106 results about "Transaction model" patented technology

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 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.

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 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 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 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.

A computer system, computer-implemented method and non-transitory computer-readable medium to predict transaction activity for a cyclical time period. A predictive transaction model is developed for individual user accounts, where the transaction model incorporates a cyclical data structure representation to detect and analyze transactions.

The invention discloses a resource collaborative and complementary low-carbon configuration method, system and device and a medium, and belongs to the technical field of power system planning, and the method comprises the following steps: building a carbon power market transaction model according to a carbon market transaction mechanism; the method comprises the following steps: establishing a double-layer optimization model of the adjustable resource collaborative complementary configuration by combining the operating characteristics of wind power, photoelectricity, energy storage and thermal power and taking the carbon transaction cost and the carbon quota income of a carbon power market transaction model as constraint conditions through the characteristics of the adjustable resource collaborative complementary configuration; and aiming at the double-layer optimization model, setting a genetic algorithm to solve the double-layer optimization model. According to the invention, by establishing the carbon power market transaction model, the carbon emission cost and the carbon quota income are organically integrated into the power system optimization decision, and deep coupling of economic benefits and environmental benefits is realized.

The invention relates to the technical field of finance, and particularly discloses a compliance risk early-warning detection method for bank-enterprise direct-connection cross-border payment, and the method comprises the steps: obtaining an original transaction sequence of bank-enterprise cross-border payment, and synchronously collecting transaction object information, fund flow data and an error-related supervision parameter sequence; obtaining a transaction mode component according to the original transaction sequence; generating a preliminary risk early warning parameter according to the transaction mode component and the error-related supervision parameter sequence; and obtaining an accumulated number of payment services, and generating an early warning threshold factor and a risk weight factor based on the accumulated number and the fund flow data. According to the method, the hidden association of the amount, the purpose and the time is mined by means of the feature association index, the risk is quantified in combination with the mode compliance deviation degree, and the early warning threshold value and the weight factor which are dynamically generated are matched, so that the risk identification accuracy is greatly improved, the missing report rate and the false report rate are reduced, and particularly, the hidden risk in complex cross-border payment can be accurately captured.

The invention provides a digital employee-based transaction method, device and business system, and the method comprises the steps: obtaining user input data under the condition that a user call instruction is received, and enabling the user call instruction to be used for initiating a transaction process; calling a pre-trained large language model to analyze the user input data so as to determine a user operation intention; determining a transaction risk level of the transaction process according to the user operation intention; and determining a target transaction mode according to the transaction risk level, and interacting with the user according to the target transaction mode to complete the transaction process, the target transaction mode including digital employee independent handling, manual supervision of digital employee handling and manual handling. According to the method, the problem that in the prior art, in the process of taking over digital employees by real employees, due to the fact that delay of voice link establishment is high, service handling efficiency is low is solved.

The application discloses a kind of multi-virtual power plant point-to-point transaction method, electronic equipment and storage medium.First, collect and aggregate virtual power plant internal distributed resources, construct multi-virtual power plant electricity-carbon-backup point-to-point transaction model;Set conditional value at risk model confidence and risk preference coefficient, construct multi-virtual power plant electricity-carbon-backup point-to-point transaction model based on conditional value at risk;Adaptive alternating direction multiplier algorithm (ADMM) based on consensus is used to solve the model in distributed manner, and the transaction strategy of each virtual power plant is obtained.The application considers that virtual power plant aggregates photovoltaic, fuel cell, energy storage, central air conditioning, flexible load and other distributed resources to participate in electricity-carbon-backup point-to-point transaction, which widens the transaction channel of virtual power plant;Value at risk quantifies the potential risk brought by photovoltaic randomness, so that virtual power plant decision makers can weigh benefits and risks;Adaptive ADMM algorithm based on consensus effectively improves the calculation efficiency of model solving.

The invention relates to the technical field of computer security, in particular to a feature space backdoor attack method, system and device based on fusion data transaction model parameter constraint and a storage medium. Obtaining an original training data set, constructing a data transaction model, calculating malicious sample loss values, and screening samples with smaller loss values as target samples; poisoning features are randomly extracted from the original data set, discrete noise is generated through a noise generator to form a trigger, the trigger is added to a target sample, a label is turned over, and an attack data set is formed; original training data are sampled, and a toll-snow information matrix of each layer is calculated; using the original model parameters to initialize the backdoor model, adding the L1 norm of the parameter disturbance difference value and the parameter difference constraint term based on the Feisnow information matrix into a loss function, and training the backdoor model; performance fluctuation caused by random sample selection is avoided through sample screening based on loss values, so that the backdoor model maintains the classification capability of clean samples while learning trigger features.

The invention discloses an abnormal transaction identification method and device, equipment, a storage medium and a program product. Comprises: obtaining a sample set; performing hash processing on samples in the sample set and then sending the samples to a preset platform, so that the preset platform performs privacy intersection operation on hash values; receiving a privacy intersection result sent by the preset platform, and performing standardization processing on the samples in the sample set based on the privacy intersection result; performing split federation learning on the abnormal transaction model based on the standardized sample set to obtain a trained abnormal transaction model; and identifying the abnormal transaction based on the abnormal transaction model. According to the abnormal transaction identification method provided by the embodiment of the invention, the abnormal transaction is identified on the basis of the abnormal transaction model obtained by split federal learning, so that the accuracy of abnormal transaction identification can be improved, and the security of data can also be improved.

The invention provides a park integrated energy system optimization scheduling method considering green certificate carbon transaction, which comprises the steps of establishing an optimization scheduling model comprising a step green certificate transaction model, a step type carbon transaction model and a demand response model, establishing a master-slave game double-layer optimization model on the basis of the model, taking an energy operator as a leader, and taking the energy operator as the leader as a leader; and an energy supplier, an energy storage operator and a load aggregator serve as followers. And then, solving the established double-layer master-slave game model by adopting a differential evolution algorithm in combination with a Cplex solver. And finally, verifying the effectiveness of the model and the solving method by adopting example analysis. After a green certificate transaction mechanism, a stepped carbon transaction mechanism and demand response are introduced, the peak clipping and valley filling effect is obvious, reduction of electric, thermal and cold loads is realized in a reasonable range, an optimization result can form a reasonable energy purchase and sale price and supply and demand balance relationship, the carbon emission of the system is reduced, and the energy consumption of the system is reduced. The method is of great significance to energy conservation and emission reduction of a park comprehensive energy system and improvement of main body benefits.

The invention provides a payment fraud real-time identification and disposal method and system based on machine learning, and relates to the technical field of computers, and the method comprises the steps: constructing a transaction relation graph, recognizing an abnormal transaction mode through a graph attention network, and calculating a transaction suspicious degree score; and when the preset threshold value is exceeded, triggering a self-adaptive verification strategy based on the knowledge graph and reinforcement learning, executing a corresponding verification means and processing a transaction result. According to the invention, accurate identification and efficient disposal of fraudulent transactions can be realized, the payment security is improved, the error interception rate is reduced, and the user experience is optimized.

The invention provides an interconnected multi-energy micro-grid point-to-point energy transaction method and system considering energy transaction consistency, and relates to the technical field of energy transaction. The method comprises the following steps: constructing a system, and establishing system models including a point-to-point energy transaction model, an energy conversion equipment model and an energy storage equipment model; a system optimization target is determined, and a cost function is constructed according to the point-to-point energy transaction cost of the multi-energy micro-grid, the energy transaction cost of the external network and the environment cost; physical constraints are constructed, wherein the physical constraints comprise energy conversion constraints, energy storage constraints and energy balance constraints; modeling a complex optimization problem including a point-to-point energy transaction and energy conversion process as a partial objective Markov decision process, and introducing energy transaction consistency; and optimizing an energy transaction and conversion strategy of the interconnected multi-energy micro-grid by using an MASAC method. According to the invention, unreasonable energy transaction behaviors can be avoided, and the total operation cost of the system is reduced.

The application discloses a kind of multi-element load linkage regulation method, system, equipment and medium, comprising: constructing the two-stage operation transaction model of multi-element load aggregation main body user, forms lower user response model;Establish two-stage robust regulation model dominated by distribution network operator;Introduce power grid topology adjustable constraint;Coupling user response model and enhanced operator regulation model, build double-layer interactive optimization framework, form two-stage robust double-layer optimization problem containing integer variable;Optimization problem is solved using nested column constraint generation algorithm, output optimal electricity price guide strategy, direct load control amount and topology adjustment scheme, as the total scheme of multi-element load linkage regulation for weak point uncertainty.

The application discloses a transaction fraud detection method based on a rule attention mechanism, and the method comprises the following steps: performing cleaning and preprocessing operations on data; learning potential transaction patterns based on a tree-based model to generate transaction decision rule information and enhance the interpretability of the method; encoding and embedding the generated cross features to enhance the flexibility of the model; capturing the interaction between transactions by using a multi-head self-attention mechanism to mine deeper transaction fraud patterns; using an attention network to focus on the importance of different rules under specific conditions, learning the interaction between specific feature conditions and transaction decision rules; and calculating the fraud score of the final transaction representation by using an activation function. The method can improve the prediction performance while enhancing the interpretability.

Techniques for item transactions including generating, based on structured transaction data indicative of values of an item over a time interval, a historical structured transaction dataset corresponding to the time interval, determining, based on the historical structured dataset, a set of historical directional values for the time interval; generating, based on unstructured transaction data comprising datasets published over the time interval, a historical unstructured transaction dataset corresponding to the time interval, determining numerical representations of the datasets; determining, based on the set of historical directional values and the numerical representations, a transaction model to determine a predicted direction of value for the item based on current structured and unstructured transaction data; determining, based on application of current structured and unstructured transaction data to the transaction model, a predicted direction of value for the item.

The present application relates to the technical field of power equipment management, and more particularly to a physical transaction quantum optimization modeling method, system, device and medium for a transformer area cluster: heterogeneous operation data is acquired and a unified decision variable set is generated according to a commitment object configuration; a preset constraint maintaining mixer is input, a target Hamiltonian is created in a feasible subspace determined by power hard constraints, and quantum bit strings are used to guide quantum approximate optimization; a parameterized quantum circuit is constructed based on the Hamiltonian and the mixer, and a target bit string is solved; and after decoding, a physical transaction model that can be directly used in multiple markets is output. The present application endogenously embeds hard constraints in quantum search, and realizes mutual exclusion of energy, capacity and mileage in a single time period through a unified variable set, thereby avoiding repeated occupation across markets, and thus solving the problem that existing algorithm models are difficult to simultaneously consider feasibility, economy and linearity, and improving the feasibility, stability and timeliness of bidding strategies for a transformer area cluster in multiple markets.

This application discloses a highly available online transaction method, apparatus, device, and storage medium combining database sharding and dynamic multi-active switching, relating to the field of computer technology. The method includes: responding to a transaction request by determining a business transaction mode based on the operating status of each transaction data shard; when the business transaction mode is a fault-random mode, determining at least one available data shard among multiple transaction data shards; and processing the transaction request according to the transaction application corresponding to the available data shard. Through this method, in fault-random mode, at least one available data shard is determined based on the operating status of each transaction data shard, and the transaction request is processed using the transaction application of that available data shard. Flexible switching of transaction modes is achieved through the operating status of the shards, improving the availability and stability of the business system, accelerating response speed, reducing transaction losses, and lowering the complexity of operation and maintenance.

The invention discloses a transaction matchmaking algorithm based on Nash equilibrium, and aims to solve the problems of extensive demand matching, single valuation model and low algorithm efficiency in a traditional transaction mode. According to the technical scheme, the method comprises the following steps: 1) realizing accurate demand matching through NLP and domain expert knowledge; 2) constructing a multi-dimensional utility function model, and quantifying the interests of the two transaction parties on the basis of a Speak prediction model; 3) optimizing the Nash equilibrium calculation efficiency by adopting an MWU-CGD hybrid algorithm, and verifying that the convergence speed is increased by 3.2 times in a simulation environment; the Nash bargaining model is applied to knowledge achievement transactions for the first time, fair and efficient benefit distribution is achieved, experiments show that the matching success rate is increased to 68% in biological medicine asset transactions, the effectiveness of the two parties is averagely improved by 15%, and the method is suitable for real-time matching of complex transaction scenes.

This invention provides a machine learning-based method and system for real-time identification and handling of payment fraud, relating to the field of computer technology. The method includes constructing a transaction relationship graph, using graph attention networks to identify abnormal transaction patterns, and calculating transaction suspiciousness scores. When a preset threshold is exceeded, an adaptive verification strategy based on knowledge graphs and reinforcement learning is triggered to execute corresponding verification measures and process the transaction results. This invention enables accurate identification and efficient handling of fraudulent transactions, improves payment security, reduces false interception rates, and optimizes user experience.

The invention discloses an automatic vending machine system and method supporting flexible giving and remote goods taking, belongs to the technical field of unmanned retail, and aims to solve the technical problems that in the prior art, an automatic vending machine is single in transaction mode and cannot support flexible cross-region giving, inventory management depends on lagged data, and allocation efficiency is low. The system comprises a cloud server, a vending machine terminal network and a user mobile terminal, and a user sets a goods taking strategy containing a goods taking person identifier and a remote goods taking terminal through the mobile terminal and initiates a transaction; the cloud server processes the request, generates a goods taking voucher and distributes the goods taking voucher to a goods taking person and a designated vending machine terminal so as to achieve flexible cross-regional transaction, the flexible transaction at the front end is combined with data intelligence at the rear end, the application scene is enriched, and the scientificity and efficiency of inventory management are remarkably improved.

The invention provides a carbon transaction method and system, computer equipment and a storage medium, and belongs to the crossing field of artificial intelligence and energy management, and the method comprises the steps: constructing a long-period carbon transaction model which fits the market reality, providing a delay reward reinforcement learning algorithm DR-RLCT, fusing randomized return decomposition with a depth deterministic strategy gradient, and obtaining a carbon transaction model; the total cost at the end of the cycle is intelligently decomposed and redistributed into the instant agency reward of each step in the performance cycle by training the reward network, so that a delayed punishment signal is converted into a dense strategy guidance signal, and the credit distribution problem in the long-cycle decision is effectively solved; and finally, optimizing the strategy network by utilizing the agency reward, so that the optimal transaction action can be output. Compared with a traditional method, the method has the advantages that stable positive earnings can be achieved, penalty defaulting can be completely avoided, and a reliable intelligent decision-making scheme is provided for enterprises to achieve low-cost compliance operation in the carbon market.