230 results about "Transaction model" patented technology

A machine learning-based fraud detection system (100) for financial transactions, comprising the following: a data collection and preprocessing module configured to capture transaction data from financial institutions, payment gateways and bank modules, and to perform data cleansing, normalization and format conversion; a module for creating user profiles and behavioral analyses, configured to create dynamic user profiles based on historical transaction patterns, spending habits, device usage, and geographic location; an anomaly detection and risk assessment module configured to use machine learning algorithms to detect deviations from normal transaction patterns and assign a dynamic risk rating to each transaction; a fraud detection module configured to classify transactions as legitimate or fraudulent based on supervised and unsupervised learning techniques, including decision trees, support vector machines (SVMs), and deep learning models; a rule-based and hybrid fraud detection module configured to integrate predefined rule-based fraud detection mechanisms with AI-driven anomaly detection to improve fraud detection accuracy; a real-time fraud detection and warning module configured to monitor transactions in real time and generate security alerts via SMS, email or mobile app when suspicious activity is detected; a transaction verification and multi-factor authentication (MFA) module configured to implement adaptive authentication measures, including biometric verification, one-time passwords (OTP), and challenge-response authentication for high-risk transactions; a self-learning and model-updating module configured to dynamically update fraud detection models using reinforcement learning and federated learning techniques; a data encryption and security module configured to protect transaction data using encryption techniques, including AES-256 and RSA, to prevent unauthorized access and data breaches; and A dashboard and reporting module configured to provide real-time analytics, visualizations, and fraud detection reports for financial institutions to monitor, track, and mitigate fraudulent activity.

The invention provides a virtual power plant-oriented multi-objective optimization model construction and regulation method and system, and is applied to the technical field of virtual power plant operation optimization. The method comprises the steps of dividing distributed resources in a virtual power plant into continuous adjustment type resources, graded adjustment type resources and switch adjustment type resources based on adjustment characteristics of the distributed resources in the virtual power plant; the aggregation cost and the aggregation income of various adjustment resources aggregated in the process that the virtual power plant aggregator participates in the power market auxiliary service are considered, and a multi-objective optimization objective function is constructed by minimizing the aggregation cost and maximizing the aggregation income; and determining constraint conditions based on the adjustment capability and the adjustment rule constraint of various adjustment resources, the production safety constraint and the power grid operation constraint so as to construct a virtual power plant-oriented multi-target optimization model. The method solves the problems that the participation mode cannot be optimized and the ability of an aggregator to participate in the auxiliary service is relatively low due to the fact that the transaction model of the virtual power plant participating in the auxiliary service market is insufficient in fineness.

The invention relates to the technical field of game transaction management, and provides a game user abnormal transaction behavior identification method and system, and the method comprises the following steps: S1, obtaining the transaction of a user, the behavior in a game, social relation data and the multi-modal data of an equipment environment, constructing a personalized behavior baseline based on the historical data of the user, generating a user normal behavior sequence; and S2, regarding the user as graph nodes, transactions, behaviors, social contact and equipment association as edges, constructing a dynamic transaction relation graph, and analyzing the updated transaction relation graph according to an edge weight calculation result. A dynamic transaction model is constructed by using a dynamic transaction construction module, and a reinforcement learning strategy module is combined, and a dynamic adjustment detection strategy and model parameters are fed back in real time according to a game environment, so that changes of economic system adjustment, new transaction mode occurrence and the like in a game can be effectively coped with, and efficient recognition capability on abnormal transaction behaviors is continuously kept; the defect of poor adaptability of a traditional static model is overcome.

The invention relates to a multi-time scale optimization regulation and control method for a multi-energy virtual power plant based on digital twinning, and the method comprises the following steps: obtaining real-time data of the multi-energy virtual power plant in a physical scene, and constructing a digital twinning model based on a deep neural network; constructing a dynamic stepped carbon transaction model by considering a carbon transaction reward and punishment mechanism; constructing a three-stage optimization framework of a day-ahead stage, an intra-day rolling optimization stage and a real-time adjustment stage based on the digital twinborn model and the stepped carbon transaction model; the three-stage optimization framework is solved, a regulation and control instruction is obtained, and the regulation and control process is completed. Compared with the prior art, the method has the advantages of realizing carbon emission reduction, improving the consumption rate of renewable energy sources and the like.

The invention belongs to the technical field of computer server management, and particularly discloses a laboratory collaborative environment-oriented user and data full-life-cycle server management system, which comprises a container virtualization layer, a server management layer and a server management layer, dynamic resource allocation is supported through a state saving-resource reallocation-environment recovery mechanism; the heterogeneous transaction coordination layer adopts a Saga transaction mode to coordinate a heterogeneous system so as to realize cross-system transaction consistency; the data collaboration ecological layer establishes typed management of a personal data volume, a shared software environment volume and a public data set volume, and supports software environment encapsulation sharing and versioning management; and the metadata management layer realizes real-time update and high-performance query of metadata based on a Redis hot update architecture. According to the method, the problems of inflexible resource allocation, incomplete environment isolation, dilemma in authority management and difficulty in data collaboration and recovery in the prior art are solved.

The invention relates to a risk control method for cross-border trade data. Cross-border trade behaviors are divided into three levels, namely a system type layer of national system / policy triggered transaction, a business type layer of a process chain and an execution type layer of data submission / customs clearance / payment behaviors. Modeling a behavior stability mapping relation among the three hierarchies through sparse matrix reconstruction and adversarial noise regression; introducing an interlayer fluctuation tension coefficient for each transaction to measure offset appearing in a behavior chain; when the tension peak value exceeds a training threshold value, even if single-point data is not abnormal, the event is marked as a potential risk transition event; according to the method, cross-border transaction behaviors are divided into a system type layer, a business type layer and an execution type layer, and an interlayer fluctuation tension coefficient is introduced, so that a cross-level collaborative imbalance phenomenon in a transaction chain is effectively captured. Even under the condition that no obvious field anomaly exists, the pseudo-legal transaction mode caused by behavior chain structure mutation can be recognized, and early warning of non-dominant structure anomaly is achieved.

The invention relates to the technical field of financial information management, and particularly discloses a financial accounting information management system based on artificial intelligence, an automatic financial processing module can automatically identify and extract financial voucher information, process repetitive tasks, analyze unstructured financial texts and convert the unstructured financial texts into structured data; the intelligent risk monitoring module constructs a transaction mode analysis model based on a machine learning algorithm, identifies abnormal transactions in real time, monitors financial indexes and gives an early warning, and also can compare financial operations with criteria and regulations to generate a compliance report; the decision support module constructs and trains a prediction model, carries out financial analysis and generates a customized report; the identity verification module performs multi-factor authentication in combination with voiceprint, facial recognition and dynamic passwords. The financial information can be efficiently and accurately processed, risks are effectively monitored, powerful support is provided for decision making, and meanwhile system information safety is guaranteed.

A device may receive a preauthorization associated with missed transaction prevention for a transaction account of a user, wherein the missed transaction prevention involves preventing a missed transaction associated with merchant accounts of the user. The device may monitor a transaction log of the transaction account and identify a transaction pattern associated with a merchant account based on a plurality of historical transactions identified in the transaction log related to the merchant account. The device may determine that a historical transaction of the plurality of historical transactions is not designated for automatic execution and that an execution of an upcoming transaction corresponding to the plurality of historical transactions is not scheduled. The device may cause an account transaction associated with the upcoming transaction to be automatically executed before a transaction period expiration, associated with the merchant account, passes.

The invention discloses a low-code platform-oriented distributed transaction engine generation method and system and a medium, mainly relates to the technical field of engine generation, and is used for solving the problems of high transaction model selection error rate, low compensation logic development efficiency and poor static strategy adaptability of an existing low-code platform. Comprising the following steps: acquiring a business process, converting the business process into a 128-dimensional feature vector, and determining a corresponding CART decision tree type; inputting the 128-dimensional feature vector into a corresponding CART decision tree to generate a transaction execution plan; inputting specific data of preset core features into the TD3 model, and generating transaction submission intervals and resource allocation data of transactions in the transaction execution plan; collecting a transaction rollback log corresponding to the transaction execution plan, encoding the transaction rollback log into a 512-dimensional semantic vector, and inputting the 512-dimensional semantic vector into the LSTM model to obtain a compensation code; and when the compensation code is updated, injecting the updated compensation code into the runtime environment through a code instrumentation technology.

The invention relates to an on-line measurement data driven multi-microgrid security collaboration and electric carbon transaction method and system. The method comprises the following steps: step 1, establishing an MMG security collaboration training framework based on on-line measurement data driven multi-agent federal deep reinforcement learning (FMADRL); step 2, establishing a dynamic pricing and trading method oriented to point-to-point electricity-carbon trading demand response between MMGs; 3, training a local double-delay depth deterministic strategy gradient TD3 electricity-carbon transaction model, and calculating the transaction demand quantity at each moment; and step 4, aggregating and generating a global collaboration model, thereby realizing experience sharing and security collaboration of MMG. According to the invention, security privacy protection in the collaboration process of each MG main body can be realized.

The invention belongs to the field of power distribution network optimization scheduling, and provides a power distribution network voltage coordination method and system considering P2P transaction risk, and the method comprises the steps: constructing a producer and consumer P2P energy transaction model and an optimization decision model of a power distribution system operator DSO; constructing a coordination model based on the guidance of the fusion dynamic operation envelope DOE and the risk network crossing cost RC-NUC; the method comprises the following steps: constructing a double-layer coordination optimization framework of multi-agent interaction of a power distribution system operator DSO and a producer and disagger based on a coordination model guided by fusion of a dynamic operation envelope DOE and risk network cost RC-NUC; and solving the double-layer coordination optimization framework of the multi-agent interaction of the operator DSO and the producer and the consumer of the power distribution system to obtain a coordination decision of the multi-agent. The DOE and RC-NUC are utilized to guide the producer and the consumer to adjust the transaction strategy so as to ensure the network security, only price and power information is exchanged among multiple subjects, the privacy and autonomy of the transaction are considered, and the coordinated optimization of the power distribution network security and the economic benefits of market participants is realized.

The invention provides a low-carbon optimal scheduling method for an integrated energy system based on a green certificate-carbon transaction mechanism, so as to reduce the carbon emission of the integrated energy system and improve the renewable energy consumption capability. In order to improve the running flexibility of the IES containing the electro-hydrogen coupling unit, a proton exchange membrane electrolytic cell is introduced, and refined modeling is carried out on the hydrogen energy using process and equipment. A transaction model of a common stepped carbon transaction is improved and combined with a green certificate transaction mechanism, and a combined heat and power energy system model with an adjustable heat and power ratio is constructed; secondly, taking the minimum total cost of the system as an objective function, coupling a carbon transaction mechanism and a green certificate strategy, and performing optimization solution by using a Cplex solver; analyzing the influence of different carbon transaction mechanism parameters and green evidence parameters on the system; finally, four typical scenes are established, the proposed strategy is obtained through comparative analysis, the carbon emission can be remarkably reduced, the consumption amount of renewable energy sources is increased, meanwhile, good economic benefits are achieved, and reference can be provided for low-carbon economic dispatching of the comprehensive energy system.

The invention provides a day-ahead electricity market-oriented multi-virtual power plant transaction decision-making method and device, and relates to the technical field of virtual power plant optimization operation. The method comprises the following steps: establishing a power distribution network operator-virtual power plant master-slave game double-layer structure according to a power distribution network operator and a multi-virtual power plant system; constructing an objective function and constraint conditions of the power distribution network operator pricing game model; constructing an objective function and constraint conditions of the robust transaction model of the deterministic virtual power plant; the uncertainty of wind power, photovoltaic and controllable loads is considered, the deterministic virtual power plant robust transaction model is converted into an uncertainty virtual power plant robust transaction model, and then a distributed robust scheduling model is obtained; solving by adopting a dynamic Kriging meta-model and introducing a local search mechanism to obtain a transaction decision; wherein the column and constraint generation algorithm is adopted to solve the distributed robust scheduling model. The method can help the virtual power plant aggregator to significantly improve the earnings in the day-ahead transaction.

The invention relates to a decentralized microgrid energy transaction method based on a two-way auction mechanism, and the method comprises the steps: building a local energy transaction model which is responsible for automatically completing the bidding and load scheduling task of P2P energy transaction; a decentralized electric energy transaction method is established, and tokens are used for transaction to realize information exchange of electric energy ownership; then, a simulation case of a power distribution network containing multiple micro-grids is researched, a scheduling strategy is formulated, and decentralized micro-grid energy transactions based on a two-way auction mechanism are subjected to optimization scheduling; the method has the advantages that the local energy transaction model is established, the energy consumption demand is effectively balanced, the decentralized electric energy transaction method is adopted, and the benefits of the two parties are guaranteed.

The invention provides an intelligent decision analysis platform and method, and relates to the field of intelligent data analysis, and the method comprises the steps: firstly obtaining a time queue of transaction data, storing the time queue, then carrying out the transaction mode recognition of the time queue of the transaction data, and if the transaction data of an abnormal transaction mode is recognized, determining that the transaction mode is abnormal; and if yes, generating a transaction abnormity early warning prompt, and generating a risk report. Therefore, transaction data can be managed and analyzed more efficiently and intelligently, abnormal transactions can be recognized in time, and corresponding decisions can be made.

The invention discloses a double-offline continuous transaction verification method, system and device for digital currency. The method comprises the following steps: acquiring related information of two transaction parties in a transaction relationship; calling an intelligent contract for generating a transaction voucher, and generating a target transaction amount transaction voucher of the payee through any two random points on the ECC curve based on the random number and the target transaction amount; obtaining an input voucher and a payer change voucher based on the transaction voucher, and carrying out continuous transaction verification on the transaction amount based on the input voucher and the payer change voucher in combination with a preset continuous transaction model; if the transaction amounts involved by the two transaction parties are the same, performing Hash calculation on the random number and the transaction data to obtain a payer signature and a payee signature; and obtaining a transaction signature based on the payer signature and the payee signature, obtaining a zero sum result based on the transaction signature, and if the zero sum result satisfies a preset condition, determining that the transaction relationship is valid. According to the invention, the transaction efficiency is obviously improved, and the time required by the transaction is greatly shortened.

A methodological technical approach is proposed herein that utilizes historical data to employ a heuristic optimization algorithm for data message routing. While not specifically limited to the banking sector and financial institutions, as a practical example, the approach can utilize historical data relating to patterns of transactions for analyzing customer behavior and detecting fraudulent activities. For example, trajectory mining can be used to analyze the transaction patterns of credit card users, or behavioural patterns of mobile banking users, such as checking account balance, transferring funds and paying bills.

The invention provides a flexible distributed energy network multi-agent collaborative optimization method based on MARL, and the method comprises the steps: building a distributed energy network multi-agent interaction framework which comprises a local energy transaction model for distinguishing an intelligent soft switching path, a network cost model based on a power transmission distribution factor, and a power distribution network operator and user income model; a physical system operation constraint model is established and covers intelligent soft switch operation characteristics, network topology radial constraint, energy storage charging and discharging dynamic states, controllable distributed power supply and photovoltaic output limitation, translational load scheduling and DistFlow power flow constraint. And finally, constructing a collaborative optimization mechanism based on a multi-agent near-end strategy optimization algorithm, modeling the system into a partially observable Markov decision process, adopting a centralized training distributed execution framework, and guaranteeing transaction security and data privacy through block chain evidence storage, RSA encryption and an intelligent contract. The system economy and the operation flexibility are improved.

The invention provides a code-free SaaS application rapid development method and system based on data source driving, and the method comprises the steps: constructing a forward business process directed acyclic graph, defining compensation association and reality binding for a forward operation node of the forward business process directed acyclic graph, and generating a visual transaction model; analyzing the transaction model to generate a transaction step sequence, constructing a finite state set and a state transfer function based on the sequence, and packaging the finite state set and the state transfer function into an SAGA process state machine definition object; generating a global transaction ID when a transaction life cycle is started, constructing a transaction context object in combination with the state machine definition object, and serializing to generate an initial transaction context instance; and loading and deserializing the instance, starting a state-driven execution loop, terminating by iterating single-step state transition when the current state belongs to a termination state set, judging a final result of the transaction, and archiving an audit log. According to the method, transaction process modeling and formalized state driving execution are fused, and the method is suitable for SaaS application development of cross-heterogeneous data sources.

The invention discloses an interconnected micro-grid online distributed point-to-point electric energy management method and system, relates to the technical field of interconnected micro-grid management, and aims to construct an electric energy transaction model by taking the minimum energy transaction cost as an optimization target and taking the electric energy of each micro-grid as a decision variable. Respectively inputting the working condition parameters of each micro-grid and the corresponding historical operation data into a preset electric energy optimization model to obtain initial electric energy corresponding to each micro-grid, and performing distributed iterative optimization on the electric energy transaction model according to the working condition parameters and the initial electric energy corresponding to each micro-grid, and obtaining the target energy transaction cost and the target electric energy corresponding to each micro-grid. The problem that a traditional micro-grid electric energy management method is generally low in convergence speed is solved, the real-time requirement of online management is difficult to meet in a large-scale interconnected micro-grid cluster scene, and the running reliability of a micro-grid is reduced.

The intelligent data processing system comprises a distributed edge probe group, a dynamic fraud strategy engine, an intelligent evidence chain construction module and a credible evidence storage interface, the distributed edge probe group is deployed in a core transaction system, a lightweight protocol analyzer is arranged in the distributed edge probe group, an SWIFT / UnionPay private protocol and a real-time desensitization module are adapted in the analyzer, and transaction account masking and behavior track hash solidification are executed at a data source end; the dynamic fraud strategy engine accesses an abnormal transaction flow of the edge probe group, and stores a cross-mechanism transaction mode vector through a graph neural network fraud feature library; the real-time threat evaluation unit detects the transaction time sequence abnormal probability based on a hidden Markov model; the self-adaptive evidence obtaining controller dynamically activates a targeted evidence obtaining instruction according to the threat value, capital flow of a cross-platform is captured through a high priority, and the evidence storage period of a suspicious session is prolonged.

The invention discloses an integrated energy system real-time scheduling method considering price fluctuation under multiple subjects, and the method comprises the following steps: S1, determining all subjects of an integrated energy system and an energy consumption mode of the subjects, and introducing a reward and punishment stepped carbon transaction mechanism to restrain a model of system carbon emission; s2, taking the comprehensive energy service provider as a leader and the other subjects as followers, and establishing a multi-subject transaction model; s3, based on the optimization target, the constraint condition and the price fluctuation, constructing an economic optimization scheduling model of the integrated energy system; and S4, calling a CPLEX toolbox in MATLAB software to solve the economic optimization scheduling model, and obtaining a real-time scheduling optimal strategy of the integrated energy system. According to the method, the minimum total operation cost of the system is taken as a target, the effectiveness of the proposed method is verified by setting an example, flexible demand response resources under real-time scheduling can be fully excavated, and balance of benefits of all subjects and economic and low-carbon operation of the system are realized.

The invention discloses a recommendation method and device for an abnormal transaction model. The method comprises the following steps: extracting model multi-dimensional tag features and a target type realized by the model from abnormal transaction model construction business requirements; disassembling each type of target to obtain a tag feature and a tag parameter corresponding to each type of target; selecting a plurality of to-be-recommended abnormal transaction sub-models containing the label parameters from a pre-constructed abnormal transaction model self-service application library according to the label characteristics and the label parameters thereof; determining a integrating degree value, a main target completion rate and an interpretability strength value of each abnormal transaction sub-model to be recommended; and according to the integrating degree value, the main target completion rate and the interpretability strength value, sorting the plurality of to-be-recommended abnormal transaction sub-models and then recommending the to-be-recommended abnormal transaction sub-models to the user. According to the method, the user can be helped to judge the interpretability strength of the model, the understanding, correction and monitoring capabilities of the user on the model are improved, and the accuracy and efficiency of abnormal transaction model recommendation are improved.

The invention provides an abnormal threshold updating method, an abnormal transaction identification method, equipment and a medium, and the abnormal threshold updating method comprises the steps: obtaining a performance evaluation data set in a transaction data flow; calculating a model performance index based on the performance evaluation data set; and when the model performance index does not meet the performance index threshold requirement, performing iterative updating on the abnormal threshold by using the target function to obtain the updated abnormal threshold. According to the scheme, when the model performance index does not meet the performance index threshold requirement, the target function is utilized to iteratively update the abnormal threshold, so that the abnormal transaction recognition model can dynamically adjust the decision threshold according to the transaction data and the model performance, and the flexibility of the abnormal transaction recognition model is improved; besides, by dynamically updating the decision threshold, the abnormal transaction recognition model can adapt to new abnormal behaviors and transaction modes, and the reliability of the abnormal transaction recognition model can be improved.

The invention discloses a hydrogen production and hydrogenation integrated station electric energy-peak regulation combined market transaction method considering hydrogen load demand response. The method comprises the following steps: 1) constructing a hydrogen production and hydrogenation integrated station day-ahead electric energy-peak regulation combined market transaction mechanism and framework; 2) constructing a cost model of the hydrogen production and hydrogenation integrated station for regulating and controlling multi-type hydrogen load demand response; 3) considering uncertainty factors such as hydrogenation load of the hydrogen fuel cell vehicle, and proposing a hydrogen production and hydrogenation integrated station market transaction model with maximum operation income as a target based on distribution robustness; 4) constructing a day-ahead electric energy-peak regulation combined market clearing model containing the hydrogen production and hydrogenation integrated station by taking social welfare maximization as a target; and 5) proposing a market transaction strategy solving method based on a differential privacy-consistent alternating direction multiplier method (DP-C-ADMM). According to the invention, a cooperative transaction mechanism in which the hydrogen production and hydrogenation integrated station participates in multiple markets is constructed, and a transaction model based on distributed robustness and a transaction strategy solving method considering privacy protection are proposing.

The invention discloses a fund allocation method and device, equipment, a storage medium and a product, and relates to the technical field of data processing, and the fund allocation method comprises the steps: responding to a fund allocation request of a transaction account, and determining a fund allocation path of the transaction account in a cash pool concurrent transaction model, the fund allocation path passes through the fund account and the position account; calculating to obtain target funds needing to be transferred by the fund account and the position account in the fund transfer path, and transferring the target funds to transfer transaction sub-households randomly selected from the fund account and the position account; and transferring the target fund stored in the transfer transaction child account to the transaction account based on the fund transfer path. According to the method and the device, the problems that transverse expansion cannot be realized due to adoption of serial fund transfer processing, mother company accounts of all levels in a cash pool become performance bottlenecks of high-concurrency transactions, and transaction processing efficiency of a bank accounting system is seriously influenced in a transaction peak period can be solved.

The invention discloses a multi-time scale peak regulation optimization scheduling method based on an electricity-carbon transaction virtual power plant. A carbon step transaction model, a virtual power plant demand side participation peak regulation scheme and a virtual power plant source side participation peak regulation scheme are improved. And establishing a day-ahead optimization model objective function and establishing a virtual power plant day-ahead stochastic programming model. And establishing an intra-day rolling optimization model objective function. And establishing an improved Shapley value method-based virtual power plant internal income distribution method. The scheme is solved, and multi-time-scale peak regulation optimization scheduling based on the electricity-carbon transaction virtual power plant is achieved. According to the method, the actual carbon emission of the virtual power plant is described more accurately, load side / original side peak regulation is effectively promoted, the influence of new energy output and load deviation can be reduced, the carbon emission, the peak regulation benefit and the output deviation are taken as correction factors to correspond to the virtual power plant carbon transaction model and the peak regulation model, and the method is more efficient and reliable. And participation of users in the virtual power plant is promoted while fairness in profit distribution is ensured.

The invention discloses a product recommendation method and device, electronic equipment and a computer program product. The method comprises the following steps: acquiring financial management information of a user in a preset period; inputting the financial management information into a transaction model to obtain an investment portrait of the user, the transaction model being obtained by training a plurality of groups of training samples, each group of training samples comprising historical financial management information and a historical investment portrait, and the investment portrait being used for representing an investment habit of the user; a predicted transaction behavior of the user is determined based on the investment portrait, and the predicted transaction behavior comprises at least one of deposit, mortgage, stock transaction and fund transaction; and determining a to-be-recommended product of the target financial institution based on the predicted transaction behavior, and pushing the to-be-recommended product to a client of the user. Through the method and the device, the problem that the recommended product is not matched with the product purchase tendency of the user in the related technology is solved.

The invention provides a heterogeneous virtual power plant cluster marketization transaction optimization method based on decision fusion, and the method comprises the following steps: firstly, constructing a framework and a transaction model of a virtual power plant cluster, and building an output model; carrying out clearing simulation on the spot market, and constructing a scheduling model; performing simulation calculation on the bid-winning electric quantity through the scheduling model; and then, establishing a virtual power plant cluster collaborative operation model, and obtaining transferred electric quantity and unit transaction cost among the virtual power plants through Nash bargaining so as to formulate a transaction plan. The method is simple and low in execution cost, the transaction strategy obtained by constructing the virtual power plant cluster collaborative operation optimization model and carrying out Nash bargaining and information gap robust decision fusion can reflect heterogeneity characteristics among virtual power plants, and the deficiency of calculation efficiency can be made up by accurately modeling strong random resources, so that the method can be applied to the collaborative operation of the virtual power plants. Even if wind and light fluctuation exists in the execution period, the whole cluster can still stably operate and supply power, and the overall power generation cost is reduced.

The invention provides a method and system for constructing a medium-and-long-term electricity purchasing and selling combined transaction model for a power grid enterprise, and the method comprises the steps: carrying out the modeling of the uncertain electricity purchasing cost of an electricity purchasing side for the scenes of different types of power supplies at the electricity purchasing side, and obtaining the uncertain factors of the electricity purchasing side; the method comprises the following steps: analyzing power demands of various types of users on a power selling side, and modeling the power purchase quantity and load of a power grid enterprise agent power purchase user on the power selling side and the uncertain total power consumption to obtain uncertain factors of the power selling side; according to the uncertainty factors of the electricity purchasing side and the electricity selling side, risk control index evaluation is carried out based on the conditional value-at-risk CVaR, and annual and monthly conditional value-at-risk is obtained; based on the annual condition value-at-risk, establishing a combined transaction model for determining monthly electric quantity by annual transaction considering an annual electricity purchase cost expected value and risk management and control; and based on the monthly conditional value-at-risk, establishing a combined transaction model for determining the daily electric quantity by monthly transaction in consideration of the expected value of the annual electricity purchase cost and risk management and control.