141results about How to "Realize identification" patented technology

The invention aims to provide a method and a device for determining the peak area of chloride ions based on ion chromatography. The method for determining the peak area of chloride ions based on ion chromatography comprises the following steps: step 1, identifying a chromatographic peak: peak separation, peak qualitation, baseline correction, peak trailing treatment and peak overlapping treatment; and step 2, calculating the chromatographic peak area: peak definition, integration algorithm, calibration curve application, repeatability verification, impurity peak deduction and main peak area calculation. According to the method, a chromatographic peak identification and chromatographic peak area calculation mode is designed, so that the technical problem that other impurity ions may be introduced to influence chloride ion chromatographic peak identification and accurate area calculation in an existing mode of determining organic chlorine through ion chromatography is solved.

The invention relates to a railway tunnel portal safety state detection method, system, device, medium and program, and the method comprises the steps: carrying out the two-dimensional data collection of a railway tunnel portal to obtain a tunnel portal image, and carrying out the three-dimensional data collection of the railway tunnel portal to obtain a tunnel portal point cloud; performing multi-layer target feature detection and two-dimensional target labeling on the tunnel portal image to obtain a two-dimensional detection result; performing point cloud denoising and spatial voting clustering on the tunnel portal point cloud to obtain a primary point cloud class group; performing bounding box detection and three-dimensional target labeling on the tunnel portal point cloud according to the primary point cloud class group to obtain a three-dimensional detection result; and performing detection target alignment and scene modeling on the two-dimensional detection result and the three-dimensional detection result to obtain a standard detection result, and performing safety analysis on the standard detection result to obtain the tunnel portal safety state, thereby improving the efficiency of the tunnel portal safety state.

The invention discloses a liquid storage type micro-fluidic chip and a working method thereof. The chip comprises a chip body, a sealing film and gum, the sealing film and the back adhesive are respectively bonded on the front surface and the back surface of the chip body; a plurality of solution storage cavities are formed in the chip body, outflow runners at the bottoms of the solution storage cavities are connected with corresponding first runners, sealing partition pieces are arranged on the first runners, the outflow runners and the corresponding first runners are blocked through the sealing partition pieces, and the first runners are communicated with second runners through corresponding gum runners. The whole experiment process does not need to be externally connected with a reagent bottle, the purposes of instant detection, preparation and sample separation of the micro-fluidic chip can be achieved, the detection time is greatly shortened, and the experiment steps are simplified. Waste liquid generated in the experiment process flows into the waste liquid cavity, and cross contamination of a laboratory is avoided. The chip provided by the invention can be used for identifying the species of pathogenic microorganisms.

The invention discloses a bearing vibration signal processing model construction method, a model and a signal classification method, which are used for constructing time domain and frequency domain loss functions by calculating a plurality of feature vector distances so as to train an encoder. According to the method, by minimizing the difference value of different distances in the time domain and frequency domain loss functions, an encoder can be guided to extract similar feature representations in signals, so that the similar feature representations are gathered in a feature space, and dissimilar features are separated. When a trained encoder (a convolutional neural network with an attention mechanism) processes a vibration signal of an unknown fault type, some feature representations in the signal are different from those of other signals, so that the feature representations are gathered in a feature space and are far away from the feature representations of other signals, and the fault detection accuracy is improved. Therefore, the recognition of the vibration signal of the unknown fault type is effectively realized.

The invention discloses a pattern recognition method and device for medical gene sequencing data, and belongs to the technical field of data processing.The method comprises the steps that a target gene sequence obtained through sequencing is preprocessed, a self-adaptive scoring matrix is constructed, and site scores are dynamically adjusted through neural network training; by means of the self-adaptive scoring matrix, a sliding window is adopted to scan sequence differences, and a Gaussian mixture model is combined for analysis, and a variation region information file is generated; reading a variation region information file, constructing a base conversion matrix for sequence comparison, and generating a variation classification result by analyzing a comparison path; and based on a variation classification result, performing standardization processing on the variation region feature vector, and generating a mutation mode type through an improved clustering algorithm. Through the scheme of the invention, the filtering quality of the sequence data can be improved, and the capability of processing a complex variation mode is enhanced.

The invention relates to the technical field of visual inspection, in particular to a stamping part defect detection method based on vision, which comprises the following steps: S1, shooting setting; s2, synchronizing video streams; s3, acquiring a part bounding box and a hole position bounding box; s4, associating part bounding boxes belonging to the same stamping part among the frame part images, and associating hole position bounding boxes belonging to the same screw hole position; s5, correspondingly associating the bounding boxes of the target parts belonging to the same stamping part in the part image; s6, when a hole position boundary frame matched with the view angle template image of the corresponding view angle exists in any target detection image under any view angle, determining that a screw hole position exists; and when the matched hole position boundary frame is not found in the target detection image under the corresponding visual angle in the visual angle template images of all the visual angles, the screw hole position is missing. The stamping part defect detection device can be used for defect detection of stamping parts in the conveying process, the consistency of detection results is improved, and the detection efficiency is improved.

The invention provides a method for extracting motion characteristic data. The method comprises the following steps: acquiring kinematics data of a muscle to be detected; establishing a finite element model of the to-be-measured muscle according to the electrical impedance imaging differential voltage signal value of the to-be-measured muscle and the anatomical prior information of the section of the to-be-measured muscle; extracting respective time domain information and frequency domain information in the conductivity data of the plurality of signal nodes from the finite element model; correlation calculation is carried out based on the time domain information, the frequency domain information and the kinematics data, the motion participation degree of each signal node is obtained, and the motion participation degree is used for representing the muscle motion participation degree of each signal node; according to the motion participation degree of each signal node, the finite element model and the conductivity data, motion feature data are obtained, the motion feature data are used for representing spatial position distribution of multiple key muscle units in the to-be-detected muscle, and therefore the morphological features of the to-be-detected muscle can be determined based on the spatial position distribution.

The invention discloses a distributed dynamic load identification method and a distributed dynamic load identification system based on a Transform framework. The method comprises the following steps: constructing a response-excitation data set in a mode of combining simulation and test, calculating a corresponding load, forming a response matrix # imgabs0 #, performing orthogonal polynomial order selection according to the highest order required by orthogonal polynomial decomposition of a load sample in the data set, and calculating an orthogonal polynomial value matrix L; performing model training on a Transform neural network architecture model according to the response matrix # imgabs1 # and the orthogonal polynomial value matrix L, wherein the model mainly comprises an embedded layer, an encoder and a decoder; and inputting a response signal by using the trained network model to obtain a corresponding distributed load. According to the method, the mapping from the limited response signal to the distributed dynamic load is directly constructed, the problem that a traditional distributed dynamic load method is not qualitative in the inversion process is effectively solved, and the method has good precision in identification of various distributed dynamic loads.

The invention relates to a double-fed fan high-low voltage ride-through control parameter identification method and system, and the method comprises the steps: analyzing a high-low voltage ride-through threshold value according to simulation data outputted by a pre-constructed double-fed fan control structured model; the high-low voltage ride-through threshold value is used as a voltage initial value of high-low voltage ride-through control, a high-low voltage ride-through control mode of the double-fed fan is identified, and a high-low voltage ride-through strategy is formulated; and on the basis of a high-low voltage ride-through strategy, identifying each parameter in the control link to obtain each control parameter value in the control link. According to the scheme, a set of complete method for identifying the fault ride-through control mode and the control parameters is formed based on the doubly-fed fan control structured model, and identification of the control parameters can be rapidly and effectively achieved.

The invention discloses an adapter card identification method and device, electronic equipment and a computer readable storage medium, the method and device are applied to a substrate management controller, the substrate management controller and a basic input and output system are both in communication connection with a preset storage medium, and the method comprises the steps that an adapter card physical link information table is traversed, and a target adapter card is determined; determining a target serial number corresponding to the target riser card in the riser card white list; determining a target storage medium channel corresponding to the target serial number in the riser card static information table; reading through the target storage medium channel to obtain a real serial number of the target riser card; if the real serial number is consistent with the target serial number, target static information corresponding to the target serial number in the riser card static information table is written into a preset storage medium to be read by the basic input and output system, and the target riser card is recognized by the basic input and output system. According to the technical scheme, the automatic identification of the adapter card by the basic input / output system in the server is realized through the baseboard management controller.

The invention belongs to the technical field of brake pad production and processing, and discloses a brake pad production and processing friction material surface treatment device and a processing method thereof. According to the brake pad production and machining friction material surface treatment device and the machining method thereof, a base is included, a conveying belt is fixedly installed on the upper surface of the base, the conveying belt is driven through a driving part arranged on the base, and a support is fixedly installed on the upper surface of the base and located on the outer side of the conveying belt; and a placing assembly is arranged on the conveying belt. According to the brake pad production and machining friction material surface treatment device and the machining method thereof, by arranging the clamping assembly and utilizing the stability brought by an internal hydraulic cylinder, certain firmness is provided for workpiece clamping, intelligent workpiece recognition can be achieved through an inductive switch, a clamping element is controlled to conduct automatic clamping, and the machining efficiency is improved. And certain working efficiency is improved.

The invention provides a radiation source signal identification method based on feature matching and mapping learning, and relates to the technical field of mode identification, intelligent information processing and signal identification. In a training stage, firstly, source domain and target domain signals are extracted from a collected radiation source signal full pulse sequence to construct a radiation source signal data set, and time-frequency characteristics Vsf and Vtf are extracted from source domain signal Vs and target domain signal Vt data in the data set respectively; and finally, inputting the P into a signal identification model, learning consistent representation of the P, and realizing a radiation source signal identification task. When a radiation source signal is identified, whether input signal data is empty or not is firstly judged, if the input signal data is empty, the whole process is terminated, if the input signal data is not empty, the input signal is preprocessed, then time-frequency characteristics of the preprocessed signal are extracted, and finally a category prediction result is given through a signal identification model. And jumping to radiation source signal input after the current input signal identification is completed, and ending the whole process until the input data is empty.

The invention provides a method for detecting the deformation quantity of a container, and relates to the technical field of container measurement, and the method comprises the steps: carrying out the whole region division of a to-be-detected container according to the structural characteristics of the to-be-detected container, and obtaining a subregion division result of the to-be-detected container; performing three-dimensional laser scanning on each sub-region in sequence based on a sub-region division result to obtain a three-dimensional point cloud model of the to-be-measured container; and for the three-dimensional point cloud model, projection area fitting and recess depth measurement of a deformation generation area are respectively carried out to obtain a projection area parameter and a deformation depth parameter of the deformation area. In order to solve the problem that deformation parts, especially small deformation, of containers in the petrochemical and refinery fields are difficult to accurately identify and position, according to the method of firstly dividing areas and then dynamically splicing, deformation positions in all divided sub-areas are identified, and then the deformation parts are identified on the whole area of the containers through dynamic splicing of the sub-areas.

The invention discloses a methane and propane component identification method based on laser absorption spectrum gas remote measurement, which comprises the following steps: S1, obtaining harmonic signals of gas to be detected, and searching a wave trough in the range of 100-350 harmonic signals; judging whether the gas to be detected is monopropane or not according to the trough width; when the to-be-detected gas is judged to be monopropane, identification is finished, and S2 is carried out to give a propane concentration value; otherwise, entering the step S3; and S3, determining that the gas is a gas mixture of methane and propane, and judging the ratio of the gas mixture. S4, acquiring a peak-valley difference value of the peak 1 and the peak 2, respectively acquiring a peak 1 concentration coefficient and a peak 2 concentration coefficient according to the peak-valley difference value and the standard gas calibration curve, acquiring the proportion of methane to propane according to the ratio of absolute values of the concentration values acquired by the peak 1 and the peak 2, and entering S4; and S4, respectively giving a methane concentration value and a propane concentration value according to the ratio of methane to propane. The method can realize component identification and accurate concentration detection, and has important guiding significance in the technical field of alkane gas remote measurement.

The present invention provides an intelligent dredging robot and a dredging management method for an elevated bridge deck drainage system, which belongs to the technical field of robots, and specifically comprises: based on the sequence of dredging treatment of drainage systems at different potential danger locations, a plurality of dredging treatment schemes are generated; the treatment time of different potential danger locations under different dredging treatment schemes is determined based on the distribution data of different potential danger locations; the treatment time effectiveness of different dredging treatment schemes and the available treatment schemes under the dredging treatment schemes are determined in combination with the water accumulation risks at different potential danger locations; based on the historical traffic flow data, the treatment time effectiveness and the treatment time effectiveness of the available treatment schemes at different potential danger locations, the dredging treatment schemes among the available treatment schemes are determined; and the dredging treatment schemes and the dredging robots are used to dredge the drainage system at the potential danger location, thereby avoiding the impact of water on the elevated bridge surface on traffic.

This invention provides a method for identifying battery degradation parameters and estimating battery health status, relating to the field of battery health management technology. This invention constructs an explicit physical correlation between the battery's voltage dynamic response and degradation parameters through an electrochemical model; it significantly improves computational efficiency by replacing the complex calculation process in the electrochemical model with a surrogate model based on a neural network; it achieves identification of battery micro-degradation parameters through self-supervised learning of voltage reconstruction based on physical mechanisms; and it estimates battery state of health (SOH) based on these mechanism-transparent micro-degradation parameters, significantly improving the interpretability and accuracy of battery SOH estimation.

This invention discloses a method utilizing nuclear magnetic resonance double-T E The method for classifying water flooding levels in well logging is as follows: First, the transverse relaxation rate of nuclear magnetic resonance (NMR), the geometric mean of the mobile fluid portion of the T2 spectrum, and the double T2 spectrum are calculated sequentially using NMR logging data. E The geometric mean difference of the mobile fluid portion of the T2 spectrum was measured. Then, the porosity of different components was obtained by inversion of echo data under long waiting times and short echo intervals. Finally, the percentage content of macroporous components in the total porosity was plotted against the T2 spectrum. E Cross plot of geometric mean difference of the mobile fluid portion of T2 spectrum and plotting of array induced resistivity AT90 and double T E The cross plot of the geometric mean difference of the mobile fluid portion of the T2 spectrum is measured to identify the flooding level of the flooded layer: unflooded layer, low flooded layer, medium flooded layer, and high flooded layer.

This invention discloses an intelligent voiceprint anti-counterfeiting method, device, and equipment based on hops signals. It collects the voiceprint signals generated by hops hitting the bottle wall, then denoises the signals and extracts PLP features. These feature signals are then input into a CNN-GMM-HMM voiceprint model for training to construct an original standard database. After collecting sample voiceprint signals, they are preprocessed and then matched and identified with the voiceprint information in the standard database to achieve authenticity verification. Compared to traditional identification methods, this method can identify genuine and counterfeit liquor faster and more accurately, greatly improving work efficiency while helping to ensure the quality and safety of liquor.

The invention discloses an enterprise tax sensitive data identification method and device, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining tax data, carrying out the data division, and obtaining first tax data for model training and to-be-identified second tax data; performing data cleaning and processing on the first tax payment data by using a diffusion time estimation network, constructing a diffusion link, inputting the processed tax payment data into the diffusion link, outputting a vector, and performing posterior distribution calculation on the vector to obtain posterior distribution; training a preset mathematical model for sensitive data identification until the model converges; and performing threshold calculation on the first tax payment data by using the converged preset mathematical model to obtain a sensitive data identification threshold, and taking data of which the numerical value is greater than the sensitive data identification threshold in the second tax payment data as enterprise tax payment sensitive data, thereby realizing enterprise tax payment sensitive data identification, and accurately identifying and effectively managing the enterprise tax payment sensitive data. And enterprise data leakage is prevented.

The invention discloses a banknote processing method and device, a storage medium and electronic equipment. Relates to the field of artificial intelligence, and the method comprises the steps: carrying out the image collection of each put banknote under the condition that the banknote is detected to be put into a banknote inlet of a banknote counting machine, and obtaining a banknote image; processing the banknote image through a banknote identification model to obtain banknote information of the banknote image, the banknote information including at least one of the following: a banknote denomination and a region to which the banknote belongs; determining a target anti-counterfeiting detection model matched with the banknote information from a plurality of anti-counterfeiting detection models; performing anti-counterfeiting detection on the banknote image through the target anti-counterfeiting detection model to obtain a first detection result; and based on the first detection result of the banknotes put in the banknote inlet, determining the number and the total amount of the normal banknotes put in the banknote inlet. According to the invention, the problem of low currency counting accuracy in the prior art is solved.

The invention discloses an operator precision verification method and device, a general-purpose computing chip, equipment and a medium. The method comprises the steps that a user computing task instruction in a computing scene is loaded and executed; when a calling instruction for a target interface of a target user operator in the user operator library is executed, real-time task data are extracted, and the target interface is called; based on the real-time task data, obtaining a precision self-checking result according to a first execution result of the programmable computing unit IP on the target user operator and a second execution result of the standard computing unit IP on the target standard operator; and returning the first execution result and the precision self-checking result, and continuing to execute the next task instruction. According to the method, the correctness of the user operator is verified based on user input by synchronously executing the standard operator through the standard calculation unit IP, so that the test process fully covers any user scene, the identification of potential problems is realized, the universality and effectiveness of operator precision verification are improved, and the reliability of a chip is improved.

The utility model relates to a throw-in type high-temperature dew point measuring sensor, which comprises a shell and a crystal resonator, a flange-shaped fixing part is formed on the outer wall of the shell, the shell is limited into an upper part and a lower part by the fixing part, and the lower half part of the shell is inserted into a gas environment or a cavity to be detected; an air chamber for refrigeration is formed in the shell, and the air chamber is provided with an air inlet and an air outlet; the bottom of the shell is provided with a containing cavity communicated with the air chamber, the crystal resonator is fixed in the containing cavity, and the crystal resonator outputs resonant frequency parameters or equivalent electrical parameters of the crystal resonator through a first lead. According to the invention, the method can achieve the judgment of the dew formation moment in a high-temperature environment, and achieves the measurement of the dew point temperature on the basis.

The invention discloses a distribution network automation feeder terminal switching method, system and device with a fault self-diagnosis function and a medium. The method comprises the steps that an original electrical signal is collected through an input interface unit; intermediate information data are obtained through the signal processing module; electrical characteristics are collected through a main control module to obtain an interface type identification result, adaptive adjustment is carried out on the intermediate information data, and an adaptive signal is obtained and output; and acquiring and analyzing the digital signal to obtain device operation state information, performing fault diagnosis in combination with the interface type identification result, and triggering an alarm when abnormality is diagnosed. Intelligent conversion is achieved by integrating multiple interfaces, the problem that equipment interfaces of multiple manufacturers are incompatible is solved, the tedious operation of equipping multiple adapters on site is avoided, the accuracy and stability of cross-interface signal transmission are guaranteed, and the operation reliability, operation and maintenance convenience and system overall safety of the device in a complex environment are improved.

The present invention provides a SAR working mode recognition method, device, equipment and medium. The method includes: obtaining a signal pulse string group of a spaceborne SAR, the signal pulse string group includes at least one signal pulse string; performing data preprocessing on each signal pulse string to obtain an azimuth time-frequency diagram sequence of the signal pulse string group; extracting the intensity variation characteristics along the azimuth time axis from the azimuth time-frequency diagram sequence, the intensity variation characteristics characterizing the receiving power intensity variation characteristics of the SAR working mode; extracting the Doppler time-frequency curve motion characteristics from the azimuth time-frequency diagram sequence, the Doppler time-frequency curve motion characteristics characterizing the Doppler course motion characteristics of the SAR working mode; determining the working mode of the spaceborne SAR according to at least one of the intensity variation characteristics and the Doppler time-frequency curve motion characteristics.

The present application provides a kind of splashing slag protects furnace and jumps slag information identification method, device, equipment and storage medium, the method is by obtaining initial image and to be identified image, based on the initial network model of labeled initial image is trained, obtains the output result of jump slag identification model, the output result includes multiple different labels, different label in output result is assigned to different pixel value, to obtain target jump slag image, identify target jump slag area in target jump slag image, to obtain the jump slag information of to be identified image;By image construction image recognition model, the intelligent identification of to be identified jump slag image is carried out, realizes the identification of converter jump slag particle, improves its identification accuracy, reduces the input of manpower, and provides reliable data source for other intelligent processing.

This application provides a data redundancy identification method, apparatus, electronic device, and storage medium. The data redundancy identification method includes: obtaining fields to be identified from at least two data tables in a preset database; obtaining the support level of the fields to be identified; and identifying reasonable redundant fields among the fields to be identified based on the support level of the fields to be identified, wherein reasonable redundant fields refer to fields whose frequency of occurrence is higher than a preset value. This application implements the identification of reasonable redundant fields.

The invention relates to the technical field of image data processing, in particular to an intelligent identification method and system for an osteoporosis area, and the method comprises the following steps: extracting edge sharpness and texture uniformity parameters from a two-dimensional image, adjusting the gray threshold of the image, and segmenting an osteoporosis candidate area. According to the invention, through integration of image data processing and three-dimensional modeling, the ability of analyzing the osteoporosis area is improved. When a two-dimensional image is processed, edge sharpness and texture uniformity parameters are extracted, and a gray threshold is adjusted, so that an osteoporosis candidate region is identified, a foreground and a background in the image are isolated, and a segmentation result is further refined by using intensity difference and connectivity between pixel points; therefore, high-precision data are provided in bone tissue structure and bone mineral density analysis. And the two-dimensional image is converted into the three-dimensional voxels, and the three-dimensional model is constructed, so that the analysis depth is increased, and the evaluation of the structural stability and the stress point is more comprehensive.

The invention belongs to the technical field of mechanical intelligent operation and maintenance, and discloses a mechanical equipment fault identification method and device and a storage medium in the technical field of mechanical equipment intelligent operation and maintenance, and the method comprises the steps: extracting a domain global feature of a vibration signal, extracting an intra-domain classification feature of the domain global feature through an intra-domain classification feature extractor, extracting inter-domain classification features of the vibration signals through the inter-domain feature extractor, performing numerical value addition operation on the inter-domain classification features and the intra-domain classification features to obtain recombination classification features, and inputting the inter-domain classification features, the intra-domain classification features and the recombination classification features into a fault classifier to obtain a fault type prediction probability; and obtaining a corresponding fault type ordinal number through the maximum value of the fault type prediction probability, thereby realizing identification of the fault type. According to the invention, the technical problem that the fault identification accuracy of the mechanical equipment is reduced due to the fact that a novel fault is wrongly identified as an existing fault type can be solved.

The utility model belongs to the technical field of license plate recognition equipment, particularly relates to intelligent community license plate recognition equipment, and aims to solve the problems of poor environmental adaptability, low recognition rate and insufficient control flexibility of traditional equipment proposed in the background technology, and provides the following scheme: the intelligent community license plate recognition equipment comprises a box body, and the outer wall of the top of the box body is fixedly connected with a binocular camera through a screw; stand columns are installed on the outer wall of the bottom of the box. Through cooperation of the millimeter-wave radar, the binocular camera, the temperature and humidity sensor and the illumination intensity sensor, high-precision license plate recognition under all-weather and multi-environment conditions is realized, the misjudgment rate is reduced, the license plate recognition device has the characteristic of multi-mode environment perception, the sliding structure of the stop lever and the compensation lever is matched with the fastening knob, the length of the stop lever can be flexibly adjusted, and the reliability of the license plate recognition device is improved. And moreover, the mounting sleeve, the protective cover plate and the box body are integrally designed, so that the wind resistance, dust prevention and weather resistance of the equipment are enhanced, and the service life is prolonged.

This invention discloses an early warning method and system for high and low voltage switchgear, belonging to the field of power system automation. The method includes: constructing a multi-dimensional sensing network to collect multi-source heterogeneous data such as temperature rise, partial discharge, mechanical characteristics, and environmental conditions in real time; preprocessing the data and aligning it with spatiotemporal features, and dynamically correcting the sensitivity threshold of the partial discharge criterion based on humidity or pressure change rate; extracting multi-dimensional fault feature vectors and dynamically assigning weights to each component according to operating conditions and environmental conditions; inputting the weighted feature vectors into a long short-term memory network integrating an attention mechanism to generate a multi-timescale predicted trajectory of the operating state; comparing the predicted trajectory with preset multi-level state thresholds in real time, and executing a three-level early warning judgment (attention, abnormal, and severe) and corresponding linkage protection strategies. This invention can reduce the risk of false alarms and missed alarms, providing reliable support for the intelligent operation and maintenance of high and low voltage switchgear.