15 results about "Forward looking" patented technology

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.

A control method and system for tart crust processing equipment based on big data modeling, belonging to the field of food industry equipment control technology, includes the following steps: obtaining dough kneading and proofing process data based on batch identifiers; obtaining pre-processing state data of the stamping equipment for the target dough stamping process, and combining the kneading and proofing process data with a trained stamping quality prediction model to obtain a predicted quality value of the finished tart crust after stamping; comparing the predicted quality value with a preset quality target to determine whether the preset stamping process parameters need adjustment; if so, adjusting the preset stamping process parameters to obtain the execution stamping process parameters; and controlling the stamping equipment to perform the stamping operation on the target dough according to the execution stamping process parameters. This approach achieves forward-looking prediction of the finished tart crust quality and enables timely intervention and adjustment, ultimately improving the stability and consistency of product quality.

The application provides an industrial equipment fault prediction and diagnosis method and system based on a multi-modal large model. First, multi-modal time series data of an industrial rotating machine during operation is collected. Second, the data is input into a pre-trained multi-modal large model to extract frequency domain features, hot spot area temperature change trend features, and abnormal sound component features. Then, a cross-modal attention mechanism is used to fuse the three extracted features to generate a unified multi-modal equipment state embedding representation. Then, a comprehensive health index is calculated, and a sequence prediction method is used to deduce the future change trajectory of the comprehensive health index. Finally, when the future change trajectory indicates that the comprehensive health index will cross the preset warning boundary value, a diagnosis conclusion and maintenance decision are output. The technical solution provided by the application not only accurately predicts the early stage of industrial equipment failure, diagnoses the type, and provides forward-looking maintenance decision support, but also improves the intelligent level of equipment health management and operation and maintenance efficiency.

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 network security, and discloses a software supply chain situation awareness method and device, an electronic device and a storage medium. The method comprises the following steps: extracting risk features of multi-source heterogeneous data in a software supply chain to be evaluated; determining direct risk factors existing in the software supply chain to be evaluated based on the risk features; calculating a probability of a top event being a supply chain risk event according to a risk evolution link of "direct risk factor-intermediate event-top event", wherein one or more direct risk factors lead to one type of intermediate event, and each intermediate event jointly determines the probability of the top event being the supply chain risk event; and calculating a prediction probability of a risk event occurring in the software supply chain to be evaluated in the future according to periodic changes of the probability of the supply chain risk event. The application actively warns future risk trends based on historical situations, provides forward-looking guidance for security decisions, and improves the accuracy, timeliness and initiative of situation awareness.

The application discloses an intelligent fund investment advisor method and device, a storage medium and a program product, and belongs to the field of financial technology. In order to solve the problems of static user portrait, lagging recommendation logic and lack of foresight of the existing intelligent investment advisor, the application proposes a method combining macroeconomic analysis and time series atlas. The method first identifies the current macroeconomic paradigm in real time through a hidden Markov model (HMM); then, according to the paradigm and real-time market data, a transmission prototype is matched and activated in a preset macro transmission prototype database, which defines the prospective influence of a specific macro event on investment plates. The scheme changes the investment logic from history-based matching to macro-conducted prediction, significantly improving the foresight, dynamic adaptability and active management capability of investment decisions.

The application discloses a kind of risk dynamic early warning system and method applied to bill platform, it is related to bill risk management technical field, this method includes: collecting bill full-volume business data and historical abnormal early warning record data, constructs multidimensional feature system;According to feature system, the category of bill is divided and risk benchmark library is established, potential risk law is mined, and risk correlation coefficient is calculated;According to feature deviation, establish risk grade determination model, divide risk grade and match early warning strategy;Real-time monitoring early warning strategy execution process and result, dynamically optimize risk benchmark library parameter and early warning strategy.The application can mine risk law from historical data, realize the accurate, dynamic, forward-looking early warning to bill risk, improve the automation level and risk prevention and control ability of bill audit.

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 application relates to the technical field of artificial intelligence, and provides a device pressure prediction method and device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring multi-modal time series data of a target device in a historical period, wherein the multi-modal time series data comprises resource state data and business load data; performing space-time correlation analysis on the resource state data and the business load data to extract space-time correlation features representing the influence of business load on resource state; and performing device pressure prediction based on the space-time correlation features to generate a pressure prediction sequence of the target device in a future period. Through deep mining and analysis of the space-time correlation between business load and resource state, the application realizes more accurate and more forward-looking prediction of device pressure, and has the advantages of high prediction accuracy, strong robustness and effective response to business burst scenarios.

The application discloses an ESG management and carbon emission reduction intelligent optimization system, relates to the technical field of carbon emission digital management and ESG sustainable development management, and comprehensively collects enterprise internal full-dimension time sequence data and external associated time sequence data through a multi-source heterogeneous time sequence data collection module, and utilizes a time sequence large model engine module to perform deep learning based on an improved Transform architecture, which is combined with multi-scale time sequence embedding and external variable cross-attention mechanism, can accurately learn the internal law of enterprise ESG, carbon emission and production operation time sequence data and the influence of external variables, thereby significantly improving the forward-looking prediction ability of ESG risk and carbon emission trend, and the improvement of accuracy and forward-looking nature helps enterprises to identify potential risks in advance, formulate scientific and reasonable emission reduction strategies, and achieve the sustainable development goal.

This application discloses an adaptive temperature field control method and system for annealing furnaces used in pipe fitting processing, relating to the field of adaptive control. It constructs a basic thermal model based on first principles and a data-driven adaptive residual model, driven by real-time perceived operating conditions, achieving a deep integration of physical mechanisms and data intelligence. This allows for real-time identification and compensation of dual dynamic disturbances caused by long-term furnace performance drift and short-term batch switching of pipe fittings. Based on this, high-precision and forward-looking prediction of future temperature field dynamics can be achieved. Finally, using a rolling optimization mechanism of model predictive control, the optimal heating power sequence that balances multiple process objectives and physical constraints is solved online. This enables a closed-loop intelligent control system that moves from passive compensation for temperature field changes to forward-looking adaptive tracking of process curves, thereby improving the accuracy and stability of the high-end pipe fitting heat treatment process.

This disclosure provides an industrial scheduling method, system, and electronic equipment for green electricity-based ammonia synthesis. The industrial scheduling method includes: acquiring predicted data on green electricity generation within a preset future time period, and task data on production tasks to be completed; quantitatively comparing the predicted data and task data, and determining a target strategy from predefined scheduling strategies based on the comparison results; and generating a production plan for the preset future time period based on the target strategy. This disclosure significantly improves the systematicness and forward-looking nature of scheduling decisions, shifting planning from reliance on manual experience to proactive prediction based on future forecasts. It achieves effective matching of green electricity resources and production demand at the source, fundamentally promoting energy supply and demand matching and improving the localization and absorption of green electricity.

The application discloses a warehouse storage space dynamic allocation and path planning method based on a multi-objective optimization algorithm, and particularly relates to the technical field of intelligent warehousing and logistics automation, comprising: constructing a dynamic digital twin model integrating multi-source data and future prediction, and extracting high-level features by using a graph neural network; defining coupled decision variables based on the model, and constructing a multi-objective optimization problem containing minimized instant logistics cost, maximized warehouse stability, maximized future response capability and minimized strategy disturbance cost; designing an interactive adaptive optimization algorithm for solution, and outputting a Pareto approximate solution set; adopting a rolling horizon control and event-driven re-planning mechanism to realize dynamic execution and real-time adjustment of the scheme; and continuously optimizing system parameters and strategies through closed-loop learning. The application realizes dynamic, collaborative and forward-looking optimization of the warehouse system, and improves operation efficiency, robustness and adaptive capability.

The application discloses an optimization adjustment method for autonomous prediction of heating demand load, comprising the following steps: constructing a heating demand load time series by collecting heating system operation data and environmental data, and introducing a Hurst index to analyze the memory characteristics of the load time series to determine the evolution state of the heating load. Based on the load evolution state, a GM(1, N) grey prediction model is dynamically constructed to predict the future heating demand load. According to the predicted load result, the corresponding heating adjustment amount is generated, and the adjustment amount is converted into executable adjustment instructions to realize the forward-looking adjustment of the heating system operation. At the same time, the actual load result after the adjustment is executed is written back to update the load time series, and the Hurst index and the GM(1, N) grey prediction model parameters are recalculated to form a closed-loop operation process of mutual interaction between prediction and adjustment. The application realizes reliable prediction and on-demand adjustment of the heating demand load under the condition of limited data samples.

The application relates to the technical field of industrial control systems and artificial intelligence, and particularly discloses a control method for a hot galvanized steel strip coating based on deep learning. The method is characterized in that a high-sensitivity microphone array is arranged in a zinc pot area, and acoustic signals, strip surface images and process sensor data are synchronously collected; after preprocessing, cross-modal attention fusion network is used to dynamically associate three-mode information to generate fusion perception features; a deep time series prediction model is used to deduce future coating thickness distribution and identify abnormalities; and coordinated adjustment instructions for air knife pressure, angle and strip speed are generated to implement closed-loop control. The application realizes reliable perception and forward-looking regulation of the coating state, improves control robustness and product quality, reduces dependence on expensive thickness measuring equipment, and is suitable for harsh industrial scenes.