CN109377016A - O&M risk supervises method, apparatus and computer readable storage medium - Google Patents
O&M risk supervises method, apparatus and computer readable storage medium Download PDFInfo
- Publication number
- CN109377016A CN109377016A CN201811125771.8A CN201811125771A CN109377016A CN 109377016 A CN109377016 A CN 109377016A CN 201811125771 A CN201811125771 A CN 201811125771A CN 109377016 A CN109377016 A CN 109377016A
- Authority
- CN
- China
- Prior art keywords
- risk
- data
- evaluation model
- metering device
- electric energy
- 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
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000003860 storage Methods 0.000 title claims description 7
- 238000013210 evaluation model Methods 0.000 claims abstract description 47
- 238000007689 inspection Methods 0.000 claims abstract description 20
- 238000011156 evaluation Methods 0.000 claims description 29
- 239000000470 constituent Substances 0.000 claims description 20
- 230000005611 electricity Effects 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 5
- 238000005457 optimization Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000005553 drilling Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012502 risk assessment Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000013075 data extraction Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
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
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention provides a kind of O&M risk inspection method and device, is applied to electric energy metering device, this method comprises: determining risk evaluation model according to learning sample data, the learning sample data include: the historical data of marketing data and table code copy data；It obtains and predicts instant data, the instant data of the prediction include: marketing error data and real-time table code copy data；It uses instant data to run the risk evaluation model as the input quantity of the risk evaluation model prediction, the O&M value-at-risk of each electric energy metering device is exported according to preset Quantitative marking rule；The O&M risk class of each electric energy metering device is determined according to the O&M value-at-risk.The result objectivity that this method analyzes risk is strong, and accuracy is high.
Description
Technical field
The present invention relates to electricity transactions to settle accounts technical field, supervises method, apparatus and calculating more particularly, to O&M risk
Machine readable storage medium storing program for executing.
Background technique
In recent years, Electricity Market Competition is increasingly fierce, electricity marketization, transaction user, sale of electricity company, power grid enterprises tripartite
High requirement is suffered to the accuracy of metering clearing, real-time；Under power marketing service lean management requirement, electricity
Net enterprise gradually popularizes low-voltage collecting meter reading lean management in an all-round way, and building " automatic data logging+intelligence accounting+noncash payment " is copied
Core receives business new system, it means that power grid enterprises will bear universal service responsibility to all kinds of Electricity customers.Due to electric energy
The inaccuracy of metering occurs again and again, not only results in transaction electricity and reruns, directly aggravates enterprise operation cost burden；But also
Cause customer complaint and for both sides' legal dispute, influences power market transaction order and electricity transaction each side economic interests.
Summary of the invention
It is an object of the invention in view of the above problems in the prior art, provide a kind of O&M risk inspection method, dress
It sets and computer readable storage medium is for solving the deficiencies in the prior art.
Specifically, the embodiment of the invention provides a kind of O&M risks to supervise method, is applied to electric energy metering device, should
Method includes:
Risk evaluation model is determined according to learning sample data, and the learning sample data include: marketing data and table code
The historical data of copy data；
It obtains and predicts instant data, the prediction includes: that marketing error data and real-time table code are made a copy of with instant data
Data；
The prediction is used into instant data as the input quantity of the risk evaluation model, runs the risk assessment mould
Type exports the O&M value-at-risk of each electric energy metering device according to preset Quantitative marking rule；
The O&M risk class of each electric energy metering device is determined according to the O&M value-at-risk.
As a further improvement of the above technical scheme, the method also includes: will investigate that there are O&M risks
Risk evaluation model described in sample data typing is to optimize the risk evaluation model.
As a further improvement of the above technical scheme, described to determine that risk evaluation model has according to learning sample data
Body includes: the table code copy data for obtaining each electric energy metering device；Classification processing is carried out to the table code copy data；It will
Table code copy data table after classification processing is generated in conjunction with the inspection rule of acquisition maintenance service and marketing data building
The risk evaluation model.
As a further improvement of the above technical scheme, the O&M value-at-risk determines the fortune of each electric energy metering device
Dimension risk class is combined using business evaluation and metrics evaluation.
As a further improvement of the above technical scheme, the business evaluation passes through setting by marketing error data
Portfolio, amount of sampling, deduction of points obtain the evaluation score of constituent parts, each business based on Y-factor method Y and Bayes's method of average；It is described
Metrics evaluation is used to be combined based on entropy assessment, enabling legislation algorithm, is analyzed result index and the Process Character index degree of correlation, is passed through
Index weights, code of points carry out quantitatively evaluating to constituent parts, the relevant index of each business.
The embodiment of the invention also provides a kind of O&M risk inspection apparatus, are applied to electric energy metering device, comprising:
Risk evaluation model determining module, for determining risk evaluation model, the study sample according to learning sample data
Notebook data includes: the historical data of marketing data and table code copy data；
Instant data acquisition module predicts instant data for obtaining, and the instant data of the prediction include: marketing
Error data and real-time table code copy data；
O&M risk obtains module, for using instant data as the input of the risk evaluation model prediction
Amount, runs the risk evaluation model, the O&M risk of each electric energy metering device is exported according to preset Quantitative marking rule
Value；
O&M risk determining module, for determining the O&M wind of each electric energy metering device according to the O&M value-at-risk
Dangerous grade.
As a further improvement of the above technical scheme, further includes: optimization module, for by investigated there are O&Ms
Risk evaluation model described in the sample data typing of risk is to optimize the risk evaluation model.
As a further improvement of the above technical scheme, the risk evaluation model determining module is specifically used for obtaining every
The table code copy data of a electric energy metering device；Classification processing is carried out to the table code copy data；By the table after classification processing
Code copy data table generates the risk assessment mould in conjunction with the inspection rule of acquisition maintenance service and marketing data building
Type.
As a further improvement of the above technical scheme, the O&M value-at-risk determines the fortune of each electric energy metering device
Dimension risk class is combined using business evaluation and metrics evaluation.
As a further improvement of the above technical scheme, the business evaluation passes through setting by marketing error data
Portfolio, amount of sampling, deduction of points obtain the evaluation score of constituent parts, each business based on Y-factor method Y and Bayes's method of average；It is described
Metrics evaluation is used to be combined based on entropy assessment, enabling legislation algorithm, is analyzed result index and the Process Character index degree of correlation, is passed through
Index weights, code of points carry out quantitatively evaluating to constituent parts, the relevant index of each business.
The embodiment of the invention also provides a kind of computer readable storage mediums, are stored with computer program, described
Computer program, which is performed, implements above-described O&M risk inspection method.
It is at least had the following beneficial effects: compared with existing well-known technique using technical solution provided by the invention
The result objectivity that the O&M risk supervises method and device analysis risk is strong, and accuracy is high.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment
Attached drawing is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not to be seen as
It is the restriction to range, it for those of ordinary skill in the art, without creative efforts, can be with
Other relevant attached drawings are obtained according to these attached drawings.
Fig. 1 is that the O&M risk that one embodiment of the invention proposes supervises method flow schematic diagram；
Fig. 2 is the O&M risk inspection apparatus structural schematic diagram that one embodiment of the invention proposes.
Main element symbol description:
100- risk evaluation model determining module；The instant data acquisition module of 200-；300- O&M risk obtains module；
400- O&M risk determining module.
Specific embodiment
Hereinafter, the various embodiments of the disclosure will be described more fully.The disclosure can have various embodiments, and
And it can adjust and change wherein.It should be understood, however, that: there is no disclosure protection scope is limited to spy disclosed herein
Determine the intention of embodiment, but the disclosure should be interpreted as in the spirit and scope for covering the various embodiments for falling into the disclosure
All adjustment, equivalent and/or optinal plan.
It hereinafter, can the term " includes " used in the various embodiments of the disclosure or " may include " instruction institute public affairs
The presence of function, operation or the element opened, and the increase of one or more functions, operation or element is not limited.In addition,
As used in the various embodiments of the disclosure, term " includes ", " having " and its cognate are meant only to indicate specific spy
Sign, number, step, operation, the combination of element, component or aforementioned item, and be understood not to exclude first one or more
A other feature, number, step, operation, element, component or aforementioned item combined presence or increase one or more spies
Sign, number, step, operation, element, component or aforementioned item combination a possibility that.
In the various embodiments of the disclosure, statement " at least one of A or/and B " includes the text listed file names with
Any combination or all combinations.For example, statement " A or B " or " at least one of A or/and B " may include A, may include B or can
Including A and B both.
The statement (" first ", " second " etc.) used in the various embodiments of the disclosure can be modified in various realities
The various constituent element in example are applied, but respective sets can not be limited into element.For example, the above statement is not intended to limit the element
Sequence and/or importance.The above statement is only used for the purpose for differentiating an element and other elements.For example, first uses
Family device and second user device indicate different user device, although the two is all user apparatus.For example, not departing from this public affairs
In the case where the range for the various embodiments opened, first element is referred to alternatively as second element, and similarly, second element can also quilt
Referred to as first element.
It should also be noted that if a constituent element ' attach ' to another constituent element by description, it can be by the first composition member
Part is directly connected to the second constituent element, and can between the first constituent element and the second constituent element " connection " third group
At element.On the contrary, when a constituent element " being directly connected to " is arrived another constituent element, it will be appreciated that in the first composition
Third constituent element is not present between element and the second constituent element.
The term used in the various embodiments of the disclosure " user " can be indicated using the people of electronic device or using electricity
The device (for example, artificial intelligence electronic device) of sub-device.
The term used in the various embodiments of the disclosure is used only for the purpose of describing specific embodiments and not anticipates
In the various embodiments of the limitation disclosure.Unless otherwise defined, otherwise all terms used herein (including technical term
And scientific term) with identical with the various normally understood meanings of embodiment one skilled in the art of the disclosure
Meaning.The term (term such as limited in the dictionary generally used) is to be interpreted as having and lead in the relevant technologies
The identical meaning of situational meaning in domain and it will be interpreted as having Utopian meaning or meaning too formal, removed
It is non-to be clearly defined in the various embodiments of the disclosure.
Embodiment 1
As shown in Figure 1, the embodiment of the invention provides a kind of O&M risks to supervise method, it is applied to electric energy metering device,
This method comprises:
S101, risk evaluation model is determined according to learning sample data, the learning sample data include: marketing data
With the historical data of table code copy data.
After establishing risk evaluation model, the foundation that just there is data reference to compare.
It determines that risk evaluation model specifically includes according to learning sample data: obtaining the table code of each electric energy metering device
Copy data；Classification processing is carried out to the table code copy data；Table code copy data table after classification processing is combined into acquisition
The inspection rule of maintenance service and marketing data building generate the risk evaluation model.
S102, it obtains and predicts instant data, the instant data of the prediction include: marketing error data and real-time table
Code copy data.
It predicts the latest data information during being electricity transaction with instant data, is to judge whether user has meter recently
The important evidence of charging error.
S103, the prediction is used into instant data as the input quantity of the risk evaluation model, runs the risk and comments
Estimate model, the O&M value-at-risk of each electric energy metering device is exported according to preset Quantitative marking rule.
S104, the O&M risk class that each electric energy metering device is determined according to the O&M value-at-risk.
O&M value-at-risk determines the O&M risk class of each electric energy metering device using business evaluation and metrics evaluation phase
In conjunction with.
The business evaluation is based on Y-factor method Y by the portfolio of setting, amount of sampling, deduction of points by marketing error data
The evaluation score of constituent parts, each business is obtained with Bayes's method of average；The metrics evaluation, which uses, is based on entropy assessment, enabling legislation
Algorithm combines, and result index and the Process Character index degree of correlation is analyzed, by index weights, code of points to constituent parts, each industry
Relevant index of being engaged in carries out quantitatively evaluating.
O&M risk inspection method may also include that wind described in the sample data typing there are O&M risk that will have been investigated
Dangerous assessment models are to optimize the risk evaluation model.
It is subsequent to carry out risk case processing analysis, wind after the O&M risk class for determining each electric energy metering device
Danger supervision effect analysis, the analysis of business short slab.Wherein, event handling analysis is exactly to show respectively to evaluate unit in current month
Risk case disposition, currently processed effective percentage, including it is processed, processing in, untreated risk amount, by drilling through
Current associated data shows risk data detail, and according to data such as accountability unit, risk amount, risk, with chart
Show each evaluation unit risk disposition and risk processing classification etc..Risk surveillance effect analysis is exactly to show current supervision
Front and back effect respectively evaluates risk amount amount, the risk score value, risk item classification number of unit including front and back.It is current by drilling through
Related data shows risk data detail, and according to data such as accountability unit, risk amount, risk, with diagrammatic representation
The information such as risk processing variation situation, risk processing classification of each evaluation unit before and after supervision.The analysis of business short slab is exactly pair
Unit abnormal data is respectively evaluated in the selected period to be shown, and shows the distribution map and wind of accountability unit in graphical form
Dangerous Map of Distributions of Types etc.；The abnormal data detail page of each evaluation unit can be obtained by drilling through data, for manually exporting.
The O&M risk is supervised method and can be flowed back using marketing system with metering automation system data and by artificial
The data imported are extracted in marketing system, constitute deeper data Layer；In operation data memory block by by the number of operation system
According to by extracting, cleaning, conversion is loaded into data warehouse later or storing process mode extracts data；It is pressed in data bins reservoir area
Data are summarized according to each subject area；Summarized in data set urban district according to each analysis theme；Comprehensively consider data extraction
Situation carries out on-line analytical processing, data mining, graphics process to data and monitors.
Embodiment 2
As shown in Fig. 2, it is applied to electric energy metering device the embodiment of the invention provides a kind of O&M risk inspection apparatus,
It include: risk evaluation model determining module 100, instant data acquisition module 200, O&M risk acquisition module 300 and O&M wind
Dangerous determining module 400.
Risk evaluation model determining module 100, for determining risk evaluation model, according to learning sample data
Practise the historical data that sample data includes: marketing data and table code copy data.
After establishing risk evaluation model, the foundation that just there is data reference to compare.
It determines that risk evaluation model specifically includes according to learning sample data: obtaining the table code of each electric energy metering device
Copy data；Classification processing is carried out to the table code copy data；Table code copy data table after classification processing is combined into acquisition
The inspection rule of maintenance service and marketing data building generate the risk evaluation model.
Instant data acquisition module 200 predicts instant data for obtaining, and the instant data of the prediction include: battalion
Sell error data and real-time table code copy data.
It predicts the latest data information during being electricity transaction with instant data, is to judge whether user has meter recently
The important evidence of charging error.
O&M risk obtains module 300, for using instant data as the defeated of the risk evaluation model prediction
Enter amount, run the risk evaluation model, the O&M wind of each electric energy metering device is exported according to preset Quantitative marking rule
Danger value；
O&M risk determining module 400, for determining the O&M of each electric energy metering device according to the O&M value-at-risk
Risk class.
O&M value-at-risk determines the O&M risk class of each electric energy metering device using business evaluation and metrics evaluation phase
In conjunction with.
The business evaluation is based on Y-factor method Y by the portfolio of setting, amount of sampling, deduction of points by marketing error data
The evaluation score of constituent parts, each business is obtained with Bayes's method of average；The metrics evaluation, which uses, is based on entropy assessment, enabling legislation
Algorithm combines, and result index and the Process Character index degree of correlation is analyzed, by index weights, code of points to constituent parts, each industry
Relevant index of being engaged in carries out quantitatively evaluating.
O&M risk inspection apparatus may also include that optimization module, optimization module be used for by investigated there are O&M risks
Sample data typing described in risk evaluation model to be optimized to the risk evaluation model.
The present invention also provides a kind of computer readable storage mediums, are stored with computer program, in the computer
Program is performed the O&M risk inspection method implemented in 1.
It will be appreciated by those skilled in the art that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, the module in attached drawing
Or process is not necessarily implemented necessary to the present invention.
It will be appreciated by those skilled in the art that the module in device in implement scene can be described according to implement scene into
Row is distributed in the device of implement scene, can also be carried out corresponding change and is located at the one or more for being different from this implement scene
In device.The module of above-mentioned implement scene can be merged into a module, can also be further split into multiple submodule.
Aforementioned present invention serial number is for illustration only, does not represent the superiority and inferiority of implement scene.Disclosed above is only the present invention
Several specific implementation scenes, still, the present invention is not limited to this, the changes that any person skilled in the art can think of
Protection scope of the present invention should all be fallen into.
Claims (10)
1. a kind of O&M risk supervises method, it is applied to electric energy metering device characterized by comprising
Risk evaluation model is determined according to learning sample data, and the learning sample data include: that marketing data and table code are made a copy of
The historical data of data；
It obtains and predicts instant data, the instant data of the prediction include: marketing error data and real-time table code copy data；
Instant data are used to run the risk evaluation model, root as the input quantity of the risk evaluation model prediction
The O&M value-at-risk of each electric energy metering device is exported according to preset Quantitative marking rule；
The O&M risk class of each electric energy metering device is determined according to the O&M value-at-risk.
2. O&M risk as described in claim 1 supervises method, which is characterized in that the method also includes: by what is investigated
Risk evaluation model described in sample data typing there are O&M risk is to optimize the risk evaluation model.
3. O&M risk as described in claim 1 supervises method, which is characterized in that described to determine wind according to learning sample data
Dangerous assessment models specifically include: obtaining the table code copy data of each electric energy metering device；The table code copy data is carried out
Classification processing；By the table code copy data table after classification processing in conjunction with the inspection rule of acquisition maintenance service and the marketing number
The risk evaluation model is generated according to building.
4. O&M risk as described in claim 1 supervises method, which is characterized in that the O&M value-at-risk determines each electric energy
The O&M risk class of metering device is combined using business evaluation and metrics evaluation.
5. O&M risk as claimed in claim 4 supervises method, which is characterized in that the business evaluation passes through marketing number of errors
According to showing that constituent parts, each business are commented based on Y-factor method Y and Bayes's method of average by the portfolio of setting, amount of sampling, deduction of points
Valence score；The metrics evaluation is used to be combined based on entropy assessment, enabling legislation algorithm, analyzes result index and Process Character index phase
Guan Du carries out quantitatively evaluating to constituent parts, the relevant index of each business by index weights, code of points.
6. a kind of O&M risk inspection apparatus is applied to electric energy metering device characterized by comprising
Risk evaluation model determining module, for determining risk evaluation model, the learning sample number according to learning sample data
According to the historical data for including: marketing data and table code copy data；
Instant data acquisition module predicts instant data for obtaining, and the prediction includes: marketing number of errors with instant data
According to real-time table code copy data；
O&M risk obtains module, for using instant data as the input quantity of the risk evaluation model, fortune the prediction
The row risk evaluation model, the O&M value-at-risk of each electric energy metering device is exported according to preset Quantitative marking rule；
O&M risk determining module, for determining the O&M risk etc. of each electric energy metering device according to the O&M value-at-risk
Grade.
7. O&M risk inspection apparatus as claimed in claim 6, which is characterized in that further include: optimization module, for will look into
Risk evaluation model described in the sample data typing there are O&M risk of card is to optimize the risk evaluation model.
8. O&M risk inspection apparatus according to claim 6, which is characterized in that the risk evaluation model determining module
Specifically for obtaining the table code copy data of each electric energy metering device；Classification processing is carried out to the table code copy data；It will
Table code copy data table after classification processing is generated in conjunction with the inspection rule of acquisition maintenance service and marketing data building
The risk evaluation model.
9. O&M risk inspection apparatus according to claim 6, which is characterized in that the O&M value-at-risk determines each electricity
The O&M risk class of energy metering device is combined using business evaluation and metrics evaluation.
10. a kind of computer readable storage medium, which is characterized in that it is stored with computer program, in the computer program
It is performed and implements the O&M risk inspection method of any of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811125771.8A CN109377016A (en) | 2018-09-26 | 2018-09-26 | O&M risk supervises method, apparatus and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811125771.8A CN109377016A (en) | 2018-09-26 | 2018-09-26 | O&M risk supervises method, apparatus and computer readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109377016A true CN109377016A (en) | 2019-02-22 |
Family
ID=65402506
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811125771.8A Pending CN109377016A (en) | 2018-09-26 | 2018-09-26 | O&M risk supervises method, apparatus and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109377016A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110334899A (en) * | 2019-05-21 | 2019-10-15 | 中国大唐集团科学技术研究院有限公司 | A kind of enhanced operation audit method based on machine learning |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103824148A (en) * | 2013-08-29 | 2014-05-28 | 国家电网公司 | Power marketing risk pre-warning method and system |
| CN104880690A (en) * | 2015-06-15 | 2015-09-02 | 国网上海市电力公司 | Method for evaluating operation of electric energy meter |
| CN104915894A (en) * | 2015-06-15 | 2015-09-16 | 广东电网有限责任公司佛山供电局 | Metering automatic terminal operation risk early warning system |
| CN106483947A (en) * | 2016-09-21 | 2017-03-08 | 国网江苏省电力公司南通供电公司 | Distribution Running State assessment based on big data and method for early warning |
| CN107862467A (en) * | 2017-11-23 | 2018-03-30 | 国网辽宁省电力有限公司 | A kind of electric network synthetic data target monitoring method and system based on big data platform |
-
2018
- 2018-09-26 CN CN201811125771.8A patent/CN109377016A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103824148A (en) * | 2013-08-29 | 2014-05-28 | 国家电网公司 | Power marketing risk pre-warning method and system |
| CN104880690A (en) * | 2015-06-15 | 2015-09-02 | 国网上海市电力公司 | Method for evaluating operation of electric energy meter |
| CN104915894A (en) * | 2015-06-15 | 2015-09-16 | 广东电网有限责任公司佛山供电局 | Metering automatic terminal operation risk early warning system |
| CN106483947A (en) * | 2016-09-21 | 2017-03-08 | 国网江苏省电力公司南通供电公司 | Distribution Running State assessment based on big data and method for early warning |
| CN107862467A (en) * | 2017-11-23 | 2018-03-30 | 国网辽宁省电力有限公司 | A kind of electric network synthetic data target monitoring method and system based on big data platform |
Non-Patent Citations (1)
| Title |
|---|
| 翟磊 等: "电能计量装置现场问题的分析处理", 《山东电机工程学会第十二届优秀论文汇编》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110334899A (en) * | 2019-05-21 | 2019-10-15 | 中国大唐集团科学技术研究院有限公司 | A kind of enhanced operation audit method based on machine learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Cody et al. | Decision tree learning for fraud detection in consumer energy consumption | |
| CN106780140B (en) | Power credit evaluation method based on big data | |
| Andres et al. | Regulatory governance and sector performance: methodology and evaluation for electricity distribution in Latin America | |
| CN110264015A (en) | It opposes electricity-stealing and checks monitoring method and platform | |
| CN107862467A (en) | A kind of electric network synthetic data target monitoring method and system based on big data platform | |
| CN111786385B (en) | Power grid operation and maintenance scheme planning method, system and equipment | |
| CN107220895A (en) | The transfer numerical statistic method and device of virtual resource | |
| CN112396437A (en) | Trade contract verification method and device based on knowledge graph | |
| CN106372969A (en) | Power user feature identification method and system | |
| CN109377016A (en) | O&M risk supervises method, apparatus and computer readable storage medium | |
| Finke et al. | Operational risk quantification: a risk flow approach | |
| CN109636627B (en) | Insurance product management method, device, medium and electronic equipment based on block chain | |
| Al-Halabi et al. | The impact of designing accounting information systems on the level of accounting conservatism-a field study | |
| Rau et al. | Heterogeneous firms and homogenising standards in agri-food trade: the Polish meat case | |
| Dong et al. | Does public data access stimulate the efficiency of corporate green innovation? | |
| Feng | Financing a black box: Dynamic investment with persistent private information | |
| CN110895757B (en) | Cheating data identification method, device, equipment and system | |
| Moss | Electricity and market power: Current issues for restructuring markets (a survey) | |
| Yih et al. | Analyzing the decision making process of certifying digital control systems of nuclear power plants | |
| TWI634498B (en) | Business operations analysis system and method | |
| Sukhareva et al. | Organizational and economic mechanism for creating a digital environment in the energy sector | |
| Hansson | Investment in project preventing deforestation of the Brazilian Amazonas | |
| Yao | Risk Management in Legal Finance in Applied Economics | |
| Tian et al. | Research on Scenario-based Intelligent Inspection Mode Based on Big Data Analysis | |
| Ogunlanaᵃ et al. | Risks behind the Variables of Financial Model for Public-Private Partnership Project |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190222 |
|
| RJ01 | Rejection of invention patent application after publication |