CN112308437B - Line loss management method, system, device and storage medium based on big data analysis - Google Patents
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Abstract
The invention discloses a line loss management method, a system, a device and a storage medium based on big data analysis. The software big data analysis function is utilized, the data difficulty of a basic group analysis and check system is reduced, the workload of on-site investigation is reduced, the basic layer burden reduction is realized, and meanwhile, the line loss problem treatment efficiency is improved. Meanwhile, a strict strategy and a loose strategy are provided, a fault-tolerant mechanism is established for data analysis, characteristic analysis is carried out on local-stage data, a large amount of abnormal data is prevented from being extracted due to oversensitivity of a research and judgment method, and unnecessary increase of field checking workload is avoided.
Description
Technical Field
The invention belongs to the technical field of power grid line loss management, and particularly relates to a line loss management method, a system, a device and a storage medium based on big data analysis.
Background
Aiming at the problems of huge amount of electric quantity data, lack of big data analysis capability and means for basic team personnel, meter electric quantity abnormal jump, meter and acquisition device abnormality, unstable signals or no signals and the like which are important attention in daily line loss management, the manual analysis system has the defects of large data workload, low efficiency, difficulty in finding practical problems and being unfavorable for guiding line loss work.
Disclosure of Invention
The invention aims to provide a line loss management method, a system, a device and a storage medium based on big data analysis so as to solve the problems.
In order to achieve the above purpose, the following technical scheme is adopted:
the line loss management method based on big data analysis comprises the following steps:
1) Extracting electric quantity data in a database, storing the electric quantity data into an electric quantity data document corresponding to the name of the electric quantity data, and dynamically updating daily electric quantity key data of all transformers or users in the electric quantity data document to form an electric quantity summary document;
2) Aiming at single-day or multi-day electric quantity data, a loose strategy or a strict strategy is adopted to search transformers or users with abnormal electric quantity, the types of the abnormal transformers or users with abnormal electric quantity are judged, and a comprehensive analysis report is generated; and according to the comprehensive analysis report, performing transformer or user abnormality detection processing on the spot.
Further, the step 2) further includes: the daily electric quantity is taken as a vertical axis, the real date is taken as a horizontal axis, and the daily change waveform of the transformer or the user electric quantity with abnormal electric quantity is displayed in groups; and exporting the electric quantity graphic data of the concerned area or the user, and storing the electric quantity graphic data into a universal picture format.
Further, in the step 2), a small probability event set a and a small probability event set B are established by using a statistical principle; comprehensively analyzing all the historical electric quantity data to form probability distribution, and dividing the electric quantity data meeting the small probability distribution into a small probability event set A; taking the electric quantity data in a period before and after the day as a sample, and analyzing whether the electric quantity data accords with a statistical rule by using a statistical method, if not, the electric quantity data is listed in a small probability event set B;
The stringent strategy: performing AND operation on the small probability event sets A and B, and simultaneously, taking data listed in the small probability event set A and the small probability event set B as abnormal data;
And the loose strategy performs OR operation on the small probability event set A and the small probability event set B, and only the data listed in one of the small probability event set A or the small probability event set B is used as warning data.
Further, in the step 1), when the transformer or the user information is changed, the information is updated in the electric quantity summary table document, and the electric quantity data is supplemented.
Furthermore, in the step 1), when the data is dynamically updated, the data of the electric quantity to be analyzed is automatically recorded according to the date, and the data is stored in the appointed position of the electric quantity summary table document, so that the change of the electric quantity is convenient to observe.
The other technical scheme of the invention is as follows: a line loss management system based on big data analysis is characterized by comprising:
the file automatic generation module is used for automatically scanning electric quantity data in the database, extracting the names of the lines or the areas and automatically generating the file according to the names;
the automatic data extraction module is used for extracting and storing daily electricity quantity key data of all transformers or users in the database;
the automatic data updating module is used for dynamically updating the daily electricity quantity key data of all transformers or users in the same document to form an electricity quantity summary document;
the big data integral comprehensive analysis module is used for carrying out comprehensive analysis on the multi-day electric quantity data and searching transformers or users with abnormal electric quantity change;
the daily data independent analysis and diagnosis module is used for analyzing and diagnosing any daily electricity quantity data on the basis of sample data and automatically searching transformers or users with abnormal electricity consumption;
The key problem automatic judging module is used for diagnosing transformers or users with greatly fluctuating judging electric quantity, long-term low electric quantity, suspected shutdown or meter damage;
the analysis diagnosis fault tolerance correction module is used for providing a strict strategy and a loose strategy and searching transformers or users with abnormal electricity consumption;
And the automatic generation analysis report module is used for generating a comprehensive analysis report aiming at the big data overall analysis module and generating a daily diagnosis report aiming at the daily data single analysis diagnosis module.
Further, the method further comprises the following steps:
the change trend image display module is used for displaying a transformer with abnormal electricity consumption or a daily change waveform of the electricity consumption of a user by taking the daily electricity consumption as a vertical axis and the real date as a horizontal axis;
The grouping screening display module is used for displaying the electric quantity greatly fluctuated, low-power consumption, zero-power consumption power station areas or users in a grouping way;
The fuzzy search query module is used for carrying out fuzzy query and displaying related electric quantity information through the number of the transformer or the user;
and the data fluctuation image export module is used for exporting the power consumption abnormal power consumption area or the power consumption graphic data of the user and storing the power consumption area or the power consumption graphic data into a universal picture format.
Further, the method further comprises the following steps:
the equipment information change identification module is used for automatically updating information in the electric quantity summary list document and automatically supplementing electric quantity data when the transformer or the user information is changed;
the ordered filling data module is used for automatically complementing the electric quantity data to be analyzed according to the date and storing the electric quantity data to the appointed position of the electric quantity summary list document;
and the batch data importing module is used for importing the continuous electric quantity data for a plurality of days in batches.
The invention also provides a technical scheme that: an apparatus for line loss management based on big data analysis includes a memory and a processor; the memory is used for storing a computer program; the processor is used for realizing the line loss management method based on big data analysis when executing the computer program.
The invention has the following technical scheme that: a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the line loss management method based on big data analysis.
The beneficial effects of the invention are as follows:
1. By utilizing the big data analysis method, the power abnormal area or user in the system can be identified efficiently, and a diagnosis analysis report can be generated automatically. The software big data analysis function is utilized, the data difficulty of a basic group analysis and check system is reduced, the workload of on-site investigation is reduced, the basic layer burden reduction is realized, and meanwhile, the line loss problem treatment efficiency is improved.
2. Providing a strict strategy and a loose strategy, establishing a fault-tolerant mechanism for data analysis, carrying out characteristic analysis on local stage data, preventing a large amount of abnormal data from being extracted due to oversensitivity of a research and judgment method, and avoiding unnecessary increase of field checking workload.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a functional frame diagram of a line loss management system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a region display with focus in the embodiment of the present invention, wherein FIG. 2a shows a region with large fluctuation of daily power in a classification manner, and FIG. 2b shows a region with zero power in a classification manner;
FIG. 3 is a schematic diagram of an exemplary abnormal area according to an embodiment of the present invention, wherein FIG. 3a is a schematic diagram of an exemplary abnormal area with unstable field signals, and FIG. 3b is a schematic diagram of an exemplary abnormal area of a metering device;
Fig. 4 is a power change curve of an important area generated in the embodiment of the present invention.
Detailed Description
The application will be described in detail below with reference to the drawings in connection with embodiments. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
The following detailed description is exemplary and is intended to provide further details of the application. Unless defined otherwise, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the application.
1. As shown in fig. 1, the embodiment of the invention provides a line loss management system based on big data analysis:
1) The system has the functions of automatically extracting and storing data, and specifically comprises the following steps:
11 The file automatic generation module is used for automatically scanning the original data in the database, extracting the names of the lines or the areas and automatically generating the file according to the names;
12 The data extraction module is used for extracting daily electricity quantity key data of all transformers or users in the database;
13 The automatic data updating module is used for dynamically updating the daily electricity quantity key data of all transformers or users in the same document to form an electricity quantity summary document;
14 Equipment information identification module, which is used for automatically updating information in the electric quantity summary list document and automatically supplementing electric quantity data when the transformer or user information is changed;
15 The ordered filling data module is used for automatically complementing the electric quantity data to be analyzed according to the date and storing the electric quantity data to the appointed position of the electric quantity summary list document, so that the electricity consumption change can be conveniently observed;
16 A batch data import module for importing continuous data for a plurality of days in batch.
2) The system has the data analysis and judgment function, and specifically comprises:
21 The big data overall comprehensive analysis module is used for carrying out comprehensive analysis on the multi-day electric quantity data and searching transformers or users with abnormal electric quantity change;
22 The daily data independent analysis and diagnosis module is used for analyzing and diagnosing any daily electricity quantity data on the basis of sample data and automatically searching transformers or users with abnormal electricity consumption;
23 The automatic judging module is used for diagnosing transformers or users with large fluctuation of judging electric quantity, long-term low electric quantity, suspected shutdown or meter damage;
24 The analysis diagnosis fault-tolerance correction module is used for providing strict diagnosis and loose diagnosis, establishing an abnormal data hypersensitive mechanism and simultaneously establishing an analysis diagnosis fault-tolerance mechanism;
25 An automatic generation analysis report module for generating a comprehensive analysis report for daily electricity analysis production day diagnosis report for big data overall analysis.
3) The data image display function is provided, and specifically includes:
31 A change trend image display module for displaying daily change waveforms of the transformer or the user electric quantity by taking the daily electric quantity as a vertical axis and the real date as a horizontal axis;
32 The grouping screening display module is used for displaying the electric quantity greatly fluctuated, low-power consumption and zero-power consumption areas which are focused on or the user grouping;
33 A fuzzy search query module for fuzzily querying and displaying related electric quantity information through the number of the transformer or the user;
34 A data fluctuation image export module for exporting the electric quantity graphic data of the concerned area or the user and storing the data into a universal picture format.
2. According to the line loss management system based on big data analysis, when line loss management is carried out, the method comprises the following steps:
1) Extracting electric quantity data in a database, storing the electric quantity data into an electric quantity data document corresponding to the name of the electric quantity data, and dynamically updating daily electric quantity key data of all transformers or users in the electric quantity data document to form an electric quantity summary document; when the transformer or the user information is changed, the information is updated in the electric quantity summary list document, and the electric quantity data is supplemented. When the data is dynamically updated, the data of the electric quantity to be analyzed is automatically recorded according to the date, and the data is stored in the appointed position of the electric quantity summary table document, so that the electricity consumption change can be conveniently observed.
2) Establishing a small probability event set A and a small probability event set B by using a statistical principle; comprehensively analyzing all the historical electric quantity data to form probability distribution, and dividing the electric quantity data meeting the small probability distribution into a small probability event set A; taking the electric quantity data in a period before and after the day as a sample, and analyzing whether the electric quantity data accords with a statistical rule by using a statistical method, if not, the electric quantity data is listed in a small probability event set B; the stringent strategy: performing AND operation on the event sets A and B, wherein data listed in the event sets A and B are taken as abnormal data; the loose policy performs an OR operation on event sets A and B, so long as data listed in one of the event sets A or B is used as warning data. Aiming at single-day or multi-day electric quantity data, a loose strategy or a strict strategy is adopted to search transformers or users with abnormal electric quantity, the types of the abnormal transformers or users with abnormal electric quantity are judged, and a comprehensive analysis report is generated; and according to the comprehensive analysis report, performing transformer or user abnormality detection processing on the spot. The daily electric quantity is taken as a vertical axis, the real date is taken as a horizontal axis, and the daily change waveform of the transformer or the user electric quantity with abnormal electric quantity is displayed in groups; and exporting the electric quantity graphic data of the concerned area or the user, and storing the electric quantity graphic data into a universal picture format.
The invention also provides a technical scheme that: an apparatus for line loss management based on big data analysis includes a memory and a processor; the memory is used for storing a computer program; the processor is used for realizing the line loss management method based on big data analysis when executing the computer program.
The invention has the following technical scheme that: a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the line loss management method based on big data analysis.
3. The method can automatically identify the transformer or the user with abnormal change of the electricity consumption, excavate important attention objects from massive data, and reduce the on-site checking range of line loss management, thereby realizing the high-efficiency checking of typical problems such as abnormal metering acquisition devices, abnormal communication signals, abnormal equipment load rate and the like, improving the line loss management efficiency, optimizing the utilization efficiency of power equipment and highlighting the economic benefit of loss reduction.
Aiming at the problems of data uploading abnormality caused by abnormality of the metering acquisition device and the field signal, the metering acquisition device and the field signal can be summarized into abnormal electric quantity change from the aspect of data. Thus, data analysis and judgment were performed in two ways.
(1) And carrying out comprehensive analysis on all historical electric quantity data by utilizing a statistical probability theory principle to form probability distribution, listing data meeting a 'small probability' dividing principle into important concerns, defining the degree of interest according to the 'small probability' event frequency, and listing the data into a small probability event set A.
(2) Considering the influence of factors such as temperature change, intermittent or seasonal electricity load and the like, the possibility of short-term fluctuation of electricity consumption is acknowledged, so that a fault-tolerant mechanism is established for data analysis, characteristic analysis is carried out on local stage data, a large amount of abnormal data is prevented from being extracted due to oversensitivity of a research and judgment method, and unnecessary increase of field checking workload is avoided. Taking analysis of certain daily electric quantity data as an example, taking data in a period before and after the day as a sample, and analyzing whether the daily electric quantity data accords with a statistical rule by using a statistical method, if not, the daily electric quantity data is listed in a small probability event set B.
The overall comprehensive analysis of the big data is beneficial to grasping the electric quantity statistics rule of the transformer area or the user in the line, and is convenient for comprehensive diagnosis; and the local stage characteristic analysis is used for studying and judging whether the daily electricity quantity data accords with the statistical rule or not so as to judge whether the equipment functions normally or not.
Aiming at different analysis requirements, from the important attention and general reminding, a strict strategy and a loose strategy are assisted in a big data analysis and statistics environment, so that different analysis purposes can be realized. The strict strategy is to perform AND operation on the event sets A and B, and the data listed in the event sets A and B are used as abnormal data, so that high attention is required; the loose strategy is to OR the event sets A and B, so long as the data listed in one of the event sets A or B is used as warning data, general attention is required. The specific operation mode is as follows:
acquiring solar energy sample data X= { X 1,X2....Xn };
Sample mean
Sample variance
1) Overall judgment: identifying abnormal electric quantity data by using a 3 sigma principle;
Meeting the above condition is noted as event a;
A＝{Xi}；
2) Local sample judgment: intercepting week sample data from the whole sample;
For day i electrical data X i, week data sample T i is constructed;
Ti＝{Xi-3,Xi-2,Xi-1,Xi+1,Xi+2,Xi+3}；
Taking a sample T i average value;
setting a lower limit coefficient m and an upper limit coefficient n;
Determination of
An event satisfying the above condition is noted B B = { X i };
Algorithm 1: strictly judging and operating to judge X i epsilon { A n B };
algorithm 2: loose judgment or operation judgment X i e { a } u B };
The satisfying and operation event is marked as C, and the abnormal data C= { X i epsilon (A and B) } is focused on;
The satisfied or event is denoted as D, as general attention anomaly data d= { X i e (a u B) }.
The strict strategy and the loose strategy are applicable to different stages of line loss management. In the initial stage of line loss management work, a strict strategy is implemented, so that the typical problems can be grasped, key points can be grasped, and the management efficiency is highlighted; and in the later period of line loss management, the achievement consolidation and promotion stage executes loose strategies, and the equipment for comprehensively checking the doubtful electric quantity data can further promote loss reduction and mining, and promote line loss indexes.
Aiming at a transformer with zero load rate or low load rate in a line, the identification method is to set a load rate threshold value and frequency, statistically analyze large data samples, and record the large data samples into a zero load rate attention area when the time and frequency of zero load rate of a certain transformer reach set conditions. Similarly, if the load factor is below the threshold and the cut duration meets the defined requirement, the low load factor zone of interest is counted.
There are various reasons for the occurrence of the zero load rate zones: on the one hand, the real load rate is zero, and the transformer can be used for disabling the process, wherein the real load rate is not really used for the electric load under the transformer; on the other hand, the reasons may be that the site meter, the metering device or the communication signal is abnormal, and the areas need to carry out treatment work. The low utilization rate of the power equipment can cause the waste of power grid resources, the line loss can be increased, and for a transformer with zero or lower real load rate, the utilization efficiency of the equipment is improved through the measures of strengthening daily operation and maintenance management, optimizing a low-voltage power supply scheme, improving technical means and the like, the line loss is reduced, and the economic benefit is improved.
4. The following demonstrates analysis in connection with specific examples of implementation:
In order to verify the actual function of the line loss analysis software, a large amount of distribution network lines and power data of the transformer areas are selected, and 10kV branching and 0.4kV transformer area line loss branching work is performed. Taking 10kV temple Hualu in Beijing Huaiyou area as an example, analyzing and searching transformers with abnormal electric quantity in the line and zero (low) load rate transformer areas. Analyzing the used electricity quantity data to obtain an integrated synchronous line loss system of a national power grid company, completing the collection of the daily line loss electricity quantity data between 1 day and 6 months and 8 days in 4 months in 2019, running a program, importing the data into an electricity quantity data file in batches, automatically extracting important attention information, and automatically generating an electricity quantity summary file according to a time sequence.
After the original data are extracted and stored in sequence, data analysis is carried out in a targeted mode according to parameter setting, and an analysis report is generated. The data analysis mode is set as 'comprehensively analyzing multi-day electric quantity data', the program carries out overall comprehensive analysis on the 10kV temple historical data, finds out electric quantity abnormal jump important focusing areas and electric quantity fluctuation general focusing areas through a strict strategy and a loose strategy, and finds out low-load-rate areas by utilizing a zero (low) load rate identification algorithm. And automatically generating a comprehensive analysis report by the program according to the automatic analysis result of the big data.
After data analysis is completed according to the set mode, the electric quantity data of all transformers under the line are displayed on a graphical interface in a curve mode. And searching the information of the concerned area by utilizing a program fuzzy query function, and searching an electric quantity change curve of the concerned area through the area index information.
And analyzing the 10kV temple China road historical data, displaying large-amplitude abnormal fluctuation of partial power data of the transformer areas, and enabling the partial transformer areas to be zero-load rate for a long time. The solar power greatly-fluctuated area and the zero power area are focus of line loss analysis, and aiming at the areas, the area number of similar problems can be automatically collected by utilizing the area display function, so that inquiry is convenient, and the problem analysis efficiency is improved.
By utilizing the image display function, the typical problem of electric quantity variation in the circuit can be effectively identified and searched. Fig. 2 shows: the electric quantity data of the two transformers with the user number 0002329947 have abnormal fluctuation, the electric quantity of the 4 month 1 day in fig. 2a is greatly jumped, the daily electric quantity of the transformers is approximately 3000kWh, the average value of the electric quantity between the 4 month 2 day and the 4 month 19 day is about 30kWh, and the electric quantity after the 4 month 20 is zero, so that the metering and collecting device is abnormal, the jump of the data of the uploaded electric quantity occurs, and the station area is stopped or the field signal is abnormal in the later stage; the transformer corresponding to fig. 2b fails to upload the power data from 19 days of 4 months, and the power is normally uploaded from 20 days of 4 months, and then the power for later use is zero, so that the power area becomes zero. Two transformers of the same user become a zero-electricity-quantity station area after 4 months and 20 days, and abnormal communication signals of a distribution room of the user are very likely to occur, so that electricity quantity data cannot be uploaded normally. The communication carrier signal device in the distribution room malfunctions and causes the distribution room to be signal-free through field verification.
The electric quantity change curve of the transformer in fig. 3a shows that the communication signal of the position where the transformer is located is unstable, the system can not be uploaded in time due to frequent occurrence of the electric quantity information of the current day, the electric quantity data uploaded every other day is about 2 times of the average daily electric quantity, the calculation of line daily loss is affected, and the field signal is reinforced aiming at the station area to ensure the stable uploading of the electric quantity data. The transformer shown in fig. 3b has frequent and large jump of daily power data, the daily normal power consumption is about 200kWh before 4 months and 19 days, and the power consumption of the transformer area is 0 at the beginning of 4 months and 21 days, which indicates that the metering collection has faults. After verification on site, the transformer is a street lamp transformer, the acquisition module has a fault, and the transformer returns to normal after the acquisition module is replaced.
The classification attention information listed by the line loss analysis report is combined, and the electric quantity change curve of the important attention area or the user is derived through analysis software, as shown in fig. 4, the difficulty of analyzing data and troubleshooting problems of basic groups can be effectively reduced, and the working efficiency of line loss management is improved. In the line loss management system provided by the embodiment of the invention, in the big data analysis method, the strict strategy and the loose strategy are set, so that abnormal data can be efficiently checked, the fault tolerance and correction capability can be improved, and the practicability is enhanced. By the line loss management system provided by the embodiment of the invention, the distribution network electric quantity historical data is analyzed, so that typical problems such as abnormal metering acquisition, abnormal communication signals, abnormal load rate and the like below a line or a station area can be efficiently identified, and the pertinence of the distribution network line loss problem investigation and management is improved. The line loss management work is carried out by utilizing the big data analysis technology, the difficulty of analyzing data and searching problems of first-line staff can be obviously reduced, the burden of basic groups can be reduced, and the line loss work efficiency can be improved.
It will be appreciated by those skilled in the art that the present invention can be carried out in other embodiments without departing from the spirit or essential characteristics thereof. Accordingly, the above disclosed embodiments are illustrative in all respects, and not exclusive. All changes that come within the scope of the invention or equivalents thereto are intended to be embraced therein.
Claims (8)
1. The line loss management method based on big data analysis is characterized by comprising the following steps of:
1) Extracting electric quantity data in a database, storing the electric quantity data into an electric quantity data document corresponding to the name of the electric quantity data, and dynamically updating daily electric quantity key data of all transformers or users in the electric quantity data document to form an electric quantity summary document;
2) Aiming at single-day or multi-day electric quantity data, a loose strategy or a strict strategy is adopted to search transformers or users with abnormal electric quantity, the types of the abnormal transformers or users with abnormal electric quantity are judged, and a comprehensive analysis report is generated; performing transformer or user abnormality detection processing on the spot according to the comprehensive analysis report;
step 2) further comprises: the daily electric quantity is taken as a vertical axis, the real date is taken as a horizontal axis, and the daily change waveform of the transformer or the user electric quantity with abnormal electric quantity is displayed in groups; exporting electric quantity graphic data of a concerned station area or a user, and storing the electric quantity graphic data into a universal picture format;
In the step 2), a small probability event set A and a small probability event set B are established by utilizing a statistical principle; comprehensively analyzing all the historical electric quantity data to form probability distribution, and dividing the electric quantity data meeting the small probability distribution into a small probability event set A; taking the electric quantity data in a period before and after the day as a sample, and analyzing whether the electric quantity data accords with a statistical rule by using a statistical method, if not, the electric quantity data is listed in a small probability event set B;
The stringent strategy: performing AND operation on the small probability event sets A and B, and simultaneously, taking data listed in the small probability event set A and the small probability event set B as abnormal data;
And the loose strategy performs OR operation on the small probability event set A and the small probability event set B, and only the data listed in one of the small probability event set A or the small probability event set B is used as warning data.
2. The line loss management method based on big data analysis according to claim 1, wherein in the step 1), when the transformer or the user information is changed, the information is updated in the electric quantity total table document to supplement the electric quantity data.
3. The line loss management method based on big data analysis according to claim 1, wherein in the step 1), when the data is dynamically updated, the data of the electric quantity to be analyzed is automatically recorded according to the date, and the data is stored in a designated position of an electric quantity summary table document, so that the change of the electric quantity is convenient to observe.
4. A line loss management system based on big data analysis is characterized by comprising:
the file automatic generation module is used for automatically scanning electric quantity data in the database, extracting the names of the lines or the areas and automatically generating the file according to the names;
the automatic data extraction module is used for extracting and storing daily electricity quantity key data of all transformers or users in the database;
the automatic data updating module is used for dynamically updating the daily electricity quantity key data of all transformers or users in the same document to form an electricity quantity summary document;
the big data integral comprehensive analysis module is used for carrying out comprehensive analysis on the multi-day electric quantity data and searching transformers or users with abnormal electric quantity change;
the daily data independent analysis and diagnosis module is used for analyzing and diagnosing any daily electricity quantity data on the basis of sample data and automatically searching transformers or users with abnormal electricity consumption;
The key problem automatic judging module is used for diagnosing transformers or users with greatly fluctuating judging electric quantity, long-term low electric quantity, suspected shutdown or meter damage;
the analysis diagnosis fault tolerance correction module is used for providing a strict strategy and a loose strategy and searching transformers or users with abnormal electricity consumption;
And the automatic generation analysis report module is used for generating a comprehensive analysis report aiming at the big data overall analysis module and generating a daily diagnosis report aiming at the daily data single analysis diagnosis module.
5. The big data analysis based line loss management system of claim 4, further comprising:
the change trend image display module is used for displaying a transformer with abnormal electricity consumption or a daily change waveform of the electricity consumption of a user by taking the daily electricity consumption as a vertical axis and the real date as a horizontal axis;
The grouping screening display module is used for displaying the electric quantity greatly fluctuated, low-power consumption, zero-power consumption power station areas or users in a grouping way;
The fuzzy search query module is used for carrying out fuzzy query and displaying related electric quantity information through the number of the transformer or the user;
and the data fluctuation image export module is used for exporting the power consumption abnormal power consumption area or the power consumption graphic data of the user and storing the power consumption area or the power consumption graphic data into a universal picture format.
6. The big data analysis based line loss management system of claim 5, further comprising:
the equipment information change identification module is used for automatically updating information in the electric quantity summary list document and automatically supplementing electric quantity data when the transformer or the user information is changed;
the ordered filling data module is used for automatically complementing the electric quantity data to be analyzed according to the date and storing the electric quantity data to the appointed position of the electric quantity summary list document;
and the batch data importing module is used for importing the continuous electric quantity data for a plurality of days in batches.
7. An apparatus for line loss management based on big data analysis, comprising a memory and a processor;
The memory is used for storing a computer program; the processor is configured to implement the line loss management method based on big data analysis according to any one of claims 1 to 3 when executing the computer program.
8. A computer-readable storage medium, wherein a computer program is stored on the storage medium, which when executed by a processor, implements the line loss management method based on big data analysis as claimed in any one of claims 1 to 3.
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