55results about How to "Reduce recognition errors" patented technology

The invention relates to the technical field of television image communication, in particular to a user information input method and system in an intelligent television all-in-one machine. The method comprises the following steps: collecting a user image, carrying out white balance correction and focusing processing, generating a moving object focusing image, carrying out identification and attention tendency analysis on a character in the focusing image, obtaining character tendency data, analyzing a historical interaction image, identifying an interaction mode and constructing a user intention prediction model, and obtaining a user intention prediction result. And when the tendency data shows that the user preferences the television content, intention drilling and auxiliary adjustment are performed on the current image based on the prediction model, predicted user input information is generated, logic conflicts in the predicted information are detected, feedback optimization is performed, and final user input information is output. According to the invention, a more accurate and more flexible user information input method is realized.

The invention discloses a hard disk backboard and a server, and relates to the technical field of servers. The power supply control interface of the backboard controller is connected with the hard disk connector, the power supply control function of the hard disk is fully utilized to control the work of the hard disk, the situation that the occupied area of the backboard is additionally increased in an electronic fuse mode is avoided, and connection is achieved only in an interface mode; meanwhile, a clock buffer is added and integrated with a power supply control function of the hard disk to realize a power-on time sequence which is strictly required by a high-speed signal of a specific hard disk, so that the hard disk identification error problem caused by a time sequence error risk is reduced, the hard disk identification accuracy is improved, and the adaptation compatibility between the hard disk and a mainboard is correspondingly improved. Besides, the backboard controller and the hard disk connector are connected through the first communication interface, the situation that key information is leaked due to the fact that conventional hard disk information acquisition occupies a polling communication protocol for a long time is avoided, and the safety of hard disk information acquisition is improved.

The invention discloses a transformer area topology identification method and system based on Kirchhoff's law, and relates to the technical field of low-voltage transformer area topology identification, and the method comprises the steps: obtaining low-voltage rural power grid transformer area LMUs and user meter data, and carrying out the preprocessing; for LMUs data, establishing an LMUs-LMU identification relation model based on the Kirchhoff's law; based on the sequence of the LMUs from high to low, establishing an LMU-LMU relation model based on the Kirchhoff's law for the LMUs to be compared and the LMUs in the existing LMU-LMU topological structure one by one, and obtaining the real positions of the LMUs to be compared in the LMU-LMU structure; establishing an LMU-user meter identification relation model; and based on the identified complete LMU-LMU structure, establishing an LMU-household meter identification model based on Kirchhoff's law for each household meter and all LMUs, and finally determining the LMU to which the current household meter belongs. According to the invention, in a power topology identification scene based on data analysis, the investment of resources such as manpower and material resources is reduced, the topology identification accuracy can be ensured, and a set of transformer area topology identification system with low cost and high efficiency is formed.

According to the on-orbit assembly robot ground test system and method based on the air floating platform, the air floating platform serves as a support, gravity unloading is provided through two air floating simulators floating on the platform, and a mechanical arm installed on one air floating simulator picks up the other air floating simulator; and enabling the large-tolerance butt joints which are respectively arranged on the flexible simulation piece cross beams of the two air floatation simulators to be close to each other until the butt joint is completed, so as to complete the verification of the related auxiliary butt joint capability of the space manipulator. In the process, the vertical rod of the flexible simulation piece is connected with the air floating simulator at the mounting end of the mechanical arm, and flexible damping simulation in the horizontal direction is provided. Different flexible simulation pieces are matched with the air flotation device, vertical gravity unloading, horizontal controllable force and torque control and flexible base damping disturbance in the mechanical arm actuation process are met at the same time, the in-orbit working condition of the space mechanical arm can be fully simulated, and the auxiliary butt joint capacity of the mechanical arm is verified.

The invention discloses a gesture recognition method, terminal equipment, a storage medium and a computer program product, and relates to the technical field of signal processing, and the method comprises the steps: obtaining a pre-trained basic gesture recognition model; obtaining user training data of the target user, and adjusting a preset offset calibration module based on the user training data to obtain an individual calibration module; a target gesture recognition model is constructed based on the individual calibration module and the basic gesture recognition model, and when gesture recognition is conducted on the to-be-recognized electromyographic signals of the target user according to the target gesture recognition model, feature vectors corresponding to the to-be-recognized electromyographic signals are adjusted according to the individual calibration module. The individual calibration module is introduced to capture and adapt to the unique features of the target user, so that the target gesture recognition model can better capture and adapt to the unique features of the target user, recognition errors caused by individual differences are reduced, and the accuracy of gesture recognition is improved.

The invention relates to the technical field of image recognition, and discloses a multi-commodity automatic correction and recognition method and an intelligent self-service cash register, and the method comprises the steps: extracting a commodity front image from a commodity image; identifying label areas, and selecting associated feature points for each label area; segmenting a commodity area from the front image of the commodity; determining an associated commodity area of each label area; extracting a commodity side image, and determining an associated commodity area of each label area in the commodity side image; reconstructing a three-dimensional model of the commodity; scanning a label area to obtain commodity information, and verifying the commodity information based on a gravity sensor and the three-dimensional model of the commodity; according to the invention, the accuracy of commodity identification and the settlement efficiency are improved, the user experience is improved, the security and reliability of the system are enhanced, and a more efficient, convenient and reliable self-service cashier solution is provided.

The present invention discloses a micro-expression recognition method, system, device and storage medium based on multivariate optical flow feature fusion. The method includes obtaining original video, generating optical flow images respectively through multiple optical flow algorithms, extracting horizontal optical flow vectors and vertical optical flow vectors therein for superposition, obtaining horizontal images, vertical images and overall images, and inputting them into the MRKCBN network model for feature extraction, outputting corresponding image output features to obtain local features; inputting the optical flow image generated by one of the optical flow algorithms into the VIT network model for feature extraction and outputting global features, splicing local features and global features to obtain splicing features; fusing the splicing features through the APSB module to obtain fused features; and classifying the fused features through the fully connected layer to obtain micro-expression recognition results. The present invention uses multiple optical flow algorithms to fuse optical flow information to improve the accuracy of recognition; and takes into account both local features and global features through MRKCBN and APSB.

The invention provides an image auditing method, device and equipment and a computer readable medium, and the method comprises the steps: carrying out the character detection of a to-be-audited image through calling a character detection model, cutting the to-be-audited image according to the detected characters, obtaining at least one character image, and carrying out the cutting of the to-be-audited image according to each character image, performing multi-dimensional feature detection on the character image, selecting a corresponding special text recognition model to perform text recognition on the character image according to a feature detection result to obtain a text recognition result, and performing violation judgment according to all the text recognition results corresponding to the to-be-audited image to obtain a violation judgment result. According to the method and the device, the to-be-audited image which is judged to be illegal is processed according to the preset illegal image processing rule, so that an image auditing solution which is high in automation degree, high in accuracy and high in processing speed is provided, the burden of manual auditing can be greatly reduced, and the accuracy and the efficiency of content auditing are improved.

The invention discloses a policy interaction relation identification method and device, and the method comprises the steps: inputting a to-be-analyzed policy text into an element extraction model, and obtaining an element set in the policy text; if the number of the elements in the element set is greater than or equal to two, generating an element pair candidate set based on the element set; if the element pair candidate set is not empty, obtaining a relation type and a credibility score corresponding to each element pair through a relation discrimination model; comparing the credibility score with a preset credibility threshold value, and screening out a target element pair from the element pair candidate set; performing time sequence consistency verification on the screened target element pairs; and outputting the target element pair passing the time sequence consistency verification and the corresponding relationship type thereof as the policy interaction relationship of the policy text. According to the method, automatic identification of the policy interaction relationship is realized, and the accuracy and reliability of an identification result are effectively improved.

The embodiment of the present invention provides a clothing recognition method, device, equipment and storage medium, the method comprising: extracting key frames at multiple different moments from a video to be recognized; detecting clothing areas and human body areas on the key frames; in response to detecting at least one clothing area and at least one human body area in the key frames, selecting a representative clothing area from the at least one clothing area, the representative clothing area meeting the following conditions: the overlap ratio between the representative clothing area and the human body area reaches a first threshold, the representative clothing area is a target clothing posture, and the clothing exposure ratio reaches a second threshold under the target clothing posture; clothing recognition is performed based on the selected representative clothing area. In this way, the recognition errors that may be caused by unstable clothing postures and incomplete exposure of noise clothing areas are reduced, thereby improving the accuracy of clothing recognition.

This application discloses a training method, application method, apparatus, and device for a text correction model. The related embodiments can be applied to various scenarios such as artificial intelligence to improve the accuracy of typo recognition. The method of this application includes: outputting a first text probability distribution corresponding to each character in the training sample to be corrected through a text correction model; calculating a correction loss value corresponding to the training sample to be corrected based on the first text probability distribution corresponding to each character; replacing any original character in the training sample to be corrected except for the original typo to construct a noise sample corresponding to the training sample to be corrected; obtaining a second text probability distribution corresponding to each character in the noise sample; calculating a noise loss value corresponding to the training sample to be corrected based on the first and second text probability distributions; and adjusting the parameters of the text correction model based on the correction loss value and the noise loss value to obtain the target text correction model.

The present invention relates to the technical field of electronic non-stop toll collection, and in particular to an electronic non-stop toll collection system based on high-precision identification, including a data acquisition module, an information processing module, and an identification and deduction module. The present invention greatly improves the accuracy of license plate recognition and the reliability of the system through advanced data acquisition and processing technology. The system collects license plate images through image sensors in the environmental monitoring area, and uses character recognition and judgment units for high-precision recognition to ensure that a high recognition rate can be maintained even under adverse weather conditions. By real-time monitoring of vehicle speed and image clarity, the system can dynamically adjust the heating temperature to prevent image blur and equipment failure. The system automatically adjusts system parameters for interfering weather such as rain and snow, thereby improving the stability of license plate recognition under various climatic conditions. It effectively solves the problem of low accuracy of license plate information recognition under extreme weather conditions due to reliance on light sources and light sensors.

This application discloses a method for training a recognition model, a recognition method, a computer device, and a computer-readable storage medium. The training method includes the following steps: controlling a single-photon lidar to emit photon pulses towards a target scene and receiving one-dimensional single-photon echoes reflected from the target scene, where the target scene includes at least one object to be recognized; constructing a training sample set based on the one-dimensional single-photon echoes; constructing an initial model and inputting the training sample set into the initial model for iterative training; validating the initial model after each iteration, marking the validated initial model as the recognition model, and outputting it. The application method involves inputting the echo signal to be recognized into the trained recognition model, and obtaining the recognition result based on the processing of the recognition model. The recognition result includes the pose and type of the object in the target scene. Therefore, this application can achieve high-precision recognition of long-distance targets.

The present invention discloses a method and system for identifying and locating underground pipeline damage based on a feature hierarchy model, and the method includes: S1, arranging a plurality of Raspberry Pi ultrasonic detectors along the direction of the underground pipeline, and constructing ultrasonic pipe wall damage sample data containing labels; S2, constructing a sample feature hierarchy label learning model to extract features from the ultrasonic pipe wall damage sample data to obtain a label feature vector and a weight coefficient vector through an attention mechanism module; S3, relying on the Raspberry Pi ultrasonic detectors arranged on the underground pipeline wall to collect underground pipeline data at the sampling point, input the sample feature hierarchy label learning model, and output the damage identification result, and use the ultrasonic detector to calculate the location information of the sampling point and store it in association with the damage identification result, and filter and output it. The present invention realizes the output of the sampling point positioning position information and the sampling point damage identification association data, which is convenient for management personnel to efficiently record and maintain work.

The invention relates to a hierarchical scanning, automatic identification and processing method based on traffic violation snapshot pictures, and belongs to the field of image processing and traffic management. The technical method aims at improving the scanning efficiency, accurately recognizing traffic violation information and achieving automatic processing and uploading so as to improve the processing efficiency and accuracy of traffic violation snapshot data. Comprising the steps of hierarchical scanning, automatic identification, data verification and uploading, and exception handling and feedback. According to the method, the stored snapshot pictures are quickly positioned and extracted by presetting the folder hierarchy depth and being compatible with various folder hierarchy structures, and each picture is marked, so that repeated scanning and missing detection are avoided, and the scanning efficiency is remarkably improved. The OCR technology is combined with the deep learning algorithm, so that the recognition accuracy is remarkably improved, and particularly, error recognition can be effectively reduced in a complex environment. After identification is completed, the system verifies the accuracy of an identification result through an automatic verification mechanism, seamless connection and processing of data are completed, manual intervention is reduced, and law enforcement efficiency is improved.

The invention provides a safety shoe mixed line identification method and device, equipment and a storage medium, and the method comprises the steps: obtaining the identification image information of a plurality of to-be-identified safety shoes, obtaining the corresponding contour information of the safety shoes, and determining a corresponding shooting angle; obtaining a preset safety shoe model library, wherein the preset safety shoe model library comprises a plurality of 3D models corresponding to preset safety shoes and preset safety shoe identifiers; obtaining 3D image information corresponding to each 3D model at the shooting angle; the 3D image information at least comprises a plane view, color information and depth information; respectively carrying out feature extraction on the color information and the depth information to obtain color feature information and depth feature information, and marking the color feature information and the depth feature information in corresponding plane views; and feature matching is carried out on the identification image information and the marked plane view, a preset safety shoe identifier corresponding to the successfully matched plane view is a target safety shoe identifier, the target safety shoe identifier is added to the corresponding identification image information, and mixed line identification of the plurality of safety shoes to be identified is completed.

The invention discloses a wireless positioning method and device, a medium and a product, and relates to the field of wireless positioning.The method comprises the steps that whether a propagation path at the current moment is a sight distance path or not is judged according to obtained real-time monitoring data of a positioning base station; if yes, determining the first arrival path of the sight distance path state and the corresponding first arrival path time delay, and determining the distance between the positioning base station and the terminal and the positioning coordinate of the terminal; if not, determining a first arrival path of the non-line-of-sight path state and a corresponding first arrival path time delay based on local cognitive feedback; judging whether the first arrival path of the non-line-of-sight path state determined based on local cognitive feedback has a problem or not; if not, determining the distance between the positioning base station and the terminal and the positioning coordinate of the terminal; and if yes, determining an estimated first arrival path of the non-line-of-sight path state at the current moment and a corresponding first arrival path time delay estimation value based on cognitive feedback of the cloud, and determining a distance between the positioning base station and the terminal and a positioning coordinate of the terminal. The positioning error can be reduced.

The invention relates to an underground cable ground penetrating radar image automatic identification method based on a similarity index, and the method comprises the steps: carrying out the detection of a plurality of points randomly selected in a detection region through a ground penetrating radar, and obtaining a plurality of ground penetrating radar image data; processing the ground penetrating radar image data to obtain a single-channel waveform under the electromagnetic wave radiation of the ground penetrating radar; the method comprises the following steps: detecting areas where typical media are located, collecting single-channel waveforms of the typical media, and constructing a single-channel waveform library of the typical media; calculating a similarity index between a single-channel waveform obtained by the ground penetrating radar in a region with an unknown medium type and a waveform in a single-channel waveform library of a typical medium; and determining the medium type of the point in the detection area according to the magnitude of the similarity index. According to the method, the medium type of the detection area is determined in combination with the calculation result of the similarity index, cable identification errors possibly caused by human subjective factors are reduced, meanwhile, the calculation process is simple and convenient, the required result can be rapidly obtained, and high effectiveness is achieved.

The invention discloses an off-line conference recording box and a voice recognition system thereof, belongs to the field of voice recognition, and aims to solve the problems of accurately and efficiently capturing and recognizing voices of a plurality of speakers and reducing the influence of environmental noise on recognition accuracy. The method comprises the following steps: capturing an audio signal from a microphone array to form audio data, receiving a voice audio signal through voice activity detection and dynamic volume adjustment, and providing early warning feedback when an audio quality problem is detected; the method comprises the steps of capturing audio data, performing data processing on the captured audio data, performing de-noising processing on original voice audio signals in the audio data, extracting audio features, and performing voiceprint recognition through clustering analysis of the audio features so as to obtain sound source separation signals with voiceprint information marks; and performing independent voice recognition according to the sound source separation signal, and organizing and managing voice data.

The invention discloses a well descending abnormity identification method and device based on dynamic time warping. The method comprises the following steps: acquiring monitoring data of a miner descending into a well; preprocessing the monitoring data of the miner descending into the well to obtain preprocessed monitoring data; extracting personnel step number and personnel vital sign data from the preprocessed monitoring data; clustering miners according to the personnel step number to obtain a plurality of miner clusters; on the basis of the same miner cluster, obtaining the personnel vital sign similarity between any two miners according to dynamic time warping; and according to the similarity of the vital signs of the personnel, well descending abnormal data are recognized, and thus well descending abnormal recognition based on dynamic time warping is completed. The method can comprehensively and effectively identify underground abnormal conditions, and significantly improves the accuracy of abnormal identification, thereby providing more reliable guarantee for underground safety.

The embodiment of the invention provides a voice recognition method, electronic equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a voice signal, and determining a target field related to the voice signal; obtaining a target speech recognition model corresponding to the target field, and inputting the speech signal into the target speech recognition model to obtain an initial speech recognition result, the initial speech recognition result comprising at least one character string; for each character string, under the condition that abnormal characters exist in the character string, candidate standard expressions corresponding to the character string are searched in a standard corpus, the character string is updated through the candidate standard expressions, and the candidate standard expressions are standard expressions with the highest similarity with the character string in the standard corpus; and determining a target speech recognition result according to all the character strings. According to the invention, the normalization and accuracy of a speech recognition result can be improved.

This invention discloses a method and system for identifying abnormal driver behavior based on continuous learning, relating to the field of driving behavior detection technology. The method includes dividing a driver monitoring image into image blocks and projecting them into feature vectors, adding positional encoding, and combining extracted prompt features with segmentation into prompt vectors to achieve dual coverage of global and key local features, improving feature extraction accuracy. The two types of vectors are concatenated into a fused feature sequence, input to a pre-trained Vision Transformer model with frozen parameters, and enhanced features are extracted through a self-attention mechanism. Then, a global feature vector is obtained through average pooling. Continuous learning is achieved by outputting a vector of all seen task logic values, using a binary mask for filtering, and classifying using the argmax function. This allows for adaptation to behavior category expansion, balancing real-time performance with stability in complex environments, improving the accuracy of similar behavior recognition, and making it easily deployable in in-vehicle scenarios.

The invention provides a material verification method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining to-be-proof data based on a preset interaction interface, and transmitting the to-be-proof data to a material collection end, so as to obtain proof material data corresponding to the to-be-proof data; receiving and analyzing proof material data sent by the material collection end, and performing image recognition on a proof material image in the proof material data to generate mechanism recognition data; and according to the mechanism identification data and the obtained exogenous verification data and supervision system data, carrying out comparison verification on the proof material data, and generating a material verification result. Compared with the prior art, the collection efficiency of the proof material can be improved; the time and cost of manual auditing can be further reduced, key information in the proof material can be extracted and analyzed more accurately, and the authenticity and accuracy of the proof material can be ensured.

This invention relates to a multimodal orbital classification and recognition method, terminal, and medium based on edge AI. The method, applied to a mobile terminal, includes the following steps: acquiring a two-dimensional color image of a face to be recognized; performing region segmentation on the two-dimensional color image of the face to obtain a color image of the orbital region; acquiring three-dimensional face data corresponding to the two-dimensional color image of the face; obtaining a face depth image based on the three-dimensional face data; performing region segmentation on the face depth image to obtain a depth image of the orbital region; and using the color image of the orbital region and the depth image of the orbital region as input to a lightweight multimodal recognition model to obtain the classification result. Compared with existing technologies, this invention has advantages such as high reliability and small scale.

The invention discloses a structural hole programming method, and relates to the technical field of machining. According to the method, the characteristics that most structural holes are high in similarity and easy to summarize are utilized, the complex structural holes are created into a module, an identification fitting method is researched, and subsequent modular integration is facilitated through feature fitting and fusion of contents such as tool information, machining parameter information and tool path information of machining in subsequent machining. The module can be applied to programming of multi-part shell structure holes, the programming speed and the machining speed are increased, the machining efficiency is improved, and the module can be vigorously popularized in other typical structures in the direction.

The application relates to the technical field of household appliances, and discloses a cleaning brush and a control method thereof. The cleaning brush comprises a main machine, a handle and a machine head. The machine head is connected with the main machine and comprises a water tank and brush hairs. Cleaning medium is arranged in the water tank, and the brush hairs are arranged at the bottom of the water tank. An atomizing water spraying device is arranged at the bottom of the water tank and communicates with the inside of the water tank. The other end of the atomizing water spraying device is provided with a nozzle. The atomizing water spraying device is used for atomizing the cleaning medium and spraying the atomized cleaning medium to a surface to be cleaned and the brush hairs. Compared with the related art, the water tank and the atomizing water spraying device are integrated in the brush head, so that an additional handheld water pipe, a shower for humidification or frequent wetting of the brush hairs is not needed, the cleaning operation process is simplified, and the use convenience is remarkably improved.

The application provides a kind of based on image semantics increase or decrease hook problem dynamic identification and hierarchical processing method, the method includes that the increase or decrease hook project area multi-source data is unified, time alignment and grid encoding are carried out to coordinate, remote sensing image is cut according to plot boundary, internal, boundary neighborhood semantic feature and shape feature are extracted, boundary reliability is calculated and plot semantic representation is constructed, direction texture and hierarchical distribution features are extracted through double passage model, are fused into structural semantic features according to boundary reliability weighting, combine topographic elevation and historical data to extract multi-temporal time series change characteristics, generate space-time semantic vector, determine construction occupation, reclamation deficiency, boundary mismatch and use deviation problem, and update boundary reliability, determine plot problem level according to the determination result, analyze adjacent similar plot group, determine boundary adjustment scheme, output processing results containing problem level and boundary optimization result.

The present application provides a kind of based on multi-source vision sensor fusion's blast furnace slag iron flow detection method and system, belongs to blast furnace slag iron flow detection technical field, including: hyperspectral image data input slag iron ratio estimation model, the composition of molten iron and slag is identified and separated, extract the spatial distribution information of iron phase and slag phase, calculate slag iron ratio;Slag iron flow time series data input time series visual flow velocity perception model, the dynamic analysis of slag iron flow flow process, extract time evolution characteristics and flow trajectory information, calculate slag iron flow velocity;Three-dimensional point cloud data input three-dimensional point cloud reconstruction flow section analysis model, the spatial form of slag iron flow is modeled and analyzed, extract three-dimensional geometric structure and cross-sectional shape characteristics, calculate slag iron flow cross-sectional area.Slag iron ratio, slag iron flow velocity, slag iron flow cross-sectional area are jointly modeled, the adaptive calculation of slag iron flow mass flow is carried out, and slag iron flow detection result is output.The present application can effectively carry out slag iron flow detection.

The application provides a workpiece free edge automatic machining method and device, the method comprises the following steps: scanning a workpiece to be machined through a scanning module to obtain scanning data, extracting workpiece contour data of the workpiece to be machined from the scanning data, identifying a workpiece free edge to be machined in the workpiece contour data, and obtaining a machining data range of the workpiece free edge; before machining the workpiece to be machined, detecting actual position data of the workpiece free edge in the machining data range through a detection module, updating the machining data range based on the actual position data; and controlling a machining module to machine the workpiece free edge based on the updated machining data range. Through the design of automation, precision and intelligence, the application solves the problems of inaccurate identification and low efficiency in large workpiece machining.

The invention relates to a cleaning robot ground material identification method. The cleaning robot ground material identification method comprises the steps that historical ultrasonic data, collected by a host, of the surrounding ground are obtained, and the historical ultrasonic data comprise historical echo integral values and historical primary echo distances; performing ground material pre-estimation classification processing based on the historical echo integral value and the historical primary echo distance to obtain a ground material pre-estimation result; acquiring real-time ultrasonic data of the surrounding ground acquired by the host, wherein the real-time ultrasonic data comprises a real-time echo integral value, a real-time afterwave integral value and a real-time primary echo distance; and performing ground material detection analysis processing on the ground material pre-estimation result based on the real-time echo integral value, the real-time afterwave integral value and the real-time primary echo distance to obtain a ground material result. According to the scheme, the ground material identification speed can be increased, and the ground material identification error can be reduced.