79results about How to "Reliable technical support" patented technology

The invention relates to the technical field of rural image inspection and recognition, and discloses a multi-source image collaborative inspection and recognition analysis system and method for a digital rural area, and the method comprises the steps: collecting multi-source image data in real time; obtaining a plurality of characteristic parameters corresponding to each image data item in the image data set, and carrying out space-time registration and multi-scale fusion processing on the plurality of characteristic parameters of each image data item; performing target detection and identification analysis on the plurality of feature parameters in the fusion feature parameter set, and constructing an abnormal point identification model; setting a plurality of abnormal point change thresholds according to the inspection coordinate data set for classification processing to obtain a plurality of abnormal point categories; and setting a corresponding co-processing scheme according to the plurality of abnormal point categories, and setting early warning information corresponding to the change trends of the plurality of abnormal point categories based on the co-processing scheme. According to the invention, the intelligent degree and response efficiency of rural inspection are improved, and the safety and sustainable development of digital rural construction are effectively guaranteed.

The application discloses a kind of multifunctional low-temperature degradation corn stalk composite microbial inoculant, preparation method and application, the composite microbial inoculant is compounded by multiple microorganisms with straw degradation, potassium solution and promoting function, comprising the following strains: bacillus subtilis K15, curvularia geniculata L11 and bacillus mycoides L31.The composite microbial inoculant constructed in the application realizes synergistic effect and functional complementation, through strain scientific compounding, straw decomposition rate is significantly improved, and decomposition period is obviously shortened;Enhance the adaptability and colonization competitiveness of microorganisms in winter low temperature and complex soil environment.The composite microbial inoculant of the application also has multiple functions such as straw degradation, potassium solution and IAA production, etc.At the same time of releasing nutrients by rapid decomposition of straw, it can convert difficult-to-use soil potassium into available potassium.

The invention relates to the technical field of electric power Internet of Things, in particular to a time-space hierarchical data compression method and system for multi-source heterogeneous data on a power distribution network side, and the method comprises the following steps: S1, carrying out the classification and lightweight compression processing of original data, and outputting intermediate data with a priority identifier; s2, based on the intermediate data obtained in the step S1, a gateway layer carries out topological grouping according to the electrical distance, and then carries out time alignment and weighted fusion on the data in the group to generate a fusion data packet; and S3, based on the fusion data packet obtained in the step S2, the cloud platform performs panoramic insight and value evaluation, generates and dynamically issues an optimization strategy instruction, and the strategy instruction is dynamically issued back to the edge layer and the gateway layer through a control channel. According to the method, the real-time advantage of edge calculation and the big data processing capability of a cloud platform can be fully exerted, modules of all levels work cooperatively, a set of complete power distribution network multi-source heterogeneous data processing solution is formed, and the overall efficiency of the system can be effectively improved.

The invention discloses a power battery pack health state monitoring system, and relates to the field of battery pack health state monitoring, and the system comprises a vehicle information input module which is used for receiving vehicle unique identification information inputted by a user; the standard data acquisition module is used for acquiring historical operation and charging and discharging data corresponding to the unique identification information of the vehicle from a cloud data source; the data fusion and feature construction module is used for constructing a battery state evaluation feature set; the battery health assessment module is used for constructing a battery pack health state assessment model and outputting a battery health state assessment result; the safety risk grading module is used for carrying out grading evaluation on the potential safety risk of the vehicle battery; and the report generation and display module is used for generating a structured detection report. The method has the advantages that the full life cycle of the power battery pack from real-time health state accurate evaluation to future safety risk grading early warning is realized, and intelligent monitoring can be explained.

The application provides a fermentation mechanism with multi-layer tray fixation, relates to the fermentation technical field, and comprises a hydraulic damping and oil path locking system formed by the cooperation of a groove, a piston block and a push spring; and a linkage mechanism which converts linear motion of a tray into a hydraulic drive signal by the cooperation of a fixing frame, the tray, a moving wheel, a fixing wheel, a belt, a rotating shaft, an intermediate sleeve, an extension rod, an extension sleeve, a double pulley and a tension spring. The mechanism automatically limits the speed by the flow damping of hydraulic oil when the tray is pulled out, and the oil path is locked by the cooperation of the elastic sheet and the groove and the blocking of the intermediate groove by the sealing sleeve when fixation is needed, so that the dough displacement or falling caused by too fast pulling out of the tray is effectively avoided, and the stability and safety of the fermentation operation are significantly improved.

The application discloses a kind of shotcrete and surrounding rock interface bonding performance multiscale analysis method and system, it is related to supporting interface mechanics analysis technical field, construct the multiscale through analysis method from nanometer scale to macro scale, by macroscopic test, microtest, molecular dynamics simulation and discrete element calculation are combined, establish the quantitative mapping relationship of wet heat environment parameter and interface bonding performance;The application is based on energy equivalence and strength matching principle, realizes the cross-scale parameter mapping of physical mechanism driving, reveals the micro-mechanism of interface bonding energy evolution and bonding degradation under wet heat environment.The application alleviates the technical problems that the accurate prediction and degradation evaluation of interface bonding performance under wet heat coupling environment cannot be realized in the prior art.

The application discloses a novel full-line fast distance protection method under a power system, which comprises the following steps: S1, continuously monitoring the voltage and current on one side of the line, and starting the protection when detecting that the current mutation variable exceeds a set threshold; S2, establishing an accurate line differential equation based on a pi type equivalent model; S3, constructing the differential equation in the time domain by using the collected voltage and current data, and directly and real-timely solving the equivalent transition resistance of the fault point; S4, establishing a relationship model of the additional measurement impedance and the equivalent transition resistance, accurately compensating the apparent impedance measured by the protection device, and accurately calculating the real distance from the protection installation to the fault point; and S5, comparing the calculated fault distance with a preset protection section, and realizing the fast isolation of the fault; the application has the advantages of weak feed adaptability improvement, strong anti-transition resistance capability, long line anti-maloperation optimization and fast action speed, and can provide effective technical support for the main protection configuration of new energy sending-out lines and high-proportion power electronic power grids.

The invention discloses a lithology identification method based on a neural network, and the method comprises the steps: obtaining a logging curve, recognizing a missing value in the logging curve, taking the parameter data in the logging curve as sequence data, inputting the sequence data into an established neural network model, carrying out the prediction through the neural network model, obtaining the complementation data corresponding to the missing value, and obtaining the lithology of the logging curve. Filling missing values in the logging curve by using the completion data to obtain a complete logging data set, namely a first data set; based on the first data set, performing equalization processing on lithology classification samples in the first data set to obtain an equalization logging data set, namely a second data set; a whale optimization algorithm is adopted to optimize hyper-parameters of the classification model, target hyper-parameter configuration is obtained, and the classification model is configured; and inputting the second data set into the classification model, and identifying and outputting the lithology category. The method is high in missing value filling precision, excellent in sample equalization effect, remarkable in model performance optimization and high in system practicability.

This invention belongs to the interdisciplinary field of artificial intelligence and geological resource assessment, specifically relating to a method and system for predicting the energy storage potential of salt caverns based on multi-source geological information. It aims to address the problems of low prediction accuracy and difficulty in balancing breadth and precision in existing technologies due to single-source data, static models, and lack of adaptive capabilities. The method includes: acquiring multi-source data such as seismic, well logging, core, and geostress data; constructing a geological feature fusion vector containing seven core parameters; establishing a high-dimensional nonlinear mapping model driven by a deep neural network; dynamically adjusting the prediction range based on data coverage density and confidence level; and achieving continuous model iteration through online incremental learning. The system integrates data quality assessment, anomaly handling, and distributed computing modules. By adopting the above technical solutions, this application can achieve a significant improvement in prediction accuracy and adaptive intelligent adjustment of the prediction range.

The application discloses a multi-parameter vital sign monitoring and early warning system based on an intelligent ambulance, and relates to the technical field of signal alarm devices.The system comprises a task receiving and parameter determining module, a model reconstruction and compression module, and a model issuing and monitoring and early warning module.The specific steps include receiving first-aid task information to dynamically determine a list of target vital sign parameters;reconstructing and compressing a cloud-integrated early warning model based on the list to generate a lightweight early warning model;finally, issuing the lightweight model to the target ambulance to perform monitoring and early warning, and adopting a local and cloud collaborative early warning mechanism based on confidence.The application solves the technical problems of insufficient early warning accuracy caused by the lack of task adaptability, the difficulty in deploying complex models due to the limitation of computing resources, and the impact of the local and cloud alarm logic on the collaborative efficiency, and realizes accurate, real-time and reliable early warning of vital signs in a mobile first-aid scene with limited resources.

This invention relates to the field of tunnel engineering, and particularly to a tunnel shotcrete design method, storage medium, and electronic device that considers spatiotemporal effects. The method includes: calculating the final value of vertical deformation pressure and the radius of the plastic zone of the surrounding rock without support based on tunnel foundation parameters and tunnel shotcrete design parameters; calculating the deformation pressure as a function of time using a deformation pressure calculation formula, combining tunnel excavation speed parameters, the radius of the plastic zone of the surrounding rock, and preset shotcrete hardening characteristic parameters; inputting the deformation pressure-time function into a shotcrete load-support structure model, calculating the safety factor of the shotcrete at each time node based on the shotcrete hardening characteristic parameters, and iteratively adjusting the tunnel shotcrete design parameters according to whether the safety factor meets preset requirements, until outputting tunnel shotcrete design parameters that meet the preset requirements. This invention dynamically calculates deformation pressure considering spatiotemporal effects and iteratively adjusts parameters to ensure the load-bearing safety of the shotcrete throughout its entire life cycle and reduces construction risks.

The invention discloses a cultivated land non-agrochemical intelligent monitoring method and system based on multi-temporal SAR and optical image fusion, and belongs to the technical field of remote sensing monitoring and land resource management. The method solves the problems of information loss and misjudgment caused by cloud and mist shielding or noise interference of a single remote sensing data source. According to the scheme, the method comprises the following steps: acquiring and preprocessing a multi-temporal SAR and an optical image sequence; carrying out space registration; constructing a double-branch deep neural network, respectively extracting radar scattering features and spectral texture features, and fusing the radar scattering features and the spectral texture features by adopting a cross attention mechanism; generating a change intensity graph based on a time sequence change detection module; and extracting non-agrochemical suspected pattern spots through threshold segmentation, and aggregating and outputting a final monitoring result. The method is mainly used for realizing the automatic and high-precision monitoring of the non-agrochemical change of the cultivated land.

This invention belongs to the interdisciplinary field of medical image analysis and deep learning, and discloses a method and apparatus for pathological slide image segmentation using multimodal supervised contrastive learning. The method generates biological prior feature maps through decoupling, extracts visual and semantic features by combining the master encoder and prior encoder, and achieves feature alignment using a cross-modal attention mechanism. Supervised contrastive learning is introduced to enhance intra-class compactness and inter-class separability. A multi-scale decoupled decoder is designed, with structural similarity loss and boundary-aware loss respectively supervising structural and detail branches. Finally, dynamic weighted fusion and pseudo-label calibration strategies are used to optimize segmentation performance, significantly improving the accuracy and robustness of pathological image segmentation.

The invention discloses a supervision-process-oriented multi-modal engineering document intelligent structured extraction and association system, and relates to the technical field of engineering supervision informatization. A multi-modal perception understanding module accurately analyzes texts, images and voice documents through a supervision field fine-tuning large model, multi-step chain reasoning and geometric parameter measurement; the dynamic affair graph construction module is used for defining relationships between nodes such as problems and rectification and triggering and response, establishing association according to an analysis result and dynamically updating attributes and weights; the closed-loop state tracking module determines and rectifies a flow state rule, and realizes overtime and associated risk early warning and flow evolution prediction; the interaction application module provides a visual billboard, voice interaction and automatic report generation functions; the model optimization module acquires new case data, calculates process deviation and adjusts model parameters, and the system improves supervision document processing efficiency and rectification management and control precision and supports intelligent decision making.

The application discloses a kind of based on reduced-order model retraining's fractured reservoir injection-production parameter optimization method, it is related to unconventional oil and gas development technical field, including: establishing fractured reservoir full-order numerical simulation model, setting initial injection-production control parameter, running main, vice training simulation;Snapshot matrix is executed singular value decomposition, and reduced-order basis matrix is constructed;Snapshot matrix and the gradient information of main training simulation are executed reduced-order preprocessing, and reduced-order model is constructed;Based on reduced-order model, injection-production parameter optimization iteration is executed and is dynamically judged, until cumulative adjustment step is not more than pre-set allowable value and has reached pre-set maximum iteration step number, then with current optimal injection-production parameter operation verification, output optimal injection-production parameter and objective function value, otherwise using current optimal injection-production parameter updates main training simulation and retraining reduced-order model.The application is through simulation, reduced-order, optimization, update closed loop process, ensure accuracy while greatly improving the calculation efficiency of fractured reservoir production optimization.

A method and system for extracting and organizing knowledge from historically accumulated data using AI assistance includes the following steps: unified preprocessing of multi-source heterogeneous aviation maintenance data to output unified JSON structured data; inputting the structured data into a large language model fine-tuned from aviation maintenance corpus for knowledge triple extraction; constructing a multimodal interaction and query interface supporting structured queries, natural language question answering, historical case retrieval, and maintenance solution recommendations; and establishing an incremental update mechanism to update newly submitted repair reports and batch-uploaded documents to the knowledge graph after preprocessing, extraction, and manual review. This invention effectively solves the problems of difficult standardization of multi-source heterogeneous aviation maintenance data, low reliability of knowledge extraction from large language models, and limited query and interaction methods, achieving efficient knowledge utilization and intelligent knowledge services from historical maintenance data.

The invention discloses a mental health service system and method, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence and health cross, and the system comprises an emotion analysis module, an intelligent pre-inquiry module and an analysis report generation module. And the emotion analysis module processes emotion-related multi-modal data provided by the target user by using the trained multi-modal large model to generate an emotion analysis result. And the intelligent pre-inquiry module performs multiple rounds of dialogues with the target user based on a natural language understanding technology, collects information related to mental health of the target user and generates a questionnaire. And the analysis report generation module performs joint reasoning on the information acquired by the intelligent pre-inquiry module and response information of the target user to the questionnaire to generate an analysis report. According to the invention, objective emotion analysis is provided through the emotion analysis module, standardized information collection is realized through the intelligent pre-inquiry module, and a scientific report is generated through combined reasoning by the analysis report generation module, so that the service accessibility and efficiency are improved.

This invention discloses a literature screening method, system, device, and storage medium based on multi-language model stability determination, relating to the fields of information technology and literature processing technology. The method constructs a complete literature screening quality assessment system through core steps such as data import, preprocessing, dual-model multi-round screening, stability determination and early stopping, arbitration judgment, and stratified sampling evaluation. The system adopts a multi-module collaborative architecture, achieving a technological improvement from "single judgment" to "quantifiable stability judgment" through prompt word rotation, quantifiable stability determination, and intelligent arbitration mechanisms. This invention effectively solves key technical problems in multi-source literature data processing, significantly improves the accuracy, stability, and traceability of screening results, and provides an efficient and reliable solution for scenarios such as system reviews and scientific and technological intelligence analysis.

This invention relates to the field of data evaluation technology, and discloses a multi-source mapping data fusion evaluation method and system for agricultural insurance. The method includes: acquiring images of a target planting area before and after the snowmelt period; extracting the reflectance difference of the snow spectrum of each plot unit in the images under shaded and unshaded conditions to obtain the change in snow cover rate of the plot unit per unit time; identifying accelerated ablation blocks in the target planting area based on the change in snow cover rate; and constructing a snowmelt runoff catchment tree based on the topological connection between the water runoff direction of the slope where the accelerated ablation block is located and the downstream plots; based on the snowmelt runoff catchment tree, this invention can solve the problems of existing technologies having ambiguity in determining the range of runoff scour caused by snowmelt, lacking a nighttime temperature correction mechanism for runoff freezing, and having large deviations between simulated snowmelt flow and actual conditions.

The invention discloses a prison abnormal behavior recognition method and system based on edge intelligent analysis, and belongs to the technical field of intelligent security protection, and the method comprises the steps: deploying edge nodes at each monitoring point of a prison, collecting a synchronous monitoring image flow, recognizing a low-light environment image frame through illumination evaluation, and storing the low-light environment image frame in a database; enhancing the image by using an improved Retinex algorithm optimized by a prison scene noise sample library; inputting the clear enhanced image into a lightweight CNN model, performing multi-dimensional matching with a prison abnormal behavior category knowledge base of a three-layer structure through an embedded behavior classification engine, and outputting a classification result of calibrated confidence; when the confidence exceeds a threshold value, triggering graded early warning, and associating the monitoring platform with peripheral sensor synchronous data to form a multi-mode alarm evidence chain; the edge node regularly encrypts and summarizes the early warning data packet and the negative sample data, and feeds back to the training end to realize model incremental learning; according to the method, the low-light environment adaptability and the feature discrimination capability are improved, and the recognition accuracy and the real-time performance are guaranteed.

This invention relates to the field of geological disaster monitoring technology and discloses a UAV-based geographic mapping method and system. The UAV-based geographic mapping method includes: achieving stable sensor operation in extremely low-temperature environments through adaptive sensor temperature compensation and performance enhancement; employing a plateau-specific power system to improve the UAV's payload and endurance in thin air; utilizing an anti-interference multi-mode communication system to ensure data transmission reliability; solving energy supply problems in polar environments through an intelligent hybrid energy management system; performing real-time data analysis and early warning based on an edge AI processing unit; and introducing a self-repairing maintenance mechanism to improve system reliability. This invention achieves a breakthrough in monitoring geological disasters in plateau snow-capped mountains from an "extensive" to a "precise and intelligent" approach, improving monitoring accuracy and reliability, and has significant practical value.

This invention relates to the field of intelligent route selection technology, and particularly to a progressive optimization method, medium, and device for railway route schemes in densely constrained environments. The method includes: constructing a multi-constraint fused geographic grid model that integrates spatial constraints and auxiliary data of the research area; generating an initial route search graph network including vertex and edge sets based on the multi-constraint fused geographic grid model; using spatial conflict as the driving force, progressively optimizing the network based on the initial route search graph network and the set of active edges to obtain a final candidate route graph network; and using a breadth-first search method to search for candidate paths based on the final candidate route graph network, and ranking each candidate path according to cost indicators to output the top N candidate paths as the preferred scheme group. This invention effectively solves the problems of existing methods in constrained and coupled scenarios, such as difficulty in systematically evaluating conflicts, exhaustively exploring feasible solutions, and easily causing repeated rework of schemes.

The invention relates to a laser SLAM fusion positioning method with environmental adaptability, and belongs to the technical field of autonomous mobile platform positioning. The method comprises the following steps: firstly, constructing a plurality of parallel sub-models comprising different laser SLAM positioning methods, then calculating the observation likelihood value of each sub-model in real time based on the matching quality of the current laser point cloud and a prior map, and updating the conformity of each sub-model in combination with the transfer relationship between the models; taking the conformity as a weight, carrying out weighted fusion on the pose estimation and covariance output by each sub-model, and generating a global positioning result; and finally, feeding back the posterior pose, the covariance and the conformity of each sub-model to the next period, and carrying out positioning information interaction to form closed-loop adaptive fusion positioning. According to the method, the robustness and the positioning precision of the laser SLAM system in a complex, dynamic or structure degradation environment are improved through adaptive fusion of a plurality of sub-models, and the problem of positioning drift or failure caused by incompatibility of the environment and the model is effectively avoided.

The present application provides a field core tank corner point extraction and perspective distortion correction method based on multi-scale Retinex fusion, comprising: S1. Core tank image preprocessing based on anisotropic diffusion and multi-scale Retinex fusion; S2. Core tank corner point extraction based on topological constraint Shi-Tomasi and graph cut optimization interaction; S3. Perspective correction based on thin plate spline transformation and Markov random field fusion; S4. Correction result precision verification and closed loop feedback based on Hough transform straight line fitting and geometric moment invariant fusion. The present application realizes accurate extraction of core tank corner points and efficient distortion correction of images, and simultaneously completes full-dimensional precision verification of the correction results, adaptive migration of processing parameters of batch images and lightweight field deployment of the algorithm.

This application discloses an online monitoring system for molten salt parameters, relating to the field of intelligent monitoring and diagnostic technology. The online monitoring system selects a first pipe with a curvature less than a preset threshold as the measurement segment in a concentrated solar power (CSP) plant, and installs pressure testing devices at both ends, constructing an online measurement structure based on the pressure difference principle. This system achieves real-time, in-situ monitoring of molten salt viscosity, overcoming the problems of poor timeliness and inability to reflect real-time operating status in traditional offline sampling and analysis. The limitation on pipe curvature ensures the stability of fluid flow, allowing the measured pressure difference to accurately reflect the viscous resistance of the molten salt. This solution has a compact structure, is easy to deploy in engineering, and provides stable and reliable technical support for real-time perception of the molten salt status, operational risk assessment, and early warning decision-making in CSP plants.

This invention proposes a multimodal driven and dynamically optimized method and system for power plant inspection image segmentation, belonging to the field of computer vision technology. The method includes: acquiring inspection sample images; generating initial pseudo-labels using a pre-trained visual language model and a multimodal large language model; inputting the inspection sample images into the visual model to obtain dense features, aligning them cross-modally with text semantic embeddings to obtain enhanced visual features; constructing a transition matrix based on image features and enhanced visual features, and inputting these features into a decoder to obtain a dual-path prediction mask, which is then fused to generate a segmentation probability map; optimizing the initial pseudo-labels based on the segmentation probability map and the transition matrix, and using the optimized pseudo-labels to construct a supervision signal to guide the generation of the transition matrix; iteratively optimizing until convergence to obtain a trained image segmentation network, which is then used as input to the inspection image to be segmented to obtain the segmentation result. This improves the segmentation accuracy and generalization ability of inspection images.

The application relates to the field of artificial intelligence and image processing technology, and discloses a multi-modal large model driven complex scene image data processing method and system. The method obtains target image data, extracts multi-scale semantic features, performs feature coding based on a scene context dependency graph, generates optimized feature representation through attention guided feature fusion and multi-level semantic reasoning, and executes a target processing operation. The application can effectively improve the complex scene image understanding accuracy, enhance the feature expression capability, improve the performance of the model in spatial relationship reasoning and semantic analysis, and is suitable for complex visual scene analysis and decision-making.

The application is a kind of online car-hailing driver space behavior analysis method based on operation record data, comprising: collecting operation record data formed in the operation process of online car-hailing, normalizing order time and space position information, and constructing order set of drivers within a set time period; based on the order set, calculating the driver behavior index according to the preset behavior quantification rule; associating the behavior index with the preset space analysis unit, performing spatial mapping and aggregation processing on the driver operation behavior, and constructing the driver space behavior feature data set of the space unit level; based on the driver space behavior feature data set, differentiating the driver operation behavior in different space analysis units for analysis, and obtaining the analysis result. The application introduces a spatial difference description mechanism in the process of driver operation behavior analysis, realizes effective analysis of the operation behavior characteristics of online car-hailing drivers in urban space, and provides technical support for travel service management.

A kind of urban rainwater pipe network and river network hydrodynamic accelerated calculation method based on data-driven surrogate model, comprising: based on bidirectional long short-term memory (BiLSTM), graph attention network (GAT) and residual connection, data-driven surrogate model Deep-Drain is constructed, for simulating the simplified process of urban rainwater pipe network and river network water flow;Mechanism model is constructed to simulate one-dimensional hydrodynamic process under measured rainfall and different return period design rainfall, and process data set of rainwater pipe network and river network flow process is constructed, while training set, validation set and test set are divided;Optimal hyperparameter combination of Deep-Drain is found by using Bayesian optimization;The efficiency improvement effect of Deep-Drain is quantitatively analyzed by typhoon storm event;The present application realizes the effective improvement of the calculation efficiency of urban rainwater pipe network and river network flow simulation process.