770 results about "Feature code" patented technology

The invention relates to the technical field of network communication, and discloses a vehicle-mounted edge computing data recording system and method based on protocol adaptive analysis, and the system comprises a multi-protocol network access controller which is used for capturing an original data frame and extracting a physical feature triple; the identification analysis engine is used for executing hash operation on the triple to generate a physical feature code and retrieving a physical logic address mapping table; the direct memory access controller is used for responding to a target memory address pointer hit by retrieval and directly writing a data load into an input buffer area of the functional operation module, and by constructing a direct addressing mechanism based on Hash mapping, thorough decoupling of vehicle-mounted heterogeneous network physical topology and edge computing logic is achieved.

The invention provides an oil and gas pipeline defect three-dimensional contour determination method and device. Acquiring a three-axis magnetic flux leakage detection signal of a to-be-detected target oil and gas pipeline and corresponding space coordinate information of the three-axis magnetic flux leakage detection signal; constructing multi-channel input data according to the three-axis magnetic flux leakage detection signal and the space coordinate information; determining a defect contour prediction result of the target oil and gas pipeline according to the multi-channel input data by using a pre-trained defect contour inversion model; wherein the pre-trained defect contour inversion model comprises a multi-axis feature extraction and fusion module, a feature coding module and a multi-task decoding module; the defect contour prediction result is used for indicating whether the target oil and gas pipeline has defects or not, and the defect contour prediction result is further used for indicating the three-dimensional contour shape of the defects under the condition that the target oil and gas pipeline has the defects. Therefore, high-precision visual reconstruction of the defect position and the three-dimensional form of the oil and gas pipeline is realized, and the accuracy of defect identification and the reliability of evaluation are improved.

The invention discloses a three-dimensional scene reconstruction method based on an intelligent LED street lamp multi-mode sensor. The three-dimensional scene reconstruction method comprises the following steps that RGB images are obtained and preprocessed; the information is input to a visual feature coding module, two-dimensional bounding box information is extracted, and an object segmentation module is guided to output a two-dimensional segmentation mask; obtaining point cloud data, projecting the point cloud data to the standardized RGB image, and screening target points in combination with the two-dimensional segmentation mask; complementing the preliminary segmentation result of the point cloud, mapping the result to a standardized RGB image, and extracting a pixel region; carrying out joint coding, implicit representation and neural decoding processing on the object-level RGB image and the point cloud complete segmentation result; and fusing into an original three-dimensional scene, and completing spatial restoration through point cloud registration, attitude optimization and semantic constraint. The invention provides an efficient three-dimensional scene reconstruction method in combination with a multi-mode sensor of an intelligent LED street lamp, and the method has high precision, real-time performance and dynamic target processing capability.

The invention belongs to the technical field of computers, and particularly relates to a software fault repairing method and system fused with intelligent analysis, which comprises the steps of collecting a multi-level running log and performing structured preprocessing, constructing a dynamic calling graph through a time sequence encoder and a graph neural network, inferring a fault root cause in combination with a Bayesian causal inference model, and repairing a fault fault according to the fault root cause. And matching the repair strategy to generate an atomization instruction sequence, and deploying the atomization instruction sequence to a production system after sandbox environment verification. The system comprises a log acquisition module, a feature coding module, a graph construction module, a causal reasoning module, a strategy matching module, an instruction generation module, a sandbox verification module, a deployment feedback module and the like. Through end-to-end intelligent analysis and a closed loop verification mechanism, the fault positioning precision and the repair safety are remarkably improved, system self-evolution is supported, and operation and maintenance are promoted to be transformed from passive response to active autonomy.

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention discloses a printed circuit board withstand voltage detection and evaluation method, which comprises the following steps: in the whole withstand voltage detection process, aiming at different process batches and environments, acquiring electrical signals, process parameters, environment variables and multi-modal visual data, and carrying out time sequence synchronization marking and standardized preprocessing; extracting multi-source features by using a feature coding algorithm, and establishing a multi-mode distribution model covering signal mutation, visual defects and working condition anomalies; through cross-modal feature fusion and a time sequence causal inference model, an abnormal event causal relationship graph is automatically constructed, and a voltage withstanding failure root cause chain is identified and positioned; according to the method, the efficient structured management of the detection data, the accurate traceability of the abnormal reasons and the cross-batch analysis are realized, and the capability of diagnosing and preventing the withstand voltage abnormality of the printed circuit board is remarkably improved.

The invention provides an atmospheric precipitable water quantity inversion method and system based on multi-source satellite remote sensing data, and relates to the technical field of remote sensing data. After clear sky pixels are screened, radiation brightness temperature data, digital elevation model data and spatio-temporal feature coding data are processed through three feature extraction branches, obtained feature representations are spliced and input into a feature fusion network, a spatial attention module is introduced in the feature fusion stage, finally, a composite loss function is adopted for training, and the feature fusion network is constructed. And high-precision atmospheric precipitable water quantity inversion is realized. According to the method, the multi-source data characteristics are fully utilized, and the inversion precision and the space continuity are improved.

The invention relates to the technical field of gift packaging three-dimensional modeling, and discloses a construction method of a gift box packaging design three-dimensional model. The method comprises the following steps: collecting natural language description, a freehand sketch image and a physical material attribute parameter; analyzing a natural language to construct a design intention map, and analyzing a sketch to generate a geometric feature code; performing knowledge alignment fusion on the two to form enhanced design semantic representation; generating an initial three-dimensional design framework by using a generative design algorithm; material parameters are integrated, compatibility simulation test is carried out, parameter self-adaptive adjustment circulation is started according to a feasibility evaluation report, and component parameters are dynamically optimized; and rendering and outputting a final model. Through deep fusion of multi-modal design information and preposed dynamic optimization of physical attributes, the accuracy of design intention expression and the producibility of a three-dimensional model are improved, and automation and intelligentization of a design process are realized.

The invention relates to the technical field of computers, and discloses an interaction control method of intelligent glasses. The method comprises the following steps: synchronously acquiring multi-modal data such as eye movement, voice, gestures and head postures and environment and application context information; carrying out independent time sequence feature coding on each modal data; generating a modulation vector in combination with the context, and outputting probability distribution of user intentions through a cross-modal attention fusion network; and a unique execution instruction is determined through an instruction arbitration module based on rules and a state machine. The system comprises corresponding function modules. According to the method, the accuracy, robustness and naturalness of interaction are improved through multi-modal synchronous fusion and a context self-adaption mechanism.

The invention discloses a three-dimensional scene reconstruction method and system based on monocular depth estimation, and belongs to the technical field of computer vision and three-dimensional reconstruction, and the method comprises the steps: carrying out the multi-scale feature coding of a monocular RGB image through a mixed attention depth coding module, and obtaining the hierarchical depth feature representation; carrying out autoregression depth decoding through a self-adaptive edge perception depth decoding module to generate an initial depth map; a depth confidence map is calculated through a geometric consistency constraint optimization module and is fed back to a coding module for iterative optimization, and a refined depth map is output; and three-dimensional Gaussian ellipsoid scene representation is constructed through the Gaussian ellipsoid scene reconstruction module. According to the invention, high-precision depth estimation and high-quality three-dimensional reconstruction are realized by constructing a depth-coupled closed-loop cooperative system.

The invention discloses a deep foundation pit crack feature coding detection method based on image recognition, and aims to solve the problems that a two-dimensional image is difficult to accurately characterize a three-dimensional crack, and the opening width and the slab staggering height are inaccurately estimated, the deep foundation pit crack feature coding detection method comprises the following steps of: calibrating and collecting through short time sequence and multi-polarization imaging, segmenting the crack to extract a center line, and dividing a left side domain and a right side domain; carrying out local reconstruction in a mask neighborhood by adopting a nerve radiation field or three-dimensional Gaussian sputtering, establishing a left implicit surface and a right implicit surface, fusing a polarization solution line, applying occupancy priori and seam-crossing repulsive potential in a gap region, combining a reference object to obtain an absolute scale, measuring a millimeter-level opening and a dislocation along a central line section, and generating a feature code; and threshold value and time sequence comparison judgment is carried out, and the technical effects of high-precision three-dimensional measurement, reliable risk assessment and traceable management of the deep foundation pit crack are achieved.

The invention relates to the field of intelligent evaluation, and particularly discloses a robot joint wear evaluation method and system based on a neural network, and the method comprises the steps: synchronously obtaining an original vibration time sequence and a working condition data time sequence of a robot joint, and constructing a feature extraction network with working condition data as a dynamic condition; and pure degradation feature codes only related to the internal wear state are decoupled and extracted. And then, based on the pure degeneration feature coding sequence, a degeneration trend time sequence modeling network is driven to accurately capture a long-term monotonous evolution law of the degeneration trend time sequence modeling network, and a time sequence hidden state representing the real abrasion degree of the joint is generated. And finally, decoding prediction is carried out on the hidden state of the time sequence, accurate evaluation of the remaining service life of the robot joint is achieved, it can be ensured that the real degradation state of the joint is accurately tracked all the time under the working condition of wide changes, and therefore accurate and intelligent health evaluation of the robot joint is achieved.

The invention discloses a bridge expansion joint abnormity diagnosis method and system based on voiceprint recognition and deep learning, and the method comprises the steps: arranging sensors on the bridge surface and under the bridge of an expansion joint, and obtaining the video data of a vehicle passing through the expansion joint and the audio data of the vehicle passing through the expansion joint; constructing a vehicle seam crossing discrimination model based on a neural network, inputting the video data of the vehicle passing through the expansion joint into the vehicle seam crossing discrimination model, and detecting a vehicle seam crossing scene; identifying a single vehicle seam crossing event in the vehicle seam crossing scene by adopting a data cleaning algorithm; cutting from the audio data of the expansion joint passing of the vehicle to obtain a bicycle seam passing audio of a time period corresponding to the bicycle seam passing event; extracting a Mel spectrogram of a single-vehicle seam-crossing audio frequency, and carrying out feature coding on the Mel spectrogram to obtain an enhanced Mel spectrogram; and constructing an expansion joint anomaly diagnosis model based on an unsupervised linear self-encoding network, inputting the high-dimensional Mel spectrum feature vector of the enhanced Mel spectrum into the expansion joint anomaly diagnosis model, and identifying the abnormal state of the expansion joint.

The invention belongs to the technical field of visual positioning, and relates to a visual positioning method based on semantic comprehension and attribute distinguishing enhancement. The framework mainly comprises a feature coding module, a semantic sensitive data enhancement module, a fine-grained attribute guiding module and a multi-stage cross-modal decoder module. Specifically, the feature coding module performs feature coding on an input graph. The semantic sensitive data enhancement module generates a plurality of queries which are consistent with long text semantics by keeping the consistency of spatial relation words in combination with a large language model, so that a training data set for the long text is expanded. The fine-grained attribute guiding module extracts attribute prior information from a text query in combination with a text graph model and an image encoder, constructs a visual feature representation with higher discrimination by using the information guiding model, and generates a target query with attribute difference at the same time.

The invention relates to the field of intelligent report generation, in particular to an intelligent insight report generation method and system based on an AI large model, and the method comprises the steps: inputting an insight demand, and generating an insight data package comprising insight contents, associated data and industry labels; extracting a basic keyword set of the insight data packet to form a mixed feature code; after mixed feature coding preprocessing, weight distribution is carried out; and after weight distribution of the AI large model, injecting a high-weight feature vector into a semantic understanding core layer, injecting a low-weight feature vector into a logical reasoning layer, and outputting analysis data to form an intelligent insight report. According to the method, word embedding parameters are optimized according to field semantic characteristics, a parameter verification mechanism is introduced, field text characteristics are adapted by adjusting vector dimensions, context windows and low-frequency vocabulary filtering threshold values, window parameter validity is verified through cosine similarity, word frequency threshold value reasonability is verified through standard deviation, and field text characteristic matching is achieved. And it is ensured that the feature vectors can accurately capture domain-specific semantics.

The invention provides an energy storage battery management system and a control method, belongs to the field of energy storage battery management, and is used for solving the problems of low battery state estimation precision, insufficient fault diagnosis sensitivity and poor full life cycle adaptability in related technologies. By combining a neural network-Kalman filtering cascaded state estimation algorithm and a twin structure feature coding fault diagnosis algorithm and matching with an edge-cloud collaborative model self-evolution mechanism, accurate estimation of a battery state and timely diagnosis and positioning of a fault are realized, and the full-working-condition adaptability and operation reliability of a system are improved.

System, method, and various embodiments for a functional code generation system leveraging LLM capabilities, are described herein. An embodiment operates by receiving a user instruction to generate a report. An information prompt is generated, and an large language model (LLM) derives a plurality of vectors from the instruction, including a first vector. A first question is identified based on comparing the first vector to a plurality of questions; the first question is associated with a first query template. A query result from a collection database based on executing a query corresponding to the first query template against the collection database. The LLM is instructed to generate an answer comprising a natural language interpretation of the query result in view of the first question. The LLM is instructed to generate a report based on the answer in view of the user instruction.

The invention relates to the technical field of ship energy efficiency management, and discloses an energy efficiency improvement method and system based on course-speed-trim collaborative optimization, which constructs a course feature code based on navigation environment forecast information and electronic chart information, and combines real-time state information and decision variables of a ship to improve the energy efficiency of the ship. Generating voyage number accumulated energy consumption expectation by using a ship energy consumption prediction model fused by physical information; taking expectation minimization as a target, coupling navigation time constraints, host working condition constraints and ship stability safety boundaries, constructing a three-variable collaborative optimization model of the route path, the planned navigational speed of each route segment and the planned trim value, solving the model, and outputting a collaborative optimization scheme; and on-line parameter adaptive updating is performed on the model based on real-time data during navigation, and dynamic re-planning is triggered. The global voyage number energy consumption optimization is realized through a three-variable collaborative optimization framework and physical information fusion prediction, and the adaptability and robustness of an optimization scheme in a dynamic environment are improved by means of an online learning and rolling optimization mechanism.

The invention discloses an interaction method and system based on multi-modal data, and relates to the technical field of man-machine interaction. The method comprises the steps that initial interaction data of a user is acquired, preprocessing and feature coding are carried out on the initial interaction data, target interaction data are obtained, and the initial interaction data comprise a hand depth image, a voice signal, a through hole center coordinate and a head rotation matrix; determining a target weight of each piece of target interaction data by using a preset weight strategy, performing conflict resolution on each piece of target interaction data based on the target weight of each piece of target interaction data or the timestamp of each piece of target interaction data, and fusing each piece of target interaction data after conflict resolution to obtain a target interaction instruction; and executing the target interaction instruction, and displaying the virtual scene and the operation target after executing the target interaction instruction. Therefore, the problems that the interaction dimension of the current virtual reality technology is single, the multi-modal signal fusion degree is low, a cooperation mechanism is missing, and misjudgment is easily caused are solved.

The invention discloses a robot obstacle avoidance assembly method and system based on a curriculum learning strategy, and the method comprises the steps: setting a plurality of subtasks according to the curriculum learning strategy, and generating a task scene which comprises barrier-free assembly, static obstacle avoidance assembly and dynamic obstacle avoidance assembly; based on the task scene, action strategy training is conducted on the robot through a reinforcement learning algorithm, an HER mechanism and a parameter migration mechanism are introduced for optimization, and system state information is obtained; and carrying out feature coding on the system state information, then splicing the system state information, constructing a course switching condition to carry out loop training, and outputting a robot obstacle avoidance assembly result. According to the invention, the learning efficiency and generalization ability of the robot in complex tasks can be improved. The robot obstacle avoidance assembly method and system based on the course learning strategy can be widely applied to the technical field of robot intelligent control.

A power transmission tower grounding resistance intelligent measurement method based on ground potential field distributed sensing comprises the steps that a ground potential sensing network is arranged around a power transmission tower grounding device, and a ground potential field distributed measurement system with an iron tower as the center is established; a broadband characteristic coding current signal is injected into the grounding system through a weak current injection module; acquiring potential response data of each node of the ground potential sensing network excited by the feature coding current signal by adopting a distributed synchronous acquisition technology, and reconstructing complete ground potential field spatial distribution from sparse node observation data based on a compressed sensing theory through a signal reconstruction algorithm; and inputting the reconstructed complete ground potential field spatial distribution map into a pre-trained map neural network model, and outputting an intelligent identification result and an anomaly diagnosis conclusion of the ground resistance. According to the invention, non-contact, distributed and high-precision grounding resistance measurement under the condition that a grounding lead does not need to be disconnected is realized, and the measurement precision and reliability under a complex working condition are remarkably improved.

The invention relates to the technical field of industrial internet of things, in particular to an automatic equipment information collection management method and system, and the method comprises the steps: 1, transmitting a multi-protocol detection instruction sequence to target equipment, analyzing response data, and dynamically generating a protocol feature code; 2, analyzing a production process logic to generate an equipment action dependency chain, and creating a time sequence trigger condition expression according to a dependency relationship; step 3, equipment fault events are detected in real time, a dynamic acquisition strategy is generated based on the fault conduction relation, and the strategy comprises an acquisition frequency adjustment rule of associated equipment; and 4, executing a dynamic acquisition strategy, starting data acquisition of specified equipment when a time sequence triggering condition is met, and dynamically adjusting the acquisition frequency according to the fault conduction strength. The dynamic acquisition strategy generated based on the fault conduction relation can adjust the acquisition frequency in real time, the flexibility and timeliness of fault response are improved, and efficient and safe operation of the production line is ensured.

The invention discloses an indoor scene image generation method and device, equipment and a storage medium. The method comprises the following steps: firstly, acquiring a to-be-processed image with furniture and information input by a user, and performing furniture elimination processing on the image to obtain an empty room image; and then generating a cue word for image generation based on a preset cue word template in combination with user input information. Meanwhile, extracting a depth map and a semantic segmentation map of the empty room image, and inputting the depth map and the semantic segmentation map into a pre-trained depth map control model and a pre-trained semantic segmentation control model for feature coding to obtain corresponding depth control features and semantic segmentation control features. And performing parameter adjustment on the large-scale graph generation model subjected to parameter fine adjustment by using the two types of control features to obtain a target graph generation model. And finally, inputting the empty room image and the cue word into the target image generation model to obtain a target image. According to the method, the spatial structure stability and the personalized design requirement of the user on the indoor layout can be considered.

The invention provides a mechanical arm movement track generation method and device, a storage medium and a program product. The method comprises the steps that observation data of the current environment and state data of the mechanical arm at the current moment are obtained; respectively carrying out feature coding on the observation data and the state data to obtain an observation feature vector and a state feature vector; the observation feature vector, the state feature vector and the state data are input into a trained mechanical arm motion trail generation model, and the motion trail of the mechanical arm is obtained; wherein the mechanical arm motion trail generation model comprises a denoising network and a probabilistic dynamic motion primitive ProDMP decoder, the denoising network is used for generating ProDMP weight parameters based on the observation feature vector and the state feature vector, and the ProDMP decoder is used for generating a motion trail according to the ProDMP weight parameters and the state data. The continuity, smoothness and boundary controllability of the motion trail of the mechanical arm are improved.

The invention discloses a multi-modal federated learning method based on self-attention, and relates to the field of data processing, and the method comprises the steps: firstly, designing a multi-modal feature coding module at a client, carrying out the deep semantic modeling of image and text modals based on a Transform architecture, and capturing the internal structure information of each modality; secondly, a local cross-modal self-attention modeling module is introduced, fine-grained semantic alignment between images and texts is modeled through a bidirectional cross-attention mechanism, and the local cross-modal understanding ability is enhanced; a multi-head collaborative fusion optimization module is further combined, inter-modal deep semantic collaborative modeling is carried out under a multi-attention perspective, and a fusion effect is optimized through a gating mechanism, so that the problems of inter-modal conflicts and inconsistent expressions are effectively relieved.

The invention relates to a same-frequency signal separation method and device based on an attention mechanism, equipment and a medium, and belongs to the technical field of wireless communication. The method comprises the following steps: acquiring a mixed signal in a same-time and same-frequency interference scene, and simulating to generate a plurality of types of modulation signals as priori pure signals; inputting all the priori pure signals into a priori signal feature extraction network trained based on an Arcfu loss function to obtain embedding centers of various modulation signals as priori information; a target signal separation network is constructed and trained, in the network, mixed signal features are extracted according to a mixed signal encoder, modulation features are coded and extracted from an embedding center according to a modulation feature coding module, bidirectional cross attention calculation is performed on the mixed signal features and the modulation features according to a cross attention module, and the target signal separation network is obtained. And a fusion feature focused on the target signal is obtained and decoded, and a target estimation signal is reconstructed and output. According to the method, accurate separation of the target signals in the same-time and same-frequency interference scene can be realized.

A robot grabbing method based on a geometric topology constraint field and adaptive calibration comprises the following steps: constructing a manifold neighborhood enhancement field based on a target object prior point cloud; carrying out feature coding on an input real-time observation point cloud and a prior point cloud template, and embedding observation features into a manifold neighborhood enhancement field to realize geometric alignment; aiming at the RGB image and the real-time observation point cloud, establishing a preprocessing and symmetry sensing mechanism of a multi-modal feature; constructing a geometric feature re-calibration module, performing adaptive weighting on feature response through a channel attention and space attention mechanism, and performing adaptive calibration on deviation between observation features and prior manifold features; the 6D pose parameters of the target object are regressed based on the re-calibrated global features, and a corresponding geometric consistency measurement value is output; and according to the consistency measurement result, the pose result is subjected to iterative correction in the reasoning stage, and a robot grabbing instruction is output. According to the method, the space postures of different objects can be accurately modeled, and the grabbing precision of the robot in a complex environment is improved.

The invention relates to a multi-modal meteorological data downscaling generation method and device and computer equipment. The method comprises the following steps: acquiring topographic data and meteorological data in a to-be-measured area; inputting the topographic data and the meteorological data into a pre-trained deep learning model, carrying out feature coding on the topographic data and the meteorological data through the deep learning model to obtain a topographic feature vector and a meteorological feature vector, carrying out feature interaction on the topographic feature vector and the meteorological feature vector to obtain interaction features, and carrying out feature extraction on the interaction features; and carrying out downscaling decoding on the interaction features to obtain downscaling meteorological data. By adopting the method, the downscaling generation efficiency and the physical rationality of the meteorological data can be considered.

The embodiment of the invention discloses a query statement optimization method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the steps of performing statement performance detection on a to-be-executed query statement set to obtain a statement performance detection information set; determining an optimization level information set and a query recommendation index set; screening the to-be-executed query statement set to obtain a target query statement set; performing level feature coding on the query recommendation index set, the target query statement and the database meta-information to obtain a multi-level embedded vector set; performing shared information coding and multi-task prediction processing on the multi-level embedded vector set to obtain an execution path feature vector set; performing hybrid sorting on the execution path feature vector set to obtain a target execution path; and performing statement optimization and execution on the target query statement. According to the embodiment, the query efficiency can be improved, the query time and resource consumption are reduced, the database performance is improved, different database environments are dynamically adapted, and the user experience is improved.

The present disclosure discloses a method for generating a garment design drawing, a method for generating a garment pattern, and a method for generating a garment pattern piece. The method for generating a garment design drawing comprises: receiving design intent data and / or design materials; initializing the design intent data to obtain a visual design drawing; encoding the design materials to obtain a material feature code; generating a target design drawing by inputting the material feature code and / or the visual design drawing into a pre-built generative network; and outputting the target design drawing.