CN108205788B - Multi-index power load curve analysis system for crawling external source data in real time - Google Patents
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Abstract
The invention discloses a multi-index power load curve analysis system for crawling exogenous data in real time, which comprises a load curve analysis index storage library, a load curve analysis index storage library and a calculation formula, wherein the load curve analysis index storage library is used for storing indexes and calculation formulas for power load curve analysis; the analysis data configuration module is used for calling information stored in the load curve analysis index storage library and transmitting the information to the load curve analysis index calculation module, and also configuring an extraction data range and an extraction frequency for the load curve analysis data acquisition module; the load curve analysis data acquisition module is used for extracting data required by analysis and external influence factor information from the power internal and external systems in real time; the load curve analysis index calculation module is used for calculating the power load curve analysis index and storing the power load curve analysis index into an index calculation result storage library; and the load curve analysis module obtains and outputs the corresponding load curve and the correlation degree of the load curve analysis index and the external influence factor.
Description
Technical Field
The invention belongs to the field of power loads, and particularly relates to a multi-index power load curve analysis system for crawling external source data in real time.
Background
At present, the electricity demand of China is continuously increased, the contradiction between the supply and the demand of electricity is intensified, and the electricity utilization structure is transforming. With the development of the power market and the improvement of the power technology level, the load curve analysis is one of the basic works of the power market analysis as an important basis for load prediction, and is more and more important for the operation and planning development of power enterprises. The load curves of different time dimensions represent the change of the power load in the time period.
At present, load analysis mainly depends on experience of business personnel, the main means is qualitative analysis of a load curve, the analysis is concentrated inside a load index, and real-time acquisition and mining of external influence factors are lacked. Meanwhile, internal and external information systems of power systems such as a power utilization information acquisition system and a government portal are widely applied, a large amount of load analysis basic data are accumulated, but the load analysis basic data are not fully mined, and the accuracy of power load curve analysis is influenced.
Disclosure of Invention
In order to solve the defects of the prior art, the invention aims to provide a multi-index power load curve analysis system for crawling external source data in real time.
A multi-index power load curve analysis system for external source data real-time crawling comprises:
a load curve analysis index storage library for storing indexes of power load curve analysis and calculation formulas thereof;
the analysis data configuration module is used for receiving a load curve analysis request of a user, calling information stored in a load curve analysis index storage bank according to the user load analysis requirement and analysis time dimension, and transmitting the information to the load curve analysis index calculation module; the analysis data configuration module is also used for configuring an extraction data range and an extraction frequency for the load curve analysis data acquisition module;
the load curve analysis data acquisition module is used for extracting data required by power load curve analysis from the power internal system in real time according to the configuration information of the analysis data configuration module and transmitting the data to the load curve analysis index calculation module; extracting external influence factor information from an electric power external system and transmitting the external influence factor information to an external influence factor information storage library;
the load curve analysis index calculation module is used for calculating a power load curve analysis index according to the received data required by power load curve analysis and an index calculation formula, and storing the calculated load curve analysis index into an index calculation result storage base;
the analysis data configuration module is also used for configuring load curve analysis index analysis processing logic for the load curve analysis index analysis logic module; the load curve analysis index analysis logic module is used for receiving the load curve analysis index analysis processing logic and providing the processing logic for the load curve analysis module;
and the load curve analysis module is used for respectively calling the index calculation result and the external influence factor information from the index calculation result storage library and the external influence factor information storage library, analyzing according to the received processing logic, further obtaining and outputting the corresponding load curve and the correlation degree of the load curve analysis index and the external influence factor.
Further, the system also comprises a load analysis result display module which is used for displaying the load analysis result.
The method can analyze and display the change trend of the power load curve in different time periods, and can also analyze and display the influence degree of external influence factors on the load curve in real time.
Further, the index of the power load curve analysis can be divided into a daily load curve analysis index such as daily maximum load, a monthly load curve analysis index such as monthly maximum load, and an annual load curve analysis index such as annual maximum load according to a time dimension.
Further, the extrinsic factor information includes meteorological factors, economic factors, and holiday factors.
The invention carries out load curve analysis from a plurality of time dimensions such as year, month, day and the like, quantitatively analyzes the influence degree of external factors on the power load on the basis of qualitative analysis, simultaneously increases the flexibility of the load analysis, realizes the analysis and display of the power load curve, and provides multi-dimensional and visual load analysis display for users.
Furthermore, the load curve analysis data acquisition module extracts data required by power load curve analysis from an internal system of the power system through a WebService interface.
According to the invention, the data required by power load curve analysis is extracted from the internal system of the power system through the WebService interface, so that the data synchronism between the internal system of the power system and the load curve analysis data acquisition module can be ensured.
Further, the external influence factor information repository and the index calculation result repository are both relational databases.
The external influence factor information repository and the index calculation result repository adopt a relational model to organize data stored in the external influence factor information repository and the index calculation result repository, so that the consistency of the data can be kept, and the accuracy of a load curve analysis result is finally improved.
Further, the load curve analysis index calculation module comprises a daily load curve analysis index calculation module, a monthly load curve analysis index calculation module and an annual load curve analysis index calculation module;
the daily load curve analysis index calculation module, the monthly load curve analysis index calculation module and the annual load curve analysis index calculation module are respectively used for calculating a daily load curve analysis index, a monthly load curve analysis index and an annual load curve analysis index according to the received data and index calculation formula required by the power load curve analysis and storing the data and the index calculation formula into an index calculation result storage base.
Further, the load curve analysis module comprises a daily load curve analysis module, a monthly load curve analysis module and an annual load curve analysis module, the daily load curve analysis module, the monthly load curve analysis module and the annual load curve analysis module respectively retrieve the index calculation result and the external influence factor information from the index calculation result storage library and the external influence factor information storage library, and carry out analysis according to received processing logic to further obtain the association degree of the daily load curve, the monthly load curve, the annual load curve and the corresponding load curve analysis index and the external influence factor.
According to the invention, load curve analysis is carried out from a plurality of time dimensions such as year, month and day according to different load curve analysis indexes acted by different influence factors, on the basis of qualitative analysis, the influence degree of external factors on the power load is quantitatively analyzed, meanwhile, the flexibility of the load analysis is increased, the analysis and display of the power load curve are realized, and multi-dimensional and visual load analysis display is provided for users.
Further, a grey correlation analysis method is adopted to process the index calculation result and the external influence factor information to obtain the correlation degree of the corresponding load curve analysis index and the external influence factor.
A measure of the magnitude of the relatedness of a factor between two systems, which varies with time or from object to object, is called relatedness. In the system development process, if the trends of the two factors are consistent, namely the synchronous change degree is higher, the correlation degree of the two factors is higher; otherwise, it is lower. Therefore, the gray correlation analysis method is a method for measuring the degree of correlation between the factors according to the similarity or difference of the development trends between the factors, i.e., "gray correlation". The grey correlation analysis method is analyzed according to the development trend, so that the sample size is not required, a typical distribution rule is not required, the calculated amount is small, the result is consistent with the qualitative analysis result, the analysis process is simple, and the analysis result is reliable.
Further, the load analysis result display module comprises a graphic display and a text display, wherein the graphic display displays the result by drawing a graph through vaddin.
vaadin is an open source product using the Apache V2 license agreement, and the greatest benefit of using vaadin is that it can be decoupled from a complex user interface. vaadin has the following functional properties:
(1) excellent Web browser compatibility; (2) strong Web application integration capability; (3) a good integrated development environment; (4) support of a wide range of application servers and Web browsers: vaadin supports the Java Servlet API 2.3 standard and the JSR-168Portlet specification, and can run on any application server compatible with both standards, such as Tomcat 4.1+, WebLogic 9.2+, WebSphere 6.1 +.
The invention has the beneficial effects that:
(1) the invention provides a multi-index power load curve analysis system with external source data crawling in real time, which extracts required load data from power internal systems such as a power utilization information acquisition system and the like, crawls weather, regional GDP, holidays and other external factor data influencing the weather, the regional GDP, the holidays and the like from external information sources of power systems such as administrative departments, web sites and the like in real time, analyzes load curves from a plurality of time dimensions such as year, month and day according to different load curve analysis indexes influencing different influence factors, quantitatively analyzes the influence degree of external factors on power loads on the basis of qualitative analysis, increases the flexibility of load analysis, realizes the analysis and display of the power load curves, and provides multi-dimensional and visual load analysis and display for users.
(2) In order to improve the interactivity of the system and the user, if the user can set a daily load curve for analyzing only a certain electricity client or a certain area, a load curve analysis index analysis logic module is arranged, and the analysis logic can be changed by the user according to the service requirement of the user.
(3) The method can be used for determining factors and influence degrees influencing power load fluctuation, realizing quantitative analysis of the influence factors on the basis of qualitative analysis of the load curve, improving the refinement degree of the load curve analysis, and providing a basis for load characteristic prediction and power market planning of a power system; the load curve analysis of various users, user groups and various industries of various levels of time dimensions can be realized, and professionals are assisted to better analyze the load change rules of various research objects.
Drawings
FIG. 1 is a schematic structural diagram of a multi-index power load curve analysis system for real-time crawling of exogenous data.
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.
FIG. 1 is a schematic structural diagram of a multi-index power load curve analysis system for real-time crawling of exogenous data. As shown in FIG. 1, the multi-index power load curve analysis system for external source data real-time crawling of the invention comprises the following parts:
(1) load curve analysis index storage library
The load curve analysis index storage library 1 is used for storing load curve analysis indexes and calculation modes thereof, and the indexes can be divided into daily load curve analysis indexes, monthly load curve analysis indexes and annual load curve analysis indexes according to time dimensions.
Wherein the daily load curve analysis indexes comprise daily maximum (small) load, daily average load, daily load rate, daily minimum load rate, daily peak-valley difference rate, daily load curve and the like; the monthly load curve analysis indexes comprise monthly maximum load, monthly average daily load, monthly maximum daily peak-valley difference, monthly average daily load rate, monthly minimum daily load rate, monthly maximum daily peak-valley difference rate, monthly load rate, maximum power load of the day with the maximum peak-valley difference, accumulated maximum load utilization hours and the like;
the annual load curve analysis indexes comprise annual maximum load, seasonal imbalance coefficients, annual maximum peak-valley difference rate, annual maximum load utilization hours, annual load curve, annual continuous load curve and the like.
The calculation mode of each index can be referred to 'deep requirement of load characteristic research content and index explanation' issued by national grid company in 2005.
(2) Analysis data configuration module
The analysis data configuration module 2 is used for receiving a load curve analysis request of a user, acquiring a corresponding load curve analysis index calculation mode and load data required by the load curve analysis index calculation mode from the load curve analysis index storage library 1 on the one hand, and configuring external influence factors which need to be crawled in real time and are used for analysis on the other hand, and configuring an analysis data extraction range and an extraction frequency, so that the load curve analysis data acquisition module 3 acquires load curve analysis basic data and external influence factor information; and sending the index calculation mode to a load curve analysis index calculation module 5.
The external influence factors refer to external factors influencing a power load curve, and comprise meteorological factors, economic factors and holiday factors, wherein the meteorological factors are mainly reflected on air temperature, and comprise the highest temperature, the lowest temperature and the like; economic factors are mainly reflected on the GDP, including the GDP, the GDP growth rate, the GDP specific gravity of the first industry, the GDP specific gravity of the second industry, the GDP specific gravity of the third industry and the like.
(3) Load curve analysis data acquisition module
The load curve analysis data acquisition module 3 is configured to acquire data required for load curve analysis from the internal and external power systems according to the data extraction and information crawling range configured by the analysis data configuration module 2, and includes a load data acquisition module 301 and an external influence factor acquisition module 302.
The load data acquisition module 301 is configured to extract, through the WebService interface, required load curve analysis basic data from internal systems of the power system such as the power consumption information acquisition system, where the load curve analysis basic data includes maximum load, minimum load, maximum load occurrence time, minimum load occurrence time, average load, load rate, minimum load rate, peak-valley difference rate, electric quantity, and the like.
The external influence factor obtaining module 302 is configured to obtain, through an information crawling technique, external influence factor information such as weather information, regional GDP information, legal holiday arrangement information, and the like, which are required by a weather department portal website, a statistical information network, and other government portal websites. The information crawling technology is adopted to automatically crawl relevant webpages of external influence factors and extract webpage contents, a specific area where the air temperature is located in a meteorological department portal website and keywords are determined by taking the real-time air temperature as an example, and the area is selected to continuously crawl the keywords to obtain the real-time air temperature.
(4) External influence factor information repository
And the external influence factor information storage library 4 is used for storing the influence factor information acquired from the load curve analysis data acquisition module 3, providing external influence factor analysis data bases for the sun load curve analysis module 501, the month load curve analysis module 502 and the year load curve analysis module 503, and storing the external influence factor analysis data bases by adopting a relational database.
(5) Load curve analysis index calculation module
The load curve analysis index calculation module 5 is configured to obtain load curve analysis basic data from the load curve analysis data obtaining module 3, calculate a load curve analysis index according to an index calculation formula formulated by a national power grid company in accordance with an index calculation manner transmitted by the analysis data configuration module, and load the load curve analysis index into a corresponding index calculation result repository, and includes a daily load curve analysis index calculation module 501, a monthly load curve analysis index calculation module 502, and an annual load curve analysis index calculation module 503.
The daily load curve analysis index calculation module 501 is configured to calculate a daily load curve analysis index according to an index calculation formula formulated by a national grid company, and load the daily load curve analysis index into the corresponding daily index calculation result storage library 6.
The monthly load curve analysis index calculation module 502 is configured to calculate a monthly load curve analysis index according to an index calculation formula established by a national power grid company, where calculation needs to be based on a daily load curve analysis index calculation result and is loaded into the corresponding monthly index calculation result storage 7.
The annual load curve analysis index calculation module 503 is configured to calculate an annual load curve analysis index according to an index calculation formula formulated by a national grid company, where the calculation is based on the daily load curve analysis index and the monthly load curve analysis index calculation result, and the calculation result is loaded into the corresponding annual index calculation result storage 8.
(6) Index calculation result storage library
The index calculation result repository includes a daily index calculation result repository 6, a monthly index calculation result repository 7, and a yearly index calculation result repository 8.
And the daily index calculation result storage library 6 is used for storing daily load curve analysis index data calculated by the daily load curve analysis index calculation module 501 in the load curve analysis index calculation module 5, providing an analysis data base for the daily load curve analysis module 10 and storing the data by adopting a relational database.
The monthly index calculation result storage library 7 is used for storing monthly load curve analysis index data calculated by the monthly load curve analysis index calculation module 502 in the load curve analysis index calculation module 5, providing an analysis data base for the monthly load curve analysis module 11, and storing the monthly load curve analysis index data by adopting a relational database.
And the annual load curve analysis result storage library 8 is used for storing the annual load curve analysis index data obtained by the annual load curve analysis index calculation module 503 in the load curve analysis index calculation module 5, providing an analysis data base for the annual load curve analysis module 12, and storing the data by adopting a relational database.
(7) Load curve analysis index analysis logic module
The load curve analysis index analysis logic module 9 is configured to receive load curve analysis index analysis processing logic provided by a user, and provide the processing logic to the load curve analysis module 10, the monthly load curve analysis module 11, and the annual load curve analysis module 12.
For example, for daily load, the user may set a daily maximum load, a daily minimum load, a daily average load, a daily load rate, a daily minimum load rate, a daily peak-to-valley difference rate, a daily load curve, etc. that shows a day.
For the monthly load, the user can set and display 5 monthly maximum load curves of the year of the month to be analyzed and the monthly maximum load of the previous 4 years; displaying the year of the month to be analyzed and the average daily load of the month of the previous 4 years; displaying the maximum peak-to-valley difference of the month to be analyzed and the month of the previous 4 years; displaying the year of the month to be analyzed and the monthly load rate of the previous 4 years; and displaying the maximum power load of the day with the maximum peak-valley difference of the months to be analyzed and the accumulated maximum load utilization hours.
For the annual load, a user can set and display the annual maximum load of the year to be analyzed and the previous 4 years, and the growth rate of each year in comparison with the previous year is calculated; displaying the season unbalance coefficients of the year to be analyzed and the previous 4 years; displaying the annual maximum peak-valley difference of the year to be analyzed and the previous 4 years; displaying the maximum peak-to-valley difference rate of the year to be analyzed and the previous 4 years; displaying the maximum load utilization hours of the year to be analyzed and the previous 4 years; and displaying the annual load curve and the annual continuous load curve of the year to be analyzed.
(8) Load curve analysis module
The load curve analysis module comprises a daily load curve analysis module 10, a monthly load curve analysis module 11 and an annual load curve analysis module 12.
The daily load curve analysis module 10 is configured to process a corresponding daily load curve analysis logic, and implement analysis on a daily load curve, and includes a daily load curve analysis index analysis module 1001 and an external factor influence analysis module (daily) 1002.
The daily load curve analysis index analysis module 1001 obtains data from the daily index calculation result repository 6 for analysis according to the corresponding index analysis logic obtained from the load curve analysis index analysis logic module 9, and sends the analysis result to the load curve analysis result display module 13 for display.
The external factor influence analysis module (day) 1002 adopts a gray correlation analysis method to form a sequence matrix of gray correlation analysis by the daily load curve, the numerical values in the daily load curve and the numerical values at the corresponding time points of the external influence factors such as meteorological factors, obtains a correlation coefficient matrix by performing normalization processing and calculating a difference sequence on the matrix, performs weighted summation on the correlation coefficients of each time period to obtain the correlation degree between the correlation coefficients, and sends the analysis result to the load curve analysis result display module 13 for display.
The monthly load curve analysis module 11 is configured to process a corresponding monthly load curve analysis logic, and implement analysis on a monthly load curve, and includes a monthly load curve analysis index analysis module 1101 and an external factor influence analysis module (month) 1102.
The monthly load curve analysis index analysis module 1101 acquires data from the monthly index calculation result storage 7 according to the corresponding index analysis logic acquired from the load curve analysis index analysis logic module 9 for analysis, and sends the analysis result to the load curve analysis result display module 13 for display.
The external factor influence analysis module (month) 1102 uses a gray correlation analysis method to combine the numerical values in the indexes such as the monthly maximum load curve and the like with the numerical values at the corresponding time points of the external influence factors such as the meteorological factors, the holiday factors and the like to form a sequence matrix of gray correlation analysis, obtains a correlation coefficient matrix by performing normalized processing and calculating a difference sequence on the matrix, performs weighted summation on the correlation coefficients of each time period to obtain the correlation degree between the correlation coefficients, and sends the analysis result to the load curve analysis result display module 13 for display.
The annual load curve analysis module 12 is configured to process corresponding annual load curve analysis logics and implement analysis on an annual load curve, and includes an annual load curve analysis index analysis module 1201 and an external factor influence analysis module (year) 1202.
The annual load curve analysis index analysis module 1201 obtains data from the annual index calculation result storage 8 according to the corresponding index analysis logic obtained from the load curve analysis index analysis logic module 9, analyzes the data, and sends the analysis result to the load curve analysis result display module 13 for display.
The external factor influence analysis module (year) 1202 adopts a grey correlation analysis method to combine numerical values in indexes such as year load curves and season unbalance coefficients with numerical values at corresponding time points of external influence factors such as meteorological factors, holiday factors and economic factors to form a grey correlation analysis sequence matrix, obtains a correlation coefficient matrix by performing normalized processing and calculating a difference sequence on the matrix, performs weighted summation on the correlation coefficients of each time period to obtain the correlation degree between the correlation coefficients, and sends an analysis result to the load curve analysis result display module 13 for display.
(9) Load analysis result display module
And the load analysis result display module 13 is used for displaying the load analysis result, and comprises a graph display 1301 and a character display 1302, wherein the graph display 1301 can display the result in various modes such as a histogram, a curve graph and the like through vaddin.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (9)
1. The utility model provides a many indexes power load curve analysis system that exogenous data crawled in real time which characterized in that includes:
a load curve analysis index storage library for storing indexes of power load curve analysis and calculation formulas thereof;
the analysis data configuration module is used for receiving a load curve analysis request of a user, calling information stored in a load curve analysis index storage bank according to the user load analysis requirement and analysis time dimension, and transmitting the information to the load curve analysis index calculation module; the analysis data configuration module is also used for configuring an extraction data range and an extraction frequency for the load curve analysis data acquisition module;
the load curve analysis data acquisition module is used for extracting data required by power load curve analysis from the power internal system in real time according to the configuration information of the analysis data configuration module and transmitting the data to the load curve analysis index calculation module; extracting external influence factor information from an electric power external system and transmitting the external influence factor information to an external influence factor information storage library; the load curve analysis data acquisition module comprises a load data acquisition module and an external influence factor acquisition module;
the load data acquisition module is used for extracting required load curve analysis basic data from an internal system of the power system through a WebService interface, wherein the required load curve analysis basic data comprises maximum load, minimum load, maximum load occurrence time, minimum load occurrence time, average load, load rate, minimum load rate, peak-valley difference rate and electric quantity;
the external influence factor acquisition module is used for acquiring required external influence factor information from a government portal website through an information crawling technology, wherein the required external influence factor information comprises meteorological information, regional GDP information and legal festival and holiday arrangement information; the method comprises the steps that a relevant webpage of an external influence factor is automatically captured by adopting an information crawling technology, webpage content is extracted, a specific area where the air temperature is located and a keyword in a portal website of a meteorological department are determined, and the keyword is continuously captured in the area to obtain the real-time air temperature;
the load curve analysis index calculation module is used for calculating a power load curve analysis index according to the received data required by power load curve analysis and an index calculation formula, and storing the calculated load curve analysis index into an index calculation result storage base;
the analysis data configuration module is also used for configuring load curve analysis index analysis processing logic for the load curve analysis index analysis logic module; the load curve analysis index analysis logic module is used for receiving the load curve analysis index analysis processing logic and providing the processing logic for the load curve analysis module;
and the load curve analysis module is used for respectively calling the index calculation result and the external influence factor information from the index calculation result storage library and the external influence factor information storage library, analyzing according to the received processing logic, further obtaining and outputting the corresponding load curve and the correlation degree of the load curve analysis index and the external influence factor.
2. The system according to claim 1, further comprising a load analysis result display module for displaying the load analysis result.
3. The system of claim 1, wherein the index of the power load curve analysis is divided into a daily load curve analysis index such as daily maximum load, a monthly load curve analysis index such as monthly maximum load, and an annual load curve analysis index such as annual maximum load according to a time dimension.
4. The system of claim 1, wherein the extrinsic factor information comprises meteorological factors, economic factors, and holiday factors.
5. The system according to claim 1, wherein the external influencing factor information repository and the index calculation result repository are relational databases.
6. The multi-index power load curve analysis system for crawling of external source data in real time as claimed in claim 1, wherein the load curve analysis index calculation module comprises a daily load curve analysis index calculation module, a monthly load curve analysis index calculation module and an annual load curve analysis index calculation module;
the daily load curve analysis index calculation module, the monthly load curve analysis index calculation module and the annual load curve analysis index calculation module are respectively used for calculating a daily load curve analysis index, a monthly load curve analysis index and an annual load curve analysis index according to the received data and index calculation formula required by the power load curve analysis and storing the data and the index calculation formula into an index calculation result storage base.
7. The system of claim 6, wherein the load curve analysis module comprises a daily load curve analysis module, a monthly load curve analysis module and an annual load curve analysis module, the daily load curve analysis module, the monthly load curve analysis module and the annual load curve analysis module respectively retrieve the index calculation result and the external influence factor information from the index calculation result repository and the external influence factor information repository, and perform analysis according to received processing logic to further obtain the association degree between the daily load curve, the monthly load curve, the annual load curve and the corresponding load curve analysis index and the external influence factor.
8. The multi-index power load curve analysis system capable of crawling external data in real time as claimed in claim 7, wherein a grey correlation analysis method is adopted to process index calculation results and external influence factor information to obtain the correlation degree of corresponding load curve analysis indexes and external influence factors.
9. The system of claim 2, wherein the load analysis result display module comprises a graphical display and a text display, wherein the graphical display displays results by drawing a graph with vaddin.
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