113results about How to "Achieve recognizability" patented technology

The invention relates to the technical field of information management, in particular to a material digital management system and method based on a cloud platform. The method comprises the following steps: acquiring material data; distributing a material storage space by using the material data; receiving an operation message of the storage space, and pushing the operation message to an inventory database; creating a circulation record table and an inventory state table; using the operation message in the inventory database to update a circulation record table, and recording circulation data; automatically updating an inventory state table according to the circulation data so as to determine the material inventory capacity; when the material inventory capacity is lower than or equal to a preset safety inventory threshold value, management early warning information is generated; and when the material inventory capacity is higher than a preset safety inventory threshold value, updating the management safety field of the inventory state table. The material inventory is monitored in real time based on the information management technology, the replenishment information is intelligently generated, inventory dynamic management is achieved, and the material management efficiency is improved.

This invention relates to the field of electricity meter operation monitoring technology, specifically to an intelligent analysis system for electricity meter operation faults based on sensor monitoring. The system includes: an operation status acquisition module, which collects the voltage, current, active power, reactive power, and power factor of the electricity meter and obtains the operation status at each moment; a periodic analysis module, which uses active power as a load parameter and obtains a basic period based on the load parameters within a preset time period, then segments the preset time period to obtain different time periods and obtains the period fit at each moment; and a fault monitoring module, which obtains the comprehensive rationality of the load linkage deviation at each moment; and uses the comprehensive rationality of the load linkage deviation at a moment and the period fit to correct the squared Mahalanobis distance corresponding to the data points composed of voltage, current, reactive power, and power factor at that moment, and uses the corrected squared Mahalanobis distance for fault monitoring. This application can effectively monitor the operation faults of electricity meters.

This invention discloses a lightweight method for detecting foreign objects (FOOs) in power transmission lines, relating to the fields of power line inspection and computer vision. A YOLOv10-based FEO detection model is constructed, comprising a feature extraction part, a feature interaction part, and a detection head part. The C2f module in the feature extraction and feature interaction parts is replaced with an expert dynamic extraction module, and the detection module in the detection head part is replaced with a parameter-sharing detection module. The trained FEO detection model is obtained by collecting and evaluating a calibrated FEO dataset of power transmission lines. FEO keyframes are input into the trained FEO detection model to obtain detection results. The expert dynamic extraction module acquires feature maps from multiple receptive fields, achieving rich representation in FEO detection environments with varying scales. The parameter-sharing detection module, based on this, reduces redundant computation and enhances the semantic representation of small targets through parameter sharing and grouping normalization, effectively improving the efficiency of FEO detection in power transmission lines.

This invention belongs to the field of materials testing technology and discloses a deep learning-based method and system for identifying the interface of SiC modified asphalt. It acquires multi-dimensional interface images through a layered time-series acquisition method using four devices: optical microscope, SEM, EDS-Mapping, and Raman spectrometer. Adaptive algorithms are used for targeted denoising, normalization, and registration, while attention mechanisms and weighted fusion strategies are combined to enhance interface features. Simultaneously, a multi-condition dataset containing a high proportion of minor defects is constructed to achieve deep fusion and accurate identification of multi-modal features, eliminating subjective errors from manual judgment and improving the fine-grained accuracy and detection efficiency of SiC modified asphalt interface identification. The U-Net model is improved for specific scenarios and designed to be lightweight. By optimizing the encoder-decoder structure, introducing a dual-channel attention mechanism and a condition-adaptive branch, and combining a loss function, the feature extraction capability of interface boundaries and minor defects is enhanced.

This invention discloses a GIS-based risk management system and method for pollutant migration in mining area watersheds, relating to the technical field of simulating pollutant migration patterns. It includes a 3D GIS functional module, a hydrodynamic-water quality model coupling module, and a mining watershed pollution risk management module. Based on watershed hydrology theory and pollutant migration and diffusion mechanisms, it fully considers the migration of pollutants in the storage area after rainfall via surface microrunoff. By studying the formation patterns of microrunoff, it simulates the watershed migration process of pollutants in the mining area under different rainfall intensities, thereby more accurately calculating pollutant migration and diffusion fluxes and managing the risk of pollutant migration in the watershed. Based on historical and real-time data and models, it uses a 3D model to display the spatial distribution patterns and development trends of pollutants, achieving accurate pollutant identification and source tracing. It can simulate pollution control measures in mining area watersheds, predict development trends, and provide early warnings for pollution events.

The application discloses a cross-cultural communication non-verbal media emotion recognition interaction system, which comprises a non-verbal media data acquisition module, a non-verbal emotion feature extraction module, a non-verbal emotion recognition model construction module, a cross-cultural feature weight parameter self-adaptive acquisition module and an emotion intelligent interaction module. The application relates to the technical field of data processing, in particular to a cross-cultural communication non-verbal media emotion recognition interaction system, which proposes a hierarchical collaborative design of a general non-verbal emotion recognition model and a cross-cultural feature weight parameter self-adaptive acquisition module, can significantly improve the accuracy of non-verbal emotion recognition in a cross-cultural scene, improves the recognition accuracy of the model by designing a three-dimensional structured model input tensor, a customized emotion feature reinforcement filter and an emotion fluctuation extraction pooling operation, and improves the accuracy of cross-cultural non-verbal emotion recognition by adopting a stage-by-stage position updating strategy in a predation stage and combining a dynamic dimension exchange mechanism to improve and optimize the algorithm.

This application discloses a code scanning method, electronic device, storage medium, and computer program product, relating to the field of image processing technology. The method includes, in response to a code update, identifying a changed file representing the code update and performing change dependency parsing on the changed file; if the parsing result indicates that the changed file has dependencies, then determining the dependent files corresponding to the dependencies based on the dependency graph corresponding to the changed file, wherein the dependency graph represents the logical dependencies between the application layer, business logic layer, and data processing layer; determining a change set containing the changed file and all dependent files, and scanning and analyzing the updated code of the changed file within the change set and the code in the dependent files that has a strong correlation with the updated code. This application improves the accuracy of code scanning.

This invention discloses a method and system for thyroid and breast surgical biopsy and surgical planning using multimodal image fusion, belonging to the fields of medical image processing and surgical computer-aided planning technology. The method includes the following steps: acquiring multimodal medical image data and dynamic body posture information of the thyroid and breast regions, and organizing them into a continuous time series according to time sequence as the input basis for subsequent analysis and calculation; comparing magnetic field interference signs and brightness extension trajectories in the continuous time series frame by frame, extracting regions with abnormal signal distributions, generating a candidate list of artifacts, and identifying the suspected artifact range. This invention achieves synchronous correction of artifact regions and restoration of image geometric accuracy through time series reconstruction and spatial offset correction, ensuring the spatial consistency of the fusion model. By generating a list of coordinate anomalies and high-risk locations, it enables dynamic path review and risk identification, ensuring that the biopsy and surgical paths correspond to the real tissue, improving sampling accuracy and surgical safety.

This invention relates to an orbit recognition method based on EfficientNet, comprising the following steps: constructing a network structure based on the EfficientNetB0 structure; obtaining an orbit recognition model based on an orbit dataset through a network training method; acquiring real-time orbit images as input to the orbit recognition model to obtain the coordinates of the recognized orbit.

The application discloses a multi-mode full-frequency compatible 5G wireless router wireless transceiving control method and belongs to the technical field of router control, and specifically comprises the following steps: acquiring the working frequency band and local oscillator frequency of each radio transceiver module; calculating the frequency band overlapping length and frequency ratio of any two modules; marking the modules with the overlapping length greater than zero as a same-frequency interference module group and marking the modules with the same frequency ratio as a preset ratio as a local oscillator leakage module group; extracting the modules belonging to both groups, taking the local oscillator frequency of the modules as an index to find a frequency mapping table to obtain a clock division coefficient; adjusting the local oscillator frequency of the extracted modules according to the clock division coefficient; repeating the above steps until there is no module belonging to both groups, and outputting the final local oscillator frequency. The application realizes unified identification and iterative suppression of the composite interference of the same-frequency interference and the local oscillator leakage.

This application belongs to the field of robot control technology and discloses a method and system for risk prediction and safety control of industrial humanoid robots in complex industrial environments. The method includes: dividing the robot's hand perception area and setting area perception confidence parameters; real-time acquisition and preprocessing of multimodal tactile time-series data (normal force, shear force, micro-vibration, and temperature) output by the electronic skin during operation; identifying contact events and contact areas based on this data and extracting a set of contact state time-series features; subsequently modulating this feature set using the area perception confidence parameters; predicting contact instability risks based on the modulated feature set; and finally combining the prediction results with the area perception confidence parameters to generate a safety control strategy. This invention achieves differentiated early identification and safety control of potential hazards in different contact areas, improving the safety, stability, and operational efficiency of robots in complex industrial scenarios.

The present application relates to the technical field of equipment maintenance, and more particularly to an intelligent predictive equipment maintenance system, comprising a data acquisition module, a data processing module, a maintenance decision module, a maintenance execution module, a user interaction module and a closed-loop optimization module. The data acquisition module collects equipment operating parameters through the deployment of a sensor array to generate standardized data streams; the data processing module extracts time sequence features and performs state deduction through a virtual model to output fault analysis results; the maintenance decision module calls enterprise resource information for multi-objective optimization to generate a dynamic maintenance plan; the maintenance execution module assists in guiding the execution of maintenance through augmented reality and records data; the user interaction module dynamically displays equipment status and risk information; and the closed-loop optimization module updates model parameters based on feedback data. The system realizes predictive maintenance under data driving through module collaboration, effectively reduces unplanned downtime and resource waste, and improves fault diagnosis accuracy and operation and maintenance efficiency.

The application provides a non-intrusive experimental evaluation system and method for pilot human-machine trust behavior, and relates to the technical field of human-computer interaction, which comprises the following steps: collecting a plurality of original data points including eye movement trajectory data points, operation instruction data points and flight instrument parameter data points of a pilot to form an objective behavior data set; receiving a plurality of subjective evaluation data points of the pilot based on the objective behavior data set to form a subjective evaluation data set; receiving a plurality of expert evaluation data points of a flight expert according to the subjective evaluation data set and the objective behavior data set to form an expert evaluation data set; and synchronously matching each data point in the objective behavior data set, the subjective evaluation data set and the expert evaluation data set according to a flight task time node to generate a multi-dimensional behavior mapping data set with time corresponding relationship. The application can realize accurate identification, dynamic evaluation and quantitative analysis of pilot human-machine trust behavior.

The application discloses a kind of based on convolutional neural network's electroencephalogram automatic feature extraction method, comprising the following steps: obtaining original electroencephalogram data, based on channel spectrum curvature rate of change carries out adaptive artifact suppression and channel amplitude dynamic re-labeling;Frequency band morphing reconstruction is executed, according to spectrum curvature rate of change sequence determines frequency band boundary, constructs frequency morphing mapping function and generates multiple frequency band component tensor;The frequency band component tensor is input into improved ConvNeXt model, extracts multidimensional electroencephalogram feature tensor;Cross-frequency domain attention fusion is executed, and fusion electroencephalogram feature vector is constructed;Task reasoning is carried out, and electroencephalogram task response result is output;According to frequency morphing consistency index and predicted offset degree, feature feedback reconstruction is executed, and morphing convolution kernel parameter is updated jointly.The application improves the precision and stability of electroencephalogram feature extraction, and is suitable for cognitive state recognition, medical auxiliary analysis and other multiple electroencephalogram intelligent processing scenarios.

This utility model discloses a high-speed real-time target recognition and tracking device based on multimodal sensor fusion, relating to the field of intelligent sensing and target tracking technology. It addresses the problems of unreasonable sensor layout, low data fusion efficiency, and difficulty in adapting to complex environments found in existing similar devices. The high-speed real-time target recognition and tracking device based on multimodal sensor fusion includes a housing, a central processing module, a lidar component, a millimeter-wave radar component, a binocular vision component, an ultrasonic sensor group, a power supply module, and a communication module. The multimodal sensor fusion architecture and innovative component structure of this utility model, through optimized sensor layout and data fusion methods, effectively solve the shortcomings of existing devices in target recognition and tracking, achieving high-speed, real-time, accurate, and stable target recognition and tracking.

This application provides a vehicle battery life management method, apparatus, electronic device, and storage medium. The method includes: collecting vehicle usage data; quantifying the usage behavior into an impact coefficient on the battery health degradation rate based on a pre-built risk quantification model; determining a warning threshold for each usage behavior in each cycle based on the impact coefficient; generating personalized battery management suggestions and pushing them to an interactive terminal when a usage behavior reaches the warning threshold in any cycle; determining the user's adoption of the management suggestions and the battery improvement based on the usage data, and optimizing the suggestion strategy and calibrating the parameters of the risk quantification model based on the adoption and improvement. This application's embodiments, through the above method, help to achieve precise correlation and dynamic optimization between user behavior and battery life management.

This application provides a method and system for detecting the location of welds in coated pipelines based on magnetic field attenuation characteristics. The method includes: applying pulsed eddy current excitation to the coated pipeline, wherein the pulsed eddy current excitation uses a square wave or step wave signal; arranging multiple magnetic field sensors along the circumference of the pipeline and receiving voltage signals induced in the pipeline by the pulsed eddy currents, which include secondary magnetic field decay signals; measuring the magnetic field decay rate of the voltage signal received by each magnetic field sensor, wherein the magnetic field decay rate is characterized by the attenuation slope of the secondary magnetic field decay signal; superimposing the attenuation rate slope values ​​of the multiple magnetic field sensors in the later stage of magnetic field decay to obtain a superimposed attenuation rate slope value; and determining the presence and location of the weld based on a comparison between the superimposed attenuation rate slope value and a preset threshold, wherein the preset threshold is set based on the attenuation rate slope value of the weldless region.

A method for determining the association and continuity combination of top-side-rock structural surfaces based on unsupervised learning is proposed. This method involves investigating the structural surfaces of the roof and side-rock masses in underground engineering projects to obtain geometric and statistical information. A unified encoding is performed to construct a joint feature set of roof-side-rock structural surfaces. An unsupervised density-based clustering algorithm is used for initial cluster analysis of the joint feature set. A swarm intelligence-based Lüperfox optimization algorithm is introduced to optimize key parameters of the DBSCAN clustering algorithm. A Latin hypercube sampling method is used to improve the population initialization process of the Lüperfox optimization algorithm, resulting in better DBSCAN hyperparameters and optimized structural surface clustering results. The method determines the structural surface system affiliation of structural surfaces in different locations, quantitatively calculates the spatial continuity and combination relationship of structural surfaces, and outputs the spatial association determination results and continuity levels of the roof-side-rock structural surfaces. This method enables intelligent identification and quantitative analysis of the spatial relationship between roof and side-rock rock structural surfaces.

The application provides a robot mechanical arm cooperative control method based on imitation learning. The method comprises the following steps: constructing a robot historical state data set, performing feature extraction on the robot historical state data set through an encoder structure to obtain a latent feature representation; obtaining a color image and a depth image of a target operated by a robot to be predicted through a binocular camera, performing feature extraction processing on the color image and the depth image through a mode classification module and a visual feature extraction network to obtain a mode feature and a visual feature vector; inputting joint state information, the latent representation, the operation mode feature and the fused visual feature into a decoder to obtain an action block prediction result, performing weighted averaging on action blocks at multiple continuous time points based on a time series integration weighting mechanism to obtain a continuous control trajectory of the robot mechanical arm. The application solves the multi-stage strategy discontinuity and error accumulation problems existing in the traditional control algorithm, and realizes smooth action, stable task and double-arm cooperative control.

The application discloses a television application recommendation method and system based on multi-physiological signal fusion, a television and a storage medium, and relates to the technical field of television application recommendation.The method comprises the following steps: acquiring physiological signals of a user collected by a remote controller, and generating a standardized physiological feature vector according to the physiological signals; acquiring behavior features, time features and scene features; inputting the standardized physiological feature vector, the behavior features, the time features and the scene features into a lightweight end-side calculation model for fusion calculation to obtain user identity data; acquiring multi-modal features and user historical use records; constructing a user portrait according to the user identity data, the multi-modal features and the user historical use records, and obtaining user portrait data; and performing application prediction and recommendation according to the user portrait data, and displaying recommended applications when a proactive recommendation trigger condition is met.The application provides personalized services according to the characteristics of different users, improves the prediction accuracy of recommended content, and thus realizes true non-sensing recognition and proactive prediction.

This invention discloses a plant leaf segmentation and cutting device, including a base, a cutting frame mounted on the base, and a cutting mechanism movably mounted on the cutting frame. A suspended area is constructed on the base opposite to the cutting mechanism. During cutting, the portion of the plant leaf to be cut is extended into the suspended area, and the cutting mechanism passes through the suspended area to segment the plant leaf. Plant leaves are placed on the base with the portion to be cut extending into the suspended area, and then the cutting mechanism descends to pass through the suspended area to segment the plant leaf. Compared with existing technologies, this invention eliminates the need for manual hand-held cutting equipment to cut plant leaves, and the mechanized cutting by the cutting mechanism results in a smoother cut on the plant leaf, improving both the efficiency and safety of the cutting operation.

This application provides a method, equipment, medium, and product for testing power line signals, relating to the field of signal processing technology. The method includes: dividing the power frequency cycle into N test time cycles using a voltage zero-crossing detection unit and generating corresponding N wideband test data packets; sequentially sending the wideband test data packets within M consecutive power frequency cycles to obtain M test signals, which are then collected to form N sub-signal sets; calculating the fluctuation variance of each sub-signal set and generating a confidence coefficient; synthesizing N reference sub-signals and dividing them into high-confidence sub-signals and sub-signals to be repaired based on the confidence coefficients; reconstructing and repairing the sub-signals to be repaired using the channel response characteristics of the high-confidence sub-signals to generate repaired sub-signals; and finally concatenating the high-confidence sub-signals and repaired sub-signals in time sequence to obtain the final test signal. This solution solves the technical problem of how to obtain high-precision power line test signals throughout the entire power frequency cycle.

This invention relates to the field of early warning technology, and discloses a cable stress intelligent sensing and early warning method and device, comprising: a force acquisition module to acquire standardized multi-point force array data; a reference separation module to extract thermal effect baseline and contact deviation data and divide complete cycle periods; a deformation reconstruction module to generate potential deformation and bending deformation based on a swarm intelligence algorithm; an offset extraction module to acquire the offset of the eccentric hysteresis center; a stress assessment module to fuse the above parameters to amplify the average force value to obtain the equivalent stress index; a dynamic threshold module to generate a safety threshold based on historical data and calculate the over-limit ratio; and finally, an early warning module to output an alarm status. This invention can accurately locate hidden bending peaks induced by micro-rotation angles, achieving early warning.

The application provides a kind of multilingual front-end resource automatic construction method and system based on construction stage, method includes: in front-end construction stage, configuration target language type set and main language type, and target language type set is executed cyclic construction processing to;In cyclic construction processing, when target language type is different from main language type, the text content in front-end source code is scanned and identified, generates and labels text mark, based on text mark, from target language type corresponding language package, target language text is obtained, and target language text is replaced with corresponding text content, and the text content that is not completed replacement processing is recorded as to-be-processed text;Call translation interface to execute translation processing to to-be-processed text, generate corresponding translation text, based on translation text, corresponding text content is executed replacement processing, and according to the text mark of to-be-processed text, translation text is added to target language type corresponding language package.

This invention provides a method and system for road condition data acquisition and analysis based on a commercial vehicle connected driving recorder. The invention utilizes a connected driving recorder, a mandatory standard feature in commercial vehicles, to acquire road images in real time via an image acquisition module and perform preprocessing such as backlight compensation, distortion correction, noise reduction, and effective area cropping. An image recognition module employs a lightweight deep learning and rule-based algorithm to locally identify congestion and traffic accidents. A positioning module acquires latitude and longitude, vehicle speed, and timestamps via GPS / BeiDou dual-mode, achieving precise spatiotemporal binding through timestamp alignment and interpolation compensation. A 4G communication module uploads data using a strategy prioritizing event triggering and supplementing with periodic heartbeats. A data monitoring platform verifies the data, performs 3D deduplication, and then visualizes it on an electronic map. This invention requires no additional hardware, is low-cost, has wide coverage, and strong real-time performance, enabling automatic and precise monitoring of road conditions across the entire area, and is suitable for traffic management and logistics scheduling.

The application discloses an industrial internet high-risk software identification system and method, and particularly relates to the technical field of industrial internet security, and comprises an industrial software gene map automatic construction module, a business context-aware dynamic risk assessment module and a map-associated precise disposal strategy recommendation module; through multi-dimensional feature acquisition and fusion technology, a unique digital identity is generated for software assets, precise identification is realized; the real security risk of software assets is dynamically quantified by comprehensively considering vulnerability severity, business criticality and network exposure; based on asset relationship map and game analysis, the optimal disposal strategy with the least impact on business is generated and recommended for high-risk software. The application solves the problems of inaccurate industrial software identification, risk assessment out of business context and lack of intelligent decision support for disposal measures in the prior art, realizes automatic closed-loop management from identification, evaluation to disposal, and significantly improves the security protection capability of the industrial internet.

The invention discloses a photovoltaic safety early warning method based on Bayesian dynamic belief propagation, and relates to the field of photovoltaic safety early warning. According to the method, a dynamic Bayesian network model of which the risk evolves along with time is constructed, belief propagation reasoning is performed on multi-source heterogeneous data by using a BD-BP algorithm, and risk weighted convergence is performed in combination with network topology centrality. Therefore, false alarms caused by environmental noise are effectively suppressed, dynamic tracking and accurate positioning of the gradient risk of the photovoltaic equipment are realized, the early warning has the characteristics of strong anti-interference capability and accurate key node identification, and the crossing from single-point passive alarm to system-level active situation awareness is realized.

This invention discloses an autonomous navigation and obstacle-crossing inspection robot for high-voltage transmission lines, comprising front and rear fixed arms, a middle movable arm, an adjustable center of gravity frame, and a control box. The adjustable center of gravity frame provides the supporting foundation for all structures of the robot. The front and rear fixed arms are used to monitor and identify obstacles on the line and to grip the line for movement. The middle movable arm, located between the front and rear fixed arms, grips the line and drives the robot to slide over obstacles. The control box, located at the lower end of the middle movable arm, moves with the sliding motion of the arm, providing power and control for the robot, and communicating with a ground-based control computer via a built-in data communication exchange. This invention features a compact overall structure, enabling accurate identification and autonomous crossing of complex line obstacles with a high success rate. It adapts to various line obstacle scenarios, solving the problems of weak obstacle-crossing ability and poor adaptability of traditional inspection robots, and significantly improving inspection efficiency.

This invention relates to a vision-based method and system for identifying and classifying the boundary crossing behavior of intelligent agents in oilfield electronic fences. Belonging to the fields of oilfield perimeter security and computer vision technology, it addresses the problems of existing electronic fences, such as inability to identify target types, inability to quantify boundary crossing risks, single warning levels, and low system integration. The method involves target detection and continuous tracking of oilfield perimeter image data to obtain target type and movement trajectory. Boundary crossing verification is performed on the target's center position based on a polygonal electronic fence, and the boundary crossing distance from the target to the fence boundary is calculated. This distance is then fused with target type risk weights to obtain an adjusted boundary crossing distance. Classified warnings are implemented based on preset thresholds. Simultaneously, the warning information is visualized and logged, achieving accurate identification and differentiated warnings for boundary crossing behavior. This invention is applicable to perimeter security monitoring and intelligent risk warning in complex environments such as oilfield mining areas, well sites, and oil and gas storage areas.

This invention relates to a real-time detection method for encryption hijacking on Linux platforms based on multi-dimensional behavioral characteristics. This method relies on a closed-loop governance system formed by the collaborative analysis and processing module and the decision output module. The triggering logic of the three-layer cascaded analysis is as follows: the resource monitoring layer performs real-time monitoring and initial anomaly screening of system-level hardware resource indicators, outputting system-level risk status identifiers; when any monitoring indicator is abnormal, the behavior analysis layer is activated; the behavior analysis layer collects and analyzes process-level behavioral characteristics, outputting a list of suspicious processes and their risk scores; when the process risk score exceeds a set threshold, the essence detection layer is triggered; the essence detection layer performs encryption hijacking essence behavior verification on suspicious processes, completing the final decision. The main objective of this invention is to eliminate reliance on variable features and instead construct a detection mechanism that is difficult to circumvent, has a low false alarm rate, and can respond in real time based on the core essence behavior of encryption hijacking.