12 results about "Market prediction" patented technology

A system and method for certifying, sharing, and monetizing via marketplaces personal and collective memories as digital assets, integrated with blockchain or other immutable registry technologies. Users upload multimedia memory content, which is authenticated via biometric, contextual, or metadata-based verification. Artificial intelligence assists in structuring the memory and generating immersive presentations incorporating visual elements and optionally one or more sensory modalities including touch, smell, hearing, taste, moisture, nervous system sensations, and temperature. The immersive presentation is then minted as a unique non-fungible token (NFT / nxNFT) or equivalent digital asset, enabling privacy, access control, transferability, and monetization through sale, financing, rental, or co-ownership models. AI further supports market prediction, including dynamic pricing strategies. The system may incorporate a social ecosystem for voting, curation, and emotional engagement, alongside a governance layer for ethical oversight and category designation. Designed for scalability, cross-platform resonance, and anticipates future technologies ensuring interoperability with evolving digital asset standards.

The invention relates to the technical field of electricity market prediction, in particular to an electricity market day-ahead electricity price prediction method and system based on an enhanced linear Stacking framework, and the method specifically comprises the steps: obtaining multi-source heterogeneous data, carrying out the preprocessing of the data, obtaining a standardized feature matrix, calculating the feature importance through a random forest, and carrying out the calculation of the feature importance; k-fold time sequence cross validation and marginal benefit analysis are combined to screen an optimal feature subset, two basic learners are constructed, fold time sequence cross validation is adopted to train the basic learners, fold-out prediction vectors are obtained, statistical features are extracted, and the fold-out prediction vectors and the statistical features serve as input to train the basic learners, so that the optimal feature set is obtained. And then the optimal hyper-parameter of the basic learner is searched through Bayesian optimization and re-fitting is completed, the meta learner is trained to obtain a Stacking integrated model, a test sample is input into the model, and a day-ahead electricity price prediction value of an original dimension is obtained through inverse transformation. The method can improve the accuracy and stability of electricity price prediction, and is suitable for an electricity market day-ahead electricity price prediction scene.

The invention relates to the technical field of bulk commodity operation, in particular to a data sharing and analysis method and system based on multi-dimensional association, and the method comprises the steps: collecting multi-source heterogeneous data through a mixed mode of interface synchronization and manual reporting, and quantifying an unstructured text into a standardized index through a bidirectional LSTM semantic analysis algorithm; a cosine similarity and grey relational degree fusion algorithm and an LSTM dynamic weight optimization model are adopted, association rules are mined in combination with an improved Apriori algorithm, and market pre-judgment is achieved through three sub-models of supply and demand, price and risk; and finally, establishing a closed-loop mechanism, and outputting an inventory adjustment scheme and a futures hedging strategy. The system correspondingly comprises four functional modules, full-process data processing and decision support are achieved, and scientificity and operability of operation decision are improved.

This invention belongs to the field of financial investment decision-making technology, specifically a financial investment decision-making assistance method and system based on game theory and multi-source big data. It collects and processes multi-source financial data, constructs a unified data foundation, and uses machine learning to generate multi-dimensional market prediction signals based on this foundation. These prediction signals are then input into a game theory model to simulate the behavior of market participants, solve for equilibrium strategies, and generate investment or risk control suggestions based on the game results, which are then visualized. This invention overcomes the limitations of single data sources by integrating multi-source big data and generating multimodal prediction signals. By combining game theory to model the complex interactive behaviors of market participants, it can deduce market evolution trends from a global perspective, thereby assisting in the formulation of more forward-looking investment or risk control strategies. It can simulate information asymmetry, strategy interaction, and dynamic evolution processes in the market, thus providing a better understanding of market fluctuations and the impact of sudden events.

The application discloses a social media-based interpretable dynamic graph network benefit prediction model training method, relates to the technical field of text analysis and market prediction, and obtains market data and related social media data of a financial asset to divide the data by weeks; for the social media data of each week, a topic is taken as a node, and similarity between topics is taken as an edge weight, a topic correlation graph of each week is constructed to obtain weekly graph data; a prediction model performs time sequence updating and market prediction according to the weekly graph data; a text time sequence memory unit obtains a current week topic memory vector according to a current week topic text embedding vector and in combination with a last week topic memory vector; a multi-layer graph attention network captures the correlation features between nodes through an attention mechanism and adopts attention weights for weighted summation to obtain a current week global graph embedding; and a classifier outputs a next week price change prediction result. The application solves the problem that the prior art cannot effectively capture the time sequence evolution of topic sentiment and the correlation between topics.

The invention discloses a power market supply and demand prediction system and method based on multi-source data fusion and an LSTM neural network, and relates to the technical field of power market prediction, and the method comprises the steps: collecting multi-source data, carrying out the time alignment and formatting, and generating standardized time series data; carrying out missing value filling, anomaly detection and denoising processing on the standardized time sequence data to generate a time sequence feature set; time sequence features of different time scales are extracted from the high-quality fusion feature sequence, a semantic memory LSTM network is constructed, output of each scale is fused through attention gating, and an initial supply and demand prediction result is generated; and calculating a residual error according to the difference between the initial supply and demand prediction result and the actual observation value, when the residual error exceeds a residual error triggering threshold value, triggering the correction network to generate a correction item, and superposing the correction item with the initial prediction result to generate a final supply and demand prediction result. According to the invention, power market scheduling, load management and new energy consumption optimization are facilitated.

The embodiment of the invention provides a laser market prediction information generation method and device. The method comprises the following steps: acquiring multi-dimensional market information corresponding to a laser in response to a received market prediction request for the laser; preprocessing the multi-dimensional market information corresponding to the laser, and classifying the preprocessed multi-dimensional market information according to a time dimension, a product type dimension and a region dimension to obtain multiple types of text information; performing feature extraction on the plurality of types of text information to obtain index information corresponding to the plurality of types of text information, and constructing knowledge graph information corresponding to the laser based on the index information corresponding to the plurality of types of text information; and calling the trained market prediction model according to the knowledge graph information corresponding to the laser, and generating market prediction information corresponding to the laser. The method improves the accuracy of market prediction information.

This invention provides a method, system, device, and storage medium for power system risk control decision-making, belonging to the field of power system technology. The method includes: acquiring power system fusion data and power market text information, and labeling the text information with market sentiment tags; training an optimal electricity price sentiment mapping and an optimal sentiment mapping correction quantity based on the tags and text information, and extracting market sentiment features based on the optimal mapping and correction quantity; acquiring a market feedback time decay mapping based on the aforementioned text information, and fusing the text information, time decay mapping, and market sentiment features to obtain power market expectation features; then combining the power fusion data to predict the base predicted electricity price and the market predicted electricity price; and obtaining the base price tendency quantity under gating parameters based on the predicted electricity price to obtain the estimated electricity price; and generating a power anomaly alarm based on the estimated electricity price for risk control decision-making. This invention can improve the reliability of power system risk control decision-making.

The application discloses an enhanced model day-ahead electricity price prediction method based on dynamic feature selection, which extracts features from the original data set through a multi-head attention mechanism for multidimensional correlation analysis, generates a feature weight vector in real time to realize dynamic screening of the optimal feature subset, thereby suppressing redundant information and nonlinear noise features at the source; then, an enhanced XGBoost model is used to deeply mine the structured relationship of power market data, simultaneously combined with a Transformer model to capture the long-range time series dependence characteristics of the electricity price sequence, and through a gating mechanism to perform dynamic weighted fusion, to realize the complementary integration of spatial features and time series features; finally, a multi-objective Bayesian optimization algorithm is introduced to obtain a trained enhanced model through multi-objective collaborative optimization, and then prediction is performed. The application significantly improves the prediction accuracy and robustness, effectively solves the deployment problem of the model in a resource-limited environment, and has high engineering application value and power market prediction practicality.

The application discloses an enterprise customer information management system and a management method, and relates to the technical field of enterprise information management; the system comprises a customer basic information module, a space-time sequence analysis module, a geographic information correlation module, a data storage module and an interactive display module; the customer basic information module accurately processes missing values and abnormal values, and improves data quality and management efficiency in combination with distributed storage; an LSTM model integrates multi-dimensional features, monthly updates guarantee procurement time prediction accuracy, and helps enterprises to reasonably plan; the geographic information correlation module directly presents regional value and the relationship between related enterprises through coordinate conversion, clustering analysis and GIS visualization, and optimizes resource allocation; in customer management, the system outputs procurement prediction and triggers collaborative early warning, and enhances customer interaction; a data verification feedback mechanism forms a closed loop, and continuously optimizes the model; each module flexibly communicates, the interface is friendly, and the enterprise customer control, market prediction and decision-making ability are comprehensively improved.

The invention relates to an intelligent period-present fusion market analysis system and method. The system comprises a data source layer, a data access and processing layer, a core calculation layer and an application presentation layer. The data source layer is configured to acquire data information of a futures exchange, a spot trading platform and a macroscopic / industry database in real time; the data access and processing layer is configured to receive, buffer and preprocess the data acquired by the data source layer; the core calculation layer is configured to perform reasoning calculation by loading a pre-trained Transform model and perform data storage; and the application presentation layer is configured to convert the calculation result into a transaction strategy and visual content of user interaction. According to the invention, the problem of data islands is solved, and automatic fusion and standardization of periodic data are realized; an AI deep learning model is introduced, so that the market prediction accuracy is remarkably improved; the full-automatic analysis process is realized, the transaction efficiency is greatly improved, and human errors are avoided.

The invention relates to the technical field of power markets, and discloses an electric energy and auxiliary service market capacity distribution decision method and system, and the method comprises the following steps: obtaining the operation parameters and market prediction data of a power generation main body, and generating a group of future operation scenes reflecting the uncertainty according to the operation parameters and market prediction data; based on the scene set, establishing risk-adjusted benefits of each power generation main body in the electric energy and auxiliary service market for each power generation main body; through a multi-objective decision conversion method, the double-market income is constructed into a single-objective optimization function of each subject; and finally, constructing the single objective functions of all the subjects into a non-cooperative game model, and solving the Nash equilibrium of the non-cooperative game model to obtain an optimal capacity allocation scheme of each subject between the two markets. According to the method, a random scene reflecting new energy output correlation is constructed, and a conditional value-at-risk (CVaR) model is introduced to quantify a decision-making tail risk into cost, so that a decision-making scheme capable of keeping robust in a changeable market environment is obtained.