46 results about "Market dynamics" patented technology

The invention provides a supply chain cost anomaly AI positioning system based on multi-mode depth tracing, and relates to the technical field of cost anomaly tracing. According to the supply chain cost anomaly AI positioning system based on multi-modal depth tracing, a three-layer architecture is adopted, a dynamic threshold algorithm and a multi-level association map are combined, real-time detection and root positioning of cost anomaly are achieved, and the three-layer structure is data acquisition, dynamic modeling and visual tracing. The system has the advantages that dynamic adaptability is improved, reasonable fluctuation and abnormal risks are accurately distinguished, the system breaks through the rigid limitation of a traditional static rule, the judgment threshold value is dynamically adjusted by fusing market dynamics (such as bulk commodity prices and policy adjustment) and real-time service data, and a supplier-production-logistics-storage association graph is constructed, so that the risk of abnormal risks is accurately distinguished. And the conduction path of the cost abnormity in each link of the supply chain is visually displayed.

The invention relates to a differential privacy-based competitive intelligence analysis system and method. The differential privacy-based competitive intelligence analysis system comprises a differential privacy processing and budget management module, a differential privacy feature engineering module, a privacy protection analysis and prediction module and a differential privacy synthetic data generation module. According to the design of the invention, an end-to-end advanced analysis framework with mathematics provable privacy protection capability is constructed, so that accurate, timely and reliable insight of market dynamics and competitor strategies is realized on the premise of strictly protecting enterprise sensitive commercial confidentials.

The invention relates to the technical field of product oil management, and discloses a product oil purchase-sale-stock multi-modal data analysis method and system. The method comprises the following steps: acquiring real-time inventory data, transportation state data and sales terminal data in a product oil supply chain to form a multi-modal data set; performing data cleaning on the data, and extracting key inventory characteristics to generate a cleaned data subset; obtaining associated historical purchase data, regional demand prediction data and market price fluctuation data based on the gas station identification information to form a multi-dimensional auxiliary data set; inputting the features into an inventory feature extraction model, generating a multi-feature vector set, and fusing the multi-feature vector set; inputting the fused feature vectors into an inventory state evaluation model set, and outputting inventory early warning level and abnormal type information; and finally, a dynamic allocation scheme is generated by combining real-time inventory data and the like. According to the method, multi-modal data are integrated, so that the comprehensiveness and accuracy of data analysis are improved, and the purchase-sales-stock management of the product oil is assisted to be more suitable for market dynamics.

The invention provides a reinforcement learning dynamic pricing method based on game feedback, and relates to the technical field of online data market dynamic pricing. Through combination of leader-follower strategy interaction of the Stackelberg game and the exploration-utilization balance principle of the dobby machine, the problems of price adjustment lag, large income fluctuation and insufficient strategy stability in a non-stable environment are solved. By introducing a data freshness evaluation mechanism, data weight is dynamically adjusted by integrating data timeliness attenuation and market correlation analysis, it is ensured that data input into a model always has high real-time performance and strong correlation, and the problem of decision lag caused by static data input in a comparison scheme is avoided; the exploration-utilization balance principle and the reinforcement learning model of the dobby machine are fused, the exploration rate is dynamically adjusted, a historical optimal strategy can be fully utilized in a non-stationary environment, a potential better scheme can be explored, and the static optimization bottleneck that a game model is limited to a preset objective function in a comparison scheme is broken through.

The invention belongs to the technical field of virtual power plants, and relates to a power spot market oriented virtual power plant resource dynamic aggregation method. The method comprises the following steps: extracting a distributed resource feature vector and mapping the distributed resource feature vector into a response type label; determining a price fluctuation period according to a spot price sequence, calculating a time sequence matching degree between each resource and a market rhythm by combining the feature vector, and constructing a comprehensive distance by fusing a clustering weight and a response type label; dividing the resource cluster into dynamic aggregation units by taking the variance of the minimum comprehensive distance as a target; a resource aggregation strategy is generated based on the aggregation unit, a control instruction is issued, and clustering weights and aggregation parameters are iteratively updated according to the clearing deviation rate fed back by market clearing. According to the method, the technical problems that the market dynamics is ignored, the clustering model is easy to merge heterogeneous resources and the closed-loop optimization is lacked in the existing static division are solved, and the effects of improving the market response capability of the virtual power plant, the control consistency of the aggregation unit and the adaptive optimization of continuous transactions are achieved.

The invention belongs to the technical field of self-media operation and business decision, and particularly relates to a self-media account business intelligent quotation method and system, and the method comprises the following specific steps: S1, collecting self-media account basic data, user portrait data, commercial value data and market dynamic data; s2, encrypting and storing the sensitive data through AES-256; s3, adopting a random forest algorithm to construct a basic quotation model, and combining account operation fluctuation and market nodes to realize dynamic adjustment; s4, displaying quotation composition details to the account party and supporting parameter adjustment preview; and S5, generating a multi-format personalized quotation list, and negotiating and recording full-link data based on the quotation and the cooperation effect. According to the method and the system, through integrating the self-media account basic data, the user portrait data, the commercial value data and the market dynamic data, the key information influencing the quotation is covered in multiple dimensions, the quotation association factors are comprehensively considered, the deviation caused by single data limitation is reduced, and the quotation accuracy is improved.

The invention relates to the technical field of data processing, in particular to a project application competitiveness analysis system which comprises an acquisition module, a preliminary screening module, a judgment module, a determination module, an adjustment module and an output module. According to the method, multi-dimensional data is acquired in real time, firstly, project keywords are matched with policy keywords, and then key projects are judged by utilizing technology integration density and task dependency density; then, a priority list is determined by integrating the competitive strength, the technical optimization rate and the policy matching degree, in addition, according to the dynamic optimization threshold value of the approval rate, self-adaption policy change and market dynamics, not only is the scientificity of analysis improved, but also the practicability and reliability of the system are enhanced, and finally, by outputting an analysis report, a decision basis of data support is provided for a user, and the user experience is improved. The problems of low project screening accuracy and response lag caused by excessive dependence on models and incapability of adapting to changeable markets and policy environments are effectively solved.

Computerized systems and methods for automated AI-driven customer and vendor subdivision and personalized insight delivery are described. The method involves collecting real-time data including purchase behavior and market trends, analyzing it using an AI / ML algorithm for subdivision, and delivering personalized insights through a user-friendly single glass pane user interface (SPoG UI). Transaction details are recorded for continuous enhancement. Based on user feedback and evolved market dynamics, the subdivision effectiveness is monitored and refined. A plurality of data sources and analysis tools are integrated for comprehensive analysis. The user may customize the tessellation parameters, and the delivery options include push notifications and email alerts. The system facilitates continuous optimization and adaptation, thereby enhancing correlation and accuracy.

The application relates to the field of power grid dispatching, and provides a medium and long term price prediction method and device, an electronic device and a storage medium. The method comprises the following steps: taking a to-be-predicted date as a starting time point, and collecting historical multi-dimensional feature data sets in reverse; determining power boundary feature data corresponding to the to-be-predicted date based on the historical multi-dimensional feature data sets; screening a target training data set from the historical multi-dimensional feature data sets based on the power boundary feature data and the historical multi-dimensional feature data sets; training an initial medium and long term price prediction model by using the target training data set, so as to obtain a trained medium and long term price prediction model; and inputting multi-dimensional feature data corresponding to the to-be-predicted date into the trained medium and long term price prediction model, and outputting a medium and long term price prediction result. The application effectively solves the problems that the existing technology has poor ability to describe complex market dynamics, the prediction accuracy is difficult to break through the existing bottleneck, and it is difficult to realize high-precision and high-reliability prediction of medium and long term prices.

The invention discloses an e-commerce platform dynamic pricing and subsidy optimization method based on a multi-agent game, and relates to the technical field of e-commerce sales management. The method comprises the following steps: step 100, constructing a multi-agent game model, determining a core game agent in an e-commerce scene, quantifying a revenue function and a constraint condition of each agent, and solving to obtain a pricing and subsidy game equilibrium feasible region; step 200, based on the game equilibrium feasible region, executing a dynamic optimization algorithm, collecting real-time operation data of the e-commerce platform, setting a multi-objective optimization function, and iteratively outputting an optimal pricing strategy and an optimal subsidy strategy through a reinforcement learning algorithm; and step 300, executing the optimal pricing strategy and the optimal subsidy strategy, and constructing a full-link feedback closed loop. The beneficial effects of improving the adaptability of the pricing and subsidy strategy to the market dynamics, optimizing the platform operation resource allocation efficiency and ensuring the long-term stable operation of the e-commerce ecology are achieved.

The application discloses a market maker pricing optimization method for water right transaction, which comprises the following steps: firstly, recording the uncompleted water right transaction; secondly, constructing an expected income function for predicting the income according to the uncompleted transaction; thirdly, establishing a stock risk constraint condition; fourthly, combining the expected income function and the constraint condition to establish a pricing optimization model; fifthly, solving the model to obtain the optimal purchase offer and the optimal sale offer; sixthly, starting the water right transaction by taking the optimal sale offer and the optimal purchase offer as the current offer; seventhly, monitoring all the water right transaction conditions and adjusting the sale offer and the purchase offer according to the transaction conditions; and eighthly, replacing the previous offer with the adjusted offer. The method has the hard constraint of the water right stock quantity, the expected income function is predicted based on the market order information, and the method has high practical value and reliability. The method can adjust the offer according to the market dynamic conditions, and has a positive effect on keeping the stock balance and promoting the transaction.

The invention relates to the technical field of electricity market transaction, in particular to an electricity spot market price prediction and transaction optimization method. According to the technical scheme, the electric power spot market price prediction and transaction optimization method comprises a work flow of electric power spot market price prediction and transaction optimization; according to the method, the robustness and the prediction precision of a price prediction model in the face of market complexity and sudden events are remarkably improved, and a long-term and short-term memory network component can effectively capture and memorize a nonlinear dependency relationship from a historical price sequence and related multi-dimensional features by virtue of a gating mechanism; the model can understand a more abstract market dynamic mode, the autoregressive integral moving average model component is used for capturing inherent linear trends and short-term laws in a time sequence, and the model is allowed to dynamically allocate different weights for input information of past different time steps by setting an attention mechanism when prediction is performed each time.

The invention relates to the technical field of data processing, and provides an asset value data determination method and device, electronic equipment and a readable storage medium. The method comprises the following steps: standardizing house attribute data to obtain standardized house attribute data; performing rationality verification processing on the standardized house attribute data to obtain effective house attribute data; performing multi-factor fusion processing on the effective house attribute data, preset asset static parameters and preset asset dynamic parameters to obtain target house asset value data; and sending the house target asset value data to the target terminal device for display, thereby improving the consistency and normalization of heterogeneous data, improving the data quality and authenticity, enhancing the response speed of asset value evaluation to market dynamics, improving the accuracy and comparability of a pricing result, and improving the user experience. And the decision transparency and the business execution efficiency are improved.

The present application relates to the technical field of intelligent product selection, and particularly relates to an intelligent product selection and infringement early warning method based on cross-platform mining and a knowledge graph, comprising: obtaining product description texts on multiple platforms, user interaction behavior sequences and market dynamic indicators; using natural language processing to analyze the product texts and extract a technical feature vector, combining time series analysis to generate a product heat index, and calculating market saturation based on market dynamics to form a product market potential evaluation indicator; constructing a multi-dimensional product knowledge graph based on the technical feature vector, and selecting candidate products according to the potential indicator; mapping the candidate product technical vector to the knowledge graph to quantitatively generate a patent infringement risk evaluation value; when the risk exceeds a threshold value, generating a design avoidance scheme based on the technical substitution path and design variant scheme in the graph; and outputting intelligent product selection decision support information. The present application improves platform product selection efficiency and accuracy.

The application provides a marketing strategy dynamic simulation optimization system data processing method and device, and relates to the field of data processing. The method comprises the following steps: establishing an industry market demand prediction model with a specified period based on industry characteristic data; establishing a market competition situation and market influence model based on historical market data, and determining the competition type of each market participant in the market competition structure in the target industry through the market competition situation and market influence model; establishing a production capacity and energy storage model based on the production process characteristic data, the upper limit of production capacity data and the inventory turnover level data of the target industry; constructing a market dynamic twin engine based on the production capacity and energy storage model, the market competition situation and market influence model and the industry market demand prediction model; and dynamically simulating the marketing strategies corresponding to different competition types through the market dynamic twin engine to obtain optimal marketing strategy data with a specified period.

The embodiment of the invention provides an LNG price prediction method and system, and belongs to the technical field of energy. The method comprises the steps of performing multi-source data acquisition in a historical database based on preset LNG price related data items; obtaining preprocessed multi-source data and time sequence data, and constructing a training sample based on the preprocessed multi-source data and time sequence data; pre-constructing a training model based on an end-to-end coding-decoding structure, and training the training sample based on the training model to obtain an LNG price prediction model; and collecting current multi-source data related to LNG price prediction, and performing LNG price prediction based on the current multi-source data related to the LNG price prediction and the LNG price prediction model to obtain an LNG price prediction result. According to the scheme, domestic and overseas LNG price prediction can be supported on the prediction space scale, the time scale covers short-term and medium-term price prediction, real-time response to market dynamic changes can be achieved, and prediction flexibility and timeliness are enhanced.

Systems and methods for predicting demand in real estate sales receive project-specific input data such as launch pricing, unit mix, project configuration, location factors, and / or market dynamics; identify comparable real estate projects within a defined geographic range based on similarity of project size, configuration, and / or launch conditions; apply an Initial Timeframe Model to forecast the number of units likely to sell within a first timeframe after launch, using a comparative analysis of the identified comparable projects and the project-specific input data; generate a project-specific demand curve for a subsequent timeframe using a demand curve model in which the model establishes a relationship between unit pricing and demand; dynamically refining the demand curve by incorporating real-time market data and / or housing market trends to align predictions with current economic conditions; and provide a graphical representation of the demand curve to guide optimal pricing and sales strategies for sustained revenue generation.

The invention discloses a multi-agent-based power spot goods dynamic pricing system and method, and relates to the technical field of multi-agents, and the system comprises a framework establishment module which constructs a multi-agent cooperation framework; the method comprises the following steps: collecting power grid data in real time and preprocessing, and constructing a current market state vector through spatial-temporal feature separation and extraction and physical information distillation; a heterogeneous action space is constructed, and based on the current market state vector, the intelligent agent outputs an action through an Actor network; the MOA agent constructs a clearing model to carry out constraint clearing, and a clearing result is generated; and calculating rewards of the power generator agent GA and the load aggregator agent LAA, calculating an action pair value through a Critic network, and performing parameter updating on a strategy network of the power generator agent GA and the load aggregator agent LAA. And the reliability and efficiency of market dynamic pricing are greatly improved.

The invention discloses a power retail market risk assessment method and electronic equipment. According to the power retail market risk assessment method provided by the invention, the analytic hierarchy process weight dynamically generated based on historical objective data is introduced, so that the defect that a fixed weight is set depending on subjective experience in a traditional method is effectively overcome, and the risk assessment result is more objective and accurate. Meanwhile, risk probability prediction is carried out in combination with a time sequence deep learning model, market dynamic changes can be caught sensitively, and the timeliness of risk early warning is remarkably improved. And finally, the objective weight, the prediction probability and the current market state information are deeply fused by using a fuzzy comprehensive evaluation method, and the generated comprehensive risk evaluation result not only has high reliability, but also has adaptive ability to market change, and provides powerful decision support for accurate supervision and risk prevention and control of the power retail market.

The invention relates to the technical field of production and purchase management, and discloses an enterprise production and purchase management system and method based on an AI technology, and the system comprises a purchase data collection module, a data integration module, an intelligent analysis module, an intelligent grading module, a decision optimization module, and a block chain evidence storage module. The system automatically generates purchase data based on a production plan and collects market dynamics in real time, so that manual intervention is greatly reduced, and the response speed is increased; integrating multi-source data to generate evaluation indexes, providing real-time service analysis, assisting strategic decision and improving the transparency of a supply chain; an index is constructed, an optimal purchasing scheme and an alternative scheme are generated through a grading result, multi-scheme cost / risk comparative analysis is provided, an enterprise is helped to select suppliers, and supply chain risks are dynamically avoided; and generating a unique hash code for the material, binding a specification, a model and a substitute material relationship, realizing full-link tamper-proof tracing, and ensuring data integrity and transparency.

The invention relates to the technical field of financial risk prediction, and discloses a financial transaction risk dynamic prediction system based on artificial intelligence. A financial data acquisition unit of the system obtains real-time market information flow and associated entity information flow of global multi-market transaction objects, a financial space-time cube is formed through space-time alignment processing, and the data flow and the information flow comprise multiple types of information such as an object asset price fluctuation sequence and a market depth change sequence; the risk factor separation unit executes deep tensor decomposition on the financial space-time cube, and extracts a core factor tensor and an abnormal mode indication tensor; a risk strategy generation unit generates a multi-market collaborative real-time risk prevention and control strategy containing subject asset position adjustment and market open control instructions based on the core factor tensor and the mechanism risk preference map; the dynamic strategy adjustment unit identifies an emergency risk event according to the abnormal mode indication tensor, revises the strategy in real time to generate a new prevention and control strategy, and can effectively deal with the dynamic risk of the financial market.

This invention discloses a method and system for precise marketing resource allocation based on the user's marketing value lifecycle, specifically relating to the field of marketing resource allocation technology. The method includes acquiring multi-source heterogeneous data of the target user group and constructing a user state representation that integrates static user attributes, dynamic behavioral sequences, and market dynamic characteristics. Based on the user state representation, a dynamic CLV prediction model is used to simultaneously predict the user's future lifecycle value and user state transition probability. This invention solves the technical problems of static prediction, disconnect between optimization and execution, and rigid budgeting in traditional marketing resource allocation by constructing a closed-loop collaborative architecture of dynamic CLV prediction, heterogeneous MDP planning, and contextual multi-armed learning. This enables the marketing resource allocation process to combine the foresight of long-term planning with the agility of short-term execution, improving the accuracy of CLV and state transition probability predictions. Through asynchronous updates and knowledge transfer mechanisms, it ensures the system's stable adaptation in dynamic environments.

The application relates to the technical field of intelligent decision-making and is applicable to the financial field, and provides a transaction dynamic prediction optimization method and device, which comprises the following steps: determining a current market state based on multi-dimensional market performance information of a current transaction space; updating model parameters of an initial transaction prediction model based on the current market state; determining a predicted yield rate and a predicted market index of the current transaction space through the current transaction prediction model; constructing a dynamic Sharpe ratio optimization function, taking the predicted yield rate and the predicted market index as input information of the dynamic Sharpe ratio optimization function, and determining the optimal asset allocation weight of the current transaction space through the dynamic Sharpe ratio optimization function. The scheme improves the real-time response capability of a transaction system to market environment changes and overcomes the defect that a transaction prediction model is difficult to adapt to real-time market dynamics due to artificial parameter adjustment.

Computerized systems and methods for automated AI-driven customer and vendor subdivision and personalized insight delivery are described. The method involves collecting real-time data including purchase behavior and market trends, analyzing it using an AI / ML algorithm for subdivision, and delivering personalized insights through a user-friendly single glass pane user interface (SPoG UI). Transaction details are recorded for continuous enhancement. Based on user feedback and evolved market dynamics, the subdivision effectiveness is monitored and refined. A plurality of data sources and analysis tools are integrated for comprehensive analysis. The user may customize the tessellation parameters, and the delivery options include push notifications and email alerts. The system facilitates continuous optimization and adaptation, thereby enhancing correlation and accuracy.

This invention provides a method, apparatus, and electronic device for dynamic pricing optimization in a market with rigid demand, relating to the field of data processing. The method includes: establishing a market forecasting model for rigid demand based on industry characteristic data of the industry; establishing a market competition situation evaluation model based on historical competition data within the industry; converting specified production capacity data and relevant parameters of specified industry safety and environmental standards into quantitative production capacity constraints; establishing an evaluation model of the impact of market prices and freight costs in neighboring regions based on market prices and freight costs in the corresponding neighboring regions; fusing the market forecasting model, the market competition situation evaluation model, and the market price and freight cost impact evaluation model to construct a multi-factor, multi-dimensional spatiotemporal model; determining the optimal market price through the multi-factor, multi-dimensional spatiotemporal model according to a specified optimization objective function and quantitative production capacity constraints, and using the optimal market price as the result of dynamic pricing optimization in the market with rigid demand.

The invention discloses a text travel consumption product popularity prediction and evaluation method based on artificial intelligence, and relates to the technical field of intelligent decision making, and the method comprises the steps: collecting text travel consumption product attribute data, user interaction data and environment event data, carrying out the data cleaning, standardization and correlation integration, and generating a market data set; defining a state space, an action space and an interaction rule according to the market data set, and creating a corresponding dynamic agent for each text travel consumption product; constructing a multi-agent reinforcement learning framework, inputting the dynamic agent into the multi-agent reinforcement learning framework, and generating a virtual market simulation environment; and driving the dynamic agents to select marketing actions according to the state attribute values in the current state space, and generating a market state evolution sequence according to the marketing actions executed by all the dynamic agents and the interaction rules. The market dynamic state is simulated through the multi-agent reinforcement learning framework, and the accuracy and reliability of popularity prediction are improved.

This invention discloses a sales volume prediction method, apparatus, device, and storage medium based on public opinion data. The method includes: acquiring public opinion data and historical sales data of a target product within a historical time period; extracting features from the public opinion data to obtain a public opinion feature set; performing spatiotemporal alignment and fusion processing on historical sales volume, historical prices, and public opinion features from the public opinion feature set according to a preset period to obtain a fused feature sequence; and inputting the fused feature sequence into a sales volume prediction model to obtain the sales volume of the target product for each prediction period within the prediction time period. This invention achieves the prediction of target product sales volume by combining historical sales data and public opinion data. By capturing dynamic changes in public opinion such as consumer sentiment and focus in the market through public opinion data, it effectively solves the problem of lagging market dynamic response when using only historical sales data in existing technologies, making sales volume prediction more forward-looking and accurate.

The invention provides a housing policy intelligent decision auxiliary method and system based on a large language model, and belongs to the technical field of housing policy decision support, and the method comprises the steps: constructing a market theory hypothesis based on a housing filtering mechanism; inputting proxy attributes and market variable data; based on an intelligent agent modeling technology, a simulation model for simulating housing decision behaviors of different income groups is constructed and operated, and market dynamics are deduced under various policy scenarios; key indexes such as transaction activeness, housing quality distribution and group structure in the simulation process are extracted and visualized; and finally, inputting the structured indexes into a large language model, and generating an intelligent decision-making auxiliary text containing scene analysis, result induction and policy suggestion. According to the method, the dynamic deduction ability of computational simulation and the semantic understanding ability of artificial intelligence are fused, refined and intelligent evaluation of the housing policy effect can be realized, and the scientificity and the foresight of housing policy design are effectively improved.

The invention provides a reinforcing steel bar guidance price and height limit price measuring and calculating method based on big data, and relates to the technical field of big data analysis. Firstly, historical reinforcement settlement data of an enterprise is preprocessed, and data sorting and cleaning are completed according to specifications, models and settlement time; secondly, removing abnormal data through outlier analysis, dividing effective data into price intervals, and matching the price intervals with differential floating ranges; thirdly, locking an effective settlement price value interval and measuring and calculating a guidance price in combination with a price rising and falling trend of an external market; and finally, based on an enterprise collection protocol and an external network price, calculating a limit price through a multi-dimensional matching strategy, and expanding a matching range to ensure the calculation precision when there is no direct collection protocol. Actual settlement data of an enterprise is taken as a core, external market dynamic and collection scenes are fused, accurate control and dynamic adaptation of the steel bar purchase price are realized, and a scientific basis is provided for enterprise purchase decision making.

The invention belongs to the field of data analysis, and discloses a market situation awareness and decision-making method based on space-time diagram convolution and causal inference, and the method comprises the steps: constructing a unified data collection framework, and fusing multi-modal data; establishing a cross-department network through quality evaluation, cleaning and clustering analysis mining association; mapping to a space-time diagram, and generating an operation portrait by means of a convolutional network; analyzing a portrait prediction trend, and mining a causal relationship; and constructing a twin network virtual scene, and carrying out reinforcement learning iteration to obtain an optimal decision. Multi-modal data fusion, accurate situation awareness and intelligent decision making are realized, enterprises are assisted to capture market dynamics and make scientific decisions, the market competitiveness is improved, and the method is suitable for enterprise operation management in a complex market environment.