352 results about "Information capture" patented technology

The invention relates to the technical field of intelligent logistics and supply chain management, and discloses a cross-border e-commerce logistics intelligent storage scheduling and transportation cooperation method based on AI path planning, and the method comprises the steps: building a monitoring module, a dynamic information capturing module, a data classification module, a data analysis module, a storage scheduling and transportation cooperation module and a management module; the monitoring module monitors storage and transportation information in real time through a sensor and monitoring equipment, the dynamic information capture module collects external environment data and internal abnormal events, the data classification module classifies the monitored data and the captured information, and the data analysis module constructs a path planning model according to the stored data of the data classification module. The system comprises a storage scheduling and transportation collaboration module, a storage scheduling and transportation collaboration module and a management module, the storage scheduling and transportation collaboration module generates an optimal path and calculates a storage efficiency index # imgabs0 #, a transportation path lifting coefficient # imgabs1 # and a storage-transportation collaboration index # imgabs2 #, the storage scheduling and transportation collaboration module optimizes the path according to a calculation result and sets an early warning mechanism, and the management module assists management personnel in monitoring and decision making through a visual interface.

In a traffic scene, a traditional target detection algorithm always faces the problems of strong background interference, difficulty in small target detection and the like, and detection precision and robustness are affected. Therefore, the invention provides an improved YOLOv12n vehicle detection method in which an EMA (Empirical Multi-scale Attention) attention mechanism and an SFA (Space Feature Aggregation) attention mechanism are fused. The invention further provides a method for detecting the YOLOv12n vehicle based on the improved YOLOv12n vehicle based on the attention mechanism of the EMA (Empirical Multi-scale Attention) and the attention mechanism of the SFA (Space Feature Aggregation). The SFA module is deployed in a shallow network, key target area expression is enhanced by aggregating spatial features, and background noise interference is suppressed; the EMA module is embedded into a neck network, and the global information capture and multi-scale sensing capabilities are improved by adopting multi-scale convolution, cross-space modeling and feature grouping mechanisms. According to the method, the real-time performance is kept, meanwhile, the detection precision in a complex scene is remarkably improved, and particularly, higher robustness is shown in the aspects of small target recognition and shielding processing.

The invention discloses a flexible job shop scheduling method based on a graph neural network and deep reinforcement learning, and relates to the technical field of flexible job shop scheduling. The method at least comprises the following steps: S1, firstly, carrying out Markov Decision Process (MDP) on a flexible job shop scheduling problem, namely, FJSP, and initializing a scheduling state; and S2, representing a complex relationship between a job and a machine by using a heterogeneity graph, and effectively mapping different entities (the job, the machine, the operation and the like) of the problem and the relationship between the different entities into a graph structure, wherein the different entities (the job, the machine, the operation and the like) of the problem and the relationship between the different entities (the job, the machine, the operation and the like) of the problem are represented by the heterogeneity graph. According to the method, the graph neural network based on the meta-relationships is provided, different graph convolution modes are innovatively adopted for different meta-relationships to extract features, original semantic information is reserved, the global information capturing capability is enhanced, and a reinforcement learning agent is more accurate when making a scheduling decision.

The invention provides a video generation method based on diffusion model optical flow trajectory control. The video generation method comprises the following steps: S1, processing a training video and modeling an optical flow trajectory; s2, optical flow trajectory calculation and dynamic attention optimization; s3, carrying out diffusion modeling; and S4, model reasoning and implicit vector reconstruction. Through three core technologies of optical flow trajectory modeling, dynamic attention optimization and implicit vector reconstruction, the precision of motion information capture and the detail quality of video generation are improved, personalized style control is realized, and compared with a traditional method, the method has remarkable advantages in motion accuracy, video coherence and personalized customization ability, and the method is suitable for popularization and application. The problems that in the prior art, the precision in few-sample video generation is insufficient, and motion information in a training video cannot be effectively extracted are solved.

Methods and systems are described for identity verification and deepfake prevention. A first video of a person is captured during an in-person interaction, the person's identity is verified, and the video is associated with the verified identity. In a subsequent electronic interaction, information about a second person is accessed, a second video of the second person is captured, and a trained neural network model is used to determine if the second person is attempting to identify as the first person. If the second person is not attempting to identify as the first person, the electronic interaction proceeds. If the second person is attempting to identify as the first person, the model determines if the first person is likely the same as the second person, transmitting a positive indicator if likely the same, and a negative indicator if not. Related networks, models, apparatuses, devices, techniques, and articles are also described.

The invention discloses a foggy day perception-based feature refinement ship instance segmentation method and equipment, and belongs to the field of instance segmentation of ship images in a foggy day scene. According to the method, YOLOv8s is used as a basic model, a joint learning framework is designed, and defogging and feature refinement enhanced perception strategies are combined. Through a state space model and a multi-scale channel aggregation gating strategy, modeling image features are refined, and foggy day long-distance dependence feature information captured by the model is enhanced. And through the adaptive defogging module, fog background interference features are reduced. Through multi-window branch feature extraction and a multi-angle similarity measurement strategy, the ability of the model to extract local detail information and global information is further enhanced, the focusing ability of a ship target area and the adaptability to a complex haze scene are improved, and the ship instance segmentation performance and generalization ability in a foggy day scene are effectively improved.

The invention provides a business path tracking and fault tracing method and device for a video monitoring system, and relates to the technical field of video monitoring fault diagnos.The video monitoring system is divided into a plurality of layers, and multiple types of data acquisition probes are deployed at key nodes of each layer; when a user initiates a video request, generating a Trace ID to be spread along with a video streaming signaling, and reconstructing an end-to-end service path in combination with node information captured by a probe; associating and fusing the multi-dimensional performance data by taking the TraceID as a key, and establishing an end-to-end total delay decomposition and dynamic health baseline; and positioning a root cause node based on the weighted directed graph, the node anomaly confidence score and the fault propagation consistency, displaying a path, data and a traceability result through a visual interface, and generating a precise alarm. According to the method, service link visualization is realized, traditional equipment alarm is upgraded to service fault root cause positioning, the troubleshooting efficiency is improved, and the method is suitable for an isomerized large-scale video monitoring system.

The invention discloses a language-guided structure-aware network architecture and method for camouflage target detection, and relates to the technical field of camouflage target detection. According to the method, the text is guided to focus on a potential target, text semantic and visual features are fused through the CLIP model, the target mask is generated, the problems that a traditional model lacks semantic guidance and is difficult to focus on a camouflage area are solved, and background interference is greatly reduced; edge details can be accurately extracted, a Fourier edge enhancement module (FEEM) is combined with spatial domain edge enhancement and frequency domain high-frequency information capture, the problem of a fuzzy pain point of a camouflage target boundary is effectively solved, and the edge positioning precision is improved; structural and local dual optimization is proposed, a structural awareness attention module (SAAM) is fused with semantic and edge information, and a coarse guidance local refinement module (CGLRM) is designed through double branches of global guidance and local refinement, so that the regional consistency and structural integrity of a segmentation result are guaranteed, and local details are prevented from being lost.

The invention discloses an agricultural hyperspectral image classification method based on a hypergraph and Mama, and the method comprises the steps: S1, constructing a spectrum-space hypergraph according to an agricultural hyperspectral image; s2, a Mama framework used for hyperspectral classification is constructed; and S3, inputting the spectrum-space hypergraph into a Mama framework for hyperspectral classification, and carrying out classification, the method effectively fuses space and spectrum information, improves the precision of hyperspectral image classification, and solves the problem of information capture in a traditional method.

The invention belongs to the field of computer science and technology, and particularly relates to a multi-mode weak supervision video anomaly detection method and device, equipment and a medium. The method comprises the following steps: firstly, carrying out dynamic modeling on inter-fragment information by utilizing an external attention mechanism to obtain cross-fragment global context information; the temporal context aggregation module and the multi-scale time network are used to capture global and local information of the visual information and the text information within the fragment, and a feature representation containing local context details and the global information is generated. In addition, a multi-modal adaptive fusion mode is adopted, key modal features are focused in combination with target weights, further processing is performed through a multi-scale convolution attention module, and feature representation with higher discrimination is extracted. According to the method, the dependence on accurate annotation data in traditional video anomaly detection is effectively reduced; through hierarchical context modeling and an adaptive attention mechanism, the feature expression ability and the key information capture efficiency are enhanced, and a reliable anomaly detection solution is provided for an intelligent monitoring system.

The invention discloses a scale and semantic feature fused power equipment instance segmentation method and device, electronic equipment and a storage medium, and belongs to the field of power equipment.The method comprises the steps that a power distribution image is acquired; inputting the power distribution image into a preset model, so that the model generates a plurality of hierarchical features corresponding to different scales according to the power distribution image through an image coding module; generating a plurality of context enhancement features according to the plurality of level features through a scale sensing module; generating a plurality of semantic alignment features according to the plurality of level features through a semantic perception module; through a feature fusion module, generating a plurality of fusion features according to the plurality of context enhancement features and the plurality of semantic alignment features; generating the category of the power equipment, the coordinate frame of the power equipment and the boundary of the power equipment according to the plurality of fusion features through an instance segmentation decoding module; by implementing the method and the device, the problem of insufficient target information capture caused by single-scale feature capture in the prior art can be solved.

The invention discloses a foundation pit early warning method based on multi-source monitoring data and dynamic evaluation, which relates to the technical field of foundation pit safety early warning and comprises the steps of S1, multi-element monitoring, S2, data processing and normalization, S3, dynamic safety level establishment, S4, dynamic weight and credibility calculation, S5, evidence fusion and safety level judgment and S6, dynamic risk evaluation and calibration. According to the method, all-dimensional data acquisition and high-quality data processing are formed through the steps S1 and S2, all-dimensional information capture of foundation pit engineering characteristics, geological conditions, surrounding environments and body states is achieved, data quality is guaranteed through data cleaning, normalization and validity verification, and the problems of monitoring data fragmentation and uneven quality are avoided; through the steps S3 and S5, a security level judgment system of dynamic probability allocation and multi-source evidence fusion is constructed, limitation of a static threshold method is prevented, and scientificity and accuracy of security level judgment are improved.

The invention discloses a multi-modal neural network-driven product style trend analysis method and system, and relates to the technical field of image processing, and the method comprises the steps: receiving product multi-modal data, including text, image and audio data, carrying out the tensor extraction of the product multi-modal data, enabling each modal data to correspond to a single-peak tensor, and carrying out the tensor extraction of the product multi-modal data; carrying out feature fusion on the single-peak tensor to obtain a multi-modal feature tensor, and then carrying out dimension reduction processing; calculating a cross-modal multi-head attention score by using the single-modal tensor based on a language tensor, wherein the cross-modal multi-head attention score is used for weighting single-modal features; fusing weighted features, dimensionality reduction tensors and manually labeled style labels, constructing a supervised training target, and generating single-mode pseudo labels for multi-task model training; cross-modal shared information capture is carried out on the unimodal tensor based on a covariance matrix, cross-modal shared information is constrained through modal alignment loss, and under a multi-task learning framework, a trained multi-task model takes output of a multi-modal main task as a final style label to realize product trend analysis.

The invention discloses a deep learning aircraft skin damage detection method suitable for a dark light condition, and the method comprises the following steps: enabling an unmanned plane to fly around an aircraft to obtain the original image data of the aircraft skin damage, carrying out the preprocessing of the collected image data, completing the screening and label marking, and carrying out the detection of the aircraft skin damage. The invention relates to the technical field of aircraft skin detection, in particular to an aircraft skin damage detection method based on a PENet network, an aircraft skin damage data set needed by a training network is made, image data of the training data set is input into the PENet network, the dark light information capture rate is increased, and the training data set processed by the PENet is input into an improved yolov8 network and a CSPDarknet backbone network for feature extraction. The SCConv module is used for carrying out CNN compression by using space and channel redundancy between features, redundancy calculation is reduced, learning of representative features is promoted, the training calculation amount is reduced, the model is lightened, the model training time is shortened, an algorithm is deployed on the unmanned aerial vehicle, consumption of manpower and material resources can be greatly reduced, and the detection cost is reduced.

The invention discloses a text-video retrieval method and system based on multi-granularity learnable interaction, and relates to the field of data retrieval, and the method comprises the following steps: encoding a query text and an unpaired video in to-be-retrieved data, and extracting query text features and video features; obtaining a plurality of learnable vectors, carrying out cross-modal interaction on the learnable vectors and fine-grained video features, carrying out cross-grained alignment on query text features and learnable variables, carrying out fine-grained alignment on each word feature and the learnable vector, and aligning the query text features and video features; and capturing fine-granularity alignment information and coarse-granularity alignment information, capturing weights of different similar vectors among modals, summing multiple similarity scores based on the weights to obtain final multi-granularity similarity scores, and sorting the final multi-granularity similarity scores to obtain a final retrieval result. According to the method, cross-modal interaction and other operations are carried out through the plurality of learnable vectors and the fine-grained visual features, the fine-grained visual information is utilized, and the retrieval accuracy is improved.

Techniques for identifying potential privacy threats are described. One example method includes receiving a first set of signals from a first sensor representing information captured from an environment of the computer system in a first field of view associated with the first sensor; receiving a second set of signals from at least one second sensor representing information captured from an environment of the computer system in a second field of view associated with the second sensor, wherein the first sensor and the second sensor are of different types and the second field of view is wider than the first field of view; processing the first and second set of signals to create a representation of an effective field of view that is wider than the first field of view; and detecting the presence of at least one person within the effective field of view based on the representation.

An environmental capture system (ECS) captures image data and depth information in a 360-degree scene. The captured image data and depth information can be used to generate a 360-degree scene. The ECS comprises a frame, a drive train mounted to the frame, and an image capture device coupled to the drive train to capture, while pointed in a first direction, a plurality of images at different exposures in a first field of view (FOV) of the 360-degree scene. The ECS further comprises a depth information capture device coupled to the drive train. The depth information capture device and the image capture device are rotated by the drive train about a first, substantially vertical, axis from the first direction to a second direction. The depth information capture device, while being rotated from the first direction to the second direction, captures depth information for a first portion of the 360-degree scene. The image capture device captures, while pointed in the second direction, a plurality of images at different exposures in a second FOV that overlaps the first FOV of the 360-degree scene. The depth information capture device and the image capture device are rotated by the drive train about the first axis from the second direction to a third direction. The depth information capture device, while being rotated from the second direction to the third direction, captures depth information for a second portion of the 360-degree scene. The image capture device, while pointed in the third direction, captures a plurality of images at different exposures in a third FOV that overlaps the second FOV of the 360-degree scene.

The invention discloses an information display method and device, a vehicle, electronic equipment and a storage medium, the information display method comprises the steps that in response to user operation, video information and / or superposition information are / is displayed, the video information is information shot by recording equipment, and the superposition information is information collected when the recording equipment shoots the video information; the superposition information is used for being displayed on the video information in a superposition manner; wherein the video information is stored in the video information storage area, and the superposition information is stored in the superposition information storage area. The superimposed information can be selectively displayed in the video picture according to the selection operation of the user.

The invention provides a use method of a high-temperature and high-pressure air compressor with an adjustable cooling system, and relates to the technical field of air compressors, the high-temperature and high-pressure air compressor comprises an adjusting system, and the adjusting system comprises an information capture system, an intelligent operation system and a cooling dynamic regulation and control system; the information capturing system comprises a temperature distribution detector, a pressure strain network, a heat flow monitoring device and an environment response sensor, and the temperature distribution detector generates an air compressor surface temperature distribution diagram through an infrared imaging technology; an information capturing system collects temperature distribution, pressure strain, a heat flow state and external environment parameters of the air compressor in real time through a temperature distribution detector, a pressure strain network, a heat flow monitoring device and an environment response sensor, and instantaneous heat flow fluctuation is monitored in combination with a heat flow dynamic monitoring network; the problem of limited data dimension in the prior art is solved.

The present application relates to the technical fields of prestressed positioning mesh welding, and especially relates to a multi-stage adjusting mechanism for prestressed positioning mesh, which is provided with a welding control module, an information capturing module, a displacement analysis module and a welding correction module. The transverse steel bars are laid on the longitudinal steel bars through the laying unit, the intersection positions of the longitudinal steel bars and the transverse steel bars are welded through the welding unit, the vibration intensity change curve is drawn through the information capturing module, it is determined whether there is vibration allowance abnormal phenomenon at the intersection positions and the vibration allowance abnormal category of the intersection positions is determined through the abnormal analysis unit, the welding correction mode is selected through the welding correction module, and then, the quick abnormal risk identification according to the actual situation in the steel bar welding and sticking process is realized, the abnormal welding positions are classified and processed, the welding correction mode is adaptively adjusted, and the production efficiency and welding reliability of the prestressed positioning mesh are improved.

Systems and methods are provided for a security and safety management system. The system utilizes a computer vision system and machine learnable algorithms, including models, to identify from information captured by one or more visions sensors, including cameras, a plurality of security related challenges. A recognition module or circuity can detect, including, for example, via image recognition, at least information pertaining to: unauthorized access to entry / exit points; a derived behavioral analysis of at least identified potential intruders; provide information that can enhance emergency responses; assist in compliance with regulatory standards; and, be utilized in connection with predictive maintenance of security hardware. By leveraging advanced algorithms, machine learning, and integration with security hardware, the systems and methods disclosed herein can represent a significant advancement in the field of security technology, offering a robust solution for enhancing safety and operational efficiency in a wide range of environments.

Helper neural network can play a role in augmenting authentication services that are based on neural network architectures. For example, helper networks are configured to operate as a gateway on identification information used to identify users, enroll users, and / or construct authentication models (e.g., embedding and / or prediction networks). Assuming, that both good and bad identification information samples are taken as part of identification information capture, the helper networks operate to filter out bad identification information prior to training, which prevents, for example, identification information that is valid but poorly captured from impacting identification, training, and / or prediction using various neural networks. Additionally, helper networks can also identify and prevent presentation attacks or submission of spoofed identification information as part of processing and / or validation.

The present disclosure provides a system and method of using telemetry data to determine wager odds by receiving time-stamped position information captured by sensors, such as telemetry data, of one or more participants of one or both is the first set of participant(s) and / or the at least second set of participant(s) in the competition, and then using the time-stamped position information to determine a first play situation of the competition, and determining the probability and wager odds of a first future event occurring at the competition based on at least the first play situation and playing data associated with at least a subset of one or both of the first set of one or more participants and the at least second set of one or more participants and historical playing data.

The invention discloses an automatic driving behavior decision-making method fusing time sequence Transform and safety constraints, and relates to the technical field of automatic driving decision-making. The method comprises the following steps: constructing an automatic driving mixed traffic scene, wherein the mixed traffic scene comprises an automatic driving vehicle and a man-driven vehicle; a man-driven vehicle model is constructed for a man-driven vehicle in a mixed traffic scene, an IDM model is adopted to control a longitudinal decision, and an MOBIL model is adopted to control a transverse decision. According to the invention, the cost constraint is introduced, so that the automatic driving system can avoid potential dangerous behaviors in a complex environment, and the safety is improved; an action regularization item is introduced into a strategy loss function, so that the stability of the system under an uncertain condition is enhanced, and decision errors caused by external interference are reduced; and meanwhile, a transform time sequence model is introduced to design a network structure, so that the information capturing capability of the model is enhanced, and the decision effectiveness is improved.

The invention relates to the technical field of image classification, in particular to an image classification method and system based on a visual language large model, and the method comprises the following steps: 1, obtaining a plurality of original images, and constructing an image classification network; 2, selecting one original image from a plurality of original images, inputting the selected original image into an image classification network, and finally obtaining a category prediction result; 3, constructing a loss function by utilizing a category prediction result and a real category; 4, circulating the steps 2 and 3, minimizing the loss function until the loss function converges or the number of iterations reaches a set number of times, and updating the weight of the image classification network to obtain a trained image classification network; and 5, deploying the trained image classification network to a device end, and classifying the images by using the device end to obtain a classification result. According to the method, the problems of insufficient global information capture and low visual and language information fusion efficiency in a traditional single-mode classification method are solved, and higher classification precision and task generalization ability are realized.

An environmental capture system (ECS) captures image data and depth information in a 360-degree scene. The captured image data and depth information can be used to generate a 360-degree scene. The ECS comprises a frame, a drive train mounted to the frame, and an image capture device coupled to the drive train to capture, while pointed in a first direction, a plurality of images at different exposures in a first field of view (FOV) of the 360-degree scene. The ECS further comprises a depth information capture device coupled to the drive train. The depth information capture device and the image capture device are rotated by the drive train about a first, substantially vertical, axis from the first direction to a second direction. The depth information capture device, while being rotated from the first direction to the second direction, captures depth information for a first portion of the 360-degree scene. The image capture device captures, while pointed in the second direction, a plurality of images at different exposures in a second FOV that overlaps the first FOV of the 360-degree scene. The depth information capture device and the image capture device are rotated by the drive train about the first axis from the second direction to a third direction. The depth information capture device, while being rotated from the second direction to the third direction, captures depth information for a second portion of the 360-degree scene. The image capture device, while pointed in the third direction, captures a plurality of images at different exposures in a third FOV that overlaps the second FOV of the 360-degree scene.

The technology disclosed includes systems and methods for preparing a segmented occupancy grid map based upon image information of an environment in which a robot moves. The image information is captured by at least one visual spectrum-capable camera and at least one depth measuring camera. The system includes logic to receive image information captured by at least one visual spectrum-capable camera and location information captured by at least one depth measuring camera located on a mobile platform. The system includes logic to extract from the image information, features in the environment. The system includes logic to determine a 3D point cloud of points having 3D information. The system includes logic to determine, from the 3D point cloud, an occupancy map of the environment. The system includes logic to segment the occupancy map into a segmented occupancy map of regions that represent rooms and corridors in the environment.

The invention discloses a rotating target detection method based on frequency division feature refinement. The method comprises the following steps: firstly, carrying out initial feature extraction on an input image; then, carrying out frequency division feature refinement on local features, global features and context features of four stages, paying attention to image key position information, modeling a global relationship, extracting surrounding environment information as supplement, and enhancing the capturing capability of the model on the information; and finally, classifying and positioning the target by using a detection head, designing a sample collaborative optimization loss, adaptively adjusting the attention degree of the model on positive and negative samples, and further improving the detection precision of the model. The method solves the problems that in the past, remote sensing image rotating target detection feature extraction capacity is insufficient, interference and shielding between targets are caused by complex scenes, and target features are sensitive to positioning. The method is suitable for a rotating target detection task of a remote sensing image in a complex environment, and is high in detection accuracy and good in robustness. The method is widely applied to the field of rotating target detection.

One aspect relates to a method for training a trajectory (12) for a vehicle (3), along which the vehicle (3) drives into a parking area (P), wherein, during a training drive, the environment (11) of the vehicle (3) is captured using a first capture device (7, 8, 9) of the vehicle (3), which first capture device constitutes a first type of function with respect to the type of capture and / or a capture state and / or the position on the vehicle and / or the orientation on the vehicle (3), and the trajectory (12) is determined on the basis thereof, wherein, during a subsequent drive which follows the training drive and in which the vehicle (3) drives to the parking area (P) at least in certain regions, the environment (11) is captured using a second capture device (7, 8, 9) of the vehicle (3), which second capture device constitutes a second type of function differing from the first type of function with respect to the type of capture and / or a capture state and / or the position on the vehicle (3) and / or the orientation on the vehicle (3), wherein the trajectory (12) is updated on the basis of the information captured by the second capture device (7, 8, 9). A further aspect relates to an electronic vehicle guidance system (5).

The invention discloses a target tracking early warning method and system based on radar photoelectric linkage and information fusion, and relates to the technical field of electric digital data processing, and the method comprises the steps: obtaining the information of a moving target, and obtaining a real-time azimuth angle and a distance sequence; obtaining an absolute difference value between adjacent distance data in the distance sequence, and obtaining a stability index; generating a correction value, obtaining a compensation azimuth angle range, obtaining a steering angle range of the photoelectric equipment, obtaining a compensation distance range, and obtaining optical zoom information of the photoelectric equipment; and driving photoelectric equipment to capture target contour features according to the steering angle range and the optical zoom information to lock a target, and calling a physical rule base to obtain a target classification and early warning strategy based on the moving target information obtained by the radar and the target contour features captured by the photoelectric equipment. The method has the advantages of dynamic self-adaption, high resource utilization rate and cooperative closed loop.