75results about How to "Shorten recognition time" patented technology

The invention discloses an intelligent real-time emotion evaluation method for social media and online text data based on a multi-modal knowledge graph, and the method comprises the steps: firstly obtaining and preprocessing multi-modal original data; secondly, designing single-mode feature extraction of dialogue texts, voices and facial expressions based on BERT-BiLSTM, AST and ResNet-50 pre-training models; furthermore, sharing among multiple modes and private emotion semantic information are considered, and complementation of cross-mode emotion information is promoted by enhancing repeated emotion information and utilizing uniqueness of private information. Meanwhile, a multi-mode knowledge graph method is provided from bottom to top to carry out emotion real-time evaluation. Analyzing user behaviors by using DeepFM and generating user features, and capturing external knowledge layer features in combination with K-BERT; finally, on the basis of a TransformerEncoder structure, cross-modal emotion information interaction and feature fusion are achieved, real-time recognition and classification of multi-modal emotion perception are completed through a Softmax layer, and therefore the purpose of intelligent real-time emotion evaluation is achieved.

The invention discloses an intelligent management method and system for IT equipment asset maintenance, and relates to the technical field of intelligent management. During system operation, multi-dimensional parameters of IT equipment are collected in real time and transmitted to a data processing module, the collected data are preprocessed, the current state of the equipment is comprehensively analyzed, and the health state coefficient Hstate of the equipment is calculated; the method comprises the following steps: acquiring a current equipment operation state, performing baseline establishment on historical operation data of the equipment, comparing the current state with the historical data, analyzing the performance trend and change of the equipment, calculating to obtain a trend analysis coefficient Ttend, and identifying potential problems and performance reduction trend of the equipment through long-term data accumulation. And calculating a final fault prediction probability index Pfault, comparing the final fault prediction probability index Pfault with a set threshold value, obtaining a grade prediction scheme, generating early warning information, prompting a system administrator to sort and distribute according to the urgency and priority of tasks, automatically generating a comprehensive report by the system, and providing decision support for the administrator.

The invention discloses a street tree safety risk feature detection method, system and device, and a storage medium. The method comprises the steps of obtaining image information of a lesion area of a trunk on a street tree; preprocessing the image information to obtain a training sample; based on the training sample, training an improved YOLOv8 network model to obtain a trained pest and disease identification model; wherein the improved YOLOv8 network model comprises a selective multi-scale convolution kernel attention mechanism module and an average pooling module; and detecting a trunk image to be recognized by adopting the pest and disease recognition model to obtain a detection result. A pest and disease identification model obtained by training the training sample and the improved YOLOv8 network model can be used for identifying large-batch and various types of tree trunk pest and disease images, and a high-precision identification result is quickly provided, so that the identification time is saved, the troubleshooting cost is reduced, and a more comprehensive reference basis is provided for urban greening management and decision making.

The invention discloses a multi-source ship target identification method based on two-stage collaborative fusion, and relates to the technical field of signal processing, and the method comprises the steps: firstly collecting target sea area data, including original optical image and radar image data, by means of an optical camera and a radar sensor on an aircraft, and then carrying out the preprocessing of the collected data through Gaussian filtering; performing sea-land segmentation by using a deep learning algorithm, constructing a loss function optimization network parameter to obtain a segmentation mask, and removing a land background area according to a mask result to obtain processed optical and radar images; an improved SURF feature and graph cut algorithm combined registration method is adopted, optimal transformation parameters are obtained by screening feature point pairs and solving the minimum value of an energy function, and image registration is achieved; for the registered images, fusing the images based on an adaptive fusion rule; and finally, extracting corresponding features through manual feature extraction and feature extraction based on deep learning, and fusing the features to obtain a final target recognition result.

The invention relates to the technical field of RFID chip manufacturing, in particular to an RFID chip image anomaly detection method and system based on intelligent learning. The method comprises the following steps: acquiring a real-time RFID chip sampling image set, performing feature alignment based on a registration template, generating an average grey-scale map by using an alignment image set, and obtaining a chip edge mask image set by using an edge search algorithm; segmenting the edge mask image set of the RFID chip to obtain an effective edge mask variance image set of the RFID chip; integrating the variance image set to obtain a pre-generated variance image; acquiring field environment data of the RFID chip and performing sensitivity coefficient estimation to obtain an edge sensitivity coefficient; and performing convolution filtering smoothing processing on the pre-generated variance image to obtain a chip edge variance image, and constructing an RFID chip image anomaly detection model based on the edge variance image and the average grey-scale image. According to the invention, the detection accuracy and stability are improved.

The invention discloses a street tree crown pest and disease detection method, system and device and a storage medium. The method comprises the following steps: acquiring image information of a street; wherein the image information is acquired by the unmanned aerial vehicle and carries lesion information of crowns on the border trees; preprocessing the image information to obtain a training sample; based on the training sample, training the initial YOLOv8 network model to obtain a trained pest and disease identification model; detecting the street image to be recognized by adopting the disease and pest recognition model to obtain a detection result; wherein the detection result comprises positioning information of the street image to be identified. According to the pest and disease damage identification model, large-batch street images can be detected, high-precision automatic identification and spatial positioning of infected street trees are realized, the identification time is saved, the troubleshooting cost is reduced, and the dynamic monitoring requirement of large-area urban street tree pest and disease damage is met.

The invention relates to the technical field of transformer substation small animal monitoring and early warning, and discloses a transformer substation small animal intrusion monitoring and early warning method and system, and the system comprises an image preprocessing module considering micrometeorological factors, an intrusion target detection module based on image recognition and target tracking, and a track prediction module without a monitoring area. According to the method, an improved YOLOv5s algorithm is adopted as a reference detection model, an improved EIOU loss function is adopted for replacement for the defect of a loss function in the model, meanwhile, the defect that irregular target features are difficult to extract in common convolution is overcome through a mode of replacing deformable convolution, the recognition accuracy of the model is improved, and the recognition efficiency of the model is improved. Therefore, the target in the transformer substation is detected, whether the target is an invading small animal or not is judged, the recognition time is effectively shortened, meanwhile, the accuracy is improved, and the increasing intelligent requirement is met.

The invention discloses a method, a processor and a system for detecting the welding quality of a stud. The method comprises the following steps: acquiring an image of a target detection area; the obtained target detection area image is input into a pre-trained detection model, and target stud welding information of the target detection area is recognized; respectively comparing the welding information of the identified target stud with corresponding standard stud welding information preset in a detection model; whether the target stud is qualified or not is determined according to the comparison result of the welding information of the target stud and the welding information of the standard stud; and outputting the quality state information of the target stud in the target detection area. The method has the advantages of being low in cost, high in efficiency, large in detection coverage rate, high in accuracy and good in human-to-effect ratio.

The invention relates to a big data strategy analysis system, which solves the technical problems of poor accuracy and low efficiency, and adopts an enterprise portrait analysis unit and a strategy push unit. The edge of the enterprise internal data acquisition and processing unit is arranged in an enterprise, the edge of the enterprise external data acquisition unit is arranged in the enterprise, and the enterprise portrait analysis and processing unit and the portrait result output unit are arranged in the cloud center; the enterprise external data acquisition unit also acquires strategy data; and the enterprise internal data acquisition and processing unit is used for processing the enterprise internal data into parameter score data required by the cloud center enterprise portrait and then transmitting the parameter score data to the enterprise portrait analysis and processing unit, so that the problem is well solved, and the method can be used for accurate strategy pushing.

The invention discloses an automatic fracture scanning electron microscope image recognition method based on a convolutional neural network, and the method comprises the steps: collecting a fracture scanning electron microscope image, and manually calibrating the type of the fracture scanning electron microscope image; secondly, performing image preprocessing to form a data set; then, based on a deep learning convolutional neural network, an improved CNN network model is adopted, and based on a plurality of convolution kernels, a maximum pooling layer and a global average pooling algorithm, the metal fracture scanning image is analyzed, and the type of the metal fracture image is determined; and finally, inputting a to-be-recognized image, and performing statistics on a classification model of the image by using a global average pooling algorithm. According to the method, the risk that the accuracy is reduced due to experience or long-time work of personnel can be avoided, the time cost of analysis work is reduced, the type of metal fracture analysis is obtained, and the analysis result is reliable and efficient.

The invention relates to an optical cable cutover auxiliary method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: measuring temperature information of a to-be-cut optical cable on the optical cable distribution frame in real time by adopting temperature detection equipment positioned on the optical cable distribution frame; cooling the candidate optical cables at the to-be-cut-over position in sequence by adopting refrigeration equipment, and judging whether the temperature information is changed or not; the candidate optical cable represents an unknown optical cable including the to-be-cut optical cable; and under the condition that the temperature information changes, determining the candidate optical cable which is currently cooled as the to-be-cut optical cable. By adopting the method, the optical cable cutover efficiency can be improved.

The invention discloses a lane changing intention recognition method and system and a computer storage medium, and the method comprises the steps: carrying out the data fusion, feature extraction and initial marking processing of collected auxiliary driving information according to a preset time sequence, and carrying out the recognition, so as to execute the lane changing control. The method has the advantages that the problem that an existing marking method lacks robustness is solved, compared with a traditional time window marking method, the recognition time is effectively shortened, the lane changing intention recognition model based on the multi-head attention mechanism is established, and higher accuracy can be obtained; the defects caused by the fact that a time window and other single-feature marking methods need to be overcome in the prior art of a lane changing initial point marking method are overcome, the method is crucial for a driving assistance system to predict lane changing risks and provide timely suggestions, and meanwhile more response time is provided for a driver.

The invention provides an ASR-based early media identification method and system. The ASR-based early media identification method comprises the following steps: collecting audio data corresponding to a target media in real time, comparing the audio data and repairing the audio data to obtain the repaired audio data; inputting the repaired audio data into an ASR model to obtain text data corresponding to the audio data; performing keyword and phrase recognition from the text data to obtain target keywords and phrases; the target keywords and phrases are compared with preset keywords and phrases and an emotional tendency table for recognition, an emotional tendency result is obtained, and the emotional tendency result is an early media recognition result; and pushing the media content according to the emotional tendency result. The system comprises modules corresponding to the steps of the method.

The present application relates to the field of artificial intelligence and the field of financial technology, and discloses a speech recognition method, device and equipment and a storage medium, the method comprising: splicing a first embedding vector and a second embedding vector to generate a third embedding vector, reconstructing the third embedding vector to generate a fourth embedding vector; obtaining a predicted speech recognition text output by a speech recognition model based on the fourth embedding vector, obtaining a first loss value and a second loss value between the predicted speech recognition text and a preset speech recognition text; generating a total loss value of the predicted speech recognition text according to the first loss value and the second loss value, training the speech recognition model based on the total loss value; obtaining a current speech and a current field label sent by a customer service system, inputting the current speech and the current field label into the trained speech recognition model, and obtaining a current speech recognition text output by the trained speech recognition model. The present application is beneficial to improving the efficiency of speech recognition.

The present invention belongs to the field of computer technology, and specifically relates to a face recognition method, system, computer equipment and storage medium. The method comprises: acquiring captured image data; initializing the acquired image data, judging whether there is a rotation flag; if not, cutting out the face and reading the characteristic value of the captured face; according to the processed characteristic value and the face direction recognition judgment, obtaining the rotation value, assigning the image and saving the rotation flag; if yes, performing the corresponding angle rotation according to the value of the rotation flag to obtain normal image data. The present invention can quickly and efficiently complete the adaptive normal display of the face direction, and all big data operations such as image processing are processed in an embedded neural network processor (NPU), which greatly improves efficiency, reduces power consumption, can identify living bodies, avoids the situation that the recognition can be passed by using photos, and greatly improves the recognition security.

The invention discloses a fault processing method and device, and relates to the technical field of network management and control, and the method comprises the steps: after a root alarm in a network element is reported to a management and control system, analyzing and recognizing a fault which causes a non-atomic function to generate the root alarm in the network element, starting a network element fault diagnosis process, and meanwhile, reporting the fault to the management and control system. The management and control system performs a fault identification process based on network connection after receiving the root alarm in the network element, and eliminates the fault causing the root alarm to be generated by the non-atomic function according to the fault diagnosis state of the network element, so that the fault processing efficiency is improved.

The invention discloses an equipment mirror image burning and testing method and system and a development panel, and the method comprises the steps: obtaining the information of a target container, building a connection with the target container, and obtaining the initial mirror image information of a stored initial mirror image disk; analyzing the burning instruction to obtain target mirror image information, and adding a corresponding target mirror image disk to carry out mirror image burning; and inputting a plurality of test instructions to obtain a plurality of mirror image burning results, and generating a final test result. According to the method and the device, the mirror image disks in the multiple containers and the target container are obtained, the corresponding connection is established, then mirror image burning is performed, and the burning process is tested from multiple angles such as the containers, the mirror images and the directories, so that the efficiency of mirror image burning on the equipment is improved, and the data integrity is ensured.

The invention relates to the technical field of small animal monitoring and early warning in substations, and discloses a small animal invasion monitoring and early warning method and system for substations. The system comprises an image preprocessing module considering micro-meteorological factors, an intrusion target detection module based on image recognition and target tracking, and a trajectory prediction module for a non-monitored area. The invention adopts an improved YOLOv5s algorithm as a benchmark detection model, and adopts an improved EIOU loss function to replace the defect of the loss function in the model. At the same time, the defect that ordinary convolution is difficult to extract irregular target features is solved by replacing the deformable convolution, and the recognition accuracy of the model is improved, so as to realize the detection of targets in the substation and judge whether they are intrusive small animals, effectively reduce the recognition time, and improve the accuracy, so as to meet the growing intelligent demand.

The invention discloses an unlocking method and device of an intelligent door lock and a nonvolatile storage medium. The method comprises the following steps: acquiring image data; identifying the image data, and comparing the to-be-identified face information with the face information of the resident personnel stored in the local edge storage device in an edge storage mode under the condition that the to-be-identified face information exists in the image data; under the condition that the comparison between the to-be-recognized face information and the face information of the resident personnel fails, determining that the personnel corresponding to the to-be-recognized face information is the external visitor; and receiving request information of the temporary password sent by the visitor, and unlocking the intelligent door lock based on the temporary password of the visitor when the request information passes. The technical problems that the face recognition process needs a long time and the unlocking efficiency of the intelligent door lock is low due to the fact that the face data needs to be continuously transmitted to the terminal in the related technology are solved.

The present invention discloses a method and system for intelligently identifying road surface defects. The method detects defects on an input road surface image based on a target detection model, and returns the defect area position coordinate information, defect type, and defect confidence of the road defect. The defect area is accurately located using a small frame of a fixed size, which is convenient for calculating the length or area of ​​a specific defect. The result of the accurate location is post-processed, including removing isolated frames and identical items, to obtain the final recognition result. The present invention can automatically identify defects, reduce the false alarm rate of detection, and improve detection efficiency.

This invention discloses a method and system for identifying threat targets in a driving environment based on the coordination of electroencephalography (EEG) and eye movement. The method includes the following steps: S1, acquiring real-time video of the road conditions in the foreground and performing multi-target detection and tracking; S2, displaying flashing arrows of different stimuli on the target and following its movement, collecting eye movement data and EEG data; S3, processing the eye movement data to obtain eye movement results, and processing the EEG data to obtain EEG results; S4, combining the eye movement results and EEG results to output the target tracking result. This invention has advantages such as reducing the false positive rate, shortening the target recognition time, and improving the recognition accuracy.

The invention provides a multispectral human body recognition control method, a recognition system, a computer readable storage medium and equipment. The multispectral human body recognition control method comprises the steps that multiple pieces of first multispectral image data are obtained, each piece of first multispectral image data comprises image data of human skin and image data of non-human skin at the same time, the multiple pieces of first multispectral image data are labeled so as to obtain a human skin sample and a non-human skin sample, and the human skin sample and the non-human skin sample are compared; training the labeled human body skin sample and the non-human body skin sample by adopting a machine learning method to obtain a pixel-level multispectral recognition model, inputting collected second multispectral image data of the target object into the pixel-level multispectral recognition model to obtain a human body skin binary image corresponding to the second multispectral image data, and outputting the human body skin binary image. And whether the human body exists in the target object is judged according to the human body skin binary image, so that the recognition precision is improved, and the time required for recognition is shortened.

The invention relates to the technical field of certificate anti-counterfeiting, and discloses a certificate anti-counterfeiting identification system and method.The system comprises a management platform end, an acquisition instrument equipment end and an intelligent identification algorithm module, the light wavelength parameters are used for displaying anti-counterfeiting characteristics under different spectrums; and the module is used for receiving and storing certificate pictures uploaded by the acquisition instrument equipment and displaying the pictures in real time, and uploading overseas driving license pictures collected by the acquisition instrument equipment under purple light, infrared light, light transmission and natural light to the platform. And multi-dimensional shooting of the certificate under different spectrum conditions is ensured. According to the design, invisible images and texts, miniature characters and special textures of the certificate under different spectrums can be clearly displayed, and counterfeiting and tampering behaviors are effectively prevented.

The invention discloses a method and device for determining the position of a rotor when a synchronous motor is started, and the method comprises the steps: firstly, injecting a voltage pulse into an alpha axis or a beta axis, and then determining a stator flux linkage vector calculation value; the stator flux linkage vector calculation value comprises a component calculation value of a stator flux linkage vector in an alpha axis under a static two-phase coordinate system and a component calculation value of the stator flux linkage vector in a beta axis under the static two-phase coordinate system; determining a plurality of stator flux linkage vector estimation values according to the position range of the rotor, wherein the stator flux linkage vector estimation values comprise a component estimation value of a stator flux linkage vector in an alpha axis under a static two-phase coordinate system and a component estimation value of the stator flux linkage vector in a beta axis under the static two-phase coordinate system; and finally, the rotor position is determined based on the plurality of stator flux linkage vector estimated values and the stator flux linkage vector calculated values, so that the identification time of the rotor position when the synchronous motor is started can be shortened, the identification precision of the rotor position is improved, and the starting time of the synchronous motor is shortened.

The invention relates to the field of artificial intelligence and financial science and technology, and discloses a verbal skill identification method, device and equipment and a storage medium, and the method comprises the steps: selecting an insurance fraud sample or a non-insurance fraud sample as sample data, training a large language model based on the sample data, and obtaining a prediction verbal skill label outputted by the large language model based on the sample data; obtaining a first loss value and a second loss value between the predicted verbal skill label and a preset verbal skill label; determining a total loss value of the predicted verbal skill label according to the first loss value and the second loss value; when the total loss value meets a predefined convergence condition, stopping training the large language model, and storing the trained large language model; and obtaining a current record of the alarm call, obtaining a current verbal skill label output by the trained large language model based on the current record, and when the current verbal skill label is an insurance fraud verbal skill label, identifying that the current verbal skill of the alarm call is the insurance fraud verbal skill. According to the invention, the identification efficiency of the insurance fraud verbal skill is improved.

The invention relates to the technical field of data processing, in particular to a target classification method and device based on image recognition and a storage medium. Comprising the following steps: acquiring at least two frames of to-be-recognized images including the cardiac muscle of the same to-be-detected subject; determining time sequence features and spatial features of the to-be-recognized image; the time sequence features are used for representing the time sequence of at least two frames of to-be-recognized images, and the spatial features are used for representing the myocardial distribution condition in each frame of to-be-recognized image; and determining the probability that the at least two frames of to-be-identified images belong to the target classification according to the time sequence features and the spatial features. The embodiment of the invention is used for solving the problem that the current image classification recognition mode consumes long time.

The invention relates to the field of pump turbine design methods, in particular to a key flow field feature extraction method for a pump turbine. The feature extraction method comprises the following steps: acquiring flow field data, and constructing a matrix set; transforming and segmenting the matrix set by adopting a discrete Fourier transform method to obtain covariance matrixes of a plurality of time periods; decomposing the covariance matrix of each time period by adopting a spectral eigenorthogonal decomposition method, and drawing a spectral eigenorthogonal decomposition modal shape; and analyzing the modal shape, and identifying key flow field characteristics which have obvious influence on the performance of the pump turbine. According to the key flow field feature extraction method for the pump turbine, information in flow field data can be efficiently extracted on the basis of a spectral intrinsic orthogonal decomposition method, an energy peak value under a specific frequency and a corresponding key feature shape are directly given, the dependence on artificial experience is reduced, and the recognition accuracy of key features is remarkably improved.

The invention discloses an X and gamma dose rate instrument automatic verification method based on machine vision, and relates to the technical field of machine vision, and the method comprises the steps: S1, reading an image of an instrument reading camera in an irradiation room, taking the image as a data source, and carrying out the preprocessing of the image to improve the text recognition speed and accuracy; s2, performing text detection and recognition on the instrument picture by adopting a neural network algorithm, converting the instrument reading into a character string, and storing the character string in a memory; s3, post-processing the output result of the neural network, wherein the post-processing content comprises dose rate extraction and digital hopping error processing; and S4, based on the data of each measurement point, calculating the statistical fluctuation, the calibration factor, the relative inherent error and the relative expansion uncertainty of the instrument, and linking the calculation result to the fixed position of the verification certificate template to complete the generation of the certificate. According to the method provided by the invention, the numerical values of the X and gamma dose rate instruments can be automatically read, the identification time is less than 0.5 s, and the verification efficiency can be remarkably improved.

The present application belongs to the field of communication technology, and specifically, relates to a stack overflow identification method, device, terminal and storage medium. Among them, a stack overflow identification method includes: when determining that a segment error occurs in the system memory, obtaining the memory address corresponding to the error reserved memory in the system memory and the stack memory corresponding to the error reserved memory, wherein the process corresponding to the stack memory is the first process; obtaining the stack top address corresponding to the stack memory; when the memory address does not match the stack top address, determining that a stack overflow occurs in the stack corresponding to the stack memory. Therefore, there is no need to receive the user's stack overflow identification instruction, nor is there a need to perform a stack overflow identification operation after each stack push operation, which can reduce the stack overflow identification operation and thus improve the stack overflow identification efficiency.

The present invention provides an industrial chain identification method and system, which obtains transaction flow data, pre-processes the transaction flow data, obtains operating transaction flow data and key information for constructing a capital map; constructs a capital map based on the operating transaction flow data and key information; based on the capital map, uses an association mining algorithm to mine industry sequences that meet a support threshold in the capital map to obtain an industry frequent sequence set; uses the k industry sequences with the highest industry association as chain sequences to obtain and display an industrial chain map. In this solution, a capital map is constructed using transaction flow data, and an association mining algorithm is used to mine industry sequences that meet a support threshold in the capital map, and the k industry sequences with the highest industry association are used as chain sequences to obtain and display an industrial chain map, thereby being able to identify the degree of association between industries, improve the integrity and adequacy of identification, reduce identification time, and improve versatility.