5700 results about "Data entry" patented technology

A 3D target detection method based on multimodal fusion and a depth attention mechanism includes the following steps: obtaining and preprocessing original point cloud data and original image data; inputting the preprocessed point cloud data and image data into a 3D target detection network based on multimodal fusion and the depth attention mechanism, where the 3D target detection network based on multimodal fusion and the depth attention mechanism includes a generation phase for 3D bounding box proposals and a refinement phase for 3D bounding boxes, and the network outputs parameters and classification confidence of a target bounding box; training the 3D target detection network based on multimodal fusion and the depth attention mechanism; and processing the collected lidar point cloud data and image data by using the trained detection network, and outputting 3D target information, to realize 3D target detection.

The invention relates to the technical field of the Internet of Things, and discloses a gas leakage monitoring method, device and equipment based on the Internet of Things and a storage medium, and the method comprises the steps: carrying out the data collection of a gas storage device, and obtaining an initial multi-dimensional data set; performing filtering and edge detection processing to obtain potential abnormal point data; inputting the potential abnormal point data into a mixed deep learning model for gas leakage detection to obtain initial leakage source position data and a leakage intensity value; executing multi-sensor cooperative accurate leakage positioning to obtain target leakage source position data; performing gas diffusion simulation to obtain gas concentration distribution data and risk indexes; based on the gas concentration distribution data and the risk index, dynamic reconstruction of the sensor network is carried out, an emergency response strategy and sensor network configuration information are output, and the method realizes whole-process management from leakage detection and source positioning to risk assessment and emergency response.

The invention relates to the technical field of stamping part detection, and discloses a stamping part size and defect synchronous detection method and system. The method comprises the steps that the multi-sensor measurement module is used for collecting line laser scanning morphology data, infrared thermal image strain data and structured light projection contour data of a stamping part; multi-sensor data registration is achieved through a feature point matching algorithm, and a three-dimensional space coordinate mapping relation is generated; calculating the thermal expansion compensation amount of the material in combination with a thermal deformation correction model; filtering the structured light projection contour data, and extracting key contour feature points and defect region boundaries; inputting the related data into a multi-source data fusion model to obtain a dimensional deviation and defect fusion detection result; based on this, a measurement path is updated through a dynamic path planning algorithm, and a synchronous detection scheme is output. The device can synchronously detect the size and defect of the stamping part, improves the detection precision and efficiency, achieves the quality grade classification, and is high in adaptability.

A system to detect, classify and track small objects in an image frame. The system includes an image data system and image analytic system. The image data system receives real-time image data from an image capturing system and inputs the image data to image analytic system. The image analytic system performs the object detection, classification and tracking of objects in an image frame using a combination of techniques.

Systems, computer program products, and methods are described herein for building material data management. With reference to an example method, the method receives a first dataset formed of first data entries associated with a building material data source and generates a building material data entity based on the building material data source. The method includes outputting a representation of the building material data entity. The first dataset may be formed of data records (e.g., construction documents, user inputs, sensor data, or machines data), and the representation may be a visual representation may include notifications, visual presentations of the building material entity, etc.

The present invention relates to a pose recognition method and system combining environmental adaptation with estimation and classification, and a medium, the method comprising: on the basis of an application scenario, obtaining a human pose estimation model; inputting image data to be tested into the human pose estimation model to output human pose keypoints; performing data enhancement and normalization on the human pose keypoints; inputting the human pose keypoints that have undergone data enhancement and normalization and high-order pose features into a pose classification model to obtain a pose recognition result, the high-order pose features comprising bone vectors, the mean of bone vector moduli, relative lengths of bones, and an included angle between the line connecting each human pose keypoint and a trunk midpoint and the horizontal direction. Thus, according to the present invention, pose recognition is divided into two processes of pose estimation and pose classification, which facilitates algorithm updating and iteration being performed when there is better estimation and classification performance, and the structure is flexible.

The invention relates to the technical field of computer vision and target detection, in particular to an improved Yolov10-oriented target detection fusion method, which specifically comprises the following steps of: selecting data to construct a data set, and then preprocessing the data in the data set to obtain a preprocessed data set; the method comprises the following steps: preprocessing a data set, enhancing the preprocessed data set, constructing a target identification network model based on a lightweight Yov10n network, improving the Yov10n network, the improved Yov10n network comprising an input end, a backbone network, a neck network and an output end, and introducing four context fusion CFM modules into the neck network, the CFM module comprises a channel adjustment convolution module, a feature splicing module, an attention module, a weighted feature recombination module and an output feature generation module; and inputting the image data in the preprocessed data set into the improved constructed target recognition network model to obtain a final detection result. According to the method, diversified targets with different scales can be better processed, so that the robustness of the model to a complex background is improved.

The invention provides a deep learning-based rapid ore sorting method under adaptive illumination intensity, and relates to the technical field of ore sorting, the deep learning-based rapid ore sorting method comprises the following steps: obtaining illumination intensity distribution information and ore three-dimensional point cloud data, and calculating ore space orientation parameters based on quaternion attitude estimation; inputting the obtained data into a layered illumination compensation network, and obtaining an illumination normalized enhanced image through adaptive brightness mapping and local light spot correction in combination with reflectivity recovery and non-local mean filtering; inputting the illumination normalized enhanced image and the ore three-dimensional point cloud data into a multi-branch feature extraction network, constructing feature high-order association, and adopting adaptive feature reconstruction and conditional random field optimization features; and inputting the reconstructed features into a classification model based on active learning, optimizing a feature manifold space by using a self-organizing mapping network, carrying out sample labeling in combination with active sampling of adaptive entropy threshold screening and uncertainty perception, and realizing ore classification and sorting through random forest integration.

The invention relates to the technical field of crack identification, and discloses a crack identification analysis method and system for a building shear wall, and the method comprises the steps: carrying out the multi-angle shooting and image standardization processing of the surface of the building shear wall, and obtaining a standardized image data matrix; performing multi-model degradation processing and multi-angle image combination to obtain target enhanced image data; inputting the image into a convolution denoising auto-encoder for noise separation to obtain a denoising feature image matrix; inputting a dynamic graph convolutional neural network to carry out local feature and global feature extraction to obtain a local crack feature matrix and a global crack feature matrix, and carrying out feature fusion to obtain a fused feature matrix; and inputting the twin metric learning network to carry out feature measurement and crack classification, and outputting a target crack classification result, thereby realizing effective capture of deep semantic information between crack areas of the building shear wall, and realizing efficient integration of crack features of different scales.

The invention discloses a combined visual question and answer method based on core-to-global semantic fusion reasoning, and belongs to the technical field of image and text processing, and the method comprises the steps: obtaining image data and text question data; inputting the image data into the semantic feature extraction model to obtain image global features and visual core features; inputting the text problem data into the semantic feature extraction model to obtain text problem global features and text core features; transmitting the image global feature, the visual core feature, the text problem global feature and the text core feature to a preset multi-modal semantic fusion model to execute multi-modal semantic learning of different levels; fusing the learning results of the single-level language; and according to the target answer prediction vector, calculating to obtain a target prediction answer. According to the method and the device, the technical problems of inaccuracy in reasoning and model instability caused by inaccurate semantic alignment, feature redundancy and noise interference of multi-modal information in the prior art are solved.

The invention relates to the technical field of subway train driving strategy optimization, and discloses a subway line real-time data driven DAS driving strategy optimization method, which comprises the following steps: acquiring multi-dimensional operation data including a train real-time position sequence, station passenger flow data and a trackside signal equipment state; extracting line operation characteristics through a convolution space-time encoder, generating a passenger flow fluctuation prediction map by using a time sequence decomposition algorithm, and performing characteristic fusion on the trackside signal equipment state to generate an equipment health degree evaluation matrix; inputting the multi-dimensional data into the driving strategy model to generate an initial driving instruction sequence, dividing operation control priorities through a regional clustering algorithm, and constructing a dynamic optimization strategy network in combination with a genetic algorithm to generate a final driving strategy scheme; and acquiring an execution state log in real time, and updating the strategy network through the abnormal decision detection model. According to the method, the dynamic optimization of the subway driving strategy is realized, and the operation safety, efficiency and comfort are improved.

The invention provides a deep learning-driven coastline change dynamic monitoring method, and belongs to the technical field of image understanding and recognizing.The deep learning-driven coastline change dynamic monitoring method comprises the steps that a time sequence attention matrix is formed by constructing a multi-source satellite data fusion network, and coastline form change features are extracted by applying a fourth-order time sequence convolutional neural network to construct a first fluctuation matrix; and performing dimension reduction processing by using a space-time attention mechanism to generate a second fluctuation matrix, and converting the second fluctuation matrix into a quantitative index through a fluctuation conversion matrix. The method comprises the steps of inputting data into a pre-trained CoastDynNet model to analyze a physical mechanism, performing fine adjustment by applying a coastline evolution optimization function, performing multi-scale decomposition by using a WaveletTempNet network to separate long and short term changes, and finally constructing a multi-model integration framework by using a Bayesian model averaging method to generate a comprehensive coastline dynamic evaluation report.

The invention discloses an intelligent regulation and control method and system for a photovoltaic energy storage air conditioner. The method comprises the steps that photovoltaic related data, energy storage related data and air conditioner operation related data are collected; inputting the photovoltaic related data into the photovoltaic power generation prediction model to obtain a photovoltaic power generation prediction result; inputting the air conditioner operation related data into the air conditioner load prediction model to obtain an air conditioner load prediction result; inputting the photovoltaic generating capacity prediction result, the air conditioning load prediction result, the energy storage related data and the photovoltaic related data into an energy storage optimization scheduling model to obtain an energy storage charging and discharging decision; constructing a power supply strategy core rule base based on the energy storage related data, the electricity price peak time period and output results of the photovoltaic power generation prediction model and the air conditioner load prediction model; and optimizing an output result of the energy storage optimization scheduling model based on the power supply strategy core rule base. The method has the outstanding advantages of high reliability, high photovoltaic utilization rate, low dependence degree on a power grid and the like.

The invention discloses a multi-scale semantic guidance image compression method and system and a storage medium, and the method comprises the following steps: obtaining input image data, carrying out the preprocessing of an input image, and obtaining standardized image data; inputting the standardized image data into a pre-trained semantic segmentation network to generate a multi-scale semantic feature map and a semantic weight map corresponding to the multi-scale semantic feature map; a three-stage pyramid encoder is constructed, and the standardized image data is subjected to the following steps of: sampling under depth separable convolution to generate multi-scale features; the reversible neural network carries out nonlinear transformation on the multi-scale features; the multi-scale feature subjected to nonlinear transformation is decomposed into a low-frequency sub-band and a high-frequency sub-band through adaptive discrete wavelet transformation, dynamic selective state space modeling is executed on the high-frequency sub-band based on a semantic weight map, and a compressed code stream is generated; and inputting the compressed code stream into a decoder, decoding based on a lightweight Mama module, and reconstructing an image in combination with inverse wavelet transform and a semantic weight map.

The invention relates to the technical field of breeding planning, in particular to an intelligent breeding planning and decision-making method and system based on a large model. The method comprises the following steps: acquiring a multi-source breeding data set; constructing a structured breeding knowledge graph based on the multi-source breeding data set; performing breeding data association on the structured breeding knowledge graph according to a preset large model to generate a special breeding basic model; obtaining a breeding instruction input by a user; performing user semantic recognition on a breeding instruction input by a user to generate breeding semantic recognition data; inputting the breeding semantic recognition data into a breeding special basic model for breeding intention analysis, and generating user breeding intention data; and determining data information needing to be called based on the breeding intention data of the user, analyzing and screening to generate germplasm resource screening data and a breeding plan / breeding decision scheme. According to the method, the intelligence and operability of breeding planning are improved through integration of multi-source data, intelligent semantic recognition, combined genetic analysis and executable evaluation.

A system for real-time data aggregation and analysis within an Internet of Things (IoT) network, consisting of: a multitude of IoT sensor nodes configured to collect data from multiple sources, each sensor node comprising a data acquisition unit for capturing data in real time and a local processing unit for preprocessing the captured data by performing data filtering, normalization and compression prior to transmission; a centralized aggregation server communicating with the multitude of IoT sensor nodes, the aggregation server comprising: a data aggregation module configured to receive, integrate, and synchronize pre-processed data streams from the multitude of sensor nodes, thereby enabling consolidated data storage; a timestamped metadata indexing unit coupled with the data aggregation module and configured to add metadata to each data entry, including a unique identifier, a geographic location, and a timestamp; an analysis engine integrated into the aggregation server, which includes the following: a data processing unit configured to perform real-time analysis of the aggregated data by applying pattern recognition and anomaly detection techniques and to generate processed results based on predefined parameters; a predictive analytics module that is connected to the data processing unit and configured to perform predictive modeling tasks by extrapolating trends from historical and real-time data; a communication interface connected to the aggregation server, configured to enable bidirectional data exchange with external monitoring devices, user interfaces, or remote management systems via secure communication protocols; and a dynamic load balancing unit embedded in the aggregation server, configured to dynamically distribute computing resources between the data processing unit and the predictive analytics module depending on data volume and complexity.

The invention discloses an intelligent emergency and management and control method for a stadium, and belongs to the technical field of stadium safety management and control. According to the method, video stream data are collected through a monitoring camera array, and coverage areas of all cameras in the array have 15%-20% of overlapping areas; inputting the data into a pre-trained people density identification model to generate a real-time people flow distribution thermodynamic diagram; the retention index of each partition is calculated, and an emergency evacuation mode is activated in combination with a preset safety threshold value; in the emergency mode, the infrared sensor group monitors the instantaneous pedestrian flow speed, and an optimal evacuation path is generated by a dynamic path planning algorithm; the path is converted into a control instruction to drive an electromagnetic access control gate, emergency lighting and an electronic guide identifier, feedback data triggers a path re-calculation mechanism, and a two-dimensional code scanning device counts the number of evacuated people so as to start manual re-checking. Real-time situation awareness, dynamic risk assessment and intelligent evacuation guidance of the stadium are achieved, emergency response and evacuation efficiency is effectively improved, and crowd safety is guaranteed.

The invention provides an enterprise intelligent diagnosis method, system and device based on a large model and a medium, and belongs to the technical field of enterprise diagnos.The method comprises the steps that enterprise heterogeneous data are collected through a multi-source data interface, cleaning, feature extraction and cross-modal alignment fusion are carried out, and enterprise real-time data are obtained; constructing a knowledge graph through a graph attention network on the basis of an industry index to which an enterprise belongs, and updating association weights among nodes at regular time; inputting enterprise real-time data into the pre-trained multi-modal large model for preliminary analysis, and outputting a risk thermodynamic diagram; key abnormal indexes are identified from the risk thermodynamic diagram, sub-graphs related to the key abnormal indexes are extracted from the knowledge graph, structured prompt words are generated from the sub-graphs, then the structured prompt words and standardized enterprise real-time data are jointly input into a multi-modal large model for joint reasoning analysis, and then a visual diagnosis report is generated. Accurate identification and intelligent diagnosis of enterprise risks are realized, and decision-making efficiency is improved.

The invention provides a limb nerve rehabilitation state evaluation method based on multi-modal sensor data fusion. Motion inertia data, electromyographic signal data and text data of a corresponding limb nerve rehabilitation action manual of a patient during rehabilitation actions are obtained in real time; inputting the data into a designed and trained multi-modal data feature extraction network to obtain motion inertial data features, electromyographic signal data features and text semantic features; inputting the motion inertia data features, the electromyographic signal data features and the text semantic features into a designed and trained multi-modal data feature fusion network to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a trained prediction output network to obtain a limb nerve rehabilitation state prediction value of the patient. Through multi-modal data fusion and time-dependent feature extraction, the limb nerve rehabilitation state of the patient is evaluated more accurately, and a more reliable diagnosis basis is provided for doctors. Meanwhile, the labor cost is reduced and the evaluation efficiency is improved.

The invention provides an object positioning and posture recognition method based on 3D vision. The method comprises the steps that 3D point cloud data and image data corresponding to the 3D point cloud data are collected; preprocessing the image data to obtain a quality evaluation result, and executing image restoration processing based on the quality evaluation result to generate optimized image data; inputting the optimized image data into a pre-constructed conditional diffusion model based on hierarchical feature guidance, and generating feature representation of the optimized image data; and inputting the feature representation of the optimized image data into a differential transformer network to obtain a position and attitude prediction result of the object. According to the invention, through innovative image quality evaluation based on dual representation interaction driving, a conditional diffusion model based on hierarchical feature guidance and the design of a differential transformer network, the precision and robustness of object positioning and posture recognition in a complex environment are improved.

The invention relates to the technical field of intelligent traffic systems, and discloses a hard shoulder driving and variable speed limit cooperative control method and system.The method comprises the steps that traffic data are collected, and the traffic data comprise traffic information and speed limit values; according to the hard shoulder lane-borrowing driving motivation, the lane-borrowing safety condition and the emergency or rescue vehicle requirement of the networked autonomous vehicle, whether the networked autonomous vehicle can run through the hard shoulder lane or not is judged; inputting the collected traffic data into a pre-constructed DDPG algorithm to determine a state, an action space and a reward function of the DDPG algorithm, and outputting an optimal speed limit value; and the optimal speed limit value of each lane of the control road section is generated through a DDPG algorithm, and the network-connected automatic driving vehicle adjusts the vehicle speed according to the optimal speed limit value. The method can be used for highway entrance ramp sections with serious congestion, the average travel time and carbon dioxide emission of the road network can be reduced, and a new method is provided for efficient and low-carbon targets of smart highways in China.

The invention provides an AI-based smart old-age care service method, system and device, and a storage medium, and relates to the technical field of smart old-age care. The method comprises the following steps: collecting vital sign data, behavior data and environment data of the elderly, and carrying out standardization processing on past case data to obtain standard case data; performing management fusion on the standard case data, the vital sign data, the behavior data and the environment data to obtain multi-dimensional health data; inputting the multi-dimensional health data into a preset health state evaluation model to obtain health state information; matching the health state information with maintenance knowledge in a preset knowledge base to obtain a plurality of alternative maintenance schemes, and inputting the plurality of alternative maintenance schemes into a preset maintenance effect prediction model to obtain a plurality of maintenance effect prediction results; and taking the alternative maintenance scheme with the highest evaluation score as a target maintenance scheme. Through the alternative scheme, the effect of the pension service is improved.

The invention discloses a large-scale sample storage management method and system based on a multi-dimensional label system. The method comprises the following steps: acquiring large-scale sample data input, and establishing a multi-dimensional label system for each piece of sample data based on a sample source, sample quality and sample feature dimensions; performing automatic auxiliary identification and classification and quality evaluation on the sample data, generating a multi-dimensional label for the sample according to a multi-dimensional label system, selecting a proper storage layer according to the multi-dimensional label of the sample, and storing the sample in a specified storage layer after the storage space is optimized; and returning a retrieval result according to a retrieval condition input by a user, and distributing the retrieved sample to a required task or analysis scene according to a requirement. According to the method, by establishing the multi-dimensional metadata labels, retrieval, positioning and management of the samples can be completed quickly and efficiently. Meanwhile, automatic auxiliary classification and recognition of the samples are achieved, and the data management efficiency is improved.

The invention discloses a battery pack health state prediction method and device, and the method comprises the steps: building a battery pack three-dimensional matrix based on collected historical data of a plurality of cells in a battery pack, carrying out the sliding window processing of the battery pack three-dimensional matrix, and generating model input data; determining a battery pack health state prediction model, wherein the battery pack health state prediction model comprises an SE-TCN network, a Performer encoder and a space-time fusion layer; and inputting the model input data into the battery pack health state prediction model to obtain a health state prediction value of the battery pack. According to the method, the time dependency characteristics of the time sequence data of the plurality of battery cells in the battery pack and the global dependency relationship among the battery cells are respectively captured, and the time sequence characteristics and the space correlation characteristics are fused, so that the model can capture the complex interaction relationship among the plurality of battery cells; and the accuracy and robustness of battery pack health state prediction are greatly improved.

The invention discloses a server health state diagnosis method based on a GAT-LP algorithm, and relates to the technical field of server health diagnosis, and the method comprises the steps: collecting multi-dimensional operation state data of a server, transmitting the multi-dimensional operation state data to a data analysis platform, and carrying out the preprocessing, inputting the preprocessed historical operation state data of the server into a GAT-LP network for training to obtain a GAT-LP network model, evaluating health conditions of the server and the process and generating an overall health condition according to a health state diagnosis result of the server and health score features extracted based on the GAT-LP network model, converting an evaluation result into a visual health state, and displaying the visual health state in the GAT-LP network model. And triggering an alarm mechanism when the health state of the server is lower than a preset threshold value. By constructing a multi-dimensional health assessment index system, the running state of the server is visually displayed, potential risks are found in advance, and prevention measures are made.

Embodiments of the present description provide a root cause location method, system and device. The root cause locating method comprises: acquiring historical link data corresponding to a microservice, and determining a target anomaly location model corresponding to the historical link data; determining link data sets to be processed corresponding to the microservice, clustering said link data sets, and determining cluster center link data on the basis of a clustering result; inputting the cluster center link data into the target anomaly location model to obtain abnormal link node information corresponding to the cluster center link data; and on the basis of the abnormal link node information, determining a target abnormal link corresponding to the microservice, and determining an abnormal link node in the target abnormal link. Model fine adjustment is carried out on the basis of an initial anomaly location model, so that the initial anomaly location model has wide applicability and high generalization ability. Cluster center link data is selected for root cause location, so that the root cause location speed of the microservice is increased.

The invention discloses a multi-source traffic data information extraction and intelligent responsibility judgment method, system and device, and relates to the technical field of traffic accident data processing and analysis, and the method comprises the steps: inputting traffic accident related data into a pre-constructed multi-modal large model, and generating a brief description text of traffic accident passing; retrieving legal provisions related to the traffic accident from a pre-constructed legal knowledge base according to the brief description text; and inputting the brief description text and the legal provisions into a pre-constructed Traffic-T5 model, and automatically generating a responsibility affirmation result. According to the method, unified modeling and processing are performed on the text and image data by adopting the multi-modal large model, the efficiency and accuracy of accident information extraction are improved, the multi-modal large model is optimized, the understanding and analysis ability of the multi-modal large model on incomplete traffic accident data is enhanced, and a legal knowledge base is combined, so that the method is suitable for popularization and application. Dynamic retrieval and application of traffic laws and regulations are realized, and the responsibility division result is ensured to have a basis.

The invention relates to a method, device and product for monitoring river flow in real time, and the method comprises the steps: obtaining tower footing video data of a target river reach which comprises a target section and a pier buoy; inputting the tower footing video data into a water level and flow velocity identification model, and obtaining the water level elevation and the surface flow velocity of at least part of the cross section in the target section through the water level and flow velocity identification model; inputting the water surface elevation into a three-dimensional digital river channel model to obtain a section water passing area of a corresponding cross section; and obtaining the cross section flow of the target cross section according to the cross section water passing area and the surface flow velocity. According to the embodiment of the invention, automatic flow estimation based on the tower footing video can be realized, the manual measurement cost is reduced, the spatial-temporal resolution of flow monitoring is improved, and the method can be used for large-range river monitoring, especially in areas lacking hydrometric stations, and can provide data support for drainage basin water resource management and flood prevention early warning.

The invention discloses a photovoltaic panel defect detection method, system and device and a medium, and relates to the technical field of photovoltaic defect detection.The method comprises the steps that an infrared image and a visible light image of a photovoltaic module are obtained; visual features are extracted and fused; marking defect areas in the fused image data set, storing marking information of the defect areas, and constructing a training data set; inputting the training data set into a CLIP model for processing, and generating embedded representation; adopting the embedded expression to initialize the layer weight of the YOLOv11 model, and performing fine adjustment on the YOLOv11 model; inputting image data of a to-be-detected photovoltaic module into the trained photovoltaic defect detection model, and detecting to obtain a defective area in the image data; according to the method, the problem that a large amount of data for photovoltaic detection cannot be obtained in a remote area, so that model training is difficult is solved.

The invention provides a wafer edge defect detection method and system based on image recognition and a storage medium, and belongs to the technical field of semiconductor manufacturing, and the method comprises the steps: processing annular image data, and obtaining target image data; inputting the target image data into the edge defect prediction model to obtain a defect probability distribution diagram; based on the defect probability distribution diagram, identifying a pixel region with a probability value greater than or equal to a preset threshold value as a candidate defect region; performing feature extraction on the candidate defect region to obtain a target feature vector; inputting the target feature vector into a defect classification model, and determining the defect type and classification confidence of the candidate defect region; based on the defect type and / or the classification confidence, screening out a target defect area from the candidate defect areas; obtaining three-dimensional point cloud data of the target defect area, and calculating three-dimensional geometric parameters of the target defect area based on the three-dimensional point cloud data; and determining a detection result of the target wafer based on the defect type and the three-dimensional geometric parameters.