10results about How to "High practical application value" patented technology

The invention belongs to the technical field of groundwater reserve prediction, and particularly relates to a physical logic driven groundwater reserve intelligent prediction method and system. Comprising the following steps: performing complementation and physical verification on multi-source heterogeneous observation data related to groundwater reserves in a target area to obtain a physical verification complete data set conforming to physical logic; performing space-time decoupling causal contribution analysis on the physical verification complete data set to generate a causal contribution matrix; according to the causal contribution matrix, constructing and training a physical constraint emergence type space-time prediction model; applying a physical anchoring adversarial migration strategy to adapt the trained emergence type prediction model to the target new region to obtain a region adaptive prediction model; and inputting the future scene conditions into the regional adaptation prediction model for simulation, and generating a groundwater reserve prediction result. According to the method, intelligent prediction with high precision, high robustness and physical interpretability on groundwater reserves can be realized, and scientific decision support is provided for water resource management.

This invention provides a lithium carbonate price prediction method based on multimodal data collaborative driving, comprising the following steps: multimodal data source identification and data acquisition; multimodal data classification and standardization; personalized noise filtering and outlier correction of multimodal data; extraction of trend and correlation features from structured data; extraction of semantic and visual / speech features from unstructured data; extraction of key information and feature quantification from semi-structured data; weighted collaborative fusion and feature optimization of multimodal features; construction and training of a two-branch collaborative prediction model; model prediction and preliminary verification of prediction results; optimization and dynamic correction of prediction results; interpretability analysis and visualization of prediction results; and method effectiveness verification and continuous improvement. This method, based on multimodal data collaborative driving, addresses the pain points of existing lithium carbonate price prediction technologies, forming a comprehensive, high-precision, and highly practical prediction solution.

A satellite remote sensing image power tower disaster damage identification method, system and related device, the method comprises collecting multi-source power tower satellite remote sensing image and constructing a data set; input the data set into the pre-constructed rotating target detection model, output the power tower spatial position and identify the orientation angle through the rotating target detection model; according to the spatial position of the power tower and the identified orientation angle, the maximum likelihood estimation model of the angle distribution of the power tower is constructed through angle specialization processing, and the maximum likelihood estimation model parameters are solved by using the expectation maximization algorithm to determine the orientation angle distribution of the power tower in the image to be measured; according to the orientation angle distribution of the power tower in the image to be measured, abnormal detection is carried out, and the objects exceeding the threshold range are screened out, that is, the power tower damaged by disaster. The present application can avoid the problem of sample scarcity, significantly reduce the data preparation threshold, realize full automation from data preprocessing to abnormal screening in the detection process, and effectively improve the disaster damage perception efficiency and accuracy.

The invention discloses an anti-loss image processing method for digital media, which is suitable for short video frame processing and comprises the following steps: S1, through multi-algorithm preprocessing, outputting a layered feature cache library and a preprocessing feature map containing a transition area; s2, a differential elastic compression strategy is adopted for the four types of image areas, and two-stage error correction and area fusion softening are matched; s3, repairing details through an attention codec and a multi-residual network, correcting dynamic artifacts and balancing light and shadow; s4, adaptively converting a color space, and scaling the resolution to adapt to different terminals; and S5, outputting the image after verification. The method gives consideration to compression efficiency and image quality, reduces transmission loss, improves the detail reduction degree of a complex scene, guarantees the consistency of cross-device display, and is suitable for a multi-digital media scene.

The application discloses an ultrahigh-vacuum-compatible high-precision nanoradian rotary table and a nanoradian angle output method, and belongs to the technical field of precise adjustment equipment. The rotary table is composed of a rotary seat assembly, a driving assembly, a displacement transmission assembly and an angle output rotary hinge. The driving assembly and the displacement transmission assembly are cores. The nanometer displacement of a piezoelectric actuator is scaled and amplified by a displacement driving hinge, driving force is accurately transmitted through the displacement transmission assembly, and linear displacement is converted into nanoradian rotation through the angle output rotary hinge. The titanium alloy material of the rotary table is suitable for an ultrahigh-vacuum environment, and the piezoelectric actuator is combined to realize overall power-off self-locking. Under a 5kg load, a minimum rotation step of 10nrad and a rotation stroke of 200μrad can be realized, and the problems of poor load, no self-locking, unsuitable vacuum and low rotation precision of existing rotary tables are solved. The structure has high rigidity and stability, and closed-loop control can also be realized.

The present application relates to the field of energy and environmental information technology, and more particularly to a traffic industry carbon emission multi-scale rolling prediction method based on deep learning, comprising: step 1, data preprocessing and sequence decomposition; step 2, rolling sample construction; step 3, CAWOA initialization; step 4, CAWOA iterative optimization, loop judgment process; step 5, component level BiLSTM training and rolling prediction; step 6, rolling judgment and information update; step 7, mode cycle and prediction output; step 8, mode reconstruction and result restoration. The present application can realize dynamic description and accurate prediction of the evolution trend of traffic industry carbon emission, and provide more realistic prediction basis for relevant emission reduction policy making.

The application discloses a kind of enteromorpha green tide salvage ship deployment determination method, belong to enteromorpha green tide prevention and control field.This method includes the following steps: a, construct the biomass estimation model based on enteromorpha green tide satellite remote sensing data and field monitoring data;B, construct biomass-salvage volume conversion model;C, construct the calculation model of salvage efficiency under different sea conditions and different salvage modes;D, based on biomass estimation model, biomass-salvage volume conversion model and the calculation model of salvage efficiency under different sea conditions and different salvage modes, construct salvage ship deployment model;E, according to the delineated sea area range, obtain the enteromorpha green tide satellite remote sensing data of the sea area range, then based on the salvage ship deployment model constructed in step d Determine the number of salvage ship demand, carry out salvage ship deployment.The present application constructs salvage ship deployment model etc., based on the dynamic deployment strategy of fine classification, significantly improves the scientificity and operation efficiency of ship resource scheduling.

The invention provides a pressure vessel gas tiny leakage fault self-adaptive diagnosis method and system, and the method comprises the steps: carrying out the preprocessing of a leakage signal through a self-adaptive signal decomposition algorithm according to a pressure vessel gas tiny leakage acoustic signal collected by an MEMS ultrasonic microphone array, extracting a plurality of leakage features, and inputting the features into a leakage diagnosis model, and the function of identifying micro leakage working conditions of various apertures of the pressure vessel is realized. According to the invention, by extracting the multi-index characteristics of leakage, the micro leakage working conditions of various different apertures are effectively identified, the accuracy of gas micro leakage fault diagnosis of the pressure vessel and the safety monitoring level of an industrial production link are improved, the detection defects caused by parameter selection by artificial experience are overcome, and the detection efficiency is improved. The stability and generalization ability of the small aperture leakage diagnosis model are effectively improved, a basis is provided for subsequent leakage positioning research, and the method has great practical application value.

The application discloses a liver cancer prognosis evaluation model generation method and related devices, and relates to the technical field of data processing. The method obtains medical multi-modal data of a liver cancer patient, the medical multi-modal data including macroscopic magnetic resonance imaging data and microscopic pathological whole slice image data; based on a graph neural network, the medical multi-modal data is converted into multi-scale graph structure data and is subjected to cross-scale topological alignment through a GroMoVe optimal transport algorithm to obtain topological fusion features; the topological fusion features are input into a causal intervention module constructed based on a structural causal model, a counterfactual generation is performed to eliminate confounding factors, and causal invariant features are extracted; a continuous time evolution component is trained based on the causal invariant features, the continuous time evolution component represents time dynamic changes of a tumor recurrence risk based on a neural ordinary differential equation, and a liver cancer prognosis evaluation model is obtained. The application improves liver cancer recurrence prediction accuracy, enhances model generalization robustness and interpretability.

The application discloses a generative document reordering method based on retrieval enhancement generation, which comprises the following steps: firstly, preprocessing an original document set to obtain a candidate document set; then, inputting a query question and the candidate document set into a large language model to generate answers and thought chains, disassembling the thought chains into atomic reasoning steps, and then performing sample screening; calculating the information gain score and the semantic similarity score of each atomic reasoning step for each candidate document, taking the maximum value as the final contribution score of the document after weighted fusion, and sorting the candidate documents according to the score to form a training data set; training a generative reordering model to make it output an ordered sequence of document identifiers; and applying the pre-trained generative reordering model in the online inference stage. The method changes the sorting target to the actual contribution of the document to the reasoning step, combines a high-quality supervision signal, constructs a sliding window global rearrangement strategy, and improves the retrieval accuracy of key documents and the practicability of the reordering model in a complex question and answer scene.