19results about How to "Solving recognition difficulties" patented technology

The invention relates to the technical field of micro-droplet detection, solves the technical problem that in the prior art, micro-droplets in multiple working condition states cannot be accurately recognized, and particularly relates to a color feature-based micro-droplet image recognition method and a micro-droplet observation device. The micro-droplet image recognition method comprises the following steps: acquiring video image data of a micro-channel in a micro-fluidic chip in an optimal visual field of a color high-speed industrial camera, and converting the video image data into an HSV color space to obtain color images of micro-droplets with different color features; and judging the attribution of the segmented region according to the area of the micro-droplet region and the characteristics of the approximate circle. According to the method, searching and positioning can be carried out based on the color features of the micro-droplets, good robustness is achieved for other complex non-target edge features such as noise, shielding and bubbles, even multiple targets with similar shapes and areas can be accurately recognized, and the method can be well suitable for multi-channel color images.

The invention belongs to the field of low-voltage power distribution of an electric power system, and discloses a multi-level low-voltage transformer area topology identification method based on linear regression and a related system. The connection relation between each meter box and a branch node, a main line and a transformer in a low-voltage transformer area can be identified more accurately, and identification errors are reduced. According to the method, the complex connection relation between the meter box and the branch nodes can be effectively processed and analyzed by using the multi-scale similarity measurement and hierarchical clustering method, and the problem that the traditional method is difficult to identify when facing a complex topological structure is solved. According to the invention, by automatically acquiring and processing the voltage load data, the current data and the correlation information thereof, manual intervention can be greatly reduced, and the efficiency of topology identification is improved.

The invention discloses a time sequence change extraction and mutation detection method for a water area of a reservoir in a plain farmland area. Acquiring a spatial position and a historical maximum water area range of the target reservoir; preprocessing the Sentinel-1 radar image covering the maximum water area of the reservoir and the surrounding land area, and then carrying out water body crude extraction; removing farmland elements by using optical land utilization data matched with the radar image, and removing mountain shadows; abnormal values are removed through sliding window detection, intra-month mean value synthesis is carried out on non-abnormal values, and a monthly water body area time sequence of the reservoir is obtained; and performing mutation detection by using a PELT algorithm, and identifying a mutation time point of water body change. The method does not depend on field observation data, efficiently monitors the water body area change of the reservoir in the plain farmland area, analyzes the response characteristics of extreme events, adapts to a complex environment, timely identifies the water body area sudden change time point, contributes to water resource management and protection, and ensures the safety of regional drinking water resources.

The present invention discloses an intelligent identification system for exhibition information, comprising an exhibit data acquisition unit, a data adaptive processing unit and an identification information output unit. The present invention relates to the technical field of exhibition information identification, and solves the technical problems that information identification cannot be performed according to different situations, resulting in low overall identification accuracy, and secondly, no reasonable planning of user routes, which reduces user experience and wastes user time. The present invention identifies information according to different situations and identifies it in combination with historical records, thereby avoiding identification difficulties caused by inaccurate information provision and further improving the overall identification accuracy. Secondly, the system automatically generates corresponding route information according to the data information of the identification result, and the generated route information can provide a clear direction for the user, thereby reducing the user's search time and further improving the user's use experience.

The invention provides a multi-round dialogue intention classification method and device, and the method comprises the steps: defining and extracting a single-valued slot which is a core keyword contained in an FAQ intention; constructing intention clusters based on the single-valued slots, wherein the intention clusters of the same entity type and the intention clusters of the same single-valued slots are constructed; a single-valued slot inheriting slot mechanism is introduced into multiple rounds of dialogues, and different dynamic score adding strategies are performed on the intention clusters of the same entity type and the intention clusters with the same single-valued slot based on the recognition result that the hit intention has the single-valued slot; and performing intention classification again according to the intention score after score addition. The intention recognition accuracy and effectiveness can be improved.

The invention discloses a network illegal transaction relation prediction method and system based on association rules, and relates to the technical field of information text analysis, and the method comprises the steps: collecting the related information information of a network illegal transaction suspect, carrying out the text preprocessing, and making a data set; based on the data set, calculating an association degree between words and marked vocabularies in the data set, and obtaining a latent language in a communication process of the two transaction parties; and based on the obtained latent language, performing semantic similarity calculation to judge a potential suspect, judging whether the suspect is a potential core person or not according to the communication active degree, and determining a character relationship network. According to the method, the latent language used in the transaction process can be effectively recognized, the recognition difficulty caused by the use of the latent language in illegal transactions can be overcome, and the efficiency and accuracy of discovering and fighting against network illegal transactions by law enforcement departments are remarkably improved. Adjustment and optimization are carried out according to different types of illegal transactions, and the method has important practical significance in maintaining network space order, fighting against network crimes and protecting public benefits.

The present invention relates to the field of remote sensing and telemetry identification technology, and more specifically, to a vegetation type identification and classification method, system, and medium based on remote sensing and telemetry data. The method provided by the present invention mainly includes determining the target area that needs to be identified by vegetation type through telemetry image data, judging it as a green vegetation area or a non-green vegetation area, and when the target area is a green vegetation area, further obtaining the average height of the vegetation in the unit to be identified through a three-dimensional model, and obtaining the basic parameters of the unit to be identified to establish a second identification model, and outputting the third judgment result of forest vegetation or grassland vegetation through the second identification model. The above method solves the problem of the difficulty in identifying forest vegetation or grassland vegetation to a certain extent, and provides a reference basis for relevant staff in preliminary vegetation survey work.

The invention relates to a cattle face recognition method. The method comprises the steps of obtaining a to-be-recognized target cattle face image; extracting a feature vector of the target cattle face image through the trained cattle face feature extraction model based on machine learning; comparing the feature vector of the target cattle face image with the feature vector of at least one prior cattle face image of which the category is known, and determining the category of the target cattle face image according to a comparison result; wherein the cattle face images of the same type belong to the same cattle; the cattle face feature extraction model comprises a convolution layer, a first dense layer, a first transition layer, a second dense layer, a second transition layer, a third dense layer and an average pooling layer which are connected in sequence, the input of the convolution layer is a target cattle face image, and the output of the average pooling layer is a feature vector of the target cattle face image. Through the cattle face recognition method, the category of the cattle face image can be accurately recognized, and then whether the cattle face image belongs to the target cattle or not can be accurately judged. The problem that a cattle face recognition method is low in recognition accuracy is solved.

The application discloses a live working anti-external damage monitoring system and method, relates to the technical field of power operation safety protection, and the system comprises a laser radar, an electric field sensor, a main control module and a strong magnetic force holder. The bottom of the strong magnetic force holder is provided with a strong magnetic force adsorption disc, the top is provided with a mounting platform, the inside is provided with a horizontal shaft motor and a vertical shaft motor, and the side is provided with a data interface and a power supply interface. The laser radar is connected with the strong magnetic force holder through the mounting platform and is used for collecting laser point cloud data. The electric field sensor is connected with the strong magnetic force holder through the mounting platform and is used for collecting electric field fluctuation data. The main control module is connected with the strong magnetic force holder through a shielding cable and is used for acquiring operation area data and performing anti-external damage monitoring according to the operation area data. Through the cooperative matching of the laser radar, the electric field sensor, the main control module and the strong magnetic force holder, effective anti-external damage monitoring of the operation area during live working can be realized, and the safety of live working is improved.

The invention discloses an object recognition method, device and equipment and a medium, and the method comprises the steps: obtaining a shot image, and carrying out the preprocessing of the shot image, and the preprocessing comprises at least one of denoising, image enhancement and cutting; on the basis of a trained feature extractor, feature extraction is carried out on an object to be recognized in the preprocessed shot image, local features and global features are obtained, the local features are obtained through a convolutional neural network of the feature extractor, and the global features are obtained through a Transform network of the feature extractor; carrying out feature fusion on the local features and the global features to obtain a comprehensive feature vector; and carrying out similarity calculation on the comprehensive feature vector and a feature vector of a known object so as to identify the to-be-identified object. According to the object recognition method, the convolutional neural network and the Transform are combined to fuse the advantages of the convolutional neural network and the Transform, so that the image features are extracted more comprehensively and greatly, and the accuracy of object recognition is improved.

The invention belongs to the technical field of dam termite nest detection and termite comprehensive control, and provides a complete method for dam termite cave positioning, termite killing and termite cave blocking. Comprising the steps of C-Scan profile acquisition, deep convolutional neural network inversion, artificial intelligence neural network discrimination, drilling position selection, drilling, video stream signal acquisition, video stream signal analysis, termite dosing and killing, cement clay mortar preparation and ant nest grouting. According to the invention, through C-Scan profile acquisition, deep convolutional neural network inversion, artificial intelligence neural network discrimination and drilling position selection, the accuracy and efficiency of ant nest detection and identification are improved; by controlling the monitoring platform and heating the volatile medicament capsule, the damage of liquid medicament permeation or other methods to the surrounding soil environment is avoided, and the effective killing of the whole space distribution of the termite nest is realized; the glass beads and ettringite are doped into the silicate superfine cement, so that efficient plugging of dam ant nest grouting is realized.

The present invention relates to an auxiliary analysis system for wiring faults in power grid stations, which belongs to the technical field of fault analysis and solves the problem of low efficiency of existing wiring fault analysis. It includes: a station drawing import module for importing the original image of the power grid station drawing, segmenting the original image to obtain each wiring diagram, and obtaining the image to be detected and the auxiliary image based on each wiring diagram; a wiring data recognition module for identifying components and text content and associating them according to each image to be detected and its auxiliary image using multiple neural network models; a wiring topology recognition module for identifying each line segment in each image to be detected, and constructing wiring link information according to the components or line segments connected by the line segments; an auxiliary analysis input module for selecting power grid station drawings, wiring diagrams, component numbers or line numbers; and a fault auxiliary analysis module for analyzing the fault source link and the influencing link from the wiring link information according to the selected component number or line number. The efficiency of fault analysis is improved.

The invention relates to a drawing classification and signature method, device and equipment and a medium, and belongs to the technical field of engineering drawing identification, and the drawing classification and signature method comprises the steps of obtaining a drawing and length and width information of the drawing, carrying out text information extraction on the drawing, obtaining a keyword, and determining coordinates of the keyword based on an established coordinate system. Establishing a feature matrix based on the coordinates of the keywords; determining the lengthening proportion and the mapsheet size of the drawings based on the length and width information, classifying the drawings based on the keywords, the feature matrix, the lengthening proportion and the mapsheet size, and determining the types of the drawings; the signature picture is obtained based on the keyword of the drawing, the feature matrix, the lengthening proportion, the mapsheet size and the drawing type, and the signature area of the drawing is signed based on the signature picture, so that the recognition efficiency and accuracy of the drawing type are improved.

The present disclosure relates to the technical field of chemical warehouse management, and discloses a chemical storage method and a chemical storage system. The central area of the bearing table of the storage system is provided with a rotatable object table for placing the chemical to be stored. A moving assembly is connected to the camera assembly. A first display is provided with a first auxiliary strip and a second auxiliary strip on the first preview area of the first display. The first auxiliary strip is a frame-shaped structure coaxially arranged with the first preview area, and the area of the frame-shaped structure is smaller than the area of the first preview area. The second auxiliary strip overlaps the symmetry axis of the first preview area, and the two ends of the second auxiliary strip are connected to the first auxiliary strip. The chemical storage method and the chemical storage system can extract and compare the packaging box and reagent bottle label information, and filter and enter the parameter value from the packaging box and reagent bottle label information according to the logical relationship and weight rules.

The invention discloses a photovoltaic cleaning robot state recognition system, and relates to the technical field of robot control, in particular to the photovoltaic cleaning robot state recognition system. The system comprises a data processing unit which is used for preprocessing collected original data, and the original data is dual-motor working current. The multi-channel convolution-gating loop unit (MCNN-GRU) is used for taking preprocessed data as input, the self-attention unit (Self-attention) is used for further enhancing features obtained after model processing and paying attention to areas with large fluctuation, and the problem that individual state recognition effects are poor can be solved. According to the method, data are processed through the MCNN-GRU-SA model, the current running state of the robot is obtained, reliable running guarantee can be provided for the unattended photovoltaic cleaning robot, and intelligent monitoring and early fault early warning of equipment are facilitated.

A method, electronic device, medium, and computer product for recognizing hand-drawn flowcharts are disclosed. The method includes: acquiring stroke features corresponding to strokes in the hand-drawn flowchart; inputting the stroke features into a graph neural network model to obtain the predicted categories of the strokes in the hand-drawn flowchart output by the graph neural network model, and symbol features obtained by clustering the stroke features, wherein the graph neural network model is trained from sample stroke features; and determining the category of the symbol features based on the predicted categories of the strokes in the hand-drawn flowchart. Implementing the technical solution provided by this invention can improve the recognition accuracy of symbols in hand-drawn flowcharts and effectively solve the problem of difficulty in recognizing hand-drawn flowcharts in the prior art.

The invention discloses a recognition method of a hand-drawn flow chart, electronic equipment, a medium and a computer product. The method comprises the following steps: acquiring stroke characteristics corresponding to strokes in the hand-drawn flow chart; the stroke features are input into a graph neural network model, prediction categories of strokes in the freehand flow chart output by the graph neural network model and symbol features obtained through stroke feature clustering are obtained, and the graph neural network model is obtained through sample stroke feature training; and determining the category of the symbol feature based on the prediction category of the strokes in the hand-drawn flow chart. By implementing the technical scheme provided by the invention, the recognition precision of the symbols in the hand-painted flow chart can be improved, and the problem that the hand-painted flow chart is difficult to recognize in the prior art is effectively solved.

The present invention provides a pipeline surface bulge detection method and system for rail trains, comprising a pipeline image acquisition step, acquiring a pipeline image of a pipeline system at the bottom of a train by a visual camera; a pipeline abnormality analysis step, analyzing a bulge area on the pipeline surface according to the pipeline image after fluid is introduced, and synchronously mapping the bulge area in the pipeline image after the fluid is extracted; a bulge tightening calculation step, analyzing the difference in pipeline size change after fluid is introduced into the pipeline and after fluid is extracted, and obtaining a pipeline surface tightening value by calculation with a fluid data set; a pipeline crack matching step, obtaining a matching value by calculation of the tightening value of the bulge area on the pipeline surface, the tightening value of the normal area on the pipeline surface, and the fluid data set, comparing the matching value with a preset matching threshold N1, and outputting the crack type according to the comparison result; the present invention has the advantage of avoiding recognition difficulties caused by visual errors or environmental errors, quickly providing accurate and reliable crack conditions, and facilitating subsequent processing solutions.