103 results about "Optimization system" patented technology

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant load prediction and dynamic adjustment optimization system and method, and the system comprises a data collection module, a preprocessing module, a prediction module, an adjustment module and a verification module. The data acquisition module acquires real-time operation data and power market signals of distributed energy nodes; the preprocessing module performs standardization processing on the data through a quantum space-time alignment and anomaly reconstruction technology, and extracts strong correlation vectors of meteorological features and loads; the prediction module adopts an adaptive noise complete set empirical mode decomposition algorithm to separate a trend term, a periodic term and a residual component of a load sequence, and the adjustment module constructs a multi-target optimization model. Efficient aggregation of distributed resources, high-precision load prediction in a meteorological sudden change scene and cooperation of multi-market dynamic scheduling strategies are realized; and the clean energy consumption capability and the virtual power plant market response efficiency are improved.

The invention provides a multi-level supply chain optimization system based on a dynamic knowledge graph and a large model, and the system comprises a dynamic knowledge graph building module which employs historical supply chain data to build a graph, formulate a risk propagation rule, evaluate a supplier risk, and update the graph; the cloud manufacturing supplier optimization large model building module is used for building an optimization model by training and adjusting a BERT model which introduces an attention mechanism and combining with a dynamic knowledge graph; the supplier priority ranking tool establishing module is used for establishing a ranking tool according to the order information and supplier productivity and risk requirements; and the cloud manufacturing supplier optimization scheme prediction module integrates the dynamic knowledge graph, optimizes a large model and a sorting tool, inputs a language model development framework, predicts and optimizes a supply chain and updates the knowledge graph. Dynamic evaluation of supply chain risks and supplier optimization are realized, and the intelligent level of supply chain management is improved. The invention further provides a multi-stage supply chain optimization method based on the dynamic knowledge graph and the large model.

The invention discloses a power transaction market risk dynamic assessment system, which comprises a multi-modal sensing module used for collecting physical equipment operation data, market transaction data and environmental parameters in real time, and performing space-time alignment processing through edge computing nodes to generate a fusion data stream; the self-adaptive evaluation engine is in data connection with the multi-modal sensing module and is used for analyzing multi-dimensional risk association in the fused data stream; the invention belongs to the technical field of electricity markets, and aims to solve the problems that in the prior art, historical data static analysis is relied on, spatial-temporal scales of multi-source data are not uniform, and data cannot be given in time. The technical effects are that a closed loop is formed through sensor data, a multi-mode sensing module (alignment processing), an adaptive evaluation engine (risk analysis), a digital twin system (simulation strategy) and a dynamic feedback module (optimization system), automatic calibration and learning are realized, and the risk prediction accuracy and the reliability of the scheme are improved.

The invention belongs to the technical field of intelligent supply chain management and optimization, discloses a product supply chain comprehensive optimization system and method based on big data, and aims to solve the problem that a traditional supply chain management system based on historical data and a static model is difficult to quickly adapt to changes. Forming a real-time evaluation vector; integrating the environment data, constructing an environment feature vector, and training to obtain a risk prediction model in combination with a real-time evaluation vector, thereby obtaining a risk probability of each node in the future, and obtaining a risk evaluation vector; based on the risk assessment vector, in combination with the inventory level and the transportation path, performing joint optimization on the inventory configuration and the distribution path to generate an optimization strategy; updating each node of the supply chain according to the optimization strategy, monitoring the state of the updated supply chain in real time, and generating a cost prediction vector when abnormity is monitored; and taking corrective measures on the supply chain according to the cost prediction vector to ensure stable operation of the supply chain.

The invention relates to the technical field of collaborative optimization, in particular to a virtual power plant power generation-utilization-price collaborative optimization system based on an AI large model, and the system comprises a load confidence matching module, a resource stability mapping module, a source-load capacity coupling module, an electricity price interval adjustment module and a comprehensive regulation and control linkage module. According to the method, the confidence interval prediction of the load demand is realized based on the hybrid neural network modeling of the load behavior data and the equipment temperature control characteristic sequence, and the scheduling matching confidence is measured according to the boundary overlapping condition of the prediction interval and the power generation response characteristic; a stability screening mechanism for adjusting resources is constructed in combination with the output fluctuation ratio and the equipment inertia characteristic, the controllability of load adjustment and the real-time performance of source side response are improved, the price adjustment rhythm is corrected through an electricity price response delay factor, dynamic closed-loop linkage between load adjustment and price guidance is achieved, and the load adjustment efficiency is improved. The execution priority is dynamically updated under the condition that multiple response conditions are matched, and the certainty of resource scheduling and the sensitivity of response are improved.

The invention relates to the technical field of big data processing and analysis, in particular to a cross-border payment path optimization system and method based on multi-dimensional data analysis. Payment transaction data and exchange rate data are collected, a multi-dimensional payment path evaluation model is constructed from the angles of payment cost, payment timeliness, payment risk, payment availability and the like, and each payment path is scored in real time; constructing a payment path optimization model, dynamically selecting an optimal path and performing intelligent scheduling; a machine learning algorithm is introduced, an abnormal transaction mode is automatically identified from mass payment data, and a real-time anomaly detection and risk prevention and control strategy is established; according to the method provided by the invention, the success rate of cross-border payment can be improved, the cost is reduced, the payment time is shortened, and the payment risk is prevented, so that the cross-border payment service quality and the user experience are comprehensively improved.

The invention provides a retail supply chain optimization system and method based on big data analysis, and relates to the technical field of big data analysis. The system comprises a data acquisition module used for acquiring order data, logistics data and user behavior data and generating structured data; the data processing module is used for preprocessing the structured data and establishing a multi-dimensional association relationship among the data based on a preprocessing result; the inventory prediction module is used for predicting future order demands, calculating regional inventory demands and generating inventory distribution data; the inventory optimization module is used for generating replenishment data and an inventory adjustment strategy based on the inventory distribution data; the behavior analysis module is used for generating commodity recommendation data and promotion priorities based on the user behavior data in combination with the adjusted inventory distribution data; and the logistics allocation module is used for dynamically adjusting the logistics distribution path and the distribution priority. According to the technical scheme, the operation efficiency and flexibility of the retail supply chain can be improved.

The invention discloses a whole-process collaborative production logistics management optimization system and method. The core of the system is intelligent inquiry and blockchain cost management. The inquiry module fuses a dynamic weight algorithm and a machine learning model, intelligently screens goods opportunities, pushes an AES encrypted inquiry sheet, and optimizes a quotation scheme in a multi-objective manner. And the delivery module is in butt joint with the ERP through the OData API, automatically analyzes the order, generates a delivery order, integrates weighing data for fee calculation, and carries out early warning on an abnormal order. And the customs declaration module identifies invoice information through OCR, automatically extracts customs declaration fields, verifies compliance through API, and generates a standardized customs declaration. The in-transit tracking module integrates a multi-platform API, pulls a logistics state in real time, pushes the logistics state to a client through WebSocket, and transparentizes an order state. The cost management module constructs a distributed account book based on a PBFT consensus mechanism block chain, ensures that cost information is safe and credible, and supports multi-currency settlement and dispute arbitration. The system comprehensively optimizes the production logistics link, realizes full-process collaboration, improves the efficiency, reduces the cost, and enhances the enterprise competitiveness.

The invention discloses a virtual power plant optimization system and method, and the system comprises a virtualization platform module which is used for integrating distributed energy, energy storage equipment, an electric vehicle, a flexible load and other resources, and achieves the virtualization and centralized management of the resources; the dynamic resource aggregation module is used for dynamically matching resource characteristics with power system requirements; the market transaction module is used for optimizing the earnings of the virtual power plant; and the core algorithm module is used for optimizing the load prediction precision. The market transaction module implements two-stage market bidding based on a multi-agent game model, analyzes the uncertainty of quotation of competitors through a multi-scene simulation technology, optimizes a dynamic transaction strategy, and achieves the maximization of spot market benefits and the controllability of risks. Through cooperative work of a virtualization platform module, a dynamic resource aggregation module, a market transaction module, a core algorithm module, a security management module and the like, efficient management of resources and maximization of market benefits are realized.

The invention relates to the technical field of electric power spot transaction, and discloses an electric power spot transaction optimization system. The system comprises a data acquisition module, a dynamic pricing module, a supply and demand prediction module, a transaction optimization model construction module, a real-time scheduling module and the like. The data acquisition module acquires market data; the dynamic pricing module generates a dynamic electricity price strategy based on a deep reinforcement learning algorithm and a mixed integer programming model; the supply and demand prediction module predicts supply and demand through a federated learning framework aggregation model; the transaction optimization model construction module constructs an objective function and constraint conditions by combining the results; and the real-time scheduling module uses an improved algorithm to solve and generate an optimal scheduling scheme. In addition, the system also has the functions of risk control, abnormal transaction detection, intelligent contract management, performance evaluation and the like. The system can optimize electric power spot transaction, improve transaction efficiency, guarantee power grid stability, prevent and control market risks, and promote healthy development of the electric power market.

Systems and methods of the present disclosure provide systems and methods for receiving an indication of criteria for a function to be performed at a commercial process facility. The criteria are used to determine one or more scenarios that meet the criteria. Coupled simulators then generate one or more route maps from the one or more scenarios. A route map is selected from the one or more route maps that causes at least one operation of the route map to be performed.

The invention discloses an AI marketing advertisement putting optimization system and method fusing multi-dimensional data, and relates to the technical field of data processing, and the system comprises a historical record obtaining module which extracts the historical putting record of a target optimization block; the terminal data interaction module interacts with the advertisement putting management platform based on the historical putting record to obtain multi-dimensional terminal data; a grid division module performs grid division on the target optimization block to generate a putting grid and time sequence label data; the mapping model construction module constructs a label mapping model based on the grid time sequence label data, the historical delivery record and the multi-dimensional terminal data; the advertisement task processing module generates an advertisement label cluster by using a label mapping model according to the advertisement task information; and the putting scheme optimization module realizes the optimal matching of the advertisement task and the label cluster through a multi-objective optimization algorithm, and outputs an advertisement putting scheme, thereby achieving the technical effects of improving the data utilization rate, providing predictive putting optimization and improving the putting income.

The invention discloses an e-commerce supply chain intelligent scheduling optimization system and method based on big data, and relates to the field of intelligent logistics scheduling, and the system comprises a demand prediction module, a balance optimization module, a path optimization module, a collaborative replenishment module and a feedback driving module. According to the method, the deep learning model and the time sequence decomposition method are combined, and multi-level modeling is performed on the historical order data and the real-time sales data, so that supply chain imbalance caused by prediction deviation is avoided. A dynamic replenishment plan is generated based on the prediction result and the inventory early warning information, real-time matching of the inventory and the demand is achieved, the stockout rate and inventory redundancy are effectively reduced, and the storage resource utilization rate is improved. By establishing the multi-constraint path optimization model and comprehensively considering order distribution, vehicle load and real-time traffic data, the distribution path can be dynamically adjusted, the vehicle utilization rate and the distribution time efficiency are improved, and the energy consumption and the cost caused by empty driving and detour are reduced.

The invention relates to the technical field of financial science and technology and natural language processing, in particular to an intelligent financial cue word generation and optimization system based on a knowledge graph, which comprises a financial field knowledge base, a cue word element extraction module, a field specialization template library, a cue word generation engine, an evaluation and optimization module and a manual feedback and labeling system, the efficiency is improved, through template generation and parameterization configuration, the prompt word writing time is shortened, and the labor cost is remarkably reduced. And quality guarantee: the accuracy of professional terms is improved, the structural integrity and compliance coverage are remarkably improved, and the output deviation of a large model is reduced. And scene adaptation: the fields of security investment, financial analysis and the like are supported, and the cue word complexity and professional depth are automatically adjusted according to user roles (investors / analysts). And dynamic evolution: through real-time updating of the knowledge graph and artificial feedback learning, the system can adapt to financial environment changes (such as new supervision policies and emerging analysis methods), and continuously optimizes and generates strategies.

The invention discloses a strategy dynamic optimization system and method based on big data analysis and prediction, and relates to the technical field of data analysis and processing, and the system comprises a data collection layer which is used for building a data source; the data processing layer is used for performing data feature extraction after performing data cleaning on the data source; the extreme scene simulation layer is used for executing coping strategy training in an adversarial environment; the competition optimization layer is used for configuring a competition agent and updating an adaptive strategy set after adaptive evaluation of strategy quality, performing game optimization and generating a game optimization result; and the strategy dynamic optimization layer is used for receiving the game optimization result and establishing a dynamic response strategy. The technical problems that in the prior art, analysis deviation is caused by poor data quality, a static strategy is difficult to adapt to a complex and changeable market environment and cannot effectively cope with dynamic games of different competitive roles, and the strategy adaptability and flexibility are insufficient are solved, and the technical effect of improving the decision-making flexibility and adaptability of the fluctuating market environment is achieved.

The invention relates to the technical field of e-commerce logistics and supply chain management, and discloses a multi-channel e-commerce platform order processing optimization system and method, and the method comprises the steps: correcting physical inventory data through a Lagrange dual variable fed back in a previous period, calculating a virtual inventory, and receiving a discrete order flow; constructing a space-time hypergraph model of the to-be-processed order according to the commodity homogeneous attribute and the spatial neighborhood attribute; performing constraint optimization solution on the hypergraph model based on a Lagrangian relaxation algorithm, and generating an optimal order allocation matrix meeting productivity constraints and a Lagrangian dual variable of a current period; and analyzing the distribution matrix to generate an operation instruction, issuing the operation instruction to the physical warehouse, and feeding back the dual variables to the inventory calculation module. According to the method, collaborative operation benefits are mined through the space-time hypergraph, and a negative feedback closed loop with physical capacity pointing to front-end sales is constructed by using dual variables, so that performance cost optimization and flow adaptive control are realized.

The invention relates to the technical field of logistics scheduling, and discloses an intelligent cross-border logistics scheduling optimization system, and the system comprises the steps: S1, obtaining cross-border logistics full-chain parameters in real time through a multi-source heterogeneous data collection module, and generating a logistics basic data set; s2, performing real-time risk quantification processing on the logistics basic data set based on a dynamic risk modeling engine, and constructing a logistics risk factor matrix; by constructing a multi-source heterogeneous data acquisition module and integrating the real-time transportation situation, policy compliance and global risk data of a cross-border logistics full chain, the problems of data islands and information lag in a traditional system are solved, comprehensive perception and deep fusion of logistics basic data are realized, a solid data support is provided for subsequent intelligent scheduling, and the real-time transportation situation, policy compliance and global risk data of the cross-border logistics full chain are integrated. And the perception capability of the system to a complex cross-border environment is improved.

The invention discloses a cloud computing bare metal hardware market distribution and management optimization system and method. The system comprises a resource access module, a distribution channel management module, an intelligent matching module, a transaction settlement module, a data analysis and optimization module, a safety and operation and maintenance management module and the like. Standardized aggregation of heterogeneous resources is realized through a resource access system, and the problem of fragmented management is solved; the distribution channel management system dynamically adjusts distribution rules and improves the channel cooperation efficiency; the intelligent matching system is combined with user behavior analysis to realize accurate supply and demand connection; the transaction settlement system integrates payment channels and marketing tools and simplifies fund circulation; the data analysis system precipitates full-link data to support decision optimization; the safety and operation and maintenance system guarantees safe and stable operation. According to the method, through the steps of resource access standardization, channel rule dynamic and the like, efficient, intelligent and standardized management of the whole distribution process is realized, and the market distribution efficiency, accuracy and safety are improved.

The invention discloses a multi-dimensional financial cost control and optimization system, and relates to the technical field of financial management. Financial data of an enterprise is collected, a financial data set S is constructed, the financial data set S is summarized and analyzed, and a material price fluctuation index CL, an exchange rate fluctuation index HL and a cash flow index XJ are obtained; obtaining a financial risk score FX according to the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ, obtaining a financial risk fluctuation coefficient sigma FX in combination with the financial risk score FX in a future period of time obtained by the financial risk prediction model, evaluating the financial risk state of the enterprise, and performing financial cost optimization. And the financial data after optimization are collected in real time, the financial risk score FXf after optimization is obtained, and the financial optimization effect is evaluated, so that an enterprise can quickly respond to market changes, the financial cost is controlled, and the flexibility and timeliness of financial management are improved.

The invention provides a Serverless function compilation optimization system and method based on a multi-level cache, and is used for solving the technical problem that compilation optimization results among function instances in a Serverless environment are difficult to reuse. The system adopts a three-level cache structure, and comprises an instance level cache for storing an optimization result of a current function instance, a function level cache for storing a sharing optimization result of different instances of the same function, and a global level cache for storing a cross-function multiplexing optimization result. The optimized code segment, the calling context information, the performance index data and the version identification are stored in each level of cache. The system realizes safe reuse of an optimization result through mechanisms such as cache query matching, version consistency verification and result validity verification. The method has the beneficial effects that the compilation optimization overhead of the function instance is remarkably reduced, the reuse efficiency of an optimization result is improved, the resource consumption of repeated compilation optimization is reduced, and the method is suitable for a large-scale Serverless computing environment.

The present invention discloses a tightening tool parameter optimization system and method based on deep learning, which relates to the technical field of tool parameter optimization. The present invention first uses an edge computing module to clean, standardize and extract features of real-time data, and uses an RNN model to predict the optimal tightening parameters. The process parameter optimization module dynamically adjusts parameters in combination with real-time working conditions, and can quickly adapt to complex environmental changes; secondly, the cloud uses a Transformer model to analyze the load balancing and operation sequence between multiple tools, and optimizes the task allocation strategy. At the same time, the IIoT platform realizes the sharing of optimization parameters between multiple tools, significantly improving the collaborative efficiency of the production line; in addition, the feedback module collects tightening operation performance and operating parameters, and uploads the data to the cloud, uses a deep learning model to continuously analyze the collaboration between tools and historical data, dynamically updates the optimization algorithm, and improves the system's adaptability to dynamic working conditions and continuous improvement capabilities.

The invention relates to the technical field of cargo loading optimization, in particular to a cross-border e-commerce logistics big data optimization system, which comprises a data acquisition unit, a data storage unit, a loading algorithm unit and a real-time guidance unit. The storage unit stores goods and transportation tool data, the loading algorithm unit divides three-dimensional grid units according to transportation tool space data, goods are classified according to the weight through a clustering analysis algorithm, and the optimal position of a single goods is determined through shape matching and a heuristic search algorithm. Goods are grouped and arranged through a path planning algorithm in combination with information such as a distribution sequence, the real-time guidance unit receives new data during loading, the algorithm is operated again, guidance information is generated, the problem of the loading space utilization rate is effectively solved, and the space utilization rate is improved.

The invention discloses a brand user portrait optimization system for cross-border e-commerce personalized recommendation, and relates to the technical field of e-commerce. The precision problem is solved. The system specifically comprises a data collection module which is used for collecting user information, user purchase information and commodity information; the commodity attribute classification module is used for recording commodity information and classifying commodities; the figure portrait module is used for establishing a multi-dimensional figure portrait according to the classification conditions of the user information, the user purchase information and the commodity information; and a matching module. According to the method, the portrait of the user is established, the established dimensions correspond to the classification types of the commodities, the type to which the commodity belongs and the matching degree of the type to which the commodity belongs are given to each commodity, and finally the favorite degree of the user in each dimension is determined according to the information of the commodities purchased by the user in the past, so that a portrait form similar to a multi-dimensional ability radar map is formed. Therefore, the favorite preference of the user can be determined, and the favorite degree of each favorite type of the user can also be determined.

The invention relates to the technical field of gas station operation optimization, in particular to an intelligent gas station operation optimization system and method based on big data and artificial intelligence. The data acquisition module acquires and uploads multi-source data in real time, the data processing module cleans and standardizes the multi-source data to generate auditable tagged data, the AI analysis module performs demand prediction based on a demand prediction model, and performs grouping through a K-means algorithm to generate user portraits. The intelligent scheduling module carries out intelligent dynamic scheduling according to a demand prediction result, optimizes service efficiency and reduces client waiting time, the operation optimization module carries out precise marketing according to a client portrait and formulates a personalized preferential strategy, and the feedback suggestion collection module monitors operation data and client feedback in real time. And the result is returned to the AI analysis module to optimize the prediction model and the marketing strategy. According to the invention, the scheduling and prediction precision is improved, an operation decision basis is provided for managers, and the gas station operation efficiency and customer satisfaction are improved.

The invention relates to the technical field of advertisement putting, in particular to an advertisement putting optimization system and method for commodity promotion, and the system comprises a data alignment unit which is used for obtaining the multi-source interaction data of a user, the multi-source interaction data comprises click stream data, transaction data and social behavior data, and the data alignment unit is used for obtaining the multi-source interaction data of the user; performing heterogeneous data space-time alignment on the multi-source interaction data to obtain aligned data corresponding to the multi-source interaction data; and the feature updating unit is used for carrying out cross-modal attention fusion on the alignment data so as to obtain attention features. According to the method, space-time alignment is performed through the multi-source interaction data, and the user characteristics are updated by using a cross-modal attention fusion technology, so that the multi-dimensional interest change and behavior pattern of the user can be accurately captured, the user demand is accurately matched in the advertisement putting process, and the advertisement effect is improved.

This invention relates to the field of power plant data processing technology, and more particularly to a virtual power plant load forecasting and dynamic regulation optimization system and method, comprising a data acquisition module, a preprocessing module, a forecasting module, a regulation module, and a verification module. The data acquisition module acquires real-time operating data of distributed energy nodes and electricity market signals; the preprocessing module standardizes the data using quantum spatiotemporal alignment and anomaly reconstruction techniques, extracting strong correlation vectors between meteorological features and load; the forecasting module uses an adaptive noise complete set empirical mode decomposition algorithm to separate the load sequence trend term, periodic term, and residual components; and the regulation module constructs a multi-objective optimization model. This invention achieves efficient aggregation of distributed resources, high-precision load forecasting under meteorological change scenarios, and coordinated dynamic scheduling strategies across multiple markets, thereby improving the clean energy absorption capacity and market response efficiency of the virtual power plant.

The invention provides an electricity market transaction optimization system based on deep reinforcement learning, and the system comprises a hybrid prediction subsystem which is used for optimizing an electricity market transaction strategy through electricity price fluctuation prediction, frequency modulation demand prediction and carbon price trend prediction; the multi-agent decision-making subsystem is used for carrying out optimization generation and dynamic adjustment on an electricity market transaction strategy through a plurality of agents with specific tasks; and the risk control subsystem is used for monitoring, evaluating and controlling the transaction risk of the power market through market risk control, ontology risk control and policy risk response, and optimizing the transaction strategy of the energy storage system. According to the technical scheme, multi-target collaborative optimization and cross-time arbitrage of energy storage participating in an electric power spot market, an auxiliary service market and a carbon emission permit market are realized.

The invention provides a mainboard performance optimization method and optimization system, and relates to the technical field of mainboards, the mainboard comprises a plurality of functional units, the method comprises the following steps: determining the connection relationship among the functional units, and collecting the historical data of each functional unit; constructing an agent model for the mainboard based on the connection relationship, the historical operation data and the historical performance data; forming a first optimization target set through random combination, and performing initial prediction of mainboard performance by utilizing the proxy model and combining a Bayesian optimization method to obtain an initial prediction result; performing iterative optimization in a Bayesian optimization framework based on the initial prediction result, and identifying a non-dominated solution by using a multi-objective optimization algorithm in the iterative optimization process to form a Pareto leading edge; and performing target matching on the input second optimization target set based on all the first optimization target sets of all the random combinations, and applying the optimal configuration corresponding to the matched first optimization target set to each functional unit so as to realize performance optimization of the mainboard.

The invention provides a college e-commerce purchasing process integration and optimization system and method, and the system comprises a demand management module which is used for pushing commodities to a commodity general pool, and grabbing the commodities to the commodity pools of all schools according to the requirements of the schools for the commodities to be purchased on shelf, and has a wish list function; the supplier management module is used for monitoring suppliers in real time and supervising and urging the suppliers to rectify; the commodity management module is used for monitoring on-shelf commodities and off-shelf non-compliant commodities of the e-commerce in real time, and labeling the commodities to prompt a purchasing user whether fixed asset filing is needed or not; the order management module is used for binding the classification of the commodity with the item fund card, performing settlement on the commodity purchased by the user according to the binding relationship, and pushing a settlement order; the after-sales management module is used for providing whole-course logistics tracking information and after-sales service of commodities purchased by the user; and the data analysis module is used for displaying the purchasing process data in real time through the data board. According to the invention, seamless connection and continuous optimization of the purchasing process are realized.

The invention relates to the field of data processing, and relates to an intelligent optimization system for enhancing programmed advertisement bidding based on trajectory data. Comprising a simulation bidding module used for performing data enhancement on original advertisement bidding data so as to obtain enhanced advertisement bidding data; the track data generation module is used for arranging the enhanced advertisement bidding data into track data capable of being learned by an IQL model; the IQLTrainer module is used for generating training parameters of an IQL model, constructing an IQL model instance in combination with the trajectory data, training the IQL model, and storing optimal model parameters after training is finished; the IQL model is used for acquiring a bidding strategy according to the current state of single real-time bidding; the training parameters comprise a budget proportion, an advertiser ID, a bidding model name and an operation parameter index. By adopting the system, the convergence efficiency of a bidding algorithm can be improved, and the noise of a training track is reduced.