146results about How to "The recognition effect is accurate" patented technology

The invention provides a target identification method and system based on multi-source information fusion, and relates to the technical field of low-altitude target detection. The method comprises the following steps: acquiring target radar track data, interception equipment track data and position area information data; based on target radar track data, extracting a first feature in an RCS form dimension, and extracting a second feature in a motion dimension; based on the target radar track data and the track data of the monitoring equipment, determining a frequency spectrum monitoring correlation factor and regional position information features; performing feature fusion on the first feature, the second feature, the spectrum interception correlation factor and the regional position information feature to obtain a target feature, and identifying a target type; and identifying a target threat level based on the target type and the target radar track data. The method and the device are used in a target identification process based on multi-source information fusion, and the technical problem that the target threat degree cannot be accurately identified in a complex environment in the prior art is solved.

The application provides a character recognition method and system. The character recognition method comprises the following steps: determining a picture to be recognized; previewing the outline of the picture to be recognized, and setting the position of each vertex on the outline; generating a closed area according to the position of each vertex; judging whether the generated closed area covers the area to be recognized; if the closed area does not effectively cover the area to be recognized, resetting the vertex on the edge of the area not completely covered until an effective area that effectively covers the area to be recognized is generated; cutting the content outside the effective area and recognizing the character in the effective area. The application solves the problem of quickly and accurately positioning the recognition area by calculating the two points or multiple points to form a closed area in the photographed picture, marking and recognizing the selected character content in the closed area, and improving the recognition accuracy.

The invention provides an anti-semantic interference training method and a semantic communication method based on electroencephalogram joint learning. The method comprises the following steps: applying semantic interference to an original image to generate a disturbance image; respectively inputting the original image and the disturbance image into an image encoder to extract image features, and inputting corresponding electroencephalogram signals into an electroencephalogram encoder to extract electroencephalogram features; and based on the original image features, the disturbance image features and the electroencephalogram features, performing comparative learning through a bidirectional InfoNCE loss function, and updating parameters of an image encoder and an electroencephalogram encoder. Joint comparative learning is carried out through the electroencephalogram signals and the image data, the attention of an image encoder to key information is enhanced by utilizing the characteristics of the electroencephalogram signals, and therefore the robustness of a semantic communication system when the semantic communication system is attacked by semantic noise is improved. According to the method, the semantic interference resistance of a semantic communication system is enhanced based on the natural robust feature of the electroencephalogram signal, and the method is suitable for the complex situation that diversified and multi-type semantic interference exists.

The invention relates to the technical field of medical informatics, in particular to a pain management-oriented physiological signal real-time monitoring and intervention system, which comprises a determination module for determining the abnormal tendency of a stimulation window updated in real time at each updating moment to obtain an abnormal tendency sequence; the determination module is also used for determining similar fluctuation segments in the abnormal tendency sequence; the determination module is also used for determining an abnormal process factor of the physiological sensing signal at the current moment based on the abnormal tendency difference value and the length of each similar fluctuation section; the determination module is also used for determining a trigger factor for evaluating the abnormal pain state at the current moment according to the abnormal process factors of each physiological sensing signal at the current moment and the previous moment, and performing normalization processing on the trigger factor to obtain a normalized trigger factor; and the intervention module is used for carrying out visualization and intervention on the pain degree of the target user based on the normalized trigger factor. According to the invention, the physiological signal abnormity identification accuracy and sensitivity are improved.

The application discloses a scalable network security risk checking method, and relates to the technical field of network security, and comprises the following steps: establishing a network information collection mechanism to collect network information from each node of a target network; establishing a risk feature library, initializing the feature library, storing feature information of common network security threats in the risk feature library in a classified manner, and establishing indexes and classification labels; extracting a network topology graph from network topology information, calculating a network scale evaluation value according to the network topology graph; and generating a checking strategy according to the network scale evaluation value. The application flexibly generates a checking strategy according to a network scale evaluation value, is suitable for networks of different scales and architectures, can timely adjust the checking strategy when the network scale changes, ensures the effectiveness of risk checking, and effectively overcomes the limitations of traditional methods designed for specific network architectures.

The invention provides an SSB time-frequency synchronization method and device, and relates to the technical field of mobile communication. The method comprises the following steps: receiving a synchronization signal block SSB signal sent by a base station; processing the SSB signal to obtain a target time domain sequence generated after processing, and performing sliding correlation on the target time domain sequence to obtain a time domain correlation value sequence; acquiring a peak value of the time domain correlation value sequence, and performing time synchronization of the SSB signal based on time offset corresponding to the peak value; after the time synchronization of the SSB signal is completed, obtaining a cyclic prefix (CP) removal sequence corresponding to the time domain sequence of the SSB signal, and obtaining a pre-frequency offset PSS time domain sequence generated after a primary synchronization signal PSS time domain sequence is subjected to multiple times of frequency offset adjustment; performing correlation calculation based on the CP-removed sequence and the pre-frequency offset PSS time domain sequence to determine a target estimated frequency offset; and completing frequency synchronization of the SSB signal based on the target estimation frequency offset. According to the invention, the PSS can be locked quickly and accurately to realize time-frequency synchronization.

This invention provides a multimodal fusion-based method for correlation analysis of deep-sea environmental data outside the Yangtze River Estuary, belonging to the field of deep-sea environmental analysis technology. This invention achieves accurate data registration by establishing a spatiotemporal standardization model for multi-source data and employing an adaptive spatiotemporal kriging interpolation algorithm. It utilizes a sparse coding marine signal separation algorithm to construct an overcomplete dictionary to separate mixed signals and extract pure features. Based on an attention mechanism, a feature extraction network is constructed to automatically learn deep feature representations of physical, chemical, and biological parameters. A matrix rank deficiency detection algorithm is used to automatically supplement feature compensation vectors to ensure feature integrity. A cross-scale attention mechanism based on wavelet transform is constructed to fuse multi-scale features. A Shapley value interpretability evaluation system is established to quantify the importance of correlated features. This invention solves the technical problem of inaccurate correlated feature extraction during the fusion of multi-source heterogeneous marine environmental data.

The application belongs to the technical field of material engineering and failure analysis, and particularly relates to a metal fatigue micro-damage detection method, device, medium and equipment, the method comprising the following steps: acquiring a longitudinal wave sound velocity of a metal material to be detected; calculating a normalized damage index of the metal material to be detected based on the longitudinal wave sound velocity; taking the normalized damage index as an input of a fatigue micro-damage evaluation model that has been constructed, and obtaining a plastic strain of the metal material to be detected, so as to realize fatigue micro-damage detection of the metal material to be detected. The application can improve the sensitivity of metal fatigue micro-damage detection.

The application provides a pavement pit intelligent real-time detection and risk assessment method based on deep learning, relates to the technical field of deep learning, and comprises the following steps: dividing a detection sub-region by acquiring geographic information data, calculating a difficulty coefficient to determine the number of unmanned aerial vehicles and task allocation, generating an optimal aerial photography path based on a deep learning algorithm, pre-processing and feature extraction after image collection, constructing pit point cloud data by using an improved multi-view stereo matching algorithm, and further generating a pit damage assessment report. The application realizes intelligent and automatic detection and assessment of pavement pits, and improves detection efficiency and accuracy.

The invention discloses a multi-modal data driven complex product installation and adjustment feature and key event cognition method and system. The method comprises the following steps: firstly, synchronously collecting and performing time sequence alignment on vision, point cloud, force sense and motion data; extracting geometric invariant features decoupled from the visual angle through a dynamic graph convolutional neural network, and generating mechanical residual time sequence features based on a physical information neural network agent model; then, improving the heterogeneous features into structured scene semantic representation by using a multi-modal fusion network and an installation and adjustment domain knowledge graph; and finally, through a vertical domain large language model integrated with a temperature scaling probability calibration mechanism, real-time identification and quantitative confidence evaluation of installation and adjustment events such as stable slippage and abnormal clamping stagnation are realized. According to the method, the robustness of state perception, the physical interpretability of event identification and the credibility of decision making in the installation and adjustment process are effectively improved.

The invention provides a flower quality grading quality inspection method and system based on AI and image processing, and belongs to the technical field of computer vision and pattern recognizing.The method comprises the steps that an original image of the surface of a flower is collected through high-resolution imaging equipment, and high-frequency sub-band data representing tiny physical characteristics are extracted through multistage discrete wavelet transform; meanwhile, a sliding window is adopted to traverse the image, the color information entropy of a local area is calculated, and a color entropy graph is constructed. And after vectoring and splicing the two types of features, inputting the two types of features into a deep belief network model based on a multilayer restricted Boltzmann machine, and extracting deep abnormal feature codes. And finally, performing linear discriminant analysis on the code by utilizing a classification projection vector based on inter-class and intra-class distance optimization to generate an insect attack infection index, and realizing automatic grading quality inspection of the flower quality according to the insect attack infection index. According to the method, precise recognition and quantitative grading of tiny insect pests and recessive lesions on the surfaces of the fresh flowers are realized.

The invention relates to a voice-based self-service wine selling device and method for self-service wine taking. The voice-based self-service wine selling device comprises a wine storage cavity, a data acquisition module, a wine amount calculation module, a two-dimensional code generation module, a locking module and a display module, a wine cover is arranged at the top of the wine storage cavity, a wine outlet module is arranged at the position, close to the bottom, of the wine storage cavity, a wine outlet is formed in the wine outlet module, a wine taking module is arranged below the wine outlet, the data acquisition module and the wine amount calculation module are arranged in the wine cover, and the data acquisition module is connected with the wine amount calculation module. The wine amount calculation module is connected with the two-dimensional code generation module, the two-dimensional code generation module is connected with the locking module, the locking module is connected with the wine taking module, and the two-dimensional code generation module is connected with the display module. By means of the device, automatic wine taking is achieved, cost calculation is automatically conducted, and a user conducts the whole process from wine taking to payment in a self-service mode.

The invention belongs to the technical field of smart city information, and particularly relates to a multi-scene adaptive communication protocol software system and an optimization method. And the whole process of communication protocol adaptation, data transmission and abnormity management and control is covered. Core algorithms such as protocol adaptation modeling, data transmission optimization and abnormal management and control early warning are adopted, the technical bottlenecks of'heavy transmission and light adaptation, heavy rate and light reliability, and heavy response and light pre-judgment 'of a traditional communication protocol software system are broken through, and adaptive adaptation, high-reliability and low-delay data transmission and full-process abnormal real-time management and control of communication protocols under multiple scenes are achieved. The adaptability, reliability and stability of a communication protocol software system are remarkably improved, deep fusion of communication protocol software and multi-scene application is promoted, and a brand new intelligent communication solution is provided for the fields of industrial communication, the Internet of Things, intelligent terminals and the like.

The invention discloses a thread hole type identification method, device and system and machining equipment, and belongs to the field of machining. After the target complete circle on the to-be-detected part is obtained, the target bolt diameter corresponding to the preset bottom hole diameter is obtained based on the parameter table; constructing a theoretical cylinder at the position of the target complete circle; the theoretical cylinder and the to-be-detected part are differenced to obtain a target entity, and the type of the surface of the target entity is a cylindrical surface; if the target entity with the diameter being the diameter of the target bolt exists, the thread hole type of the position where the theoretical cylinder is located is determined to be the target type. According to the scheme, two parameters of the bottom hole diameter and the bolt diameter are actually adopted to identify the thread hole. Even if the thread hole is a non-standard geometry, the thread hole can be accurately identified and classified, and the identification efficiency and the automation level are greatly improved.

This invention discloses a method for controlling the downward speed deviation of a hydraulic press slide, belonging to the field of hydraulic press control technology. The method first collects real-time signals of slide position, main cylinder pressure, and oil temperature, and calculates the speed deviation. When the speed deviation continuously exceeds the limit, the system automatically switches from basic PID control to adaptive fuzzy PID control mode. In this mode, the system comprehensively considers the speed deviation, its rate of change, and pressure signals, determines the dominant disturbance type online according to preset rules, and generates targeted compensation commands accordingly. Through a multi-disturbance coordination strategy, the commands are output in an orderly manner to actuators such as proportional valves, cooling valves, and hydraulic pumps. The system monitors the speed deviation in a closed loop, achieving automatic mode recovery or abnormal protection. This invention achieves accurate identification and dynamic compensation of multiple disturbances, significantly improving the control accuracy, anti-interference capability, and system intelligence level of the slide speed.

The invention relates to the field of antennas, and particularly discloses a dual-polarized antenna, which comprises a slot antenna layer for radiating a first polarized wave and a microstrip antenna layer for radiating a second polarized wave, wherein the slot antenna layer comprises a waveguide structure, the waveguide structure comprises a plurality of radiation units arranged along a first direction, and each radiation unit comprises a plurality of radiation slots extending along a second direction; the micro-strip antenna layer comprises a plurality of micro-strip oscillator units arranged along a first direction, and each micro-strip oscillator unit comprises a plurality of micro-strip oscillators arranged along a second direction; the microstrip antenna layer is arranged on the slot antenna layer, at least one microstrip array unit is arranged between the radiation units, and projections of the microstrip arrays and the radiation slots on the waveguide structure are not overlapped; the antenna is simple in structure, has the characteristics of low profile, miniaturization and light weight, can obtain the electromagnetic scattering characteristics of a target in different polarization directions, and is suitable for a high-frequency dual-polarized antenna.

This invention relates to the field of bid document management technology, and in particular to an automatic bid document review method and system based on natural language processing. The method includes: identifying target sections; determining historical performance and financial information; judging whether false performance exists; analyzing the complete sentences containing target keywords; calculating material scores; determining price deviation; and determining the review result. This invention achieves multi-dimensional and comprehensive review of bid documents by intelligently analyzing multiple sections such as qualifications, risks, materials, and pricing. This effectively improves the comprehensiveness and depth of the review. Utilizing natural language processing technology, it can accurately identify and extract key information from bid documents, overcoming the subjectivity and limitations of traditional manual review. Quantitative assessment based on risk level significantly improves the accuracy and relevance of risk identification. By comprehensively weighing material scores and price deviation, it significantly improves review efficiency.

The application relates to the technical field of low-voltage electrical apparatus, in particular to a magnetic flux tripping device, a disconnecting switch and a power supply system, which comprise a support, an electromagnetic coil and a top rod; the electromagnetic coil is installed on the support, and the top rod is slidably arranged in the electromagnetic coil; at least two moving iron cores are arranged on the top rod in the electromagnetic coil and are axially spaced; when the electromagnetic coil is electrified, at least one moving iron core moves along the top rod in the axial direction and drives the top rod to move. According to the embodiment of the application, at least two moving iron cores are arranged in the magnetic flux tripping device, which is beneficial to realizing differentiated responses for different faults.

The application relates to a power grid risk detection method, device and equipment based on a knowledge graph. The method comprises the following steps: acquiring multi-source detection data of a power grid to be detected; performing feature extraction on the multi-source detection data to obtain a plurality of feature data; performing mapping processing on the feature data and a preset knowledge graph according to the label of each feature data, and determining a target knowledge graph comprising the plurality of feature data according to the mapping result; wherein the preset knowledge graph is used for representing various types of preset entities, preset attributes of each type of preset entity, and constraint relationships between the preset entities; clustering target entities corresponding to the feature data with a category label according to the target knowledge graph to obtain a plurality of feature data clusters; performing risk detection on each feature data cluster, and determining a risk detection result of the power grid to be detected according to the detection result. The method can improve the accuracy of power grid risk detection.

The invention relates to the technical field of glass manufacturing, in particular to a control system of a lump beating machine for synthetic quartz glass deposition, and the system comprises an acquisition module, a construction module, a mark generation module, a type generation module, a calculation module and an adjustment module. According to the method, geometric, thermal field and feeding rotation multi-dimensional data are collected and fused in real time, quantifiable feature vectors are constructed to accurately evaluate the working condition state, risk types such as local disturbance, thermal morphology instability and process mismatch are intelligently recognized, coupling adjustment indexes are calculated on the basis of different risks, and the working condition state is accurately evaluated. According to the method, the control dimension is controlled to be single, cooperative proportion correction and self-adaptive write-back optimization are conducted on multiple parameters such as flame power and feeding flow through driving, so that fine control over the lump growth morphology is achieved, and the problem that the morphology defect rate of a finished product is high under the complex deposition working condition due to the fact that the self-adaptive capacity is poor due to the single control dimension is effectively solved.

The application relates to a typical traffic event detection system based on deep learning and belongs to the field of vehicle-road cooperation and intelligent traffic. The system is sequentially provided with a target detection model module, a time flow motion feature model module and a traffic event detection module; the target detection model module is provided with a multi-branch hollow fusion module for increasing coordinate attention and an overlapping detection frame reservation mechanism module in parallel; the time flow motion feature model module is a Gaussian linear difference time flow model module; and the traffic event detection model module is a multi-dimensional visual feature event detection model module. The application fully utilizes roadside monitoring to obtain real-time traffic video images, optimizes a model to improve the precision of typical traffic events, can effectively improve event detection accuracy, balances detection performance and real-time performance, timely solves road traffic events, improves traffic efficiency and reduces intersection congestion.

The embodiment of the application provides a kind of early warning method and system of energy storage system based on dynamic threshold, belong to safety monitoring technical field.The method includes: the multi-source operating data of energy storage system is collected, and monitoring data set is formed;Feature parameters are extracted based on the monitoring data set, and fusion feature vector is generated based on the extracted feature parameters;According to the fusion feature vector, dynamic threshold determination is executed, and the determined dynamic threshold is obtained;The safety state of the monitoring data set is evaluated based on the dynamic threshold, and the early warning result of energy storage system is output.The application scheme can realize more accurate identification and prompt in the early risk of energy storage system thermal runaway, thereby significantly improve the reliability and adaptability of early warning result, reduce the delay of operation and maintenance intervention and the probability of accident.

Embodiments of the present application disclose a multi-screen demonstration page overflow identification method and device, equipment and a storage medium. The technical scheme provided by the embodiments of the present application configures the screen page number of a demonstration document for each screen in the order of screen display. For the case that the display order value is less than 1 or exceeds the total page number value of the demonstration document, the screen page number of the corresponding screen is configured as 1 or the total page number value. Thus, it is avoided that the screen page number exceeds the page number value range of the demonstration document, so that the corresponding display content of the page number cannot be found. By positioning the screen page number of the corresponding screen as a reference page number, according to the display order determined by the reference page number, for the screen whose display order is within the page number value range of the demonstration document, the corresponding page content of the document is displayed according to the screen page number, and for the screen whose display order exceeds the page number value range of the demonstration document, an overflow page prompt is performed, so that the overflow identification and prompt of the multi-screen demonstration page number are accurately performed.

The embodiment of the application relates to the technical field of optical fiber communication, and discloses a link state monitoring method of an optical communication network, an electronic device and the optical communication network, the link state monitoring method of the optical communication network is applied to the optical communication network, a link of the optical communication network is used for transmitting optical signals back and forth, the method comprises the following steps: acquiring a digital signal corresponding to the optical signals transmitted back and forth; converting the digital signal into a Stokes sequence representing an optical polarization state; identifying a plurality of disturbance points in the link according to the Stokes sequence; and determining the positions of the disturbance points in the link according to time offsets corresponding to the plurality of disturbance points respectively. The application can accurately monitor disturbances occurring in the link of the optical communication network.

This invention discloses a method and system for identifying equity structure diagrams based on data processing, relating to the field of data processing technology. The method includes: performing feature recognition on the equity structure diagram to obtain entity features and relationship features; constructing an identification model based on a graph neural network, using entity features as nodes and relationship features as edges; identifying nested relationships in the equity structure diagram through the identification model; wherein, nested relationships include relationships between equity entities; equity entities include shareholders and shareholders; and determining multi-level equity penetration paths, actual control paths, cross-shareholding relationships, and potential equity change relationships in the equity structure diagram based on the nested relationships.

The application discloses a memory management method, related equipment and computer program product, and relates to the technical field of data processing. Based on entity information contained in to-be-stored memory content in a new memory write request, target candidate memory content in contradiction with the to-be-stored memory content is retrieved from a memory library; based on memory causal tracing information corresponding to the to-be-stored memory content and causal dependency relationships between different memory contents maintained by the memory library, the memory sources corresponding to the to-be-stored memory content and the target candidate memory content are traced upwards respectively; according to a credibility comparison result of contradiction root positioning results of the two parties in contradiction, a write request processing result of the to-be-stored memory content is determined, and processing is performed on the to-be-stored memory content according to the write request processing result. According to the application, more accurate contradiction identification and credible arbitration can be realized in the new memory write stage, thereby improving the consistency and reliability of memory content in the memory library.

The application discloses a kind of cloud edge end collaborative hot continuous rolling process hydraulic system oil leakage monitoring system, belong to industrial fault monitoring technical field, the system uses cloud edge end architecture;Wherein, end side is used to collect the working parameter of hot continuous rolling process finishing mill hydraulic screwdown system;Edge side is used to the working parameter of end side collection is preprocessed, and the working parameter after preprocessing is input into the detection model trained by cloud, utilizes the detection result output by the detection model, to detect whether the hot continuous rolling process finishing mill hydraulic screwdown system appears oil leakage fault;Wherein, detection model fuses convolutional neural network, long short-term memory network and multi-head attention mechanism;Cloud is used for the training and updating of detection model.The application uses deep learning fusion model to carry out feature extraction to multi-source time series signal, intelligently identifies to leakage type, and can realize the rapid early warning and visual display of hydraulic system oil leakage.

The application relates to a laser radar SLAM and vegetation feature library matching method and system of a mowing robot, and relates to the technical field of data processing. The method comprises the following steps: determining initial laser radar parameters based on laser radar test data of a plurality of sample vegetation; establishing a vegetation feature library based on the initial laser radar parameters and the laser radar test data of the plurality of sample vegetation; determining environmental influence factors based on the initial laser radar parameters; acquiring real-time environmental information of the mowing robot; adjusting the initial laser radar parameters based on the environmental influence factors and the real-time environmental information to generate current laser radar parameters; acquiring point clouds of vegetation to be matched based on the current laser radar parameters; determining a vegetation type based on the point clouds of the vegetation to be matched and the vegetation feature library; and updating a two-dimensional grid map or a three-dimensional point cloud map of an environment where the mowing robot is located based on the point clouds of the vegetation to be matched and the vegetation type, so that the intelligent level of the mowing robot is improved.

The invention discloses an enamelled wire surface defect detection device for enamelled wire production, and relates to the technical field of enamelled wire detection, the enamelled wire surface defect detection device comprises a rack, a straightening machine is arranged on one side of the top end of the rack, and the straightening machine is used for straightening and conveying an enamelled wire; and the mounting frame is fixedly arranged at the top end of the rack, and fixed guide rails are arranged on the two sides of the interior of the mounting frame. Through the vibration suppression frame and the vibration suppression assembly, harmful high-frequency vibration is attenuated, wires are stabilized, meanwhile, low-frequency floating of the wires is converted into a power source for driving the mirror surface to swing, and in the swinging process, the angle and distance between the mirror surface plate and the surface defect position of the enameled wire change in real time; the brightness and direction change rule of reflected light at the defect are obviously different from those of a normal area, characteristic'dynamic smear 'is formed in camera images, the upper and lower detection cameras continuously shoot, and the surface defect can be accurately positioned and identified by analyzing the unique dynamic optical characteristics in an image sequence.

The invention provides a kidney transplantation rejection reaction serum polypeptide detection device and method, and the method comprises the steps: obtaining a kidney transplantation serum sample, and extracting a polypeptide characteristic signal group of the serum sample from a polypeptide mass spectrum of the serum sample in a preset mass-to-charge ratio range; the polypeptide characteristic signal group comprises three characteristic polypeptide peak signals corresponding to different protein fragments, and further determining the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic polypeptide peak in the polypeptide characteristic signal group; carrying out fusion judgment on the rejection risk of the serum sample according to the mass-to-charge ratio of each protein fragment and the relative intensity value of each characteristic polypeptide peak in the polypeptide characteristic signal group to obtain a risk index representing the rejection state in the serum sample; and comparing the risk index with a rejection risk threshold value of the serum sample so as to make multi-level judgment on the rejection risk level to which the serum sample belongs. Based on the scheme, rejection reaction monitoring based on kidney transplantation serum polypeptide fingerprints can be realized.