CN116681521A - Multi-stage electric quantity transaction settlement method based on blockchain technology - Google Patents
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Abstract
The application discloses a multi-stage electric quantity transaction settlement method based on a blockchain technology, which relates to the technical field of electric quantity transaction settlement, wherein after a plurality of searched transaction objects are summarized, a transaction object library is established, and a transaction information set and a credit record set are respectively established based on transaction information of the transaction objects in the transaction object library; the method comprises the steps that a transaction information set and a credit record set are both sent to a first processing unit, the first processing unit generates a credit coefficient and a transaction coefficient respectively, and according to the values of the credit coefficient and the transaction coefficient, the first processing unit screens transaction objects in a transaction object library; the quotation unit is used for quoting the target objects in turn according to the quotation sequence, and if the target objects accept the quotation, the collecting unit is used for collecting the transaction and delivery information of the target objects accepting the quotation; the value coefficient is generated according to the credit coefficient and the value of the transaction coefficient, so that the transaction value and the credit degree of the transaction object are relatively high, and the risk of the transaction is reduced.
Description
Technical Field
The application relates to the technical field of electric quantity transaction settlement, in particular to a multistage electric quantity transaction settlement method based on a blockchain technology.
Background
Power trading refers to the process of buying and selling power between power generators and consumers, with the purpose of achieving a balance of supply and demand in the power market and ensuring that the power is distributed and used in an efficient, reliable and economical manner. The power transaction is typically conducted in a power market, which may be a national, regional, or local market. In these markets, power plants, transmission companies, distribution companies, and consumers may purchase or sell electricity through transactions. The main forms of power transactions include the following: point-to-point transactions, power exchanges, bilateral negotiations, etc.
The conventional trading system of the electric power exchange carries out matching trading independently according to quotation, and the trading mode has high trading speed and high efficiency, but only considers the trading price during trading, but ignores the actual trading value and the trading credit of a trading object, so that a certain trading risk exists during electric power trading of the trading system.
Therefore, the application provides a multi-stage electric quantity transaction settlement method based on a blockchain technology.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the application provides a multi-stage electric quantity transaction settlement method based on a blockchain technology, which is characterized in that a transaction object library is established after a plurality of searched transaction objects are summarized, and a transaction information set and a credit record set are respectively established based on transaction information of the transaction objects in the transaction object library; the method comprises the steps that a transaction information set and a credit record set are both sent to a first processing unit, the first processing unit generates a credit coefficient and a transaction coefficient respectively, and according to the values of the credit coefficient and the transaction coefficient, the first processing unit screens transaction objects in a transaction object library; the quotation unit is used for quoting the target objects in turn according to the quotation sequence, and if the target objects accept the quotation, the collecting unit is used for collecting the transaction and delivery information of the target objects accepting the quotation; the value coefficient is generated according to the credit coefficient and the transaction coefficient value, so that the transaction value and the credit degree of the transaction object are relatively high, the risk of transaction is reduced, and the problem in the background technology is solved.
(II) technical scheme
In order to achieve the above purpose, the application is realized by the following technical scheme: the multi-stage electric quantity transaction settlement method based on the blockchain technology comprises a correlation judging unit, a price predicting unit, a data acquisition unit, a first processing unit, a control unit, a quoting unit and a collecting unit, wherein when the electric quantity transaction amount is gradually increased, a plurality of weather temperatures Tq and the transaction amount Jy of the electric quantity in an electric power transaction system are continuously obtained along a time axis at fixed time intervals, the correlation coefficient Rs between the weather temperatures Tq and the transaction amount Jy is obtained by the correlation judging unit through pearson correlation analysis, and when the correlation coefficient Rs is larger than a corresponding correlation threshold value, the price predicting unit predicts the electric power transaction price to obtain an electric power predicted price Dyj on an expected transaction day;
when the predicted power price Dyj exceeds a preset expected transaction price, generating a transaction price interval by using the predicted power price Dyj, searching transaction objects in the transaction price interval by using the data acquisition unit, building a transaction object library after integrating a plurality of searched transaction objects, and respectively building a transaction information set and a credit record set based on transaction information of the transaction objects in the transaction object library;
the method comprises the steps that a transaction information set and a credit record set are both sent to a first processing unit, the first processing unit generates a credit coefficient Xy and a transaction coefficient Jx respectively, and according to the values of the credit coefficient Xy and the transaction coefficient Jx, the first processing unit screens transaction objects in a transaction object library;
and determining the screened transaction objects as target objects, sequencing the target objects according to the quotations of the target objects, generating and outputting a quotation sequence, sending the quotation sequence to a control unit, generating a control instruction by the control unit, enabling the quotation unit to sequentially quote the target objects according to the quotation sequence, and collecting transaction and delivery information of the target objects receiving the quotations by a collecting unit if the target objects receive the quotations.
Further, the system also comprises a second processing unit, an analysis unit, a selection unit, a record fixing unit and a communication unit, wherein the transaction date and the transaction place of the target object electric quantity transaction are identified and acquired from the transaction and the transaction information collected by the collection unit, and a transaction information base is established after summarization; if the number of the target objects exceeds the number threshold, the second processing unit establishes a power transmission model after training and testing; acquiring a delivery place and weather conditions in a transmission process from a delivery information base, performing simulation analysis on the power transmission process by a power transmission model, and acquiring the loss of power transportation from simulation analysis results;
obtaining the loss cost of the electric power in transaction in the transportation process by the product of the loss quantity and the corresponding quotation; acquiring power delivery cost by adding service cost in the transmission process to the loss cost; and collecting the electric power delivery costs when the electric power delivery system is in transaction with each target object, and sequencing the electric power delivery costs to acquire the delivery sequence.
Further, according to the delivery sequence, the quotation and the transaction quantity of each target object are sequentially obtained, the product of the quotation and the transaction quantity is taken as income, the corresponding electric power delivery cost is taken as expenditure, and on the basis of income and expenditure, the analysis unit judges the income which can be brought by the target object and takes the income as expected income; and summarizing expected benefits possibly brought by each target object, and selecting the expected benefits which are larger than a benefit threshold by a selection unit to determine the expected benefits as the objects to be transacted.
Further, when the object to be transacted does not exist, the communication unit sends out communication to the outside; when the object to be transacted is one, the object to be transacted is taken as a transaction object; when the objects to be transacted are more than two, acquiring the credit coefficient Xy and the transaction coefficient Jx of the objects to be transacted, and generating a value coefficient Js according to the following formula;
wherein ρ is a correlation weight, which is a correlation coefficient between the credit coefficient Xy and the transaction coefficient Jx, and C is obtained by obtaining a plurality of groups of credit coefficients Xy and transaction coefficients Jx for correlation analysis 1 The specific value of the constant correction coefficient can be set by user adjustment or generated by fitting an analysis function; taking the object to be transacted with the highest value coefficient Js as a transaction object; transaction with the transaction object, and recording and fixing the transaction process by the recording and fixing unit through the blockchain.
Further, the first processing unit includes an evaluation module, a screening module, a marking module and a sorting module, where the establishing manner of the transaction information set is as follows: according to a linear retrieval model, acquiring an electric quantity limit De of a transaction object to be transacted by a data acquisition unit, if the electric quantity limit De is higher than a limit threshold, taking an intermediate price in a transaction price interval as an expected price, taking the difference between the expected price and the price of the transaction object as an expected profit Qr, and after acquiring the transaction date of the transaction object, determining the interval between the transaction date and the current date to acquire a transaction interval Jg; and summarizing the electric quantity limit De, the expected profit Qr and the transaction interval Jg, and establishing a transaction information set.
Further, the transaction coefficient Jx is obtained as follows: the transaction information set is sent to an evaluation module, the evaluation module performs dimensionless processing on the electric quantity limit De, the expected profit Qr and the transaction interval Jg, and a transaction coefficient Jx is generated according to the following formula;
wherein, alpha is more than or equal to 0 and less than or equal to 1, beta is more than or equal to 0 and less than or equal to 1, alpha+beta=1, alpha and beta are weights, and the specific values can be adjusted and set by a user.
Further, the credit record set is established as follows: according to the linear retrieval model, a data acquisition unit acquires a historical transaction record of a transaction object from the electric power transaction system, acquires total number of violations of the transaction object and historical total number of transactions from the historical transaction record, generates a violation ratio Wb according to the ratio of the total number of violations of the transaction object and the historical total number of transactions, and acquires total transaction amount Ze from the historical transaction record; after the identity information of the transaction object is determined, inquiring and acquiring the liability rate Zc of the transaction object, and evaluating the repayment capability of the user through the liability rate Zc; and summarizing the default ratio Wb, the total liability Ze and the liability rate Zc, and establishing a credit record set.
Further, the credit record set is sent to an evaluation module, and after the evaluation module performs dimensionless processing on the default ratio Wb, the total liability Ze and the asset liability Zc, a credit coefficient Xy is generated according to the following formula:
wherein, gamma is more than or equal to 0 and less than or equal to 1, theta is more than or equal to 0 and less than or equal to 1, and gamma+theta is more than or equal to 0.8 and less than or equal to 1.6, and gamma and theta are weights, C 2 The specific value of the constant correction coefficient can be set by user adjustment or generated by fitting an analysis function.
Further, the generated credit coefficient Xy and transaction coefficient Jx are sent to a screening module, and the transaction object with the credit coefficient Xy and the transaction coefficient Jx higher than the corresponding threshold value is determined to be a target object; acquiring quotations of a plurality of target objects, and sequencing the target objects by a sequencing module according to the height of the quotations to generate a quotation sequence; in the bidding sequence, marking, by a marking module, the target object whose bid is higher than the predicted power price Dyj; in the transaction, the marked target object is preferentially transacted.
Further, the method for predicting the price of the electric power transaction and obtaining the predicted price Dyj of the electric power is as follows: continuously acquiring a plurality of power price changes from historical transaction data at fixed time intervals along a time axis, and predicting the power price changes by a three-time exponential smoothing method to generate a first predicted value Dy1;
acquiring a plurality of groups of electric power trading prices in the same year from historical trading data, establishing a trading condition data set according to weather conditions such as temperature, rainfall and the like corresponding to the electric power trading prices, constructing a deep learning model by using a nerve convolution network, generating a price prediction model after training and testing the trading condition data set, and predicting the electric power trading prices by the price prediction model to generate a second predicted value Dy2; after the first predicted value Dy1 and the second predicted value Dy2 are obtained, a power predicted value Dyj is generated according to the following formula;
wherein F is 0.ltoreq.F 1 ≤1，0≤F 2 Not more than 1, and F 1 +F 2 =1, whose specific value is set by user adjustment.
(III) beneficial effects
The application provides a multi-stage electric quantity transaction settlement method based on a blockchain technology, which has the following beneficial effects:
1. after the credit coefficient Xy and the transaction coefficient Jx are generated, the target object is screened out, so that the time cost of manual screening can be reduced, the power transaction risk brought by manual participation is reduced, and the efficiency of power transaction can be improved through automatic quotation; generating a value coefficient Js according to the values of the credit coefficient Xy and the transaction coefficient Jx, screening a bargain object from a plurality of objects to be transacted according to the value of the value coefficient Js, so that the transaction value and the credit of the bargain object are relatively high, and the risk of transaction is reduced; after the transaction is completed, the record fixing unit records and fixes the transaction process, so that the process of electric power transaction delivery is also convenient to trace back, and the loss of data and counterfeiting are avoided.
2. After the power transmission process is subjected to simulation analysis, the running loss of the power in the power transmission process is determined according to the result of the simulation analysis, the cost of the power transmission is further judged, the target object is screened according to the loss of the power transmission obtained from the simulation analysis result, the energy waste and the ineffective loss in the power transaction process are reduced, different weights are given to the power transmission cost during the power transaction, and then expected benefits are calculated, so that the performance of the power transaction with overlarge part loss can be avoided, and the energy waste is reduced in a mode of increasing the power transaction cost.
3. When the electricity quantity predicted price is generated, the first predicted value Dy1 and the second predicted value Dy2 are respectively generated through matching of two different prediction models, and then the electricity predicted price Dyj is finally generated, so that influence factors which can be fused in are more when the generated electricity predicted price Dyj is obtained, deviation between a predicted result and an actual result is smaller, and at the moment, the risk of electricity transaction can be reduced and the safety of customer transaction is ensured by providing a new price prediction method.
Drawings
FIG. 1 is a schematic diagram of a multi-stage power transaction settlement method according to the present application;
fig. 2 is a schematic diagram of a second flow chart of the multi-stage power transaction settlement method according to the present application.
In the figure: 10. a correlation judgment unit; 20. price prediction unit; 30. a data acquisition unit; 40. a first processing unit; 41. an evaluation module; 42. a screening module; 43. a marking module; 44. a sequencing module; 50. a control unit; 60. a quotation unit; 70. a collection unit; 80. a second processing unit; 90. an analysis unit; 100. a selection unit; 110. a record fixing unit; 120. and a communication unit.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1 and 2, the present application provides a multi-stage electric quantity transaction settlement method based on a blockchain technology, which includes a correlation judging unit 10, a price predicting unit 20, a data collecting unit 30, a first processing unit 40, a control unit 50, a quoting unit 60 and a collecting unit 70, wherein after entering a high-temperature season, when the electric quantity is gradually increased and the electric quantity transaction amount is also gradually increased, a plurality of weather temperatures Tq and the transaction amount Jy of the electric quantity in an electric power transaction system are continuously obtained along a time axis at fixed time intervals, the correlation judging unit 10 obtains a correlation coefficient Rs between the weather temperatures Tq and the transaction amount Jy through pearson correlation analysis, and when the correlation coefficient Rs is greater than a corresponding correlation threshold, the price predicting unit 20 predicts the electric power transaction price to obtain an electric power predicted price Dyj on an expected transaction day;
when the predicted price Dyj of the electric power exceeds a preset expected trade price, a trade price interval is generated by the predicted price Dyj of the electric power, for example, 95% to 105% of the predicted price Dyj of the electric power is taken as the trade price interval, and quotations in the trade price interval can be taken as potential trading quotations;
searching a transaction object in the electric power transaction system by the data acquisition unit 30, after collecting a plurality of searched transaction objects, establishing a transaction object library, and respectively establishing a transaction information set and a credit record set based on transaction information of the transaction objects in the transaction object library;
when a proper transaction object is required to be found, the established transaction information set and the credit record set are both sent to the first processing unit 40, the first processing unit 40 respectively generates a credit coefficient Xy and a transaction coefficient Jx, and the first processing unit 40 screens the transaction objects in the transaction object library according to the values of the credit coefficient Xy and the transaction coefficient Jx;
the screened transaction objects are determined as target objects, the target objects are ordered according to the quotations of the target objects, a quotation sequence is generated and output, the quotation sequence is sent to the control unit 50, the control unit 50 generates a control instruction to enable the quotation unit 60 to sequentially bid the target objects according to the quotation sequence, and if the target objects accept the quotation, the collection unit 70 collects the transaction and delivery information of the target objects accepting the quotation.
When the system is used, after the credit coefficient Xy and the transaction coefficient Jx are generated, the target object is screened out, so that the time cost of manual screening can be reduced, the power transaction risk brought by manual participation is reduced, and the efficiency of power transaction can be improved through automatic quotation.
Referring to fig. 1 and 2, the system further comprises a second processing unit 80, an analysis unit 90, a selection unit 100, a record fixing unit 110, and a communication unit 120, wherein,
identifying and acquiring a delivery date and a delivery place of the target object electric quantity transaction from the transaction and delivery information collected by the collection unit 70, and establishing a delivery information base after summarizing; if the number of the target objects exceeds the number threshold, the second processing unit 80 establishes a power transmission model after training and testing; acquiring a delivery place and weather conditions in a transmission process from a delivery information base, performing simulation analysis on the power transmission process by a power transmission model, and acquiring the loss of power transportation from simulation analysis results;
obtaining the loss cost of the electric power in transaction in the transportation process by the product of the loss quantity and the corresponding quotation; acquiring power delivery cost by adding service cost in the transmission process to the loss cost; and collecting the electric power delivery costs when the electric power delivery system is in transaction with each target object, and sequencing the electric power delivery costs to acquire the delivery sequence.
When the power transmission system is used, the power transmission model is built, simulation analysis is carried out on the power transmission process after the delivery, the running loss of the power in the delivery process is determined according to the simulation analysis result, and then the cost of power transportation is judged; at this time, the loss of the power transportation is obtained from the simulation analysis result, so that the target object is screened, the energy waste and the ineffective loss in the power transaction process can be reduced, further, when the power is in the power transaction, different weights are given to the power delivery cost, the expected benefits are calculated, and therefore, the performance of the power transaction with overlarge part of loss can be avoided, and the energy waste can be reduced in a mode of increasing the power transaction cost.
Referring to fig. 1 and 2, according to the delivery order, the quotation and the transaction number of each target object are sequentially acquired, the product of the quotation and the transaction number is taken as income, the corresponding electric delivery cost is taken as expenditure, and based on the income and the expenditure, the analysis unit 90 judges the income which can be brought by the target object, and the income is taken as expected income; the expected benefits possibly brought by the target objects are summarized, and the selection unit 100 selects the target objects with expected benefits larger than the benefit threshold and determines the target objects as the objects to be transacted.
When the method is used, when expected benefits possibly brought by each target object are obtained, objects to be traded are screened and generated, the benefits in the electric power trading process are ensured, and the electric power trading and the delivery can be smoothly carried out.
Referring to fig. 1 and 2, when the object to be transacted does not exist, communication is sent to the outside by the communication unit 120; when the object to be transacted is one, the object to be transacted is taken as a transaction object; when the objects to be transacted are more than two, acquiring the credit coefficient Xy and the transaction coefficient Jx of the objects to be transacted, and generating a value coefficient Js according to the following formula;
wherein ρ is a correlationThe weight is the correlation coefficient between the credit coefficient Xy and the transaction coefficient Jx, and C is obtained by obtaining a plurality of groups of credit coefficients Xy and transaction coefficients Jx for correlation analysis 1 The specific value of the constant correction coefficient can be set by user adjustment or generated by fitting an analysis function.
Taking the object to be transacted with the highest value coefficient Js as a transaction object; transaction with the transaction object, and the transaction proceeding process is recorded and fixed by the recording fixing unit 110 through the blockchain.
When the system is used, when a plurality of selected objects to be transacted exist, a value coefficient Js is generated according to the values of the credit coefficient Xy and the transaction coefficient Jx, and the value of the value coefficient Js is used for screening the transacted objects from the plurality of objects to be transacted, so that the transaction value and the credit of the transacted objects are relatively high, and the risk of transaction is reduced; meanwhile, after the transaction is completed, the record fixing unit 110 records and fixes the transaction process, so that the process of electric power transaction delivery is also convenient to trace back, and data loss and counterfeiting are avoided.
Referring to fig. 1 and 2, the first processing unit 40 includes an evaluation module 41, a screening module 42, a marking module 43, and a sorting module 44, where the transaction information set is established as follows:
according to the linear retrieval model, the data acquisition unit 30 acquires the electric quantity limit De of the transaction object to be transacted, if the electric quantity limit De is higher than the limit threshold, the middle price in the transaction price interval is taken as an expected quotation, for example, when the transaction interval is 95-105%, the expected quotation is taken as 100%, the difference between the expected quotation and the quotation of the transaction object is taken as an expected profit Qr, after the transaction date of the transaction object is acquired, the interval between the transaction date and the current date is determined, and the transaction interval Jg is acquired;
and summarizing the electric quantity limit De, the expected profit Qr and the transaction interval Jg, and establishing a transaction information set.
Further, the transaction coefficient Jx is obtained as follows: the transaction information set is sent to the evaluation module 41, the evaluation module 41 performs dimensionless processing on the electric quantity limit De, the expected profit Qr and the transaction interval Jg, and a transaction coefficient Jx is generated according to the following formula;
wherein, alpha is more than or equal to 0 and less than or equal to 1, beta is more than or equal to 0 and less than or equal to 1, alpha+beta=1, alpha and beta are weights, and the specific values can be adjusted and set by a user.
When the system is used, the transaction information set is searched and established, the transaction coefficients Jx are further generated, and the transaction values of the transaction objects are ordered according to the transaction coefficients Jx, so that when the electric power transaction is carried out, the transaction objects with high values are subjected to the transaction preferentially, the risk caused by the fact that the electric power transaction is difficult to reach or difficult to cut is reduced, the electric power transaction can be successfully completed, and the safety of the electric power transaction process is guaranteed.
Referring to fig. 1 and 2, the credit record set is established as follows:
according to the linear retrieval model, a data acquisition unit 30 acquires a historical transaction record of a transaction object from the electric power transaction system, acquires the total number of violations of the transaction object and the historical total number of transactions from the historical transaction record, generates a violation ratio Wb according to the ratio of the total number of violations of the transaction object and the historical total number of transactions, and acquires a total transaction amount Ze from the historical transaction record; after the identity information of the transaction object is determined, inquiring and acquiring the liability rate Zc of the transaction object, and evaluating the repayment capability of the user through the liability rate Zc; summarizing the default ratio Wb, the total liability Ze and the asset liability rate Zc, and establishing a credit record set;
the credit record set is sent to the evaluation module 41, and after the evaluation module 41 performs dimensionless processing on the default ratio Wb, the total transaction amount Ze and the asset liability rate Zc, a credit coefficient Xy is generated according to the following formula:
wherein, gamma is more than or equal to 0 and less than or equal to 1, theta is more than or equal to 0 and less than or equal to 1, and gamma+theta is more than or equal to 0.8 and less than or equal to 1.6, and gamma and theta are weights, C 2 Is a constant correction coefficient, the specific value of which can be adjusted and set by a user, orGenerated by an analytical function fit.
When the method is used, the credit record set is obtained through searching, the credit coefficient Xy is further generated, and the transaction credit of the transaction object can be evaluated and screened according to the value of the credit coefficient Xy, so that the problem that the transaction is difficult to normally cut and finish due to the low credit of the transaction object can be avoided.
Referring to fig. 1, the generated credit coefficient Xy and transaction coefficient Jx are sent to the screening module 42, and the transaction object with both the credit coefficient Xy and the transaction coefficient Jx higher than the corresponding threshold is determined as the target object;
acquiring quotations of a plurality of target objects, and sequencing the target objects by a sequencing module 44 according to the height of the quotations and generating a quotation sequence; in the order of quotation, the target object whose quotation is higher than the predicted price Dyj of power is marked by the marking module 43; in the transaction, the marked target object is preferentially transacted.
When the transaction system is used, the transaction objects are screened by the credit coefficient Xy and the transaction coefficient Jx, so that when a plurality of transaction objects exist in the transaction, the transaction value and the credit degree can be selected to be higher, the transaction process can be successfully completed, the default risk of the transaction objects is reduced, and the security in the transaction process is ensured.
Referring to fig. 1 and 2, the method of predicting the price of power trade and obtaining the predicted price Dyj of power is as follows:
continuously acquiring a plurality of power price changes from historical transaction data at fixed time intervals along a time axis, and predicting the power price changes by a three-time exponential smoothing method to generate a first predicted value Dy1; acquiring a plurality of groups of electric power price in the same period of the past year from historical transaction data, and establishing a transaction condition data set according to the climate conditions such as temperature, rainfall and the like corresponding to the electric power price; building a deep learning model by using a nerve convolution network, generating a price prediction model after training and testing a transaction condition data set, and predicting the price of the electric power transaction by using the price prediction model to generate a second predicted value Dy2;
after the first predicted value Dy1 and the second predicted value Dy2 are obtained, a power predicted value Dyj is generated according to the following formula;
wherein F is 0.ltoreq.F 1 ≤1，0≤F 2 Not more than 1, and F 1 +F 2 =1, the specific value of which is set by user adjustment; when the method is used, when the electric quantity predicted price is generated, the two different prediction models are matched, the first prediction value Dy1 and the second prediction value Dy2 are respectively generated, and then the electric power predicted price Dyj is finally generated, so that when the generated electric power predicted price Dyj is obtained, more influence factors can be integrated, the deviation between a predicted result and an actual result is smaller, and at the moment, the risk of electric power transaction can be reduced and the safety of customer transaction is ensured by providing a new price prediction method.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (10)
1. A multi-stage electric quantity transaction settlement method based on a blockchain technology is characterized in that: comprises a relevance judging unit (10), a price predicting unit (20), a data collecting unit (30), a first processing unit (40), a control unit (50), a quoting unit (60) and a collecting unit (70), wherein,
continuously acquiring a plurality of weather temperatures Tq and transaction amounts Jy of electric quantity in an electric power transaction system at fixed time intervals along a time axis when the electric quantity transaction amount is gradually increased, acquiring a correlation coefficient Rs between the weather temperatures Tq and the transaction amounts Jy by a correlation judging unit (10) through Pearson correlation analysis, and predicting an electric power transaction price by a price predicting unit (20) when the correlation coefficient Rs is larger than a corresponding correlation threshold value to acquire an electric power predicted price Dyj on an expected transaction day;
when the predicted price Dyj of the electric power exceeds a preset expected transaction price, generating a transaction price interval by the predicted price Dyj of the electric power, searching transaction objects in the transaction price interval by the data acquisition unit (30), building a transaction object library after integrating a plurality of searched transaction objects, and respectively building a transaction information set and a credit record set based on transaction information of the transaction objects in the transaction object library;
the established transaction information set and the credit record set are sent to a first processing unit (40), the first processing unit (40) respectively generates a credit coefficient Xy and a transaction coefficient Jx, and according to the values of the credit coefficient Xy and the transaction coefficient Jx, the first processing unit (40) screens transaction objects in a transaction object library;
the screened transaction objects are determined as target objects, the target objects are ordered according to the quotation of the target objects, a quotation sequence is generated and output, the quotation sequence is sent to the control unit (50), the control unit (50) generates a control instruction, the quotation unit (60) sequentially quotes the target objects according to the quotation sequence, and if the target objects accept the quotation, the collecting unit (70) collects the transaction and delivery information of the target objects accepting the quotation.
2. The blockchain technology-based multi-stage power transaction settlement method of claim 1, wherein: the system further comprises a second processing unit (80), an analysis unit (90), a selection unit (100), a record fixing unit (110) and a communication unit (120), wherein the transaction date and the transaction place of the target object electric quantity transaction are identified and acquired from the transaction and the transaction information collected by the collection unit (70), and a transaction information base is established after the transaction date and the transaction place are summarized; if the number of the target objects exceeds the number threshold, a second processing unit (80) establishes a power transmission model after training and testing; acquiring a delivery place and weather conditions in a transmission process from a delivery information base, performing simulation analysis on the power transmission process by a power transmission model, and acquiring the loss of power transportation from simulation analysis results;
obtaining the loss cost of the electric power in transaction in the transportation process by the product of the loss quantity and the corresponding quotation; acquiring power delivery cost by adding service cost in the transmission process to the loss cost; and collecting the electric power delivery costs when the electric power delivery system is in transaction with each target object, and sequencing the electric power delivery costs to acquire the delivery sequence.
3. The blockchain technology-based multi-stage power transaction settlement method of claim 2, wherein: sequentially acquiring quotations and transaction amounts of all target objects according to the delivery sequence, taking the product of the quotations and the transaction amounts as income, taking corresponding electric delivery cost as expenditure, and judging the benefits brought by the target objects by an analysis unit (90) on the basis of the income and the expenditure, wherein the benefits are taken as expected benefits; expected benefits possibly brought by the target objects are summarized, and the expected benefits are selected by a selection unit (100) and are determined as objects to be traded.
4. The blockchain technology-based multi-stage power transaction settlement method of claim 3, wherein: when the object to be transacted does not exist, the communication unit (120) sends out communication to the outside; when the object to be transacted is one, the object to be transacted is taken as a transaction object; when the objects to be transacted are more than two, acquiring the credit coefficient Xy and the transaction coefficient Jx of the objects to be transacted, and generating a value coefficient Js according to the following formula;
wherein ρ is a correlation weight, which is a correlation coefficient between the credit coefficient Xy and the transaction coefficient Jx, and C is obtained by obtaining a plurality of groups of credit coefficients Xy and transaction coefficients Jx for correlation analysis 1 The specific value of the constant correction coefficient can be set by user adjustment or generated by fitting an analysis function; taking the object to be transacted with the highest value coefficient Js as a transaction object; transaction with the transaction object, and the transaction is recorded and fixed by a recording fixing unit (110) through a blockchain.
5. The blockchain technology-based multi-stage power transaction settlement method of claim 1, wherein: the first processing unit (40) comprises an evaluation module (41), a screening module (42), a marking module (43) and a sequencing module (44), wherein the transaction information set is established as follows: according to a linear retrieval model, acquiring an electric quantity limit De of a transaction object to be transacted by a data acquisition unit (30), if the electric quantity limit De is higher than a limit threshold, taking an intermediate price in a transaction price interval as an expected price, taking the difference between the expected price and the price of the transaction object as an expected profit Qr, and after acquiring a transaction date of the transaction object, determining the interval between the transaction date and the current date to acquire a transaction interval Jg; and summarizing the electric quantity limit De, the expected profit Qr and the transaction interval Jg, and establishing a transaction information set.
6. The blockchain technology-based multi-stage power transaction settlement method of claim 5, wherein: the transaction coefficient Jx is obtained as follows: transmitting the transaction information set to an evaluation module (41), performing dimensionless processing on the electric quantity line De, the expected profit Qr and the transaction interval Jg by the evaluation module (41), and generating a transaction coefficient Jx according to the following formula;
wherein, alpha is more than or equal to 0 and less than or equal to 1, beta is more than or equal to 0 and less than or equal to 1, alpha+beta=1, alpha and beta are weights, and the specific values can be adjusted and set by a user.
7. The blockchain technology-based multi-stage power transaction settlement method of claim 3, wherein: the credit record set is established as follows: according to the linear retrieval model, a data acquisition unit (30) acquires a historical transaction record of a transaction object from the electric power transaction system, acquires the total number of violations of the transaction object and the historical total number of transactions from the historical transaction record, generates a violation ratio Wb according to the ratio of the total number of violations of the transaction object and the historical total number of transactions, and acquires total transaction amount Ze from the historical transaction record; after the identity information of the transaction object is determined, inquiring and acquiring the liability rate Zc of the transaction object, and evaluating the repayment capability of the user through the liability rate Zc;
and summarizing the default ratio Wb, the total liability Ze and the liability rate Zc, and establishing a credit record set.
8. The blockchain technology-based multi-stage power transaction settlement method of claim 6, wherein: the credit record set is sent to an evaluation module (41), and the evaluation module (41) generates a credit coefficient Xy according to the following formula after dimensionless processing is carried out on the default ratio Wb, the total sum of liabilities Ze and the asset liabilities Zc:
wherein, gamma is more than or equal to 0 and less than or equal to 1, theta is more than or equal to 0 and less than or equal to 1, and gamma+theta is more than or equal to 0.8 and less than or equal to 1.6, and gamma and theta are weights, C 2 Is a constant correction coefficient, the specific value of which can be set by user adjustment, or by analysis functionFitting and generating.
9. The blockchain technology-based multi-stage power transaction settlement method of claim 8, wherein: the generated credit coefficient Xy and transaction coefficient Jx are sent to a screening module (42), and a transaction object with the credit coefficient Xy and the transaction coefficient Jx higher than the corresponding threshold value is determined to be a target object;
acquiring quotations of a plurality of target objects, and sequencing the target objects by a sequencing module (44) according to the height of the quotations to generate a quotation sequence; in the bidding sequence, marking, by a marking module (43), a target object whose bid is higher than a predicted power price Dyj; in the transaction, the marked target object is preferentially transacted.
10. The blockchain technology-based multi-stage power transaction settlement method of claim 1, wherein: the method for predicting the price of the electric power transaction and obtaining the predicted price Dyj of the electric power is as follows: continuously acquiring a plurality of power price changes from historical transaction data at fixed time intervals along a time axis, and predicting the power price changes by a three-time exponential smoothing method to generate a first predicted value Dy1;
acquiring a plurality of groups of electric power trading prices in the same year from historical trading data, establishing a trading condition data set according to weather conditions such as temperature, rainfall and the like corresponding to the electric power trading prices, constructing a deep learning model by using a nerve convolution network, generating a price prediction model after training and testing the trading condition data set, and predicting the electric power trading prices by the price prediction model to generate a second predicted value Dy2; after the first predicted value Dy1 and the second predicted value Dy2 are obtained, a power predicted value Dyj is generated according to the following formula;
wherein F is 0.ltoreq.F 1 ≤1，0≤F 2 Not more than 1, and F 1 +F 2 =1, whose specific value is set by user adjustment.
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