Power market transaction rule optimization method and system based on smart contract

Abstract

The invention discloses an electricity market transaction rule optimization method and system based on a smart contract, and particularly relates to the technical field of electricity market transactions, and the technical key points are as follows: inputting a model training set into an initial support vector machine model to train the initial support vector machine model, and obtaining a trained support vector machine model; inputting the power market supply and demand data, the energy cost data and the influence index data at the current moment into the trained support vector machine model to obtain a market supply and demand relationship prediction result, an energy cost fluctuation prediction result and an influence adjustment direction prediction result; based on the market supply and demand relationship prediction result, the energy cost fluctuation prediction result and the influence adjustment direction prediction result, calculating a weight ratio result of a market supply and demand relationship change index, an energy cost fluctuation change index and an influence adjustment condition index by using a combined weighting method; and setting a dynamic adjustment rule according to a weight ratio result so as to optimize the power transaction information.

Description

Technical Field

[0001] The present invention relates to the technical field of power market transactions, and in particular to a method and system for optimizing power market transaction rules based on smart contracts. Background Art

[0002] At present, the technology of collaborative interaction between the electricity retail and wholesale markets is developing in all directions. Smart contracts are tools that reflect the price linkage mechanism of the electricity retail and wholesale markets. Therefore, setting dynamic adjustment rules for smart contracts here means dynamically adjusting the rules for interaction between the electricity retail and wholesale markets. In the wholesale electricity market, participants such as power generation companies and power sales companies can set transaction conditions such as price, electricity volume, transaction time, etc. through smart contracts. Smart contracts can automatically match qualified buyers and sellers. Once the conditions are met, the transaction is automatically executed without manual intervention, which improves transaction efficiency. Smart contracts can monitor price changes in the wholesale electricity market in real time and automatically adjust the retail electricity price according to preset rules. For example, when the wholesale market price rises, the smart contract increases the retail price accordingly according to a certain ratio or formula, so that the retail price can reflect the cost changes in a timely manner while maintaining the balance of supply and demand in the market.

[0003] However, there are certain defects in the automatic execution of transactions according to preset rules in the collaborative interaction between the electricity retail and wholesale markets: First, the electricity market is complex and changeable, and it is difficult for preset rules to take into account special situations such as sudden increases in electricity demand or sudden drops in power generation caused by extreme weather, and new models brought by new market participants, thereby limiting the flexibility of the market. Secondly, the security risks are great, and the automatic trading system is vulnerable to cyber attacks. Once hackers invade and tamper with the rules or data, or software and hardware fail, it may cause market chaos.

[0004] Therefore, the present invention aims to provide a method and system for optimizing electricity market transaction rules based on smart contracts to solve the above-mentioned related problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is that in the prior art, due to the complexity and variability of the electricity market, it is difficult for the preset rules to take into account special situations such as sudden increase in electricity demand or sudden drop in power generation caused by extreme weather, new models brought by new market participants, etc., thereby limiting the flexibility of the market. The purpose is to provide a method and system for optimizing electricity market transaction rules based on smart contracts. The SVM model is trained through historical electricity market supply and demand data, historical energy cost data and historical impact indicator data of the electricity trading market, so that the trained SVM model can accurately predict changes in market supply and demand relationships, energy cost fluctuations and impact feature adjustments, and can provide data basis for enterprises to optimize production plans, adjust pricing strategies, and reasonably allocate resources; at the same time, the combined weighting method is used to calculate the weight ratio of changes in market supply and demand relationships, energy cost fluctuations and policy adjustments, and dynamic adjustment rules are constructed accordingly, which can make related decisions such as electricity trading more scientific and accurate, effectively balance the interests of all parties, enhance the market's adaptability to complex environments, and ensure stable and efficient operation of the energy market.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for optimizing power market transaction rules based on smart contracts, the method comprising:

[0008] Use blockchain to record electricity transaction information in electricity retail and electricity wholesale models;

[0009] Obtain historical power market supply and demand data, historical energy cost data, and historical impact indicator data from the power trading market to build a model training set;

[0010] Input the model training set into the initial support vector machine model, and select the kernel function and model hyperparameters to train the initial support vector machine model to obtain a trained support vector machine model;

[0011] Input the current power market supply and demand data, energy cost data and impact index data into the trained support vector machine model to obtain the market supply and demand relationship prediction results, energy cost fluctuation prediction results and impact adjustment direction prediction results;

[0012] Based on the market supply and demand relationship forecast results, energy cost fluctuation forecast results and impact adjustment direction forecast results, the combined weighting method is used to calculate the weight ratio results of the market supply and demand relationship change index, energy cost fluctuation change index and impact adjustment situation index. According to the weight ratio results, dynamic adjustment rules are set to optimize the electricity trading information.

[0013] Furthermore, the electricity transaction information of the electricity retail model includes the user number, power sales company number, transaction time, transaction electricity volume, retail electricity price and transaction type; the electricity transaction information of the electricity wholesale model includes the power generation enterprise number, power sales company number, transaction time, transaction electricity volume, wholesale electricity price, transaction method and power generation cost information.

[0014] Furthermore, when the current electricity market supply and demand data, energy cost data and influencing index data are input into the trained support vector machine model to obtain the market supply and demand relationship prediction results, energy cost fluctuation prediction results and impact adjustment direction prediction results, the method also includes: setting dynamic adjustment rule trigger conditions, specifically: setting the electricity supply and demand difference threshold, setting the electricity market price fluctuation threshold and setting special time periods in the electricity market.

[0015] Furthermore, the kernel function adopts the radial basis kernel function, specifically:

[0016] K(x i ,x j ) = exp(-γ||x i -x j || 2 )

[0017] Among them, γ is the kernel parameter, which controls the width of the kernel function; x i and x j All are historical electricity market supply and demand data.

[0018] The present invention also provides a system for optimizing power market transaction rules based on smart contracts, which is used in any one of the above-mentioned methods for optimizing power market transaction rules based on smart contracts, and the system includes:

[0019] The first module is used to record electricity transaction information in the electricity retail model and the electricity wholesale model using blockchain;

[0020] The second module is used to obtain historical power market supply and demand data, historical energy cost data, and historical impact indicator data of the power trading market to build a model training set;

[0021] The third module is used to input the model training set into the initial support vector machine model, and select the kernel function and model hyperparameters to train the initial support vector machine model to obtain a trained support vector machine model;

[0022] The fourth module is used to input the current power market supply and demand data, energy cost data and impact index data into the trained support vector machine model to obtain the market supply and demand relationship prediction results, energy cost fluctuation prediction results and impact adjustment direction prediction results;

[0023] The fifth module is used to calculate the weight ratio results of the market supply and demand relationship change index, the energy cost fluctuation change index and the impact adjustment situation index based on the market supply and demand relationship forecast results, the energy cost fluctuation forecast results and the impact adjustment direction forecast results, and set dynamic adjustment rules according to the weight ratio results to optimize the power trading information.

[0024] Furthermore, the electricity transaction information of the electricity retail model includes the user number, power sales company number, transaction time, transaction electricity volume, retail electricity price and transaction type; the electricity transaction information of the electricity wholesale model includes the power generation enterprise number, power sales company number, transaction time, transaction electricity volume, wholesale electricity price, transaction method and power generation cost information.

[0025] Furthermore, when the current electricity market supply and demand data, energy cost data and influencing index data are input into the trained support vector machine model to obtain the market supply and demand relationship prediction results, energy cost fluctuation prediction results and impact adjustment direction prediction results, the method also includes: setting dynamic adjustment rule trigger conditions, specifically: setting the electricity supply and demand difference threshold, setting the electricity market price fluctuation threshold and setting special time periods in the electricity market.

[0026] The present invention also provides a computer device, comprising a system memory and a processor, wherein the system memory stores a computer program, and the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0027] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any one of the methods described above are implemented.

[0028] The present invention also provides a computer program product comprising instructions, and when the instructions are executed by a computer device cluster, the computer device cluster executes any of the above methods.

[0029] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0030] In the present invention, the SVM model is trained through the historical power market supply and demand data, historical energy cost data and historical impact index data of the power trading market, so that the trained SVM model can accurately predict the changes in market supply and demand, energy cost fluctuations and impact characteristic adjustments, and can provide data basis for enterprises to optimize production plans, adjust pricing strategies and reasonably allocate resources; at the same time, the combined weighting method is used to calculate the weight ratios of market supply and demand changes, energy cost fluctuations and policy adjustments, and dynamic adjustment rules are constructed accordingly, which can make relevant decisions such as power trading more scientific and accurate, effectively balance the interests of all parties, enhance the market's adaptability to complex environments, and ensure the stable and efficient operation of the energy market. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0032] Figure 1 This is a method flow chart of a method for optimizing power market transaction rules based on smart contracts in this embodiment;

[0033] Figure 2 This is a system module diagram of a power market transaction rule optimization system based on smart contracts in this embodiment;

[0034] Figure 3 It is a structural schematic diagram of a computer device in this embodiment. DETAILED DESCRIPTION

[0035] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0036] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0037] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0038] Example 1

[0039] See also Figure 1 As shown, this embodiment provides a method for optimizing power market transaction rules based on smart contracts, the method comprising:

[0040] S1: Using blockchain to record power transaction information in power retail mode and power wholesale mode. It should be noted that in this embodiment, the power transaction information in the power retail mode includes the user number, power sales company number, transaction time, transaction volume, retail power price and transaction type; the power transaction information in the power wholesale mode includes the power generation enterprise number, power sales company number, transaction time, transaction volume, wholesale power price, transaction method and power generation cost information;

[0041] Specifically, in this embodiment, firstly, according to the scale of power transactions, the number of participants and security requirements, etc., a suitable blockchain type is selected. For a large number of dispersed market participants, the public chain can provide a high degree of decentralization and transparency; for a specific alliance composed of power companies, regulatory agencies, etc., the alliance chain is applicable to improve transaction efficiency and privacy protection while ensuring a certain degree of decentralization; then the blockchain nodes are configured for the relevant participants of power retail and wholesale transactions, including power generation companies, power sales companies, power grid companies, regulatory agencies, etc. Each node needs to install the blockchain software and perform corresponding network settings to ensure that it can communicate with other nodes; finally, in order to ensure the authenticity and integrity of the source of the transaction data, each transaction initiator needs to use its own private key to digitally sign the transaction data. The digital signature is generated by a specific encryption algorithm, which can prove that the data is sent by a specific private key owner and has not been tampered with during the transmission process. The encryption algorithm is a conventional technical means in this field and will not be described in detail here.

[0042] S2: Obtain historical power market supply and demand data, historical energy cost data, and historical impact indicator data of the power trading market to build a model training set;

[0043] It should be noted that, in this embodiment, the historical power market supply and demand data of the power trading market are obtained specifically as follows: first, the historical power market supply and demand data are obtained from the power company database, economic statistics department, meteorological department, etc., including power supply (power generation) and demand (power consumption), price data, economic data (such as GDP growth rate, industrial production index), weather data (temperature, humidity, etc.) and time series data; then, outliers and erroneous data in the data are removed, and the obtained data are converted into feature vectors suitable for SVM model processing;

[0044] The specific steps to obtain historical energy cost data of the power trading market are as follows: first, collect energy cost data, such as price data of coal, natural gas, oil, etc., and factors affecting energy costs, including energy extraction costs (such as labor costs, equipment costs), transportation costs (such as oil prices, transportation distances), market supply and demand data (global energy supply and demand), policy factors (energy subsidies, tax policies), etc.; then clean the data to remove noise and outliers, and then normalize the data to make the numerical ranges of different features comparable;

[0045] The specific steps for obtaining historical impact indicator data of the electricity trading market are as follows: first, collect policy text data, policy impact indicator data, social opinion data, etc., among which policy text data can be obtained from the official government website, and social opinion data can be collected from news media websites and social media platforms through text mining tools; then, perform text cleaning on the policy text data, remove irrelevant punctuation and stop words, and convert the text into word vectors or document vectors, and then integrate the vector representation of the policy text, policy impact indicator data, and social opinion data into a feature vector.

[0046] S3: Input the model training set into the initial support vector machine model, and select the kernel function and model hyperparameters to train the initial support vector machine model to obtain the trained support vector machine model. It should be noted that, in this embodiment, the kernel function adopts the radial basis kernel function, specifically:

[0047] K(x i ,x j ) = exp(-γ||x i -x j || 2 )

[0048] Among them, γ is the kernel parameter, which controls the width of the kernel function and affects the complexity and generalization ability of the model; x i and x j All are historical electricity market supply and demand data;

[0049] Specifically, in this embodiment, the model hyperparameters are determined, wherein the model hyperparameters include a penalty parameter, and the penalty parameter C controls the degree of penalty for misclassification of training samples. The larger the C, the higher the degree of fit of the model to the training data, but it may lead to overfitting. A cross-validation method, such as k-fold cross-validation (dividing the data set into k subsets, training with k-1 subsets each time, and validating with 1 subset) can be used to select the best hyperparameter combination;

[0050] The SVM model is trained through the model training set. According to the selected kernel function and hyperparameters, the optimal decision boundary is found through the optimization algorithm, so that the model can accurately classify or regress the training data and minimize the error between the predicted value and the actual value.

[0051] S4: Input the current power market supply and demand data, energy cost data and impact index data into the trained support vector machine model to obtain the market supply and demand relationship prediction results, energy cost fluctuation prediction results and impact adjustment direction prediction results;

[0052] It should be noted that, in the present embodiment, when the set dynamic adjustment rule triggering conditions are triggered, step S4 is executed, wherein the dynamic adjustment rule triggering conditions include the power supply and demand difference threshold, the power market price fluctuation threshold and the power market special time period; for example, when the power supply and demand difference exceeds the set power supply and demand difference threshold (including the power supply and demand difference upper limit (oversupply) or lower limit (undersupply)), when the power market price rises or falls by more than a certain proportion in a short period of time, or before and after special time periods such as peak power consumption periods and trough power consumption periods.

[0053] S5: Based on the market supply and demand relationship forecast results, energy cost fluctuation forecast results and impact adjustment direction forecast results, the combined weighting method is used to calculate the weight ratio results of the market supply and demand relationship change index, energy cost fluctuation change index and impact adjustment situation index. According to the weight ratio results, dynamic adjustment rules are set to optimize the power trading information.

[0054] It should be noted that, in this embodiment, the market supply and demand relationship change indicators include the power supply and demand difference, the demand elasticity coefficient and the supply elasticity coefficient. The power supply and demand difference reflects the basic supply and demand balance of the market. The demand elasticity coefficient measures the sensitivity of power demand to changes in price, economic factors, etc. The supply elasticity coefficient reflects the supply adjustment ability of power generation enterprises to market changes; energy cost fluctuation change indicators include the energy raw material price change rate, the transportation cost change rate and the equipment maintenance and renewal cost change rate. The energy raw material price change rate directly affects the power generation cost, the transportation cost change rate considers the cost fluctuation of the energy transportation link, and the equipment maintenance and renewal cost change rate involves the fixed cost change of the power generation enterprise; the impact adjustment indicators include the policy release frequency, policy intensity change and policy orientation change. The policy release frequency indicates that the introduction of new policies may have a significant impact on the market. The policy intensity change measures the degree of incentive or constraint of the policy on market entities. The policy orientation change reflects the impact of the policy direction on the market structure.

[0055] In order to eliminate the influence of different indicator dimensions, the data is standardized. The formula is:

[0056]

[0057] in: is the jth index value of the ith sample, is the mean value of the jth index, s j is the standard deviation of the jth indicator;

[0058] The analytic hierarchy process is used to obtain the subjective weight of each indicator, specifically: setting up a target layer and a criterion layer. The target layer is "comprehensive consideration of dynamic adjustment rules", and the criterion layer includes "changes in market supply and demand", "fluctuations in energy costs" and "policy adjustments". Each criterion layer contains corresponding indicators.

[0059] Then, we invite power industry experts, market analysts, etc. to compare the relative importance of each factor in the criterion layer based on their experience and professional knowledge, and construct a judgment matrix. For example, for the criterion layer, experts believe that changes in market supply and demand are slightly more important than changes in energy cost fluctuations, and changes in energy cost fluctuations are slightly more important than policy adjustments. We use the 1-9 scaling method to construct a judgment matrix; then, by solving the characteristic roots and eigenvectors of the judgment matrix, we obtain the subjective weights of each factor. For example, for the criterion layer, the subjective weights of changes in market supply and demand are calculated. Subjective weighting of energy cost fluctuations and subjective weight of policy adjustments

[0060] Then the entropy value of each indicator is calculated by the method of power, and the objective weight of each indicator of the change in market supply and demand relationship is obtained. Objective weights of various indicators of energy cost fluctuations Objective weights of various indicators of policy adjustments The calculation formula of entropy value is:

[0061]

[0062] e j is the entropy value of the jth indicator; k is the coefficient; p ij is the proportion of each index of the i-th index under the j-th index; is the weight of the jth indicator in the change of market supply and demand relationship.

[0063] Then, the combination coefficient is determined according to the importance of the subjective weighting method and the objective weighting method. In this embodiment, the combination coefficient of the subjective weighting method is α=0.6, and the combination coefficient of the objective weighting method is β=0.4. The combination weights of each indicator are obtained by combining the combination coefficients, which are specifically:

[0064] Combination weights of market supply and demand change indicators:

[0065] Combined weights of energy cost fluctuation indicators:

[0066] The combined weights of the indicators that affect the adjustment situation are:

[0067] At the same time, the preset adjustment rules are as follows: if the weight of the change in market supply and demand is the highest and the difference in power supply and demand is positive, the electricity price will be reduced according to the weight and the degree of the difference in power supply and demand. For example, if the weight of the change in market supply and demand is w1 = 0.4, the difference in power supply and demand is 10% of the total power generation capacity, the adjustment range of the electricity price is Δp1 = -0.4 × 0.1 × k1, where k1 is the price adjustment coefficient.

[0068] If the weight of energy cost fluctuation is high, and the increase in energy raw material prices leads to an increase in power generation costs, the electricity price will be increased. Assuming that the energy cost fluctuation weight w2 = 0.35, the energy raw material price has increased by 15%, and the electricity price adjustment range is Δp2 = 0.35 × 0.15 × k2, k2 is the cost transmission coefficient.

[0069] If the policy adjustment weight is high, and the policy orientation is to encourage the consumption of renewable energy, the grid-connected electricity price will be reduced for renewable energy power generation companies, and the electricity price for traditional energy power generation companies will be appropriately increased. Assume that the policy adjustment weight w3 = 0.25, and determine the specific electricity price adjustment range Δp3 based on the policy strength, such as the subsidy adjustment range;

[0070] When the weight of the change in the market supply and demand relationship is prominent, and the demand elasticity coefficient shows that the demand is insensitive to price and the supply elasticity coefficient is low, the trading power limit is tightened in the case of insufficient supply. For example, if the weight of the change in the market supply and demand relationship w1 = 0.45, the supply gap is 5% of the total demand, and the adjustment range of the trading power limit is Δq1 = -0.45 × 0.05 × k3, where k3 is the power adjustment coefficient. For energy cost fluctuations, if the increase in energy costs leads to a decrease in the willingness of power generation companies to generate electricity, the trading power limit is appropriately reduced according to the weight and the increase in costs. Assuming that the weight of the energy cost fluctuation change w2 = 0.3, the energy cost has increased by 20%, and the power generation company has stated that it will reduce the power generation plan by 10%, the trading power limit adjustment range is Δq2 = -0.3 × 0.1 × k4, where k4 is the response coefficient of the power generation company. If the weight of the policy adjustment situation is large, and the policy requires the guarantee of electricity consumption in key industries, the trading power limit of key industry users will be increased accordingly, and the trading power limit of non-key industries will be reduced. Assume that the policy adjustment weight w3 = 0.25, and determine the specific transaction power limit adjustment range Δq3 based on the policy priority classification of different industries.

[0071] Example 2

[0072] See also Figure 2 As shown, this embodiment also provides a system for optimizing power market transaction rules based on smart contracts, which is used in any one of the above-mentioned methods for optimizing power market transaction rules based on smart contracts, and the system includes:

[0073] The first module 100 is used to record power transaction information of power retail mode and power wholesale mode by using blockchain;

[0074] The second module 200 is used to obtain historical power market supply and demand data, historical energy cost data and historical impact indicator data of the power trading market to build a model training set;

[0075] The third module 300 is used to input the model training set into the initial support vector machine model, and select the kernel function and model hyperparameters to train the initial support vector machine model to obtain a trained support vector machine model;

[0076] The fourth module 400 is used to input the current power market supply and demand data, energy cost data and impact index data into the trained support vector machine model to obtain the market supply and demand relationship prediction results, energy cost fluctuation prediction results and impact adjustment direction prediction results;

[0077] The fifth module 500 is used to calculate the weight ratio results of the market supply and demand relationship change index, the energy cost fluctuation change index and the impact adjustment situation index based on the market supply and demand relationship prediction results, the energy cost fluctuation prediction results and the impact adjustment direction prediction results, and set dynamic adjustment rules according to the weight ratio results to optimize the power trading information.

[0078] Furthermore, the electricity transaction information of the electricity retail model includes the user number, power sales company number, transaction time, transaction electricity volume, retail electricity price and transaction type; the electricity transaction information of the electricity wholesale model includes the power generation enterprise number, power sales company number, transaction time, transaction electricity volume, wholesale electricity price, transaction method and power generation cost information.

[0079] Furthermore, when the current electricity market supply and demand data, energy cost data and influencing index data are input into the trained support vector machine model to obtain the market supply and demand relationship prediction results, energy cost fluctuation prediction results and impact adjustment direction prediction results, the method also includes: setting dynamic adjustment rule trigger conditions, specifically: setting the electricity supply and demand difference threshold, setting the electricity market price fluctuation threshold and setting special time periods in the electricity market.

[0080] It should be noted that the modules in the system of Example 2 correspond to the steps in the method of Example 1. The steps in the method of Example 1 have been described in detail in Example 1. In this Example 2, the contents of the modules in the system will not be described in detail.

[0081] Example 3

[0082] See also Figure 3 As shown, this embodiment further provides a computer device, including a system memory 1005 and a processor 1001, wherein the system memory 1005 stores a computer program, and the processor 1001 implements the steps of any one of the above methods when executing the computer program.

[0083] It should be noted that the processor 1001 is used to execute the steps in the above method embodiments according to the instructions in the program code. Alternatively, the processor 1001 implements the functions of each module / unit in the above system / device embodiments when executing the computer program.

[0084] Specifically, in this embodiment, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the system memory 1005, and are executed by the processor 1001 to complete the present application. One or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0085] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor 1001 and a system memory 1005. Those skilled in the art will appreciate that this does not constitute a limitation on the terminal device, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include an input / output device 1003, a network access device 1002, a bus 1006, etc.

[0086] The processor 1001 may be a central processing unit (CPU), or other general-purpose processors 1001, digital signal processors 1001 (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor 1001 may be a microprocessor or any conventional processor, etc.

[0087] The system memory 1005 may be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. The system memory 1005 may also be a storage device 1004 of the terminal device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the terminal device. Further, the system memory 1005 may also include both the internal storage unit of the terminal device and the storage device 1004. The system memory 1005 is used to store computer programs and other programs and data required by the terminal device. The system memory 1005 may also be used to temporarily store data that has been output or is to be output.

[0088] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0089] Example 4

[0090] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0091] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, system or device, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk. Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM), registers, hard disks, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above, or any other form of computer-readable storage medium known in the art.

[0092] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (ASIC). In an embodiment of the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system, or device.

[0093] Example 5

[0094] This embodiment also provides a computer program product including instructions. When the instructions are executed by a computer device cluster, the computer device cluster executes the method described in Embodiment 1.

[0095] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing power market transaction rules based on smart contracts, characterized in that: Methods include: Use blockchain to record electricity transaction information in electricity retail and electricity wholesale models; Obtain historical power market supply and demand data, historical energy cost data, and historical impact indicator data from the power trading market to build a model training set; Input the model training set into the initial support vector machine model, and select the kernel function and model hyperparameters to train the initial support vector machine model to obtain a trained support vector machine model; Input the current power market supply and demand data, energy cost data and impact index data into the trained support vector machine model to obtain the market supply and demand relationship prediction results, energy cost fluctuation prediction results and impact adjustment direction prediction results; Based on the market supply and demand relationship forecast results, energy cost fluctuation forecast results and impact adjustment direction forecast results, the combined weighting method is used to calculate the weight ratio results of the market supply and demand relationship change index, energy cost fluctuation change index and impact adjustment situation index. According to the weight ratio results, dynamic adjustment rules are set to optimize the electricity trading information.

2. A method for optimizing trading rules in a power trading market according to claim 1, characterized in that: The electricity transaction information of the electricity retail model includes the user number, power sales company number, transaction time, transaction volume, retail electricity price and transaction type; the electricity transaction information of the electricity wholesale model includes the power generation enterprise number, power sales company number, transaction time, transaction volume, wholesale electricity price, transaction method and power generation cost information.

3. A method for optimizing trading rules in a power trading market according to claim 1, characterized in that: The current electricity market supply and demand data, energy cost data and influencing index data are input into the trained support vector machine model to obtain the market supply and demand relationship prediction results, energy cost fluctuation prediction results and impact adjustment direction prediction results. The method also includes: setting dynamic adjustment rule trigger conditions, specifically: setting an electricity supply and demand difference threshold, setting an electricity market price fluctuation threshold and setting a special time period for the electricity market.

4. A method for optimizing trading rules in a power trading market according to claim 1, characterized in that: The kernel function uses the radial basis kernel function, which is: K(x i ,x j )=exp(-γ||x i -x j || 2 ) Among them, γ is the kernel parameter, which controls the width of the kernel function; x i and x j All are historical electricity market supply and demand data.

5. A power market transaction rule optimization system based on smart contracts, characterized in that: The system is used in a method for optimizing power market transaction rules based on smart contracts as described in any one of claims 1 to 4, and the system comprises: The first module is used to record electricity transaction information in the electricity retail model and the electricity wholesale model using blockchain; The second module is used to obtain historical power market supply and demand data, historical energy cost data, and historical impact indicator data of the power trading market to build a model training set; The third module is used to input the model training set into the initial support vector machine model, and select the kernel function and model hyperparameters to train the initial support vector machine model to obtain a trained support vector machine model; The fourth module is used to input the current power market supply and demand data, energy cost data and impact index data into the trained support vector machine model to obtain the market supply and demand relationship prediction results, energy cost fluctuation prediction results and impact adjustment direction prediction results; The fifth module is used to calculate the weight ratio results of the market supply and demand relationship change index, the energy cost fluctuation change index and the impact adjustment situation index based on the market supply and demand relationship forecast results, the energy cost fluctuation forecast results and the impact adjustment direction forecast results, and set dynamic adjustment rules according to the weight ratio results to optimize the power trading information.

6. A method for optimizing trading rules in a power trading market according to claim 5, characterized in that: The electricity transaction information of the electricity retail model includes the user number, power sales company number, transaction time, transaction volume, retail electricity price and transaction type; the electricity transaction information of the electricity wholesale model includes the power generation enterprise number, power sales company number, transaction time, transaction volume, wholesale electricity price, transaction method and power generation cost information.

7. A method for optimizing trading rules in a power trading market according to claim 5, characterized in that: The current electricity market supply and demand data, energy cost data and influencing index data are input into the trained support vector machine model to obtain the market supply and demand relationship prediction results, energy cost fluctuation prediction results and impact adjustment direction prediction results. The method also includes: setting dynamic adjustment rule trigger conditions, specifically: setting an electricity supply and demand difference threshold, setting an electricity market price fluctuation threshold and setting a special time period for the electricity market.

8. A computer device comprising a system memory and a processor, wherein the system memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer program product comprising instructions, characterized in that When the instructions are executed by a computer device cluster, the computer device cluster executes the method according to any one of claims 1 to 4.

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