215 results about "Transaction price" patented technology

The invention relates to the technical field of electric power system risk assessment, and discloses a risk assessment system based on an electric power transaction market, which realizes real-time acquisition and dynamic processing of electric power transaction price data through a price data real-time acquisition module in combination with a real-time deviation analysis module, calculates a price deviation value in real time and captures related influence factors, thereby realizing risk assessment of the electric power transaction market. The dynamic parameter adjustment module can establish a correlation model, automatically trigger parameter adjustment when real-time factor fluctuation exceeds a threshold value, and realize automatic updating of computational logic by combining scene adaptation and emergency adjustment subunits, so as to ensure that a price deviation value accurately reflects an actual risk; the risk assessment module combines dynamic parameters to construct a model, calculates risk indexes and divides grades, the risk early warning module generates early warning information in time, and the risk conduction analysis and pressure test unit provides comprehensive risk pre-judgment, assists market subjects to scientifically make plans, and maintains the stability of the power transaction market.

The invention provides an identification early warning and law prompting method based on big data, and the method comprises the steps: collecting the multi-dimensional behavior data of a user in real time, including identity features, historical transaction prices and real-time environment variables; performing space-time association processing on the data to generate a space-time aligned feature tensor; on the basis of user price sensitivity grading, differential weighted fusion is carried out on the feature tensors, and a classification feature matrix is generated; a dynamic deviation index is calculated, a price rationality comprehensive score is generated, and early warning is triggered by a low score; inputting a legal clause matching engine to generate an early warning report; and executing a multi-level intervention strategy according to the early warning level. The method can help a user identify potential price differences, provides technical support for a related platform, and enables the user to find and process price abnormity behaviors more accurately.

The invention discloses an intelligent supply and demand recommendation matching system based on scientific and technological achievement conversion, and relates to the technical field of resource services, the system comprises an information input module, an information management module, a supply and demand matching module, a pricing prediction module and a conversion recommendation module, user input information is subjected to distributed storage and classified labeling, and a knowledge graph is constructed; after preliminary supply and demand matching is carried out, semantic path reasoning is used for calculating the matching degree and sorting is carried out, a reinforcement learning model is adopted, the transaction price is dynamically predicted based on the transaction frequency, the industry permeability and the technical life cycle, based on a multi-factor evaluation algorithm, the matching degree, the transaction price satisfaction degree and the historical transaction rate are integrated, and a personalized recommendation list is generated; the problems that in the existing scientific and technological achievement conversion process, information of a recommendation matching system is scattered, opaque and single in evaluation dimension, and deep semantic association and user demand diversity are neglected are solved, and accurate and efficient matching services are provided for a scientific and technological achievement provider and a technical demander.

The invention discloses a virtual item processing method and device based on a block chain, equipment and a storage medium, and relates to the technical field of block chains. The method comprises the following steps: receiving a virtual item transaction request sent by a client, wherein the virtual item transaction request comprises a request transaction user of a virtual item, transaction item information, a transaction target user, transaction price information and transaction tamper-proof verification information; respectively verifying the transaction requesting user, the transaction prop information, the transaction target user, the transaction price information and the transaction tamper-proof verification information to obtain a transaction user verification result, a transaction prop verification result, a transaction price verification result and a transaction data consistency verification result; and combining the plurality of verification results to obtain a transaction request verification result, and writing the virtual item transaction request into the block chain when the transaction request verification result is that the verification is passed. According to the scheme, the virtual item transaction request is written into the block chain, so that the security of virtual item transaction is enhanced.

The invention provides a distributed power battery energy transaction and scheduling method and system for a smart power grid, and relates to the technical field of smart power grids, and the method comprises the steps: collecting load distribution data and battery state information, and dividing transaction groups; the method comprises the following steps: constructing a federated learning network, calculating a credit evaluation score, setting a differentiated transaction quota, constructing a time-space association diagram, outputting an optimal transaction price interval, generating a transaction matching strategy, optimally calculating a charging and discharging scheduling instruction and executing transaction settlement, so that efficient transaction and scheduling of power battery energy are realized, and the operation efficiency of a power grid is improved; and the scheduling cost is reduced.

A facility for estimating the value of a distinguished home is described. The facility trains a forest of decision trees to estimate valuations for homes within the geographic area where the distinguished home is located using data including both previous home sale transaction prices and synthetic sale transaction prices based on listing prices. The facility accesses information about the distinguished home's attributes and applies each decision tree in the forest to that information, generating a number of estimated valuations. The facility determines an overall valuation for the distinguished home based on the valuations generated by the decision trees.

The invention discloses a multi-virtual power plant energy carbon transaction price determination method and system based on a master-slave game. The method comprises the following steps: determining a master-slave game framework between a power distribution system operator and a plurality of virtual power plants; based on a master-slave game framework, a double-layer optimization model of energy and carbon sharing of the multiple virtual power plants is established, the upper layer of the double-layer optimization model is a dynamic pricing model with the purpose of maximizing the income of a power distribution system operator, and the lower layer of the double-layer optimization model is an energy management model with the purpose of minimizing the operation cost of the virtual power plants; and establishing a double-layer Kriging meta-model taking the transaction electricity price and the carbon price as inputs and the transaction electricity quantity and the carbon quota quantity as outputs, fitting and replacing the energy management model of the double-layer optimization model by adopting the double-layer Kriging meta-model, and solving to obtain a global optimal solution. According to the method, sharing and circulation of energy and carbon quotas among virtual power plants are effectively promoted, the economic and environmental benefits are improved, and the privacy security of each subject can be protected.

The invention provides a transaction and scheduling optimization method and system for a virtual power plant participating in a spot market, relates to the technical field of energy systems, and solves the problem of electricity purchase cost minimization of the virtual power plant participating in spot transaction and the problem of how to report and regulate adjustable resources in the virtual power plant containing multiple distributed energy main bodies. The method comprises the following steps: acquiring first data and second data; according to the spot transaction price, the static data, the first data and the second data, constructing an optimization target for minimizing the overall power purchase cost of the virtual power plant, and setting a comprehensive power constraint condition and an energy storage system constraint condition; and solving through a linear programming solver to obtain an optimal spot declaration strategy and a regulation and control strategy of corresponding equipment. The method is used in the virtual power plant transaction and scheduling optimization process, the declaration strategy of the electric power spot market is considered, the electric quantity of the real-time market is adjusted through adjustable resources such as adjustable equipment and energy storage equipment on the user side, and the electricity purchase cost can be reduced and the marketization risk can be controlled.

The invention relates to an optimal control method and system for an energy storage system to participate in a medium-and-long-term and day-ahead electricity market, and a medium. The method comprises the steps of collecting historical load and historical spot transaction price of a user and local medium-and-long-term transaction time-of-use electricity price data; predicting the hourly load of the user in the future day and the local day-ahead power spot transaction price; constructing an energy storage model based on the energy storage physical property parameters; maximized energy storage system income is set as an optimization target, and constraint conditions are set according to battery capacity, efficiency, transformer capacity and the like; a genetic algorithm is adopted to obtain an energy storage system charging and discharging strategy, including energy storage charging and discharging power at each moment and a market participation mode, for 24 hours in the next day. The method can comprehensively consider the medium and long term transaction time-of-use electricity price, the day-ahead market price and the user load characteristics, and maximizes the economic benefit while meeting the electricity demand of the user.

The embodiment of the invention provides a pollution discharge right transaction intelligent matching method and system and electronic equipment. According to the technical scheme provided by the embodiment of the invention, the supply-demand relationship model corresponding to the pollution discharge right index is utilized to predict the transaction price interval of the target pollution discharge right index for user reference; the invention further provides a transaction matching algorithm for matching at least one transaction object suitable for matching for the user, so that the pollution discharge right transaction is changed into an efficient matching mode from a listing or registration long-period searching and waiting mode. In addition, at least one transaction matching scheme is generated for the user, the user transaction target is higher, the transaction period is shortened, the efficiency is high, and the transaction rate of the discharge right transaction can be improved. Visibly, according to the scheme provided by the embodiment of the invention, the intelligent degree of the pollution discharge right transaction platform is high, the user can be assisted to make a transaction decision, and the method has a positive effect on maximizing the value of the pollution discharge right on the premise of meeting the duration demand.

The invention relates to the technical field of bulk commodities, in particular to a bulk commodity transaction price prediction method, which comprises the following steps: acquiring multi-modal data of bulk commodities in the current period; the multi-modal data comprises meteorological data, macroeconomic index quantized values and news event analysis parameters processed by natural languages; the news event analysis parameters comprise market emotion indexes and policy change influence factors; performing data preprocessing on the multi-modal data of the bulk commodities in the current period to obtain preprocessed multi-modal data; inputting the preprocessed multi-modal data into a trained price prediction model to obtain a final prediction price; the price prediction model is a hybrid neural network model fused with an LSTM-Transformer (LSTM-Transformer). According to the method, the accuracy of bulk commodity price prediction is improved, and the calculation efficiency is high.

The invention relates to the technical field of comprehensive energy, and provides a source-load-price multi-task medium and long term prediction method and system based on small sample learning. The method comprises the steps that a target data set and a source domain big data set in a short period are acquired, and each of the target data set and the source domain big data set comprises transaction electric quantity, new energy generating capacity, electric power transaction price and weather data; and training a prediction model by using the source domain big data set, migrating the trained prediction model to the target data set for secondary training, and predicting the enhanced target data set by using the secondarily trained prediction model to obtain a medium and long term prediction result of the source load price. In order to solve the problem of small power transaction data volume, transfer learning and data enhancement are utilized, and in combination with transaction data of other power markets, medium and long term prediction of the source charge price of the target power market is realized.

The invention relates to the technical field of power system power purchase decision making, in particular to an agent quotation method and device in an out-of-area power purchase auxiliary decision making and a medium, and the method comprises the steps: setting a transaction scene and a declaration strategy, and determining a sending end and receiving end agent modeling range and an output data item; acquiring historical data according to the modeling range and the output data item, and preprocessing the historical data; performing agent modeling by using an actor commentator algorithm RNN-Actor-Critic improved on the basis of a recurrent neural network, and simulating a transaction by using an agent on the basis of a transaction scene; according to the change trend of agent boundary data, prediction boundary data of a transaction target time period is calculated, then an extreme gradient is utilized to promote an Xgboost model, and the predicted boundary data is input to calculate an expected transaction price; and calculating the declaration price based on the expected transaction price, and finally outputting the predicted transaction price, the predicted boundary data, the prediction model and the predicted declaration price.

The invention relates to an optimal scheduling method and system for participation of actual energy storage and virtual energy storage in a power market, and a medium. The method comprises the steps of collecting user historical load, historical spot transaction price and local medium and long term transaction time-of-use electricity price data; predicting the user total load, the virtual energy storage capacity and the local day-ahead electric power spot transaction price in the next day; constructing a hybrid energy storage model based on the actual energy storage physical property parameters; the minimum user operation cost is set as an optimization target, and constraint conditions are set according to the actual energy storage capacity, the virtual energy storage capacity, the battery charging and discharging efficiency, the transformer capacity and the like; and a genetic algorithm is adopted to obtain a 24-hour actual energy storage and virtual energy storage collaborative scheduling strategy in the next day. According to the method, through accurate load, schedulable capacity and price prediction, the optimization algorithm is adopted to carry out collaborative optimization of actual energy storage and virtual energy storage, and the economic benefit and flexibility of the whole system are remarkably improved.

The invention provides a carbon transaction method and system for the transportation industry, and the method comprises the following steps: dividing the transportation modes of the industry into land transportation, air transportation and waterway transportation according to the comprehensiveness of the transportation industry; respectively carrying out carbon emission accounting on the three types of transportation modes; summarizing land transportation, air transportation and waterway transportation to calculate the total carbon emission amount of the enterprise; a carbon emission accounting model is constructed, and the total carbon emission amount of each enterprise is calculated; each enterprise allocates the carbon quota according to the allocation proportion; carbon quota transaction: dynamically collecting demand and supply conditions of the buyer and the seller by adopting a principle of combining bilateral auction and dynamic pricing, and jointly determining a final transaction price by the buyer and the seller; identity verification of the buyer and the seller is completed in the transaction process, the transaction is executed through an intelligent contract, and carbon quota transfer and fund payment are achieved. And recording the transaction data in a database in real time. According to the invention, a more accurate, transparent and sustainable operation mechanism is constructed for carbon transaction in the transportation industry.

The invention discloses an agricultural product transaction data processing method and device based on multi-index linkage analysis, and the method comprises the steps: obtaining a plurality of transaction data of a target agricultural product transaction, and the transaction data comprises the foreign index value, transaction volume and transaction price of the target agricultural product; processing the transaction data based on a pre-constructed price expectation model, and giving an expectation transaction price corresponding to the transaction data; matching a corresponding data processing model by analyzing an expected transaction price; and processing the transaction data by combining the expected transaction price and based on the data processing model. According to the agricultural product transaction data analysis method, the transaction data are analyzed and processed through the analysis processes of price expectation and data processing in sequence, and the rational transaction information and the irrational transaction information in the transaction data are extracted, so that the accuracy of agricultural product transaction data analysis is improved.

The invention provides a data transaction method and device, equipment, a medium and a program product, relates to the technical field of block chains, and aims to solve the problem of relatively poor efficiency of data transaction in related technologies. The method comprises the following steps: based on data demand information, creating a demand task pool by using an intelligent contract, and broadcasting the data demand information to each node in a block chain system; receiving at least one data abstract and guarantee fund submitted by the at least one data owner node through the smart contract, and writing the at least one data abstract into a demand task pool; under the condition that the first data demander node determines m first data owner nodes, price negotiation is carried out based on m price parameters submitted by the m first data owner nodes and the smart contract, and a transaction result is determined; and under the condition that the first data demander node pays the resource corresponding to the transaction price, distributing the first target data to the first data demander node. According to the invention, the data transaction efficiency can be improved.

The invention provides a government purchase commodity transaction price prediction method and device based on a CMPSOLSTMTLM model, and belongs to the technical field of data analysis. The method comprises the following steps: acquiring purchase price historical data of the same commodity in an administration and acquisition contract, preprocessing and normalizing the purchase price historical data, and dividing the purchase price historical data into a training set and a test set; constructing a government purchase commodity transaction price prediction model; adopting an improved algorithm CMPSO based on particle swarm optimization to carry out parameter optimization on the government purchase commodity transaction price prediction model; and segmenting data of the test set, inputting the segmented data into the trained government purchase commodity transaction price prediction model for prediction, and carrying out reverse normalization on an output result to obtain a commodity price prediction result. According to the method, the advantages of the LSTM and the Transform model are combined, and the particle swarm optimization algorithm is used for parameter adjustment and optimization, so that the price prediction precision and efficiency are improved.

The invention provides an electricity market bidding strategy optimization method, system and device and a medium, and belongs to the technical field of electricity market transaction. The method comprises the following steps: acquiring public data of a power market, performing structured processing, extracting key information, and generating power market data; establishing a data optimization prediction model, and predicting the electricity price, the power demand load, the supply load, the enterprise power consumption cost and the power consumption at the future moment based on the power market data; receiving and configuring risk preference parameters input by a user, and generating an enterprise feature configuration file; sequentially calculating a market supply and demand influence factor, a real-time transaction price deviation degree, a reference income fluctuation value and a risk constraint term, and generating a final premium proportion based on a calculation result; on the basis of the final premium proportion, determining a bidding price at a future moment, constructing an objective function to optimize the bidding cost, solving an optimal premium proportion sequence and generating a bidding plan; and monitoring the income fluctuation rate in real time, triggering a feedback mechanism, and updating data.

The invention discloses a connection method suitable for green electricity transaction and an electric energy market, and relates to the technical field of green electricity transaction, and the method specifically comprises the steps: 1, determining a green electricity transaction point, and carrying out the real-time monitoring of green electricity and electric energy transaction data in the green electricity transaction point; step 2, determining a supply-demand relationship balance index of the electric energy market, determining an electric energy basic price and an electric energy price error interval based on the supply-demand relationship balance index of the electric energy market, and determining a theoretical electric energy price formula by integrating the electric energy basic price and the supply-demand relationship balance index of the electric energy market; step 2, determining a supply-demand relationship balance index of green electricity transaction, and determining a unit green electricity average transaction price formula by adding a green electricity added value based on an electric energy basic price and a theoretical electric energy price formula, and step 4, constructing a green electricity transaction platform of a green electricity transaction point, and providing a transaction service for a green electricity power supplier and an electricity purchaser.

The invention relates to an interval value carbon price prediction method based on multi-source data fusion and deep learning integration, and the method comprises the following steps: obtaining the actual historical transaction data of a carbon market, collecting the maximum price and the minimum price of the daily actual transaction of the carbon market, and determining an interval value carbon price sequence; key external driving factors influencing the carbon transaction price are screened through feature importance analysis, and data alignment is conducted on the key external driving factors of different market transaction days through a cubic spline interpolation method; cEEMDAN-VMD dual decomposition is adopted, and higher-precision multi-scale characterization of an original interval value carbon price sequence is achieved; and by taking the decomposition result and the determined external key driving factor as input and the carbon price prediction interval as output, constructing a carbon price interval prediction model, and finally outputting to obtain the carbon price prediction interval containing upper and lower boundary values. The method can effectively cope with the non-stationary and non-linear characteristics of the carbon valence sequence, and is good in expansibility and adaptability.

The invention belongs to the technical field of virtual power plants, and particularly relates to a virtual power plant day-ahead strategy optimization method in a spot mode, which comprises the following steps of: performing correlation analysis and feature extraction on Spearman time-phased medium and long-term markets, spot prices and market boundary information; a time-phased spot market price prediction model is formed, and time-phased spot market prices are predicted; adopting a bidding space linear regression method to carry out time-phased medium-and-long-term market transaction price prediction; a difference algorithm is adopted to establish a virtual power plant electric power spot market price declaration strategy optimization model; and generating a virtual power plant spot market declaration strategy. According to the method, the correlation between the market boundary factor-based time-sharing spot price and the medium and long term transaction price is analyzed, the market boundary data is utilized to predict the future time-sharing thermal power load rate mean value, and the future time-sharing thermal power load rate mean value is input into the pre-trained model, so that the accurate prediction of the time-sharing average price of the electric power spot market is realized.

The invention discloses a power transaction auxiliary decision processing method and system based on extreme weather, and relates to the technical field of power transaction decision, and the method comprises the steps: obtaining an extreme weather early warning message to form an original weather early warning data set; performing space-time reference alignment on the data set to generate a grid chart containing a meteorological intensity matrix and a power generation facility distribution index; mapping the meteorological intensity matrix to obtain a meteorological sensitivity coefficient matrix, and carrying out topological association on power generation facility distribution indexes to obtain a power generation side asset vulnerability association table; inputting the two into a risk conduction calculation engine for coupling deduction to generate a power supply-demand imbalance risk thermodynamic diagram; constructing a transaction price elasticity prediction model based on the thermodynamic diagram, and forming a transaction strategy reference matrix; quotation interval suggestions are generated through matching of the decision rule base and are converted into standardized declaration instruction streams to be pushed to the transaction interface end. According to the method, the extreme weather information and the power transaction decision can be accurately joined, and the accuracy and the normalization of the transaction decision are improved.

A real estate transaction system that facilitates integrated real estate transactions by enabling a seller to list a property at a specified price with negotiation parameters, and allowing a buyer to submit offers for those properties. The system supports direct online negotiations where buyers and sellers can place offers and counteroffers in real time. A matching module optimally matches buyers and sellers with real estate agents based on property information, user preferences, agent performance ratings, and geographic location. The system includes a user interface module providing a graphical interface for users to interact with the transaction and matching modules. A data analysis module accesses and analyzes real estate transaction price data from government sources to generate price trend analyses that assist in accurate pricing.

The invention discloses a source-load two-way interactive transaction method and system based on electricity-carbon real-time coupling, and belongs to the field of data processing and energy management. The method comprises the steps of real-time carbon flow accounting and account management, source-load matching, electricity-carbon coupling bargaining, dynamic contract generation, performance accounting and real-time settlement adjustment. The method comprises the following steps: firstly, acquiring real-time marginal carbon emission factors and electric energy metering data, and calculating and updating real-time carbon flow values and accounts of participants; and constructing an electricity-carbon coupling bargaining model for the source-load transaction pair, negotiating to generate an electricity-carbon coupling transaction price, and generating a dynamic contract comprising a price-response function and a carbon flow-penalty reward function according to the electricity-carbon coupling transaction price. And after the contract is executed, settlement is carried out according to the actual carbon flow deviation. According to the method, a dual Nash bargaining mechanism and a dynamic interoperation contract are adopted, real-time carbon flow contribution is taken as core excitation, multi-subject interest conflict and transaction uncertainty can be effectively solved, and low carbon, efficiency and performance robustness of source-load interactive transaction are remarkably improved.

The invention relates to the technical field of data processing, in particular to a transaction data processing method of a gold recovery intelligent terminal. The method comprises the following steps: performing quality detection on to-be-recycled gold of a user through a gold recycling intelligent terminal to obtain quality data of the to-be-recycled gold of the user; acquiring a real-time gold transaction price; performing price prediction on gold to be recycled of the user based on the real-time gold transaction price by utilizing a machine learning method, generating a gold recycling estimated price, displaying the gold recycling estimated price on an intelligent terminal page, and generating a gold recycling estimated price interface; the payment mode of the user is confirmed according to the gold recovery predicted price interface, user payment mode data are generated, and the user payment mode data comprise the first payment mode data of the user and the second payment mode data of the user. According to the invention, through real-time price prediction, multiple payment modes, identity verification and secondary quality detection, the transaction efficiency and security are improved.

The invention relates to the technical field of transaction data processing, in particular to a transnational energy transaction risk assessment method and system, and the method comprises the steps: obtaining transaction volume data, transaction price data and transaction order response time data under a plurality of transaction times; comprehensively analyzing the sensitive condition of the data affected by the outside world based on the change condition of the transaction volume data, the transaction price data and the transaction order response time data under each transaction frequency, and obtaining the interference degree under each transaction frequency; the fluctuation stability condition of the overall interference degree of the transaction times is analyzed, and the transaction data safety credibility under each transaction time is obtained; according to the security credibility of the transaction data, grading all transaction times to obtain a plurality of security level categories; and performing security assessment according to the security level category. According to the invention, the interference of external interference on transaction data security judgment is reduced, and the accuracy of data security risk assessment is improved.

The invention provides a distributed electric energy transaction method and system based on a virtual power plant, and belongs to the technical field of energy. The method comprises the following steps of: scheduling and trading among power plants; taking electric energy prices and allocation time as comprehensive indexes in the scheduling process among the power plants; the lowest scheduling scheme of the comprehensive indexes is obtained through calculation, and the electric energy scheduling work of the virtual power plant in a period of time in the future is completed through the lowest scheduling scheme of the comprehensive indexes; the unit price of each power plant for supplying power to the corresponding user is obtained through the actual condition of each power plant in the virtual power plant, and the total transaction price between the virtual power plant and the user is obtained by combining the total consumed electric quantity of the corresponding user; according to the method, electric energy transactions of virtual power plants are divided into scheduling and transactions among the power plants and transactions between the power plants and users; therefore, the scheduling and transaction process is clearer and simpler, the scheduling algorithm is simplified, and the technical implementation cost and security are improved.

The invention discloses a carbon emission transaction cost data prediction method and system, and the method comprises the steps: collecting historical data, carrying out the preprocessing of the historical data through employing an LOF algorithm, and obtaining the preprocessing data; the historical data comprises carbon emission transaction price data and historical fossil fuel price data; calculating feature values of the preprocessed data, obtaining importance degrees of the feature values of the preprocessed data through a random forest algorithm, and screening the preprocessed data according to the importance degrees of the feature values to obtain a first carbon emission transaction price sequence; performing empirical mode decomposition on the first carbon emission transaction price sequence to obtain a second carbon emission transaction price sequence; extracting a long-term dependency relationship of the second carbon emission transaction price sequence through a long-short-term memory network; and according to the long-term dependency relationship and the historical carbon emission transaction price data, measuring and calculating a carbon emission transaction price after a preset time. By adopting the embodiment of the invention, the complexity of data processing can be reduced, and the measurement and calculation precision is further improved.

The invention discloses a method for processing vehicle tasks through cooperation of multiple road side units. Comprising the following steps: training a prediction model supporting online learning by using historical data, and regularly updating the model by using new data; vehicle data of a task to be unloaded is predicted through the model, the final position of the vehicle after the task is completed is predicted, and task processing delay is estimated; using a resource transaction algorithm to calculate a trading price according to the task unloading related data; and judging whether the transaction is performed smoothly by using a coordinator, determining an actual transaction price, and selecting a road side unit cooperation scheme. According to the method, the dynamic change and elastic requirements of the urban vehicle load information in time and space are met, the success rate of vehicle task processing is improved, and the task processing delay is reduced at the same time.