7results about How to "Improve predictive ability" patented technology

This invention discloses a liquid-cooled collaborative intelligent temperature control method and system based on layered PCM, relating to the field of liquid-cooled temperature control technology. The method includes: S1, real-time acquisition of liquid-cooled temperature control data and preprocessing of the data; S2, calculation of the sensible and latent heat absorption of the outer and inner PCM layers, assessment of the risk of thermal imbalance on the conductor surface, determination of the current thermal state, and output of a thermal risk warning signal; S3, assessment of the degree to which cable bending weakens thermal coupling performance, triggering a layered PCM collaborative temperature control strategy, and execution of graded control commands for the power pump, circulation pump, and three-way valve; S4, assessment of the deviation between the current heat exchange capacity and the heat load demand, correction of the cooling response coefficient, and optimization of the PCM collaborative temperature control strategy. This solves the problems of unstable thermal coupling between the coolant pipe and the heating conductor in existing liquid-cooled cables, and the tendency for pipe deformation and contact thermal resistance and localized hot spots to occur with frequent bending.

The invention discloses a decision task-oriented graph ontology relationship modeling method and device, and the method comprises the steps: carrying out the entity recognition and extraction processing of text data in the technical field of decision, and obtaining an ontology text set; the ontology text set comprises an attribute text set and a relation text set of each ontology; the attribute text set comprises various attribute texts; the relation text set comprises various kinds of relation texts; performing ontology relationship modeling processing on the ontology text set to obtain a graph ontology relationship prediction model; and processing a newly collected ontology attribute text set by using the graph ontology relationship prediction model to obtain relationship information of the ontology.

The invention discloses a multi-modal brain function connection brain tumor postoperative post-traumatic stress disorder classification system and method. The system comprises a multi-modal data acquisition module, a multi-map brain network construction module, a multi-kernel map convolution feature extraction module, a dynamic prediction model construction module and an interpretability visualization module. Structural magnetic resonance, functional magnetic resonance and electroencephalogram multi-modal data are integrated, a functional connection network is constructed based on multi-graph division, and multi-scale features are extracted by using a multi-kernel graph convolutional network, so that high-precision PTSD classification and prediction are realized. The problems that existing brain tumor postoperative PTSD diagnosis depends on subjective evaluation and lacks objective biological markers are solved, an integrated solution of early recognition, individualized diagnosis and interpretable analysis is provided, and the diagnosis precision and clinical effectiveness of brain tumor postoperative mental complications are remarkably improved.

The application discloses an active phased-array radar target detection correction method and particularly relates to the field of detection correction, and comprises the following steps: S1, a control center triggers radar scanning, calculates and extracts detection data, and transmits the detection data to a data preprocessing unit; S2, a preprocessing unit extracts echo parameter, acquires an echo signal validity coefficient, judges signal validity, and processes; S3, the control center triggers a three-axis gyroscope attitude measurement unit to collect attitude data, and transmits the attitude data to a coordinate conversion unit to call a conversion matrix; and S4, the coordinate conversion unit corrects an angle, calculates relative three-dimensional coordinates, and transmits the relative three-dimensional coordinates to a multi-source data fusion unit. The application constructs a detection correction system in which a phased-array radar, a gyroscope, a GPS and multiple data processing units are cooperated, improves the cooperation and intelligent level of the whole detection correction system, and is suitable for various target detection scenes with complex carrier attitude changes, and is more practical and adaptive.

The application discloses a network security situation awareness system and method based on deep learning, comprising the following steps: collecting and processing multi-source heterogeneous network security data in a target network security scene; constructing an event semantic unit set and an event semantic coupling relationship set; performing constraint filtering and attack chain fragment arrangement processing on discrete security events; constructing a heterogeneous time sequence relationship topology and performing time sequence embedding coding and correlation fusion; extracting a behavior evolution sequence and mapping to an attack chain stage atlas; generating a network security situation representation vector set through an improved TGN model; identifying complex attack behaviors, confirming a propagation path and performing situation risk grading to generate a network security situation awareness result. The application fully utilizes a deep learning method, constructs a multi-source data attack chain analysis model, realizes network situation awareness, and has the advantages of high accuracy, strong risk prediction and timely response.

The application relates to the technical field of data processing, in particular to a financial management method and system based on big data, which comprises the following steps: S1, performing semantic-visual fusion cleaning and standardization processing on unstructured financial data of a small and medium-sized enterprise, and outputting a standardized financial data matrix; and S2, based on the standardized financial data matrix, combining ERP data and external supply chain credit data, and performing cash flow health degree prediction and supply chain payment strategy linkage; when the application is used, semantic-visual fusion cleaning is used to reduce the workload of manual arrangement of unstructured data, improve the data accuracy, and solve the pain points of difficult data entry, many errors and omissions of the small and medium-sized enterprise; through real-time cash flow health degree prediction, the small and medium-sized enterprise can identify the fund gap risk 7-30 days in advance, and passive supply interruption can be avoided; through a dynamic payment strategy, the overdue payment probability is reduced, the cooperation stability of the core supplier is improved, an advance payment discount can be obtained, and the procurement cost is reduced.