6results about How to "Improve characterization accuracy" patented technology

This invention discloses a planning method and system for a hydrogen-containing integrated energy system. The method includes: constructing equipment instance models of various energy types in the system by configuring parameters; wherein the equipment includes electrolyzer equipment; constructing a load model in the system by configuring the load demand of the area to be planned, the load demand supporting configuration at multiple time scales; obtaining the energy price model of the area to be planned and completing the energy price configuration; constructing a system topology by graphically associating the equipment instance models, load models, and energy price models through a visual configuration interface, and configuring constraint rules on the system topology; wherein the constraint rules include start-up and shutdown optimization constraints for the electrolyzer; transforming the system topology with configured constraint rules into a mixed-integer linear programming model and performing optimization to obtain the configuration scheme and operation strategy of each device in the system. This invention solves the problems of insufficient characterization of equipment operating losses and low modeling efficiency in existing planning methods by introducing refined electrolyzer start-up and shutdown constraints and a visual modeling process, thus achieving efficient and accurate planning for hydrogen-containing integrated energy systems.

The application discloses a geological comprehensive information analysis system for exploration, comprising: a data acquisition module: acquiring general characteristic data of an exploration area collected by a multi-source sensor in real time; a probability density estimation unit: obtaining a probability density function of the general characteristic under a corresponding geological type for each predefined geological type; a continuous type representation unit: inputting the general characteristic data acquired by the data acquisition module into a kernel density estimation model of all geological types, generating a fuzzy membership vector for continuously describing the geological attribution of the current exploration area, and taking the fuzzy membership vector as a continuous type representation; an anomaly threshold database: storing judging standards according to different geological types; and an anomaly judgment module: dynamically determining an anomaly threshold matched with a current exploration geological state from the anomaly threshold database based on the continuous type representation, comparing real-time fusion data with the anomaly threshold, judging whether an anomaly exists in the current exploration geology, and triggering an early warning.

The application discloses a crop environment quantitative evaluation and decision system based on growth stage self-adaptation and relates to the technical field of agricultural wisdom decision-making.The data acquisition module collects original environmental data sequences of multiple types of sensors in the field;the dynamic identification module analyzes the sequences by using a preset crop growth stage identification model, identifies the current growth stage, and outputs a corresponding key environmental parameter weight template;the feature fusion module fuses the original data according to the template to generate a stage-adaptive environmental feature vector;the state evaluation module converts the vector into a growth state quantitative evaluation value through a growth state evaluation model;and the decision trigger judgment module compares the preset dynamic threshold interval corresponding to the growth stage according to the growth state quantitative evaluation value, and generates a decision trigger instruction when the growth state quantitative evaluation value deviates.The application realizes stage self-adaptive intelligence of crop growth monitoring and decision-making, and improves the accuracy of environmental state evaluation and the timeliness of management decision-making.

This invention relates to a disease evolution prediction system based on cross-modal contrastive learning and counterfactual graph reasoning, belonging to the field of medical information processing technology. It includes: a multimodal heterogeneous data alignment module for aligning multimodal heterogeneous clinical data using cross-modal contrastive learning; a dynamic evolutionary time-series graph construction module for abstracting the complex evolutionary process of chronic diseases into a dynamic graph structure that changes over time; and a counterfactual intervention effect evaluation module that uses a conditional diffusion model as a counterfactual sample generator to construct multiple parallel potential evolutionary paths to evaluate prognostic differences under different treatment strategies. This invention significantly improves the representation accuracy of heterogeneous medical data through cross-modal contrastive learning, effectively compensating for the limitations of missing information in single modalities. This invention not only enhances the robustness of the prediction system in dynamic intervention environments but also endows the model with deep clinical interpretability.

The application discloses a kind of fusion multivariate earthwork distribution space interpolation method and device, electronic equipment, it is related to water transport engineering planning technical field, the method includes: based on the topographic surveying and mapping and geological drilling data of canal engineering, determine topographic data, borehole soil layer data and lithology data, adopt analytic hierarchy process to determine multivariate coefficient and the weight parameter of different lithology, based on the weight parameter of different lithology and topographic trend residual variation function is constructed, and spatial interpolation calculation is carried out using machine learning strategy, obtain initial earthwork distribution interpolation result, to construct the stratigraphic interface model corresponding to each stratum, and generate three-dimensional geological model based on all stratigraphic interface model, for each grid element is assigned parameter, output earthwork space distribution chart.The application solves the technical problems that the earthwork calculation mode in the related art has missing geological feature expression, weak adaptability to complex terrain, resulting in large error of output earthwork result.

The application provides a traffic flow prediction method fusing curvature gradient, and relates to the technical field of traffic flow prediction. The method first acquires traffic data and geographic information system data of visible area detectors in a road network; extracts curvature and gradient characteristics of each road section, and constructs a geographic enhanced road network graph structure, wherein the edge weight between nodes is dynamically generated according to the curvature difference and the gradient difference; the traffic data is input into a graph neural network model as node features and graph structure, the model performs spatio-temporal modeling through a graph convolution operator dedicated to curvature and gradient, and explicitly fuses road alignment constraints to capture traffic flow propagation rules; finally, the prediction value of the traffic state of the monitored blind area road section is output, and dynamic prediction is realized. The application effectively solves the problem of inaccurate traffic state perception caused by monitoring blind area under complex terrain, and improves the prediction accuracy and reliability.