualization Methods 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 230000008030 elimination Effects 0.000 claims description 6
- 238000003379 elimination reaction Methods 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an evaluation method of power big data quality aiming at user behavior analysis, which comprises the following steps: s1: historical network data of a plurality of users are collected through a data collection module, and the historical network data are integrated through a data integration module; s2: layering the characteristic data; s3: sampling each layer of data by adopting a simple random sampling method, obtaining a plurality of groups of layered sampling data, summarizing the plurality of groups of layered sampling data, and obtaining data samples; s4: evaluating the data samples under multiple dimensions according to rules preset by a central processing module to obtain index evaluation results corresponding to each evaluation index, and then comprehensively evaluating the multiple evaluation results according to weights; according to the invention, through the distribution of the weight, the accuracy of data evaluation is improved; and then, comprehensively evaluating the multiple evaluation results according to the weights, so that the accuracy of the evaluation results is improved.
Description
Technical Field
The invention belongs to the technical field of evaluation of electric power big data quality, and particularly relates to an electric power big data quality evaluation method aiming at user behavior analysis.
Background
With the progress and development of society, the use of electric power is more and more extensive, and the problem of power shortage appearing in different places and places needs to analyze the power utilization behavior of customers, and then control power supply and make scientific, reasonable and individual power utilization guide strategies according to the analysis result. Various power consumption data are collected to form big data, once the quality of the big data is unqualified or inaccurate, an accurate analysis result is difficult to obtain, and therefore, the method for evaluating the quality of the power big data aiming at user behavior analysis is provided to solve the problems mentioned in the background technology.
Disclosure of Invention
The invention aims to provide an evaluation method of power big data quality aiming at user behavior analysis, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a method for evaluating the quality of electric power big data aiming at user behavior analysis comprises the following steps:
s1: historical network data of a plurality of users are collected through a data collection module, and the historical network data are integrated through a data integration module;
s2: in the data after the integration and classification, feature data are called according to data features preset by a central processing module, and the feature data are layered;
s3: sampling each layer of data by adopting a simple random sampling method, obtaining a plurality of groups of layered sampling data, summarizing the plurality of groups of layered sampling data, and obtaining data samples;
s4: evaluating the data samples under multiple dimensions according to rules preset by a central processing module to obtain index evaluation results corresponding to each evaluation index, and then comprehensively evaluating the multiple evaluation results according to weights;
s5: and displaying the comprehensive evaluation result through a visualization module.
Preferably, the data integration module in step S1 is configured to filter the historical network data, where the filtering includes removing abnormal data, classifying the removed data, and performing weight assignment on the classified data according to the category.
Preferably, the elimination of the abnormal data includes eliminating data which does not have significance to the sample, eliminating inaccurate data and eliminating data which has larger fluctuation before and after elimination.
Preferably, the feature data in step S2 includes the historical network data corresponding to a plurality of feature parameters.
Preferably, the multiple dimensions in step S4 include data access condition, accuracy, completeness, consistency and timeliness, and the accuracy includes data syntax accuracy, data semantic accuracy, data accuracy measurement coverage, metadata accuracy, data range accuracy and data value accuracy.
Preferably, the visualization module is convenient for viewing the comprehensive evaluation result, and is suitable for viewing the comprehensive evaluation result by data evaluation service personnel who do not have deep knowledge of the algorithm and the interface.
Compared with the prior art, the invention has the beneficial effects that: according to the method for evaluating the quality of the electric power big data aiming at user behavior analysis, historical network data are integrated, the feature data are called according to preset data features, the feature data are layered, the classified data possibly have different weights according to different categories, and the accuracy of data evaluation is improved through weight distribution.
Sampling each layer of data by adopting a simple random sampling method, obtaining a plurality of groups of layered sampling data, summarizing the plurality of groups of layered sampling data to obtain data samples, evaluating the data samples in a plurality of dimensions according to rules preset by a central processing module to obtain index evaluation results corresponding to each evaluation index, and then comprehensively evaluating the plurality of evaluation results according to weights, so that the accuracy of the evaluation results is improved;
the visualization module is convenient for checking the comprehensive evaluation result, and is suitable for checking the comprehensive evaluation result by data evaluation service personnel who do not have deep knowledge of the algorithm and the interface.
Drawings
Fig. 1 is a schematic flow chart of an evaluation method of power big data quality for user behavior analysis according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The invention provides an evaluation method of power big data quality aiming at user behavior analysis, which comprises the following steps:
s1: historical network data of a plurality of users are collected through a data collection module, and the historical network data are integrated through a data integration module;
s2: in the data after the integration and classification, feature data are called according to data features preset by a central processing module, and the feature data are layered;
s3: sampling each layer of data by adopting a simple random sampling method, obtaining a plurality of groups of layered sampling data, summarizing the plurality of groups of layered sampling data, and obtaining data samples;
s4: evaluating the data samples under multiple dimensions according to rules preset by a central processing module to obtain index evaluation results corresponding to each evaluation index, and then comprehensively evaluating the multiple evaluation results according to weights;
s5: and displaying the comprehensive evaluation result through a visualization module.
Specifically, the data integration module in step S1 is configured to filter the historical network data, where the filtering includes removing abnormal data, classifying the removed data, performing weight distribution on the classified data according to categories, and classifying the data, so as to facilitate subsequent retrieval, and the classified data may have different weights according to different categories, so that the accuracy of data evaluation is improved through weight distribution.
Specifically, the elimination of the abnormal data includes eliminating data which does not have sample significance, eliminating inaccurate data and eliminating data which has large fluctuation before and after elimination, so that the data accuracy is improved, and the accuracy of a subsequent evaluation result is improved.
Specifically, the feature data in step S2 includes the historical network data corresponding to a plurality of feature parameters.
Specifically, in step S4, the multiple dimensions include data access condition, accuracy, completeness, consistency, and timeliness, and the accuracy includes data syntax accuracy, data semantic accuracy, data accuracy measurement coverage, metadata accuracy, data range accuracy, and data value accuracy.
Specifically, the visualization module is convenient for checking the comprehensive evaluation result, and is suitable for checking the comprehensive evaluation result by data evaluation service personnel who do not have deep knowledge of the algorithm and the interface.
In summary, compared with the prior art, the method and the device have the advantages that the historical network data are integrated, the feature data are called according to the preset data features, and the feature data are layered, so that the accuracy of the processed data is improved;
sampling each layer of data by adopting a simple random sampling method, obtaining a plurality of groups of layered sampling data, summarizing the plurality of groups of layered sampling data to obtain data samples, evaluating the data samples in a plurality of dimensions according to rules preset by a central processing module to obtain index evaluation results corresponding to each evaluation index, and then comprehensively evaluating the plurality of evaluation results according to weights, so that the accuracy of the evaluation results is improved;
the visualization module is convenient for checking the comprehensive evaluation result, and is suitable for checking the comprehensive evaluation result by data evaluation service personnel who do not have deep knowledge of the algorithm and the interface.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (6)
1. A method for evaluating the quality of electric power big data aiming at user behavior analysis is characterized by comprising the following steps of: the method comprises the following steps:
s1: historical network data of a plurality of users are collected through a data collection module, and the historical network data are integrated through a data integration module;
s2: in the data after the integration and classification, feature data are called according to data features preset by a central processing module, and the feature data are layered;
s3: sampling each layer of data by adopting a simple random sampling method, obtaining a plurality of groups of layered sampling data, summarizing the plurality of groups of layered sampling data, and obtaining data samples;
s4: evaluating the data samples under multiple dimensions according to rules preset by a central processing module to obtain index evaluation results corresponding to each evaluation index, and then comprehensively evaluating the multiple evaluation results according to weights;
s5: and displaying the comprehensive evaluation result through a visualization module.
2. The method for evaluating the quality of the power big data for the user behavior analysis according to claim 1, wherein the method comprises the following steps: the data integration module in step S1 is configured to filter the historical network data, where the filtering includes removing abnormal data, classifying the removed data, and performing weight distribution on the classified data according to the category.
3. The method for evaluating the quality of the power big data for the user behavior analysis according to claim 2, wherein the method comprises the following steps: the elimination of the abnormal data comprises eliminating data which does not have sample significance, eliminating inaccurate data and eliminating data with large fluctuation before and after elimination.
4. The method for evaluating the quality of the power big data for the user behavior analysis according to claim 1, wherein the method comprises the following steps: the feature data in step S2 includes the historical network data corresponding to a plurality of feature parameters.
5. The method for evaluating the quality of the power big data for the user behavior analysis according to claim 1, wherein the method comprises the following steps: the multiple dimensions in step S4 include data access condition, accuracy, completeness, consistency, and timeliness, and the accuracy includes data syntax accuracy, data semantic accuracy, data accuracy measurement coverage, metadata accuracy, data range accuracy, and data value accuracy.
6. The method for evaluating the quality of the power big data for the user behavior analysis according to claim 1, wherein the method comprises the following steps: the visualization module is convenient for checking the comprehensive evaluation result, and is suitable for checking the comprehensive evaluation result by data evaluation service personnel who do not have deep knowledge of the algorithm and the interface.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911255343.1A CN111553550A (en) | 2019-12-10 | 2019-12-10 | Power big data quality assessment method aiming at user behavior analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911255343.1A CN111553550A (en) | 2019-12-10 | 2019-12-10 | Power big data quality assessment method aiming at user behavior analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111553550A true CN111553550A (en) | 2020-08-18 |
Family
ID=72007215
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911255343.1A Pending CN111553550A (en) | 2019-12-10 | 2019-12-10 | Power big data quality assessment method aiming at user behavior analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111553550A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112529677A (en) * | 2020-12-22 | 2021-03-19 | 四川新网银行股份有限公司 | Automatic data quality evaluation method and readable storage medium |
| CN113779150A (en) * | 2021-09-14 | 2021-12-10 | 杭州数梦工场科技有限公司 | Data quality evaluation method and device |
-
2019
- 2019-12-10 CN CN201911255343.1A patent/CN111553550A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112529677A (en) * | 2020-12-22 | 2021-03-19 | 四川新网银行股份有限公司 | Automatic data quality evaluation method and readable storage medium |
| CN113779150A (en) * | 2021-09-14 | 2021-12-10 | 杭州数梦工场科技有限公司 | Data quality evaluation method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110991786B (en) | 10kV static load model parameter identification method based on similar daily load curve | |
| CN110634080A (en) | Abnormal electricity utilization detection method, device, equipment and computer readable storage medium | |
| CN112598343B (en) | Risk inspection method, device, equipment and storage medium for data center | |
| CN107527114A (en) | A kind of circuit taiwan area exception analysis method based on big data | |
| CN104504508B (en) | The taiwan area closed-loop data analysis method returned based on step analysis and small echo | |
| CN108011367B (en) | Power load characteristic mining method based on depth decision tree algorithm | |
| CN110210719B (en) | Power equipment static data quality assessment method and system | |
| CN111553550A (en) | Power big data quality assessment method aiming at user behavior analysis | |
| CN111008193A (en) | Data cleaning and quality evaluation method and system | |
| CN110690701A (en) | Analysis method for influence factors of abnormal line loss | |
| CN116933010A (en) | Load rate analysis and evaluation method and system based on multi-source data fusion and deep learning | |
| CN111325435A (en) | Power transmission and transformation equipment data quality evaluation method and system | |
| CN111552686A (en) | Power data quality assessment method and device | |
| CN111242430A (en) | Power equipment supplier evaluation method and device | |
| CN113689079A (en) | Transformer area line loss prediction method and system based on multivariate linear regression and cluster analysis | |
| CN107884646A (en) | Transformer Substation Online Monitoring System Critical method | |
| CN111178676A (en) | Power distribution network project investment assessment method and system | |
| CN112651622A (en) | Electric energy quality evaluation method and system | |
| CN111275292B (en) | Grounding grid state evaluation method based on fuzzy analytic hierarchy process | |
| Wenjie et al. | A multi-index evaluation method of voltage sag based on the comprehensive weight | |
| CN110070256B (en) | Zero-power user investigation priority weight calculation method based on CRITIC method | |
| CN111191946A (en) | Fault line identification method and device and electronic equipment | |
| CN116211309A (en) | System and method for judging situation awareness state of operator based on eye movement and brain electrical characteristics | |
| Chongde et al. | Evaluation of the models for Bradford's law | |
| CN112348066A (en) | Line uninterrupted power rating evaluation method based on gray clustering algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200818 |
|
| WD01 | Invention patent application deemed withdrawn after publication |