CN115983999A - Electric power transaction data risk analysis method and system for electric power market - Google Patents
Electric power transaction data risk analysis method and system for electric power market Download PDFInfo
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Abstract
The invention relates to a method and a system for analyzing risk of electric power transaction data of an electric power market, which relate to the technical field of the electric power industry. According to the method, historical transaction data are collected, the risk data characteristics and the risk quantitative analysis result of each historical transaction data are determined, the historical transaction data are used as sample data to be trained to obtain the electric power market risk assessment model, therefore, the risk condition of the electric power transaction data is assessed in real time according to the electric power market risk assessment model, the possible risk of the electric power transaction data is analyzed, the risk early warning effect is achieved, and the risk of electric power market operation caused by electric power market violation behaviors is effectively avoided.
Description
Technical Field
The invention relates to the technical field of power industry, in particular to a method and a system for analyzing risk of power transaction data of a power market.
Background
The electric power trading center undertakes operation management work such as electric power market construction, trading organization, market subject settlement and the like. The electric power trading center is used as an important technical support system for electric power market operation, and provides business such as trade declaration, market clearing, electric charge settlement and the like for various market main bodies in provinces. The electric power trading data is multiplied along with the rapid increase of the trading electric quantity and frequency of the electric power trading center, so that the electric power market risk prevention difficulty is increased.
At present, the electric power market lacks necessary monitoring, can not carry out necessary treatment to market violations in time, lacks basis and means, leads to electric power market risk form various, risk degree is high, the risk tolerance is low, can not carry out the analysis of market risk characteristic through to electric power transaction data, effectively monitors the potential market violations of different market subjects, needs an effective technique to solve the above-mentioned problem urgently.
Disclosure of Invention
In view of this, the present application provides a method and a system for risk analysis of power trading data in a power market, and the method can effectively monitor potential market violations of different market subjects by analyzing the power trading data, and improve accuracy and efficiency of risk evaluation in the power market.
In order to achieve the purpose, the invention provides the following technical scheme:
in a first aspect, the present invention provides a method for risk analysis of power trading data in a power market, including the following steps:
acquiring historical transaction data of the power market, and processing the historical transaction data to obtain risk data characteristics and risk quantitative analysis results of each historical transaction data;
taking the processed historical transaction data as sample data, and dividing the sample data into a training data set, a verification data set and a test data set;
carrying out data modeling training by using the training data set to obtain a risk preliminary evaluation model;
verifying and testing the risk preliminary evaluation model by adopting the verification data set and the test data set, and dividing the training data set and the test data set again for training and model tuning to obtain an electric power market risk evaluation model;
inputting the real-time collected electric power transaction data into the electric power market risk assessment model to obtain a risk assessment result for monitoring the electric power market in real time;
and screening out the abnormal data of the electric power transaction in the risk evaluation result, and determining the risk grade according to the risk quantitative analysis result of the abnormal data of the electric power transaction.
As a further aspect of the present invention, the historical transaction data includes basic information of the power transaction participants, power transaction time information, transaction electricity price data, transaction electricity quantity data, risk behavior information, and a risk quantification index of each risk behavior information.
As a further aspect of the present invention, the processing the historical transaction data to obtain a risk data feature includes:
sequencing the acquired historical trading data of the power market according to a time sequence to obtain power trading time sequence data;
carrying out sectional processing on the electric power transaction time sequence data according to electric power transaction time to obtain discrete electric power transaction data;
extracting risk transaction data and non-risk transaction data in the discrete electric power transaction data, and performing feature extraction;
and taking the characteristics in the risk transaction data different from the non-risk transaction data as risk data characteristics.
As a further aspect of the present invention, the processing the historical transaction data to obtain a risk quantitative analysis result includes:
clustering discrete risk data characteristics obtained from historical transaction data to obtain a plurality of risk data characteristic types;
reading risk behavior information in the risk transaction data corresponding to each risk data characteristic type and a risk quantitative index of each risk behavior information;
and estimating the characteristic type of the risk data corresponding to the risk behavior information according to the risk quantitative index of each risk behavior information to obtain a risk quantitative analysis result of the risk transaction data.
As a further aspect of the present invention, the method for analyzing risk of electric power trading data in an electric power market further includes determining a risk estimation curve of historical trading data, where the determining of the estimation curve of electric power trading data includes:
fitting discrete risk data characteristics obtained by historical transaction data, and generating an electric power risk transaction characteristic curve according to a time sequence;
and generating a risk estimation curve of the next time period according to the curvature of the electric power risk transaction characteristic curve to obtain a risk estimation curve.
As a further scheme of the present invention, the training data set and the test data set are re-divided for training and model tuning to obtain an electric power market risk assessment model, which includes:
extracting sample data from the historical transaction data according to a preset sample extraction rule;
dividing the sample data into risk transaction data samples and non-risk transaction data samples according to risk quantitative analysis results;
inputting the risk transaction data samples and the non-risk transaction data samples into a currently trained risk preliminary evaluation model to obtain risk identification results of the risk transaction data samples and the non-risk transaction data samples;
and determining a loss function according to the risk identification result, training and optimizing the model to obtain the electric power market risk assessment model.
As a further aspect of the present invention, the method for analyzing risk of electric power transaction data in an electric power market further includes:
constructing a risk monitoring database of the power market according to the acquired historical transaction data of the power market;
screening risk transaction data in the historical transaction data, and determining the risk transaction data as a transaction data risk block according to risk behavior information in the risk transaction data, wherein the risk behavior information comprises a risk name, a risk type and a risk grade;
storing the transaction data risk block in a power market risk monitoring database.
As a further scheme of the present invention, screening out abnormal data of power transaction in the risk assessment result, and determining a risk level according to a risk quantitative analysis result of the abnormal data of power transaction, includes:
determining a risk data characteristic of the power transaction abnormal data;
extracting related transaction data risk blocks from the electric power market risk monitoring database according to the risk data characteristics;
sorting the transaction data risk blocks according to the similarity of the risk data characteristics to obtain an optimal transaction data risk block;
and determining the risk level according to the risk behavior information of the optimal transaction data risk block.
In a second aspect, the present invention further provides an electric power trading data risk analysis system for an electric power market, configured to execute the electric power trading data risk analysis method for the electric power market, where the electric power trading data risk analysis system for the electric power market includes:
the historical data processing module is used for acquiring historical transaction data of the power market, and processing the historical transaction data to obtain risk data characteristics and risk quantitative analysis results of each historical transaction data;
the sample processing module is used for taking the processed historical transaction data as sample data and dividing the sample data into a training data set, a verification data set and a test data set;
the model training module is used for taking the processed historical transaction data as sample data and dividing the sample data into a training data set, a verification data set and a test data set; carrying out data modeling training by using the training data set to obtain a risk preliminary evaluation model; verifying and testing the risk preliminary evaluation model by adopting the verification data set and the test data set, and dividing the training data set and the test data set again for training and model tuning to obtain an electric power market risk evaluation model;
and the data risk analysis module is used for inputting the electric power transaction data acquired in real time into the electric power market risk assessment model to obtain a risk assessment result for monitoring the electric power market in real time.
As a further scheme of the invention, the system further comprises a risk level evaluation module, which is used for screening out the abnormal data of the electric power transaction in the risk evaluation result and determining the risk level according to the risk quantitative analysis result of the abnormal data of the electric power transaction.
In a third aspect, the present invention provides a computer device, including a memory, a processor, and a computer program running on the processor, where the processor implements the steps of the power trading data risk analysis method for the power market when executing the computer program.
In a fourth aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the above-described electric power trading data risk analysis method for an electric power market.
The technical scheme provided by the invention can have the following beneficial effects:
according to the electric power market electric power transaction data risk analysis method and system, historical transaction data are collected, risk data characteristics and risk quantitative analysis results of each historical transaction data are determined, the historical transaction data are used as sample data to train to obtain an electric power market risk assessment model, therefore, the risk condition of the electric power transaction data is assessed in real time according to the electric power market risk assessment model, risks which may occur are analyzed, the risk early warning effect is achieved, and risks caused by electric power market illegal behaviors to electric power market operation are effectively avoided.
The application provides an electric power market's electric power transaction data risk analysis method and system, still can reduce many risk supervision costs for electric power market's electric power transaction, establish electric power market risk assessment model, carry out the analysis to electric power transaction data through electric power market risk assessment model in real time, improve the coincidence degree of electric power transaction data and electric power market's actual condition, make balance between the power supply and utilization and the risk behavior of electric power transaction, carry out real-time dynamic monitoring, analysis to electric power market power risk, can reduce potential risk hidden danger for electric power market, very extensive application prospect has.
These and other aspects of the present application will be more readily apparent from the following description of the embodiments. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application. In the drawings:
fig. 1 is a flowchart of a risk analysis method for power transaction data of a power market according to an embodiment of the present disclosure;
fig. 2 is a flowchart illustrating an implementation of a risk analysis method for power trading data of a power market according to an embodiment of the present disclosure;
fig. 3 is a flowchart illustrating a risk data feature obtained in an electric power trading data risk analysis method of an electric power market according to an embodiment of the present disclosure;
fig. 4 is a flowchart illustrating a risk quantitative analysis result obtained in the method for risk analysis of power trading data in a power market according to the embodiment of the present application;
fig. 5 is a flowchart of constructing a transaction data risk block in an electric power transaction data risk analysis method for an electric power market according to an embodiment of the present disclosure;
fig. 6 is a flowchart of risk level determination in a method for risk analysis of power transaction data in a power market according to an embodiment of the present disclosure;
fig. 7 is a block diagram illustrating a structure of a risk analysis system for power trading data of a power market according to an embodiment of the present disclosure;
FIG. 8 is a diagram of the hardware architecture of a computer device in some embodiments of the invention.
The implementation of the objectives, functional features, and advantages of the present application will be further described with reference to the accompanying drawings.
Detailed Description
The present application is further described with reference to the accompanying drawings and the detailed description, and it should be noted that, in the present application, the embodiments or technical features described below may be arbitrarily combined to form a new embodiment without conflict.
It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The present application will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures.
In the related technology, due to the fact that necessary monitoring is lacked in the current electric power market, necessary handling basis and means cannot be lacked in time for market violation behaviors, the electric power market is multiple in risk forms, high in risk degree and low in risk tolerance, and the market risk characteristics of electric power transaction data cannot be analyzed to effectively monitor the potential market violation behaviors of different market main bodies.
The application aims to provide a method and a system for analyzing the risk of electric power transaction data in an electric power market, which can effectively monitor the potential market violation behaviors of different market subjects through analyzing the electric power transaction data and improve the accuracy and the efficiency of electric power market risk assessment.
In some embodiments of the present application, referring to fig. 1 and fig. 2, an embodiment of the present invention provides a method for risk analysis of power trading data of a power market, including steps S10 to S30:
step S10, obtaining historical transaction data of the power market, and processing the historical transaction data to obtain risk data characteristics and risk quantitative analysis results of each historical transaction data;
s20, taking the processed historical transaction data as sample data, and dividing the sample data into a training data set, a verification data set and a test data set;
s30, performing data modeling training by using the training data set to obtain a risk preliminary evaluation model;
s40, verifying and testing the risk preliminary evaluation model by adopting the verification data set and the test data set, and dividing the training data set and the test data set again for training and model tuning to obtain an electric power market risk evaluation model;
s50, inputting the electric power transaction data acquired in real time into the electric power market risk assessment model to obtain a risk assessment result for monitoring the electric power market in real time;
and S60, screening out abnormal electric power transaction data in the risk evaluation result, and determining a risk grade according to a risk quantitative analysis result of the abnormal electric power transaction data.
According to the electric power market electric power transaction data risk analysis method, the collected historical transaction data are used as sample data for model training, the model training set is optimized in a mode of re-dividing the sample data, and finally the electric power market risk assessment model is obtained. And carrying out risk analysis on the power transaction data acquired in real time by using the power market risk assessment model to obtain a risk assessment result, and further determining the risk assessment result on the power transaction abnormal data existing in the power market risk assessment model.
In this embodiment, the historical transaction data includes basic information of the power transaction participants, power transaction time information, transaction electricity price data, transaction electricity quantity data, risk behavior information, and a risk quantification index of each risk behavior information.
The historical transaction data is divided into risk transaction data and non-risk transaction data according to the risk behavior information. The risk transaction data are obtained from events which are qualified as power transaction events, can be typical cases selected from the events which are qualified, and can also be randomly selected from the events which are qualified; in this embodiment, the number and specific selection of risk transaction data are not limited.
In this embodiment, the risk transaction data includes transaction power data determined as a risk event by the risk transaction data, risk behavior information, and a risk quantization index of each risk behavior information.
Referring to fig. 3, when the historical transaction data is processed to obtain the risk data characteristic, the method includes the following steps:
s101, sequencing the acquired historical transaction data of the power market according to a time sequence to obtain power transaction time sequence data;
step S102, carrying out sectional processing on the electric power transaction time sequence data according to electric power transaction time to obtain discrete electric power transaction data;
s103, extracting risk transaction data and non-risk transaction data in the discrete electric power transaction data, and extracting features;
and step S104, taking the characteristics different from the non-risk transaction data in the risk transaction data as risk data characteristics.
In this embodiment, the transaction time of the historical transaction data of the electricity selling company in the historical operation process is sorted, the scattered transaction data is sorted and then processed in sections according to time periods, the sorted transaction data is divided into discrete electric power transaction data with time periods of days, weeks, months or years, for example, the electric power transaction data processed in sections is divided into risk transaction data and non-risk transaction data according to types, feature extraction is respectively performed, and the feature of differentiation of the risk transaction data is used as the risk data feature causing risk transaction.
In this embodiment, referring to fig. 4, when processing the historical transaction data to obtain a risk quantitative analysis result, the method includes:
step S201, performing clustering processing on discrete risk data characteristics obtained by historical transaction data to obtain a plurality of risk data characteristic types;
step S202, reading risk behavior information in risk transaction data corresponding to each risk data characteristic type and a risk quantitative index of each risk behavior information;
and S203, estimating the characteristic type of the risk data corresponding to the risk behavior information according to the risk quantitative index of each risk behavior information to obtain a risk quantitative analysis result of the risk transaction data.
Determining historical transaction data according to historical order conditions of the power selling company, dividing historical orders containing risks in the power selling company into a plurality of risk data characteristic types by using risk data characteristics, and evaluating according to the recorded risk behavior information and the risk quantitative index of each risk behavior information to obtain a risk quantitative analysis result.
In this embodiment, the method for analyzing the risk of the electric power trading data in the electric power market further includes determining a risk estimation curve of historical trading data. The determination of the predictive curve of the power transaction data comprises:
fitting discrete risk data characteristics obtained from historical transaction data, and generating an electric power risk transaction characteristic curve according to a time sequence;
and generating a risk estimation curve of the next time period according to the curvature of the electric power risk transaction characteristic curve to obtain a risk estimation curve.
In this embodiment, fitting analysis is performed according to risk data characteristics of risk transaction data in historical transaction data of an electricity selling company, after an electric power risk transaction characteristic curve is obtained, fitting is performed according to curvature of the curve, and the electric power risk transaction characteristic curve in a future end time length is calculated and estimated for predicting whether risks exist or not so as to be used for risk analysis reference.
In this embodiment, the training data set and the test data set are re-divided for training and model tuning to obtain an electric power market risk assessment model, which includes:
extracting the historical transaction data according to a preset sample extraction rule to obtain sample data;
dividing the sample data into risk transaction data samples and non-risk transaction data samples according to risk quantitative analysis results;
inputting the risk transaction data samples and the non-risk transaction data samples into a currently trained risk preliminary evaluation model to obtain risk identification results of the risk transaction data samples and the non-risk transaction data samples;
and determining a loss function according to the risk identification result, training and optimizing the model to obtain the electric power market risk assessment model.
For example, sample data can be extracted from historical transaction data in a random extraction mode, or the sample data is extracted in each time period according to a specified quantity after segmentation processing is performed according to the electric power transaction time, the extracted sample data is a risk transaction data sample and a non-risk transaction data sample which are not distinguished, the extracted sample data is distinguished according to risk behavior information, and then the extracted sample data is input into a risk primary evaluation model for optimization.
In some embodiments, referring to fig. 5, the method for risk analysis of power trading data of a power market further includes:
step S301, constructing a risk monitoring database of the power market according to the acquired historical transaction data of the power market;
step S302, risk transaction data in the historical transaction data are screened, and a transaction data risk block is determined according to risk behavior information in the risk transaction data, wherein the risk behavior information comprises a risk name, a risk type and a risk grade;
and S303, storing the transaction data risk block in an electric power market risk monitoring database.
According to the embodiment, risk behavior information is defined through a transaction data risk block, the risk behavior information is stored in a database in the form of a transaction data risk block, various quantified parameters of risks are represented, the transaction data risk block can be directly extracted from the power market risk monitoring database when risk analysis operation is executed, and the transaction data risk block can be stored in the power market risk monitoring database in the formats of doc, docx, pdf, excel and the like and can be retrieved and called as a parameter file at any time.
In this embodiment, referring to fig. 6, screening out abnormal data of power transaction in the risk assessment result, and determining a risk level according to a risk quantitative analysis result of the abnormal data of power transaction includes:
step S401, determining risk data characteristics of the abnormal data of the power transaction;
step S402, extracting relevant transaction data risk blocks from the electric power market risk monitoring database according to the risk data characteristics;
s403, sorting the transaction data risk blocks according to the similarity of the risk data characteristics to obtain an optimal transaction data risk block;
and S404, determining a risk level according to the risk behavior information of the optimal transaction data risk block.
And determining a risk level corresponding to the risk behavior information according to the transaction data risk block closest to the risk data characteristic, performing early warning according to the risk level, starting a corresponding mechanism, and warning and processing the market subject of risk behavior monitoring, early warning and suspected abnormal transaction behavior.
According to the electric power market electric power transaction data risk analysis method provided by the embodiment of the application, historical transaction data are collected, risk data characteristics and risk quantitative analysis results of each piece of historical transaction data are determined, the historical transaction data are used as sample data to be trained to obtain the electric power market risk assessment model, therefore, the risk condition of the electric power transaction data is assessed in real time according to the electric power market risk assessment model, the risk which possibly occurs is analyzed, the risk early warning effect is achieved, and the risk caused by electric power market illegal behaviors to electric power market operation is effectively avoided.
It should be understood that although the steps are described above in a certain order, the steps are not necessarily performed in the order described. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, some steps of the present embodiment may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or in turns with other steps or at least a part of the steps or stages in other steps.
Referring to fig. 7, some embodiments of the present invention further provide an electric power trading data risk analysis system for an electric power market, including:
the historical data processing module 100 is configured to obtain historical transaction data of the power market, and process the historical transaction data to obtain a risk data characteristic and a risk quantitative analysis result of each historical transaction data. The historical transaction data comprises basic information of power transaction participants, power transaction time information, transaction electricity price data, transaction electricity quantity data, risk behavior information and risk quantitative indexes of each risk behavior information.
In this embodiment, the historical data processing module 100 is configured to, when processing the historical transaction data to obtain risk data characteristics, sort the obtained historical transaction data of the power market according to a time sequence to obtain power transaction time sequence data; carrying out sectional processing on the electric power transaction time sequence data according to electric power transaction time to obtain discrete electric power transaction data; extracting risk transaction data and non-risk transaction data in the discrete electric power transaction data, and performing feature extraction; and taking the characteristics in the risk transaction data different from the non-risk transaction data as risk data characteristics.
In this embodiment, the historical data processing module 100 is further configured to process the historical transaction data to obtain a risk quantitative analysis result, where the risk quantitative analysis result includes performing clustering processing on discrete risk data features obtained from the historical transaction data to obtain a plurality of risk data feature types; reading risk behavior information in risk transaction data corresponding to each risk data characteristic type and a risk quantitative index of each risk behavior information; and estimating the risk data characteristic type corresponding to the risk behavior information according to the risk quantitative index of each risk behavior information to obtain a risk quantitative analysis result of the risk transaction data.
And the sample processing module 200 is configured to use the processed historical transaction data as sample data, and divide the sample data into a training data set, a verification data set, and a test data set. The sample processing module 200 is further configured to extract sample data from the historical transaction data according to a preset sample extraction rule, divide the sample data into a risk transaction data sample and a non-risk transaction data sample according to a risk quantitative analysis result, and input the risk transaction data sample and the non-risk transaction data sample into a currently trained risk preliminary evaluation model for training set tuning.
The model training module 300 is configured to use the processed historical transaction data as sample data and divide the sample data into a training data set, a verification data set and a test data set; carrying out data modeling training by using the training data set to obtain a risk preliminary evaluation model; and verifying and testing the risk preliminary evaluation model by adopting the verification data set and the test data set, and reclassifying the training data set and the test data set for training and model tuning to obtain the electric power market risk evaluation model.
And the data risk analysis module 400 is used for inputting the electric power transaction data acquired in real time into the electric power market risk assessment model to obtain a risk assessment result for monitoring the electric power market in real time.
And the risk level evaluation module 500 is configured to screen out the abnormal data of the electric power transaction in the risk evaluation result, and determine a risk level according to a risk quantitative analysis result of the abnormal data of the electric power transaction. The risk level evaluation module 500 is configured to determine a risk data characteristic of the power transaction abnormal data; extracting relevant transaction data risk blocks from the electric power market risk monitoring database according to the risk data characteristics; sorting the transaction data risk blocks according to the similarity of the risk data characteristics to obtain an optimal transaction data risk block; and finally, determining the risk level according to the risk behavior information of the optimal transaction data risk block.
The utility model provides a power market's electric power transaction data risk analysis system, can reduce many risk supervision costs for the electric power transaction in electric power market, establish electric power market risk assessment model, carry out the analysis to electric power transaction data through electric power market risk assessment model in real time, improve the coincidence degree of electric power transaction data and electric power market's actual state, gain balance between the power supply and utilization and the risk behavior of electric power transaction, carry out real-time dynamic monitoring, the analysis to electric power market power risk, can reduce potential risk hidden danger for electric power market, and the device has very extensive application prospect.
It is to be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the method according to an exemplary embodiment of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed, for example, synchronously or asynchronously in multiple modules.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the invention. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
In this embodiment, as shown in fig. 8, the computer device includes a plurality of computer devices 1000, in the embodiment, components of the power transaction data risk analysis system of the power market may be distributed in different computer devices 1000, and the computer devices 1000 may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a cabinet server (including an independent server or a server cluster formed by a plurality of servers) that executes a program, and the like. The computer device 1000 of the present embodiment includes at least but is not limited to: a memory 1001 and a processor 1002 communicatively coupled to each other via a system bus. It is noted that fig. 8 only shows the computer device 1000 with a component memory 1001 and a processor 1002, but it is to be understood that not all shown components need to be implemented, and more or fewer components may instead be implemented.
In this embodiment, the memory 1001 (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 1001 may be an internal storage unit of the computer device 1000, such as a hard disk or a memory of the computer device 1000. In other embodiments, the memory 1001 may also be an external storage device of the computer device 1000, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 1000. Of course, the memory 1001 may also include both internal and external storage devices of the computer device 1000. In this embodiment, the memory 1001 is generally used to store an operating system and various application software installed on a computer device, for example, a power transaction data risk analysis system of the power market of the embodiment. Further, the memory 1001 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 1002 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 1002 generally operates to control the overall operation of the computer device 1000. In this embodiment, the processor 1002 is configured to execute program codes stored in the memory 1001 or process data. The method for risk analysis of electric power transaction data of an electric power market of the embodiment is implemented when the processors 1002 of the plurality of computer devices 1000 of the computer device of the embodiment collectively execute a computer program, and includes:
acquiring historical transaction data of the power market, and processing the historical transaction data to obtain risk data characteristics and risk quantitative analysis results of each historical transaction data;
taking the processed historical transaction data as sample data, and dividing the sample data into a training data set, a verification data set and a test data set;
carrying out data modeling training by using the training data set to obtain a risk preliminary evaluation model;
verifying and testing the risk preliminary evaluation model by adopting the verification data set and the test data set, and dividing the training data set and the test data set again for training and model tuning to obtain an electric power market risk evaluation model;
inputting the real-time collected electric power transaction data into the electric power market risk assessment model to obtain a risk assessment result for monitoring the electric power market in real time;
and screening out the abnormal data of the electric power transaction in the risk evaluation result, and determining the risk level according to the risk quantitative analysis result of the abnormal data of the electric power transaction.
In this embodiment, the method for analyzing risk of electric power transaction data in an electric power market further includes determining a risk estimation curve of historical transaction data, where the determining of the estimation curve of electric power transaction data includes:
fitting discrete risk data characteristics obtained from historical transaction data, and generating an electric power risk transaction characteristic curve according to a time sequence;
and generating a risk estimation curve of the next time period according to the curvature of the electric power risk transaction characteristic curve to obtain a risk estimation curve.
In this embodiment, the method for analyzing risk of electric power transaction data in an electric power market further includes:
constructing a risk monitoring database of the power market according to the acquired historical transaction data of the power market;
screening risk transaction data in the historical transaction data, and determining the risk transaction data as a transaction data risk block according to risk behavior information in the risk transaction data, wherein the risk behavior information comprises a risk name, a risk type and a risk grade;
and storing the transaction data risk block in an electric power market risk monitoring database.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a general hardware platform, and certainly can also be implemented by hardware. It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by computer programs, which may be stored in a computer-compatible storage medium, and which, when executed, may include processes of the embodiments of the methods described above.
Embodiments of the present application also provide a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App, etc., on which a computer program is stored, which when executed by a processor, implements corresponding functions. The computer-readable storage medium of the present embodiment stores the power trading data risk analysis system 10 of the power market of the embodiment, and when executed by a processor, implements the power trading data risk analysis method of the power market of the embodiment, the method including:
acquiring historical transaction data of a power market, and processing the historical transaction data to obtain risk data characteristics and risk quantitative analysis results of each historical transaction data;
taking the processed historical transaction data as sample data, and dividing the sample data into a training data set, a verification data set and a test data set;
performing data modeling training by using the training data set to obtain a risk preliminary evaluation model;
verifying and testing the risk preliminary evaluation model by adopting the verification data set and the test data set, and dividing the training data set and the test data set again for training and model tuning to obtain an electric power market risk evaluation model;
inputting the real-time collected electric power transaction data into the electric power market risk assessment model to obtain a risk assessment result for monitoring the electric power market in real time;
and screening out the abnormal data of the electric power transaction in the risk evaluation result, and determining the risk grade according to the risk quantitative analysis result of the abnormal data of the electric power transaction.
In this embodiment, the method for analyzing risk of electric power transaction data in an electric power market further includes determining a risk estimation curve of historical transaction data, where the determining of the estimation curve of electric power transaction data includes:
fitting discrete risk data characteristics obtained from historical transaction data, and generating an electric power risk transaction characteristic curve according to a time sequence;
and generating a risk estimation curve of the next time period according to the curvature of the electric power risk transaction characteristic curve to obtain a risk estimation curve.
In this embodiment, the method for analyzing risk of electric power transaction data in an electric power market further includes:
constructing a risk monitoring database of the power market according to the acquired historical transaction data of the power market;
screening risk transaction data in the historical transaction data, and determining the risk transaction data as a transaction data risk block according to risk behavior information in the risk transaction data, wherein the risk behavior information comprises a risk name, a risk type and a risk grade;
and storing the transaction data risk block in an electric power market risk monitoring database.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape systems; computer system memory or random access memory such as DRAM, DDRRAM, SRAM, EDORAM, lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet). The second computer system may provide program instructions to the first computer for execution. The term "storage media" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application includes computer-executable instructions, and the computer-executable instructions are not limited to the power trading data risk analysis operation of the power market as described above, and may also perform related operations in the power trading data risk analysis method of the power market provided in any embodiment of the present application.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all the equivalent structures or equivalent processes that can be directly or indirectly applied to other related technical fields by using the contents of the specification and the drawings of the present application are also included in the scope of the present application.
Claims (10)
1. A power trading data risk analysis method for a power market is characterized by comprising the following steps:
acquiring historical transaction data of the power market, and processing the historical transaction data to obtain risk data characteristics and risk quantitative analysis results of each historical transaction data;
taking the processed historical transaction data as sample data, and dividing the sample data into a training data set, a verification data set and a test data set;
carrying out data modeling training by using the training data set to obtain a risk preliminary evaluation model;
verifying and testing the risk preliminary evaluation model by adopting the verification data set and the test data set, dividing the training data set and the test data set again, and training and optimizing the model to obtain an electric power market risk evaluation model;
inputting the real-time collected electric power transaction data into the electric power market risk assessment model to obtain a risk assessment result for monitoring the electric power market in real time;
and screening out the abnormal data of the electric power transaction in the risk evaluation result, and determining the risk grade according to the risk quantitative analysis result of the abnormal data of the electric power transaction.
2. The electric power trading data risk analysis method of the electric power market according to claim 1, wherein the historical trading data comprises basic information of electric power trading participants, electric power trading time information, trading electricity price data, trading electricity quantity data, risk behavior information and a risk quantification index of each risk behavior information.
3. The electric power market electric power transaction data risk analysis method according to claim 2, wherein processing the historical transaction data to obtain risk data characteristics comprises:
sequencing the acquired historical trading data of the power market according to a time sequence to obtain power trading time sequence data;
carrying out sectional processing on the electric power transaction time sequence data according to electric power transaction time to obtain discrete electric power transaction data;
extracting risk transaction data and non-risk transaction data in the discrete electric power transaction data, and performing feature extraction;
and taking the characteristics in the risk transaction data different from the non-risk transaction data as risk data characteristics.
4. The electric power market electric power transaction data risk analysis method according to claim 3, wherein the processing the historical transaction data to obtain a risk quantitative analysis result comprises:
clustering discrete risk data characteristics obtained from historical transaction data to obtain a plurality of risk data characteristic types;
reading risk behavior information in the risk transaction data corresponding to each risk data characteristic type and a risk quantitative index of each risk behavior information;
and estimating the risk data characteristic type corresponding to the risk behavior information according to the risk quantitative index of each risk behavior information to obtain a risk quantitative analysis result of the risk transaction data.
5. The electric power market electric power transaction data risk analysis method according to claim 4, further comprising determining a risk prediction curve of historical transaction data, wherein the determination of the prediction curve of the electric power transaction data comprises:
fitting discrete risk data characteristics obtained from historical transaction data, and generating an electric power risk transaction characteristic curve according to a time sequence;
and generating a risk estimation curve of the next time period according to the curvature of the electric power risk transaction characteristic curve to obtain a risk estimation curve.
6. The electric power market electric power transaction data risk analysis method according to claim 1, wherein the training data set and the test data set are re-divided for training and model tuning to obtain an electric power market risk assessment model, and the method comprises the following steps:
extracting the historical transaction data according to a preset sample extraction rule to obtain sample data;
dividing the sample data into risk transaction data samples and non-risk transaction data samples according to risk quantitative analysis results;
inputting the risk transaction data samples and the non-risk transaction data samples into a currently trained risk preliminary evaluation model to obtain risk identification results of the risk transaction data samples and the non-risk transaction data samples;
and determining a loss function according to the risk identification result, training and optimizing the model to obtain the electric power market risk assessment model.
7. The electric power market electric power transaction data risk analysis method according to claim 2, further comprising:
constructing a risk monitoring database of the power market according to the acquired historical transaction data of the power market;
screening risk transaction data in the historical transaction data, and determining the risk transaction data as a transaction data risk block according to risk behavior information in the risk transaction data, wherein the risk behavior information comprises a risk name, a risk type and a risk grade;
and storing the transaction data risk block in an electric power market risk monitoring database.
8. The electric power transaction data risk analysis method of the electric power market according to claim 7, wherein screening out electric power transaction abnormal data in the risk assessment result, and determining a risk level according to a risk quantitative analysis result of the electric power transaction abnormal data comprises:
determining a risk data characteristic of the power transaction abnormal data;
extracting relevant transaction data risk blocks from the electric power market risk monitoring database according to the risk data characteristics;
sorting the transaction data risk blocks according to the similarity of the risk data characteristics to obtain an optimal transaction data risk block;
and determining the risk level according to the risk behavior information of the optimal transaction data risk block.
9. A risk analysis system for electric power trading data of an electric power market, which is used for executing the electric power trading data risk analysis method of the electric power market according to any one of claims 1 to 8, the electric power trading data risk analysis system of the electric power market comprising:
the historical data processing module is used for acquiring historical transaction data of the power market, and processing the historical transaction data to obtain risk data characteristics and risk quantitative analysis results of each historical transaction data;
the sample processing module is used for taking the processed historical transaction data as sample data and dividing the sample data into a training data set, a verification data set and a test data set;
the model training module is used for taking the processed historical transaction data as sample data and dividing the sample data into a training data set, a verification data set and a test data set; carrying out data modeling training by using the training data set to obtain a risk preliminary evaluation model; verifying and testing the risk preliminary evaluation model by adopting the verification data set and the test data set, and dividing the training data set and the test data set again for training and model tuning to obtain an electric power market risk evaluation model;
and the data risk analysis module is used for inputting the electric power transaction data acquired in real time into the electric power market risk assessment model to obtain a risk assessment result for monitoring the electric power market in real time.
10. The electric power trading data risk analysis system of the electric power market of claim 9, further comprising a risk level evaluation module for screening out electric power trading abnormal data in the risk evaluation result and determining a risk level according to a risk quantitative analysis result of the electric power trading abnormal data.
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