46results about How to "Guaranteed recognition accuracy" patented technology

The invention relates to the technical field of wearable intelligent equipment, and further relates to a method and device for determining the home position of a user and wearable intelligent equipment. The method comprises the following steps: when the wearable intelligent equipment is in an indoor environment, acquiring wireless signal data and multi-source sensing data; determining wireless signal feature data according to the wireless signal data, and determining a standing proportion, a charging behavior similarity and a position aggregation degree according to the multi-source sensing data; performing weighted summation on the wireless signal feature data, the standing proportion, the position aggregation degree and the charging behavior similarity; and when the weighted summation result is not less than the total threshold, determining that the target coordinate is the user home position. According to the method, the wireless signal data and the multi-source sensing data are effectively fused, the problem that a single judgment condition is prone to interference or misjudgment is avoided, the accuracy and reliability of user home position recognition are remarkably improved, a solid foundation is provided for subsequent intelligent services based on home scenes, and therefore the user experience is optimized.

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

This invention discloses a hybrid quantization method for image recognition models based on bit-flipping attacks, comprising: Step 1, performing initial quantization on the image recognition model to convert it into an initial quantized model; starting from the first layer of the initial quantized model, performing bit-flipping attacks layer by layer, and recording the sensitivity of each layer under attack; forming a sensitivity set of the initial quantized model from the sensitivities of all layers; constructing an optimization objective function and determining optimization constraints, and determining the optimal hybrid precision quantization strategy for the image recognition model based on the optimization objective function and the optimization constraints; Step 3, setting the bit width for storing the parameters of each layer of the image recognition model according to the optimal hybrid precision quantization strategy, thereby obtaining the optimal hybrid quantization model; Step 4, acquiring the image to be recognized and performing image recognition using the optimal hybrid quantization model.

This invention discloses a lightweight few-shot incremental learning method for power line defects. The method includes: first, constructing an inspection image dataset containing basic and incremental categories, and designing a lightweight feature extraction network; training the network using the basic category data to generate prototype vectors for each category, forming an initial prototype classification model; in the incremental stage, constructing incremental prototypes for a small number of samples of the new category, and introducing a prototype transfer mechanism based on inter-class tension, adaptively adjusting the new prototype based on the geometric similarity and discrimination conflict strength between the new and existing prototypes to alleviate category conflicts; simultaneously, introducing an adaptive feature fusion mechanism that perceives discrimination uncertainty, dynamically fusing the discrimination results of the main and auxiliary branches to improve recognition stability. This invention, employing the above-mentioned lightweight few-shot incremental learning method for power line defects, can achieve efficient and stable power line defect recognition in scenarios with few samples and continuously expanding categories.

The invention provides a substation drawing processing method and device based on deep learning, equipment and a medium. The method comprises the steps of receiving a transformer substation drawing and a design specification file which needs to be followed by the transformer substation drawing, dividing layout into a drawing area and a table area, processing the drawing area and the table area respectively, obtaining first structured data and second structured data, establishing semantic association of the first structured data and the second structured data, achieving association of the drawing area and the table area, obtaining structured semantic information, and displaying the structured semantic information. Analyzing the design specification file, and constructing a graph database knowledge graph and a vector database according to an analysis result; and searching and combining results by using structured semantic information, and judging whether the results accord with specifications or not. According to the invention, high-precision semantization and automatic compliance auditing of batch drawings can be realized, and the labor cost is reduced.

The present disclosure relates to a model decimation method, device, medium and electronic equipment. The method comprises: dividing a model surface into a plurality of sub-regions; rendering the model and determining state information of each sub-region under multiple perspectives, wherein the state information comprises brightness, contrast and color; determining a decimation degree of each sub-region according to the state information; and performing a decimation operation in the corresponding sub-region according to the decimation degree. The sensitivity of the human eye to different brightness, contrast and different color wavelengths is different, and the decimation degree of each sub-region determined according to the state information can ensure the recognition accuracy of the human eye. By determining the state information under different perspectives, the accuracy of the decimation degree of each sub-region determined can be improved. In this way, the complexity of the model can be reduced while ensuring the visual experience of the user.

The invention discloses a search agent training method and device and a storage medium, and the method comprises the steps: collecting a current state vector under the condition that a search agent carries out the key sign search of any sample cardiovascular three-dimensional diagram; inputting the current state vector into a search agent, and outputting an action instruction through the search agent; the observation window is controlled to execute the action instruction, an experience tuple is collected and stored in an experience playback pool, and the experience tuple comprises the current state vector, the action instruction, the instant reward value and a next moment state vector; and performing iterative training on the search agent based on an experience tuple stored in the experience playback pool, so that the trained search agent can output an action instruction sequence enabling the observation window to quickly cover a key sign area according to the state vector.

The utility model relates to the technical field of paper currency identification, and discloses an adjustable multi-currency automatic identification and classified storage mechanism, which comprises a machine body, a first conveying device is arranged in the machine body, a second conveying device is arranged on the lower side of the first conveying device, a third conveying device is arranged on the lower side of the second conveying device, and a third conveying device is arranged on the lower side of the third conveying device. An inclined plate is fixedly connected between vertical plates on the two sides of the machine body, and through cooperation of a suction fan, a second connecting pipe, a rotating flange, a conveying roller, an air suction opening, an arc-shaped plate and other structures, single paper money stripping is achieved through controllable adsorption force (dynamic adjustment can be conducted according to paper money materials). And the adsorbed banknotes are conveyed to the upper side of the second conveying device, so that the recognition accuracy of the subsequent recognition module on the banknotes is effectively improved, and the separation effect is improved more remarkably.

The utility model relates to early warning equipment technical field, and disclose an emergency lighting system based on wisdom building, including prop, the circumference of prop is fixedly connected with disc, the top of disc is fixedly connected with the ring of teeth, the circumference of prop rotatably connected with the board of turning, the bottom of board of turning is fixedly connected with the round angle board, the inner wall of round angle board rotatably connected with output shaft, the circumference of output shaft is fixedly connected with gear, the top of board of turning is fixedly connected with detector, the front of board of turning is fixedly connected with photovoltaic board, the present application can carry out the detection of larger range between building, when detecting the fire, can give the signal to the detection center and carry out the early warning and open the lighting lamp simultaneously and carry out the emergency lighting.

The application provides a lightweight identity recognition method combining voiceprint and earprint features. The method comprises the following steps: obtaining voice and ear canal echo signals of a known registered person and a person to be verified; extracting and fusing 13-dimensional mel-frequency cepstral coefficients (MFCC) of the voice and ear canal echo signals, i.e., obtaining voiceprint and earprint fusion features; inputting the fusion features into a lightweight identity recognition model to extract 128-dimensional embedding features of the known registered person and the person to be verified; calculating the similarity of the embedding features of the two types of persons by using a probabilistic linear discriminant analysis (PLDA) method; and determining whether the person to be verified is the known registered person according to the similarity. In the application, the fusion of earprint and voiceprint features improves the recognition performance of the classification model. The lightweight identity recognition model is obtained through a pre-training process, which reduces the equal error rate (EER) and greatly reduces the parameter quantity of the identity recognition model.

This invention discloses a method and system for geological hazard identification based on multimodal semi-supervised learning. The method includes the following steps: acquiring labeled and unlabeled orthophotos and elevation models of geological hazard areas; constructing teacher and student networks containing dual-branch encoders, extracting features from the orthophotos and elevation models respectively, and linearly fusing them, generating ensemble predictions using learnable dynamic weights; in semi-supervised training, calculating the uncertainty of the student network's prediction results in real time, and triggering an exponential moving average update of the teacher network using the student network parameters only when the decrease in uncertainty compared to the historical mean exceeds a preset evolution threshold; optimizing the student network parameters by combining supervised loss, confidence-weighted consistency loss, and student historical consistency loss; inputting the test data into the trained student network, and outputting fine-grained geological hazard segmentation results. This invention can achieve high-precision automatic identification of geological hazards under small sample conditions.

This invention discloses a method for detecting subsidence using simple linear iterative clustering, applicable to the road infrastructure industry. It can simply, accurately, and efficiently acquire subsidence information on asphalt pavements. Step 1: Scaling the image; Step 2: Dividing the image into multiple small regions using simple linear iterative clustering, significantly reducing the relative weight of spatial and gray-level distance to obtain a reasonable number and shape of regions; Step 3: Analyzing the hierarchical relationships between regions based on their geometric features to establish inclusion pairs and filtering out non-target regions; Step 4: Performing local planar fitting on the periphery of the remaining bottom-level sub-regions to eliminate the influence of pavement slope on subsidence depth calculation; Step 5: For the remaining bottom-level sub-regions, calculating the gray-level difference between each point on the outer edge of the sub-region and the center of the sub-region, and determining whether it is a subsidence area based on the proportion of depth differences exceeding a threshold. This invention can quickly segment images and identify potential subsidence areas.

The invention discloses a kitchen open fire hidden danger early warning model based on YOLO lightweight, and relates to the technical field of artificial intelligence, and the model comprises the following steps: S1, obtaining an original monitoring image of a kitchen environment, employing a preprocessing algorithm fusing bilateral filtering and adaptive histogram equalization, carrying out the denoising and enhancement of the original monitoring image, and outputting a standardized image; and S2, based on the standardized image, applying a structured pruning strategy to a pre-trained YOLOv5 model, and removing redundant convolutional layers and channels in a backbone network of the pre-trained YOLOv5 model. According to the kitchen open fire hidden danger early warning model based on YOLO lightweight, through a preprocessing mode of fusing bilateral filtering and adaptive histogram equalization, image noise is removed, flame edges and key features are completely reserved at the same time, the anti-interference capability of flame recognition in a complex kitchen environment is greatly improved, and the method is suitable for popularization and application. Clear risk guidance is provided for the user, the hidden danger can be quickly handled, and the kitchen fire protection level is comprehensively improved.

This invention discloses a method for ship traffic detection and analysis based on lidar, relating to the field of lidar positioning technology. The method includes: using historical point cloud detection data sequences from lidar to predict the distribution sequence of ship states in a target water area within a preset time window; optimizing the concurrent video monitoring angle sequence based on this prediction sequence, and collecting video stream data according to the adapted monitoring angle sequence; formulating an adapted image dimensionality reduction mechanism based on the current image interference intensity and the predicted ship state distribution sequence to perform data dimensionality reduction on the video stream data and obtain a key image frame sequence; acquiring the current point cloud detection data sequence of the target water area within the preset time window through lidar monitoring, combining it with the key image frame sequence to perform ship traffic statistics and ship type identification, and outputting a ship traffic detection result sequence. This invention effectively improves the perception performance of ship traffic detection while saving computing power.

The application provides a sensitive information identification method and device, network equipment, a medium and a program product. The method comprises: performing semantic coding processing on a target text to obtain a global semantic representation of the target text and a context vector representation of a word element; performing classification identification processing on sensitive information in the target text according to the global semantic representation to obtain a category identification result; performing sequence labeling processing on the sensitive information in the target text according to the context vector representation to obtain a first identification result; performing sequence labeling processing on the sensitive information in the target text according to the context vector representation to obtain a first identification result of the sensitive information in the target text; and cross- verifying the category of the sensitive information in the first identification result and the category identification result to update the first identification result. The method provided in the embodiments of the application has high identification accuracy and efficiency for sensitive information.

The invention discloses an intelligent identification linkage protection method for 35kV and below equipment based on multi-source adaptive fusion and a deep and shallow double-path network. The method comprises the following steps: acquiring multi-source data of target equipment, splitting the multi-source data into instantaneous and steady-state feature flows, extracting features by using a deep and shallow dual-path network, and performing dynamic self-adaptive fusion according to a real-time operation scene to obtain comprehensive features; inputting a time sequence formed by continuous comprehensive features into an LSTM network subjected to equipment exclusive gating adjustment, and outputting a combined recognition result of an equipment type and a working state and an initial confidence coefficient in combination with a hierarchical time sequence matching mechanism; the confidence coefficient is calibrated according to the characteristic fluctuation degree and the sample difficulty, and finally the protection strategy is adjusted in a hierarchical linkage mode according to the calibrated confidence coefficient. According to the method, the dynamic scene adaptability and the identification accuracy of a small number of devices are improved, the risk of misjudgment and missed judgment is effectively reduced through linkage of confidence coefficient calibration and a dynamic constant value, and the reliability of a protection system is enhanced.

The application provides a human action recognition and quality evaluation method and system based on multi-modal data fusion, the application collects RGB visual data, 3D skeleton data, inertial IMU data and electromyography sEMG data of human action, and carries out corresponding pretreatment; through branch parallel feature extraction, RGB visual feature vectors, 3D skeleton feature vectors, IMU inertial feature vectors and sEMG electromyography feature vectors are obtained respectively; based on the modal reliability values of each branch, multi-modal fusion feature vectors are obtained by dynamically distributing and fusing the weights of the cross-modal attention mechanism to the multi-path features; the action recognition result is obtained by using a lightweight action recognition network; and the action recognition result is quality evaluated through the comprehensive action quality score. The application can not only accurately recognize the action category, but also quantize the action quality from three dimensions of form, dynamics and force, thereby improving the robustness and accuracy of action recognition in a complex scene.

The invention belongs to the technical field of natural language processing, and particularly relates to a multi-choice question misunderstanding reordering method based on a causal big language model, which comprises the following steps of: aiming at the problems of noise existing in semantic vector retrieval candidate misunderstanding, insufficient logic discrimination capability of a traditional model and high resource consumption of big model deployment; the method comprises the following steps: firstly, obtaining a query context containing a question and a student error option and a Top-K candidate misunderstanding, and splicing the query context and the Top-K candidate misunderstanding into a to-be-discriminated sequence; inputting a causal large language model subjected to LoRA fine tuning, calculating a correlation score through a generative or classification head scoring mechanism, and optimizing sorting robustness in combination with list-level cross entropy loss; and finally, the perception weight quantization compression model is activated through 4-bit. According to the scheme, by means of the strong logical reasoning ability of the causal big language model, interference terms with similar texts but different logics are accurately removed, and misunderstanding recognition precision is greatly improved; meanwhile, video memory occupation is reduced to about one fourth of an original model, reasoning delay is shortened, low-resource edge equipment such as a learning machine and a mobile terminal APP is successfully adapted, and an efficient and feasible landing scheme is provided for intelligent education personalized tutoring.

This application discloses a detection device and its control method, control device, and readable storage medium, relating to the field of equipment detection technology. The control method of the detection device includes: controlling a current generator to send a first current signal to a current transformer and a current processor respectively; controlling the current processor to receive a second current signal sent by the current transformer, the second current signal being a feedback current signal after the first current signal passes through the current transformer; when the current processor receives the first and second current signals, controlling the current processor to determine the current difference between the first and second current signals; and determining the performance state of the current transformer based on the current difference. This application improves the detection accuracy of the detection device for current transformers.

The application discloses a signal modulation recognition method based on multi-modal feature fusion and SNR perception, and belongs to the technical field of wireless communication.The method of the application is as follows: a wireless signal to be recognized is acquired, and complex baseband discretization processing is performed on the wireless signal to obtain a discrete signal; a multi-branch deep neural network structure is constructed by fusing time domain IQ features, frequency domain STFT features and fused features composed of physical statistics, phase difference and SNR; an SNR embedding mechanism is adopted to encode and integrate the signal-to-noise ratio information of the input signal into the feature fusion process, so that the model can perceive the current signal-to-noise ratio condition and affect the model training process through a loss optimization mechanism, adaptive modeling of signal features under different signal-to-noise ratio conditions is realized, and the accuracy and robustness of modulation recognition are improved.The application comprehensively utilizes time domain structure information, frequency domain energy distribution information and statistical features of the signal, so that the model can still maintain high recognition performance under complex electromagnetic environment and low signal-to-noise ratio conditions.

The application provides a method and system for intelligent early warning of violation behavior, and relates to the technical field of intelligent early warning.The method comprises the following steps: logically associating and reconstructing a normal behavior baseline of a target behavior to obtain a constraint dependency graph of the normal behavior baseline; performing conflict detection on real-time monitoring data of the target behavior to obtain contradictory nodes of the real-time monitoring data; implanting inductive constraints into the contradictory nodes to obtain virtual constraint conditions of the contradictory nodes; performing reverse tracking and capturing on the contradictory nodes based on the virtual constraint conditions to obtain response data streams of the contradictory nodes; performing deviation quantization mapping on the response data streams based on the normal behavior baseline to obtain a response deviation matrix of the contradictory nodes; performing confidence discrimination on the contradictory nodes to obtain a judgment result of the real-time monitoring data; and outputting an early warning instruction of the real-time monitoring data when the judgment result points to a violation.The application can improve the efficiency of intelligent early warning of violation behavior.

The invention discloses an electric power Internet of Things terminal equipment identification method and system based on network traffic, and belongs to the technical field of electric power Internet of Things terminal equipment identification. Traffic data acquisition is carried out on accessed terminal equipment, feature extraction is carried out on the acquired traffic data, and model training is carried out according to known equipment types; according to the method, automatic identification of the newly accessed terminal equipment is realized without modifying the configuration of the field terminal equipment or installing an agent program, the field deployment difficulty is greatly reduced, the vector is constructed through the multi-dimensional flow characteristics, the terminal equipment is identified from a multi-dimensional angle, the probability of misidentification is reduced, the identification accuracy is ensured, and the whole-process automatic identification is realized, so that the efficiency is improved. Manual participation is not needed, and the recognition efficiency is remarkably improved.

This application relates to the field of privacy protection technology and discloses a face recognition privacy protection system and method, comprising: an edge device dynamically generating noise from an acquired original face image to obtain a target perturbation image, and sending the target perturbation image to a corresponding edge server; the edge server training a local model based on the target perturbation image, generating local model parameters, and uploading the local model parameters to a central server; the central server aggregating the local model parameters to obtain updated global model parameters, and distributing the global model parameters to the edge servers; the edge server updating the local model based on the global model parameters, and performing face recognition on the perturbation image sent by the edge device according to the updated local model. This achieves end-to-end privacy protection while ensuring face recognition accuracy, eliminating the risk of original face data leakage.

This application relates to a text processing method, apparatus, computer device, and storage medium. The method includes: acquiring text to be parsed; converting the text to be parsed into a text string; the text string including prompt information and answer information from the text to be parsed; filling the prompt information and answer information from the text string into a prompt template according to a pre-determined prompt template; inputting the filled prompt template into a pre-trained reinforcement learning agent; outputting the answer information from the prompt template; and converting the answer information into a prediction result of the reinforcement learning agent. This method can process the text to be parsed into a standardized format using prompt templates, ensuring that the reinforcement learning agent can handle new scenarios with unlabeled or poorly labeled text, allowing the reinforcement learning agent to adapt to new text processing scenarios and guaranteeing the recognition accuracy of new texts to be parsed.

A method and system for image similarity recognition based on multi-feature fusion, relating to the field of image processing technology, is disclosed. The method includes: calculating the hash features of an input image and generating hash similarity and hash confidence values; when the hash confidence value is less than a hash confidence reference value, performing local feature extraction on the input image to generate local similarity and local confidence values; when the local confidence value is less than the local confidence reference value, performing deep learning feature extraction on the input image to obtain deep features and deep confidence values; determining the weight coefficients of the hash features, local features, and deep features based on the hash confidence value, local confidence value, and deep confidence value, respectively; performing feature fusion to generate a fused feature vector; and calculating the final similarity between the input image and the image to be compared based on the fused feature vector. Implementing this application can improve processing efficiency while ensuring the accuracy of image similarity recognition.

The invention relates to the technical field of fault identification, and provides a wind power plant unmanned aerial vehicle inspection data fault identification system based on artificial intelligence, which comprises an unmanned aerial vehicle, a server, a data acquisition module, an interaction module, an analysis module, a decision module and an inspection control module, the data acquisition module acquires appearance image data of a wind driven generator in a wind power plant, the interaction module interactively transmits the appearance image data acquired by the data acquisition module and ground data receiving equipment, and the inspection control module acquires the position of the wind driven generator and real-time position data of an unmanned aerial vehicle. The data acquisition module acquires the position of the wind driven generator, evaluates the inspection route of the unmanned aerial vehicle according to the position of the wind driven generator and the real-time position data of the unmanned aerial vehicle, adjusts the acquisition posture of the data acquisition module according to the evaluation result, and analyzes the wind driven generator according to the appearance image data acquired by the data acquisition module to form an analysis result. And the decision module evaluates the state decision of the wind driven generator according to the analysis result.

The invention relates to an intelligent event analysis method and system based on AI, and the method comprises the following steps: obtaining multi-modal original data related to an event, and extracting the feature representation of each modal data; the method comprises the following steps of: extracting features of an event, constructing a hierarchical multi-modal feature graph containing intra-modal sub-graphs and a cross-modal association graph on the basis of the extracted features, sequentially performing intra-modal GAT aggregation and modal perception cross-modal attention calculation by utilizing a graph attention network to obtain global features after cross-modal aggregation, and finally outputting event type probability distribution through a classification layer and identifying event types; and based on the identified event type and the multi-modal feature graph, constructing a layered multi-modal event knowledge graph, analyzing a logical relationship and an evolution path between the events by using time sequence modeling and a causal reasoning model, and outputting an analysis result. Compared with the prior art, the method has the advantages that the multi-modal event type identification precision and accuracy can be improved, the analysis efficiency is improved, and the like.

The invention provides an unmanned aerial vehicle airborne everything recognition method and system based on multi-modal semantic guidance. The method comprises the steps of obtaining a voice signal, a video image and a first text instruction collected by an unmanned aerial vehicle; semantic vectors of the voice signal and the first text instruction are extracted and fused, and a semantic fingerprint vector is generated; performing feature extraction on the video image by adopting a cascade heterogeneous feature extraction network, and determining a visual feature vector; according to the method, the semantic fingerprint vector and the visual feature vector are subjected to semantic alignment, the semantic similarity between the semantic fingerprint vector and the visual feature vector is calculated, the candidate target is determined based on the semantic similarity, target identification is completed, and the semantic fingerprint vector generated through multi-modal fusion can be used for identifying the target without external communication. Local semantic matching and retrieval can be realized on the collected visual feature vectors, so that the problem of target recognition lag caused by remote communication delay is avoided, and the real-time performance is improved; and meanwhile, reasoning is performed through the heterogeneous feature extraction network, so that the energy consumption is remarkably reduced.

The application provides a kind of pole piece burr automatic detection method and a kind of pole piece burr automatic detection system.Pole piece burr automatic detection method includes the following steps:S1: start detection system;S2: pole piece positioning;S3: diffraction light acquisition;S4: signal analysis judgment: signal processing module carries out image reconstruction to digital signal, obtains diffraction light intensity distribution image, extracts the feature parameter in diffraction light intensity distribution image and compares with preset standard threshold range value, if feature parameter is in preset standard threshold range value, then confirm pole piece qualified, if feature parameter is not in preset standard threshold range value, then confirm that pole piece exists burr;S5: result output.Greatly improve the precision and accuracy of burr detection, greatly improve the detection efficiency and automation level, real-time image reconstruction and judgment ensure burr identification accuracy, guarantee battery pole piece quality stability, reduce labor cost, can satisfy the requirement of lithium battery production line pole piece burr on-line comprehensive detection.