8results about How to "Strong engineering practical value" patented technology

This invention relates to the field of audio circuit hardware technology, and in particular to a low-latency ARC circuit configuration method and system based on I2S audio switching. External video data is received through an ARC circuit compatibility interface and input to a video conversion chip for data separation, achieving synchronous separation of I2S audio signals and MIPI video signals. The separated I2S audio signals are then simulated and sent to the host SOC and external I / O terminals respectively by a first-stage audio switching chip. Through link delay analysis and instantaneous delay judgment, the audio path is dynamically configured to achieve switching control between the internal processing path and the direct path. A second-stage audio switching chip realizes multi-channel audio multiplexing and direct transmission simulation, achieving a low-latency, highly compatible ARC audio output scheme while ensuring audio and video timing alignment. This invention can significantly reduce the direct transmission latency of ARC audio while ensuring audio and video synchronization and system compatibility, and improves the flexibility and reliability of audio path configuration.

The present application belongs to the field of blast furnace system health management and prediction, and specifically discloses a blast furnace degradation model calibration and residual life prediction method under a noise environment. The method is based on a fractional Brownian motion with historical dependence, constructs an initial degradation model of the blast furnace system, estimates the model parameters by the maximum likelihood estimation method, and determines the optimal model structure of the blast furnace system temperature change by the Akaike information criterion. In addition, the present application also designs a model calibration trigger mechanism; when the mechanism is not triggered, the Bayesian fusion particle filter is used to estimate the potential degradation state of the system and update the model parameters; when the mechanism is triggered, a prediction error model is constructed and the degradation model structure is calibrated to adapt to complex working conditions. Finally, the probability distribution function of the residual life of the blast furnace system is derived, and the residual life prediction of the blast furnace system is carried out according to the calibrated degradation model.

This invention relates to the field of machine tool condition monitoring and health assessment technology, specifically to a feature analysis method and system for ranking machine tool health based on acoustic emission technology. The method involves: cleaning the raw acoustic emission signals collected by multi-channel acoustic emission sensors along the X, Y, and Z axes of the machine tool, removing invalid data, and generating mean and fluctuation sequences respectively; extracting four core feature indicators based on the preprocessed mean and fluctuation sequences; dynamically allocating sensor weights to integrate the indicators according to the test focus object, and after minimization and normalization, calculating and ranking the health score based on weights trained by machine learning. This invention enables accurate quantitative assessment and batch ranking of machine tool health status, supporting intelligent operation and maintenance throughout the entire lifecycle of the machine tool.

This invention discloses a method, system, and application for classifying the adjustable potential of photovoltaic (PV) power distribution areas, relating to the fields of smart distribution areas and power big data analysis technology. The invention first acquires source-load characteristic data of users in the distribution area, and then performs net load calculation and preprocessing on this data; the net load represents the power fed back to or drawn from the distribution area by the user. The preprocessed source-load characteristic data is then classified to obtain user categories and corresponding electricity consumption behavior characteristic curves. Based on the electricity consumption behavior characteristic curves, the adjustable load power ratio of each user type is calculated, constructing a PV adjustable potential classification standard. This invention can fully exploit the temporal and local characteristics of source-load data, significantly improving classification accuracy and providing a scientific basis for developing differentiated PV consumption and control strategies for distribution areas.

This invention provides a finite element method for solving generalized axisymmetric plane problems using stress as the fundamental variable, relating to the field of engineering analysis. The method includes: S1. Selecting basis functions that satisfy the equilibrium equations; S2. Applying the complementary energy principle to establish the element equations for each element; S3. Assembling all element equations to form the overall stiffness equation; S4. Applying forced boundary conditions to the stiffness equation; S5. Solving the stiffness equation; S6. Obtaining all stress components at any point within the element through basis function interpolation; S7. Repeating S1 to S6 to obtain the element stress distribution under each basic load condition; S8. Obtaining the stress state under any composite load based on the principle of linear superposition; S9. Evaluating the strength of the stressed body according to material failure criteria. This invention does not require users to possess professional modeling skills; through simple parameter input, the stress solution of complex axisymmetric problems can be obtained quickly and accurately, and strength evaluation and performance characterization can be performed accordingly.

The application discloses a ground subsidence evolution mechanism analysis method based on GeoShapley and belongs to the field of geological disasters. The method is based on synthetic aperture radar interferometric measurement image data, adopts time-series InSAR technology to obtain regional long-time-series ground surface deformation information, comprehensively integrates geographical space driving factors such as layered compressible layers and layered underground water levels and position coordinate data, constructs a spatially matched regional ground subsidence evolution attribution analysis data set, and integrates an optimally selected subsidence simulation model; position characteristics are taken as single joint characteristic factors and are integrated into a GeoShapley framework, inherent position effects, non-spatial factor position invariant effects and position-non-spatial factor spatial interaction effects of ground subsidence are quantified, and different regional subsidence evolution mechanisms are explored from three dimensions of factor driving, spatial interaction coupling and spatial aggregation. The method can accurately disassemble subsidence multidimensional effects, adapt to subsidence dynamic evolution characteristics, and can provide certain scientific basis for regional ground subsidence partition management and precise prevention and control.

The application discloses a kind of based on context multi-arm tiger machine's self-adapting text classification model selection method and system, to solve the problem of inaccurate model selection caused by different classification models in online text classification system Different adaptability difference of different text samples.The application maps multiple text classification models as different machine arms in multi-arm tiger machine, and extracts context features from the text samples to be classified, to depict the statistical distribution characteristics, sparsity features, position distribution features and sentiment features of text.Through dimension division on context features, construct low-dimensional and structured context representation, and evaluate the expected performance of each classification model based on context multi-arm tiger machine mechanism, adaptively select the classification model suitable for the current text sample.Experiments show that the application can effectively reduce the cumulative error rate and average decision regret in the text classification process, improve the overall performance of online text classification system, and has good engineering application value.