7results about How to "Address missing data" patented technology

The application discloses a kind of short tower cable-stayed bridge closure jacking force optimization methods for environmental adaptability, belong to jacking force calculation technical field, comprising: the coupling model of environmental temperature field and structural response is constructed, temperature gradient and strain data are collected in real time by arranging distributed temperature and strain sensor array, temperature-strain mapping function is established;Define multi-objective optimization function, with jacking force as decision variable, comprehensively consider structural stress, design linear deviation and construction safety risk, realize multi-objective optimization by dynamically adjusting weight coefficient;Set constraint condition including structural stress, elevation deviation and axis error;Improved particle swarm optimization algorithm is used to solve optimal jacking force;Output optimal solution set, select the solution with the lowest construction safety risk in Pareto frontier as the final jacking force.The method improves the precision and efficiency of jacking force optimization, enhances the adaptability under complex environment, and provides a scientific and reliable solution for bridge construction.

The application provides a kind of adaptive 3D grid reconstruction and error compensation method for die frame square iron reverse engineering, including using laser scanner to carry out 360 ° scanning on die frame square iron to obtain original point cloud data, and the original point cloud data is registered to the same coordinate system, the original point cloud data is processed by multi-scale bilateral filtering, while constructing point cloud quality evaluation field, identifying data missing area and edge aliasing area, generating point cloud confidence weight map, establishing octree space index and calculating structure tensor to obtain principal curvature value, based on principal curvature and confidence weight, the square iron surface is divided into large plane area, transition feature area and boundary feature area, and the geometric topology skeleton line is extracted.The application decomposes the deviation into systematic error and random error and carries out reverse correction, which not only eliminates the overall deformation error of the workpiece, but also retains the real machining texture, realizes high-quality reverse reconstruction.

The application discloses a power transmission line multi-modal data enhancement and intelligent fault identification method under extreme weather, comprising: multi-modal disaster data collaborative collection, standardized treatment and space-time alignment; a long-tail distribution-oriented deep data enhancement pipeline, containing a unified enhancement operator framework based on Bernoulli random variable control, a multi-class geometric transformation and photometric disturbance operator combination, a physical defogging enhancement step based on a dark channel prior and a channel-by-channel statistical normalization processing; visual-language large model fault intelligent identification and reasoning deployment based on LoRA fine-tuning. The application realizes the structured management of multi-source heterogeneous inspection data, the effective expansion of long-tail low-frequency fault samples, and the precise identification of power faults by Qwen2.5-VL under the condition that only about 0.1% of parameters are updated, and can directly output structured natural language fault diagnosis results.

The application belongs to the technical field of industrial process control, and particularly discloses a multi-mode process virtual sample generation method based on multi-view graph feature fusion, which comprises the following steps: S1, collecting sensor data in an industrial process by means of offline detection and distributed control, and establishing an industrial process database; S2, performing normalization processing on the collected sensor data based on a Z-Score method to obtain a normalized data set. The multi-mode process virtual sample generation method based on multi-view graph feature fusion realizes mode discrimination by means of a Gaussian mixture model and combines a metric learning method of mode preserving embedding, so as to solve the problem of data loss caused by insufficient initial data of a new process, sensor failure or working condition switching in an industrial scene, accurately reflect the feature distribution of different process modes, and enable a soft measurement model to capture more comprehensive dynamic characteristics of an industrial process after the original samples are combined and trained.

The present application belongs to the field of meteorological remote sensing and geographic information technology, and particularly relates to a mountainous area temperature long-time series high-resolution estimation method based on multi-source satellite remote sensing, comprising: obtaining multi-source satellite land surface temperature product data, meteorological station observation data and multi-dimensional auxiliary factor data; constructing a temperature estimation model based on different observation scenarios of the multi-source satellite land surface temperature product data, wherein the temperature estimation model is constructed based on a random forest regression model, and the multi-dimensional auxiliary factor data is input of the temperature estimation model; training the temperature estimation model by using the meteorological station observation data to generate multiple sets of temperature estimation initial value products; weighting and fusing the multiple sets of temperature estimation initial value products according to the deviation of each set of temperature estimation initial value product relative to the meteorological station observation data to generate fused temperature data; and performing spatiotemporal continuity interpolation filling on the gaps existing in the fused temperature data to obtain a spatiotemporal continuous mountainous area temperature data set.

The application relates to the technical field of remote sensing image processing and environment monitoring, and discloses a water pollution supervision method and system based on remote sensing images, which comprises the following steps: acquiring multispectral image data, and performing image preprocessing, water body boundary extraction and spectral feature extraction to obtain a water body spectral feature set; performing pollution analysis and processing according to the water body spectral feature set to obtain a pollution classification result; if the pollution classification result shows an abnormal spectral mode, acquiring historical time series data, and combining the historical time series data with a water body exclusive sub-image to perform dynamic trend analysis and determine pollution diffusion dynamic characteristics; performing pollution hotspot area identification according to the pollution diffusion dynamic characteristics to obtain pollution hotspot distribution characteristic data; and performing supervision decision analysis on the pollution hotspot distribution characteristic data to obtain a water pollution supervision result. The method can realize accurate identification, dynamic monitoring and intelligent early warning of lake water pollution, and provides a scientific basis for water resource protection.

The application discloses a kind of based on data-driven conventional steel grade laminar cooling self-adapting coefficient pre-matching method, comprising the following steps, data acquisition and pre-processing: according to standard acquisition layer before cooling control, process data of setting data during layer cooling production, establish database.Feature screening and target data set establishment: key features are screened by pearson correlation analysis and contribution analysis, and target data set is generated.Model training and optimization: the target data set is divided into training sample, verification sample and test sample, the model is trained and verified using training sample and verification sample, and the model parameters are optimized by grid search.Model testing and evaluation: the model is tested and evaluated using test sample.Model application: the optimized model is applied to actual production, and the setting adaptive parameter is updated.The application accurately predicts the laminar cooling self-adapting coefficient, optimizes the production parameters, and assists in the stable control of the laminar cooling coiling temperature in the production process.