79 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 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 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.

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 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.

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 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.

The invention discloses a quantitative transaction strategy dynamic optimization system and method based on deep learning, and belongs to the technical field of quantitative transactions, and the system comprises a data collection module which is used for collecting data of a target market invested by an investor from a data source; the data source comprises a security exchange and financial media; the acquisition frequency setting module is used for setting the frequency of data acquisition from the data source according to the transaction demand of the investor, and the acquisition frequency setting module comprises a distinguishing unit and a data setting unit; the distinguishing unit is used for distinguishing the transaction demands into high-frequency transaction demands and intermediate-frequency transaction demands according to the transaction demands. Through dynamic data adaptation, quantitative risk grading, multi-feature prediction and hierarchical fund management, accurate optimization and risk controllability of a quantitative transaction strategy are realized, and adaptability and income stability of the strategy in a complex market environment are improved.

The invention relates to the technical field of management systems, and particularly discloses a marketing scene dynamic simulation and strategy optimization system, which comprises a multi-source data fusion module, a dynamic scene simulation engine, a multi-agent strategy game module and a strategy iterative optimization and deployment module, and a closed loop from market environment perception to optimal marketing strategy generation and execution is realized. According to the invention, through a microcosmic consumer heterogeneity model, in combination with deep survival analysis and an attention mechanism, decision laws of individual consumers and differentiated responses to marketing stimulation can be accurately captured, the limitation of macroscopic analysis of a traditional system is broken through, full-dimension conversion prediction from individuals to groups is realized, and the prediction efficiency is improved. Refined data support is provided for strategy making, and the marketing conversion efficiency is effectively improved.

The present application relates to the field of strategy optimization, in particular to a credit data dynamic arrangement and strategy optimization system based on order perception. The credit report order is parsed to extract enterprise identification, analysis dimension and precision threshold; the internal historical database is searched, and the timeliness score is calculated based on the update frequency, industry volatility and risk level weighting; the timeliness score is compared with the precision threshold, and the gap analysis model is triggered to identify the external data dimension that needs to be supplemented in response to the condition not being met; the supplier selection engine is started based on the external data dimension, the multi-objective optimization algorithm is used to solve the supplier evaluation matrix to obtain the optimal supplier combination; the optimal supplier combination is called to obtain external data, which is fused with historical records after verification to generate a report. The present application dynamically allocates data sources on demand, avoids blind purchasing, and makes the data acquisition cost and data quality reach a quantitative balance under the condition of meeting the analysis accuracy.

The invention relates to the technical field of intelligent optimization combination of products. The financial portfolio optimization system comprises an investment association effect analysis module, a financial portfolio investment value analysis module, an investment risk analysis module and an investment portfolio rescreening and distribution module. The investment association effect analysis module analyzes association coefficients among the financial products and generates a financial product investment association chart; the financial portfolio investment value analysis module analyzes the overall investment value through the investment value of a single financial product and the correlation coefficient; the investment risk analysis module is used for judging whether the overall risk of the financial investment portfolio is increased or not by analyzing the whole investment portfolio; and the investment portfolio optimization module screens and distributes the financial investment portfolio again. According to the method, the overall risk of the investment portfolio is analyzed through the association effect between the financial products, and the investment portfolio is re-screened and distributed according to the association effect, so that the investment risk is reduced.

The invention discloses a dynamic weight optimization system and method based on difference proportion and level conservation, and the method comprises the steps: setting a to-be-scored subject in a personnel quality scoring system as a first-level index, and setting a plurality of to-be-scored items under the to-be-scored subject as second-level indexes; and according to the guidance probability of the weight of each index unit in the secondary index and the original weight ratio of each index unit in the secondary index, obtaining a difference driving adjustment coefficient of each index unit in the secondary index, and according to the difference driving adjustment coefficient, carrying out optimization processing on the original weight of each index unit in the preset secondary index in the personnel quality scoring system. And obtaining the final optimization weight of each index unit in the secondary index. According to the method, the relative importance change among the indexes can be dynamically captured, the self-adaptive weight adjustment is realized while the hierarchical constraint is maintained, the static weighting logic of the traditional method is broken through, and a new way is provided for constructing an intelligent and explainable comprehensive evaluation system.

The invention discloses a merchant agent hierarchical pricing and multi-agent e-commerce transaction optimization system and method, and relates to the field of telemarketing. The method is realized through combination of a user agent Agent-U, a market agent Agent-M and a merchant agent Agent-S. The merchant agent Agent-S comprises a user agent Agent-U, a market agent Agent-M and a merchant agent Agent-S. The user portrait analysis module is used for generating a value evaluation coefficient in real time; the third-level price strategy library is used for storing a new customer erosion pricing strategy, a VIP value addition strategy and a conventional competitor benchmarking strategy; and the dynamic adjustment engine responds to market intelligence data pushed by the market. According to the invention, the value evaluation coefficient calculation model is fused with the historical consumption, the service sensitivity and the potential value index to realize the accurate user portrait, and then differential response is implemented by adopting the three-level price strategy library, so that the problems existing in the prior art are solved.

The invention relates to the technical field of big data, in particular to a push optimization method and system based on e-commerce big data feedback, and the system comprises a data acquisition module, a model construction module, a push data generation module and a negative feedback optimization module. Obtaining multi-source heterogeneous data and a real-time e-commerce data stream, and performing compliance verification on all the obtained data; constructing a corresponding user portrait based on the verified e-commerce data, extracting a corresponding push feedback feature, and constructing a feedback driving model at the same time; inputting the user portrait and the push feedback feature into the feedback driving model, and calculating push data by using output expected feedback data; the negative feedback data of the push data is acquired in real time, and push optimization is performed based on the negative feedback data, so that feedback optimization of the e-commerce push data is realized, and the push conversion rate of the e-commerce data is improved.

The present application relates to the field of coating parameter self-optimization, and discloses a coating process parameter self-optimization system and method fusing model self-learning, wherein a coating process parameter self-optimization method fusing model self-learning comprises the following steps: collecting process parameters, production conditions, equipment power states and detection result data of multiple batches of coating production in real time; aligning and correlating the data according to the process sequence to form a unique mapping relationship; reversely determining corresponding process parameter nodes and working condition node sets to generate a consistency index; jointly comparing the consistency index and the corresponding data chain distribution to output corresponding working condition drift identifiers; introducing the consistency index and the working condition drift identifiers as constraint conditions into a parameter update model to output a constrained parameter update result; and writing the corresponding new round of detection results into the unique mapping relationship to generate an incremental updated mapping relationship and a reachable state space. The present application has the advantage of improving the stability of the optimization result.

The invention relates to the technical field of data processing, in particular to a multi-parameter coupling optimization system and method for a spinning bath, and the system comprises a data analysis module which obtains a multi-parameter combination in historical data, divides a process window boundary, calculates a robustness index, divides parameter space sub-regions according to the process window boundary and the robustness index, and outputs the parameter space sub-regions; and calculating the influence sensitivity of the stabilizable index to obtain a parameter importance measurement value. The numerical solution module is used for establishing a technological process mathematical equation set, microstructure characteristic parameters and defect precursor information are obtained according to the multi-parameter combined solution equation set, and the parameter evaluation module is used for obtaining a stabilization index and a defect probability. And the iterative operation module is used for generating a to-be-evaluated multi-parameter combination set according to the parameter space sub-region and the parameter importance measurement value and a differentiation mechanism, establishing a function group according to a stabilization index and a defect probability, and performing evaluation and iterative operation on the to-be-evaluated multi-parameter combination set to obtain an optimal multi-parameter combination.

The application discloses a distributed decoupling optimization method for a comprehensive energy system considering carbon trading, and comprises the following steps: establishing a centralized optimization framework of an electrically coupled system, initializing global data and information of a power system and a gas system; constructing a centralized model of a regional comprehensive energy system, wherein an objective function of the model comprises the power system, the gas system and carbon emission cost, and corresponding operation constraints are added to the objective function; converting an original non-convex problem into a mixed integer second-order cone programming based on SOCP relaxation, and performing reactive power optimization by using an ADMM consistent distributed algorithm; introducing consensus variables to represent the synergistic effect between the power and natural gas networks based on the ADMM method of consistent variables, so as to solve a distributed optimization problem of the regional comprehensive energy system; and finally, a case analysis of a regional electric and gas comprehensive energy integrated system is performed, and the distributed decoupling optimization of the system is realized by using the ADMM method of consistency. The application also comprises a distributed decoupling optimization system for a comprehensive energy system considering carbon trading.

The invention relates to the technical field of financial science and technology, in particular to an intelligent transaction optimization system based on source network load storage. According to the technical scheme, the intelligent transaction optimization system based on source network load storage comprises an electric power balance optimization module, a system security and stability module, a cost control optimization module, an intelligent scheduling control module, an intelligent operation and maintenance management module and a market transaction optimization module; according to the invention, through cooperative work of the matching engine and the electronic contract module, a direct transaction channel of a source network load storage whole link is established. Through a standardized electronic contract and an intelligent matching algorithm, real-time and efficient matching between green electricity producers and consumers is realized, information barriers and institutional cost in traditional transactions are eliminated, mobility of green electricity transactions is improved, transaction cost is reduced through a direct transaction mode, and participation enthusiasm of market subjects is enhanced.

The invention provides a second harmonic characterization and optimization system, which comprises an incident light path system, an emergent light path system and an annealing light path system, and is characterized in that the incident light path system is used for enabling a light beam emitted by an incident laser source to enter a tested sample to generate second harmonics; a signal reflected by the second harmonic waves passes through the emergent light path system and then enters a spectrograph to analyze a result; and the annealing light path system is used for annealing a tested sample. According to the second harmonic characterization and optimization system, the annealing light path system capable of achieving in-situ laser annealing is arranged, accurate characterization of interface signals is achieved, meanwhile, accurate annealing is conducted on the same position, the annealing effect can be fed back in real time, time and material losses of the steps of electrode introduction and the like are reduced, and the service life of the second harmonic characterization and optimization system is prolonged. And meanwhile, the laser annealing effect can be subjected to secondary harmonic measurement and real-time feedback through integrated arrangement of laser secondary harmonic and laser annealing, and the result of increasing the yield is achieved.

The invention discloses a commodity marketing strategy optimization system and method based on multi-source data analysis, and relates to the technical field of commodity marketing strategy optimization, and the method comprises the steps: S10, carrying out the analysis of an effective browsing frame track of a customer, and obtaining the real-time marketing data of a target commodity under a current marketing strategy; s20, selecting marketing content to be optimized in the current marketing strategy of the target commodity; s30, determining an associated frame of the to-be-optimized marketing content of each frame; and S40, determining an optimization scheme of the current marketing strategy of the target commodity. According to the method, the to-be-optimized condition of the current marketing strategy of the target commodity is determined based on the marketing data, frame-by-frame analysis of the marketing strategy is achieved, and the optimization effect of the marketing strategy is improved. The marketing strategy obtained according to the optimization scheme can be highly accepted by customers.

The invention discloses a reinforcement learning-based house resource dynamic market valuation prediction optimization system, which comprises a data processing module for collecting real estate transaction, geographic information, economic indicators and policy event data and generating a house resource state vector; the expert data module is used for constructing a weighted expert track set according to expert valuation records and transaction prices; the state recognition module is used for executing point change detection and determining market state identification; the confrontation signal module is used for generating a hetero-variance weighted confrontation reward signal based on the improved GAIL model; the risk constraint module introduces conditional value risk constraints based on quantile regression to generate risk weighted signals; the strategy optimization module is used for updating valuation strategy parameters by the risk weighting signals and distilling the valuation strategy parameters into a lightweight reasoning model; and the deployment updating module is used for executing incremental training and parameter updating based on the newly added data. According to the invention, robust optimization and risk adaptive adjustment of the valuation strategy are realized.