97 results about "Identification scheme" patented technology

The invention relates to the technical field of electroencephalography, in particular to a sleep staging automatic identification method and system based on multi-mode electroencephalography, and the method comprises the following steps: extracting a waveform period to construct dominant distribution, correcting label boundary positioning mutation response, unifying a time axis to form an alignment structure, clustering mutation fragments to establish an alternating relation, and identifying the sleep staging based on multi-mode electroencephalography. And identifying a main channel range to construct an automatic identification scheme. According to the method, a reference channel is positioned through area distribution difference, a boundary change section is judged in combination with a frequency band energy change trend, a channel region with dense and stable mutation points is extracted as an anchor point, time axis unified adjustment is completed according to main response starting and ending time, and an alternating fragment graph is further constructed through a cross-channel time coverage relation of the mutation points. The sequential structure coordination ability and mutation form aggregation expression efficiency among multi-source signals are improved, and accurate recognition of boundary drift and asynchronous response and dynamic extraction of steady-state rhythm in sleep stage division are achieved.

The invention relates to the technical field of power systems, in particular to a load identification capability comprehensive evaluation method based on fusion decision, which comprises the following steps: constructing an evaluation index system of load identification; constructing a fuzzy cognitive map model, connecting the subjective weight matrixes through a fuzzy relation by adopting a fuzzy cognitive map, and obtaining an optimized weight matrix W'by utilizing fuzzy C-means clustering; entropy calculation is carried out through an entropy weight method, a membership degree value of the to-be-evaluated load identification method is obtained through relation matrix multiplication of a fuzzy comprehensive evaluation analysis method, a final evaluation score is calculated, and a fuzzy comprehensive evaluation result is obtained; by multiplying a comprehensive evaluation value obtained by quantization of a comment set by a normalized fuzzy comprehensive evaluation matrix, a final evaluation score integrating all evaluation index influences can be obtained, and non-intrusive load identification capability can be sorted through the score. Therefore, the optimal load identification scheme can be selected in an auxiliary manner or the existing scheme can be improved.

The invention provides a method for identifying the characteristics of a metal foreign body in a GIS, and the method comprises the steps: building an electric field simulation model, and setting the metal foreign body; a multi-modal sensor is adopted to collect signals generated by partial discharge of the metal foreign matter so as to obtain a multi-modal atlas database; performing multi-domain feature extraction on the multi-modal atlas database, and generating a lightweight fingerprint feature vector through feature dimension reduction; the lightweight fingerprint feature vector sequentially passes through all steps of a combinatorial algorithm to solve identification items of the metal foreign matter; and selecting a globally optimal group of identification items to be combined into a metal foreign matter feature identification scheme. According to the invention, fine analysis and processing are carried out on partial discharge signals through a signal processing and pattern recognition algorithm, so that the detection and classification precision of metal foreign matters can be effectively improved; real-time monitoring and quick response under complex working conditions can be realized, a quantitative basis can be provided for fault early warning and equipment maintenance, and the operation reliability and safety of GIS equipment are further enhanced.

The invention relates to the technical field of construction management, and discloses a construction progress recognition method and system based on image recognition. The method comprises the steps that real-time image data of a construction scene are obtained, a construction progress feature monitoring database mapped with the real-time image data is established, and database monitoring indexes comprise image definition, feature matching degree and the like; configuring a directional recognition target, including key node recognition, construction stage division and the like, and then determining a target classification identifier; key identification parameters are selected through the main identification target and the auxiliary identification target, directional identification target influence analysis is carried out, parameter directional identification is executed after parameter identification constraints are established, and construction progress identification is completed. When parameter directional recognition is executed, a time efficiency and accuracy evaluation function is also established, and a balance adaptation function is constructed to screen recognition schemes. The system comprises a data acquisition module, a target configuration module and the like and can implement the method. Through multi-dimensional analysis and optimization, efficient and accurate recognition of the construction progress is achieved, and the construction progress management requirement is met.

The invention discloses a multiphase flow real-time flow pattern recognition method based on updip pipe pressure and a lightweight deep neural network model, adopts updip pipe pressure data, designs a training strategy, proposes a YI-Net network model, belongs to the field of deep learning, and is used for flow pattern recognition. The method comprises the steps of data preprocessing, model reasoning, loss calculation, back propagation optimization of model parameters and the like. In the data preprocessing process of the training stage, the generalization ability of the model is improved through a data enhancement training strategy, such as Gaussian noise addition, amplitude scaling, overall time translation and the like. The YI-Net model comprises two parts of a backbone network (backbone) and a head (head), and various one-dimensional calculation operations such as one-dimensional convolution and the like are adopted. And the backbone is used for extracting the characteristics of the flow pattern sample. And the head is a Classify layer and is used for determining the category of the backbone output characteristics. The flow pattern recognition scheme provided by the invention has the characteristics of good real-time performance and small parameter quantity, and provides a solution for the field of flow pattern recognition.

The invention relates to the technical field of ore recognition, in particular to a method and a system for quickly and preliminarily recognizing field ore quality of coal-series trilean metal mineral products, which comprises the following steps of: acquiring ore samples and acquiring main peaks, structures and infrared images, extracting image features to generate mutation numbers, analyzing sampling coordinates to reconstruct a connection structure, and calculating the quality of the coal-series trilean metal mineral products. Drawing a closed boundary to judge coordinate attribution, and outputting an ore quality identification scheme. According to the method, main peak reflection, microscopic image layers and infrared spectrum acquisition results are numbered in a unified manner and are subjected to time sequence binding, a synchronous corresponding relation among multi-source images is established, continuous change nodes and sudden change areas are identified in combination with offset tracks and structural features, spatial concentration blocks are generated by using number and coordinate mapping, and a connected structure is reconstructed; and drawing a closed boundary according to the track to form a region classification basis, verifying a mass change direction through image sequence coherence, and outputting a tagging identification result, thereby realizing rapid positioning and accurate classification of the field ore sample.

The invention relates to the technical field of defect identification, in particular to a braided tube appearance defect detection method and system based on machine vision, and the method comprises the following steps: obtaining an image to generate a brightness fracture region, calculating a peak difference to generate a structure abnormal region, extracting a light spot path to identify a burr abnormal region, and analyzing a grid texture to generate a deformation identification region. And a defect identification scheme is obtained by combining gray scale and jump. According to the method, a brightness fracture area is extracted through gray difference symbol change, segmentation of image jump features is realized, a structure dislocation area is positioned through symmetrical wave crest difference, a form mutation zone is revealed through light spot extension and edge consistency comparison, and burr identification stability is improved. Local trend abnormity is judged through texture length and cross frequency quantification, and a multi-dimensional standard is constructed by combining gray span and disturbance frequency screening, so that defect features are clearly presented in a detail level, and the recognition precision and stability in a complex surface environment are ensured.

The invention discloses an open source environment software hidden vulnerability patch identification method, which is based on a multi-stage architecture and collaborative relationship modeling, and provides a new patch group identification scheme: firstly, obtaining candidate code submission from an open source warehouse, extracting correlation characteristics of vulnerabilities and candidate code submission, including rule-based characteristics and semantic characteristics, calculating a correlation score and screening high-correlation submission; pairwise pairing the high-correlation submissions, and fusing multi-dimensional features to predict a cooperative relationship between the submissions; and finally, constructing an undirected graph based on the correlation score, dividing a maximum connected sub-graph, fusing the features in the group through maximum pooling, calculating the correlation with the vulnerability, and outputting an optimal patch group. Meanwhile, an existing patch identification technology based on sorting learning is combined, an enhancement method based on a submission cooperative relation is provided, ranking logic submitted by candidate codes is updated through internal association between code submission and by means of correlation between group vectors and vulnerabilities, and the identification precision in a multi-patch scene is improved.

The invention belongs to the technical field of motor control, and particularly relates to a permanent magnet synchronous motor parameter identification method, which comprises the following steps of: acquiring voltage, current and speed data of a motor in two states that direct-axis current is equal to 0 and not equal to 0 in the same period as input characteristic quantities through a permanent magnet synchronous motor vector control system; synchronously collecting motor stator resistance, direct-axis inductance, quadrature-axis inductance and permanent magnet flux linkage as output characteristic quantities for training a back propagation neural network model; and after reverse neural network training is completed, motor parameter identification is completed by inputting related characteristic quantities in real time. According to the method, the training efficiency and generalization ability of the neural network model are improved, the problems that a traditional parameter identification scheme depends on high modeling precision and low identification precision are solved, and the method is suitable for parameter identification behaviors of the permanent magnet synchronous motor under various working conditions. The invention also provides a non-transitory readable recording medium storing the program of the method and a system comprising the medium, and the program can be called through a processing circuit to execute the method.

The invention discloses a five-axis machine tool geometric error identification method based on single-axis and multi-axis measurement, and belongs to the field of numerical control machine tool precision detection. Error kinematics and multi-axis motion models are established based on a multi-body system theory, and error identification models of a linear axis and a rotating axis are respectively constructed. On the aspect of a linear axis, an identification method based on measurement of a positioning error and a straightness error is provided, two measurement / identification schemes of single-axis motion and two-axis linkage are designed, and 18 linear axis geometric error parameters related to the position and 3 linear axis geometric error parameters irrelevant to the position are obtained through calculation. In the aspect of a rotating shaft, 12 rotating shaft geometric error parameters related to the position and 4 rotating shaft geometric error parameters irrelevant to the position are obtained on the basis of an existing linear shaft result based on a measurement and identification scheme of a double-ball-rod instrument in three directions and six positions. According to the method, positioning and straightness error detection are completed in combination with auxiliary equipment such as a laser interferometer and a double-ball-rod instrument, and the method has good universality.

The present invention provides a fault diagnosis method and system for an intelligent water jet loom, belonging to the technical field of data processing, specifically including: determining a reference diagnosis model for the remaining textile type by using the similarity between the remaining textile type and the control parameters of different reference types, determining the identification deviation situation of the fault types in different dimensions based on the identification results of the reference diagnosis model for the matching data of the remaining textile type, and when it is determined that there is no reference diagnosis model with the identification reliability meeting the requirements by combining the matching data of the fault types in different dimensions, performing a combined processing on the reference diagnosis models to generate multiple groups of identification schemes, determining the identification schemes by using the identification deviation situations of the fault types in different dimensions of the remaining textile type for different identification schemes, and performing a fault identification process on the remaining textile type by using the selected identification schemes, ensuring the accuracy of the fault diagnosis results of the water jet loom.

The invention provides a logistics management system and method and a storage medium, and belongs to the technical field of logistics management, and the method specifically comprises the steps: carrying out the determination of an updating recognition scheme of a license plate in the logistics management system according to the license plate data of temporary authority in different dates, on the basis of the updating identification scheme and the similar condition of the license plate and other license plates in the logistics management system, determining the updated required license plate in the license plates, and updating the similar condition between the required license plates and the similar license plate data of the required license plates in the logistics management system; and the permission adjustment strategy for updating the required license plate is determined, so that the security of license plate management is improved.

A method for securely starting device software is provided, in particular an operating system, of an electronic device, wherein a plurality of successive software modules which contain software code are run by the device. The method has the steps of: a) running the first software module, b) loading the subsequent software module using the preceding software module, c) checking the software code of the subsequent software module and identifying security features using an identification scheme, d) evaluating the identified security features using a security, e) running the subsequent software module if the evaluation results in a value of trustworthiness that lies above a specified threshold, and f) running steps b) to e) for each of the subsequent software modules.

The embodiment of the invention discloses a target recognition method, device and equipment, a medium and a computer program product. The method comprises the steps of obtaining continuous multi-frame point cloud data corresponding to each target object in a target space; dividing the point cloud data corresponding to the target range according to the center of the point cloud data of each target object in each frame; based on the point cloud features corresponding to the divided point cloud data, generating target features corresponding to each target object in each frame; and performing target identification processing on each target object based on the target features to obtain a target identification result. According to the target identification scheme provided by the invention, the point cloud is associated with the target object, so that each target has exclusive point cloud information, identification is carried out according to the point cloud information of each target, the effect of tracking and identifying each target in the space is realized, multiple targets in the space are accurately identified, and the accuracy of target identification is improved.

This application discloses a method, apparatus, electronic device, medium, and product for identifying household customers based on a federated model, relating to the field of federated learning technology. The method for identifying household customers based on a federated model includes: collecting raw customer data, wherein the raw customer data includes at least one of the following: operator data, social data, e-commerce data, vehicle usage data, location data, or smart home data; inputting the raw customer data into a preset household group identification federated model to obtain multiple sets of household group data; inputting each household group data into a preset high-value customer identification federated model to obtain high-value customer identification results, wherein the high-value customer identification results include high-value tags corresponding to the household group data. The technical solution of this application solves the problems of low accuracy and data security risks in current identification schemes for high-value household customers.

The invention discloses a nuclear power intelligent labeling platform construction method based on semantic understanding, and relates to the technical field of data processing, and the method comprises the steps: determining a classification recognition scheme of a nuclear power file format; performing manual annotation to form annotated sample data; establishing an annotation task identification model, forming an annotation task decomposition mechanism, and forming an annotation precision setting mechanism; forming an annotation implementation mechanism, and integrating the task recognition model, an annotation task decomposition mechanism and an annotation precision setting mechanism into a nuclear power intelligent annotation platform; after iterative training, completing the construction of the nuclear power intelligent labeling platform; and after format recognition, the nuclear power intelligent labeling platform labels contents of different formats respectively. By forming a classification identification scheme, an annotation task identification model, an annotation task decomposition mechanism, an annotation precision setting mechanism and an annotation implementation mechanism, annotation is determined by using different modes based on text identification of different formats, and different annotations are performed according to different annotation requirements.

The present invention provides a method for distance segment III protection with automatic dispatching correction, which is a method for distance segment III protection integrating DC control information. The method first analyzes the problem of misoperation of AC side distance segment III protection caused by DC line removal; then proposes a flow transfer identification scheme integrating DC control information; finally, according to the flow transfer identification result, a revised distance segment III protection criterion is proposed. The present invention solves the problem of misoperation of existing AC distance protection caused by DC line removal, and reduces the probability of major power outages.

The invention discloses a multi-industrial equipment-oriented ELM fault identification method fusing feature attention and manual LU decomposition, and belongs to the technical field of industrial equipment fault diagnosis. The core of the method is that a feature attention mechanism and a manual LU decomposition module are fused: firstly, the feature contribution degree is quantified through a channel attention mechanism, key fault features are automatically focused, weak correlation feature interference is inhibited, and the quality of a hidden layer output matrix is improved; secondly, the manual LU decomposition module realizes autonomous controllability and numerical stability of matrix inverse operation through row principal component selection and regularization optimization, and is separated from the dependence of a third-party math library. According to the method, the accuracy of equipment fault identification is remarkably improved, the omission ratio and the false alarm rate are effectively reduced, memory limitation of edge equipment is overcome, and an efficient and reliable real-time identification scheme is provided for bearing abrasion and gearbox abnormity of a wind power variable pitch system and faults of other industrial equipment such as a machine tool and an industrial motor.

This invention discloses a method, system, and medium for measuring stress and earth pressure behind retaining walls / piles under localized overload. Addressing the shortcomings of existing technologies in simultaneously considering stress or earth pressure measurement under localized overload and rotational displacement of retaining walls / piles, this invention first solves the problem of measuring additional vertical stress behind retaining walls / piles under localized overload. The calculation model is constructed by combining theoretical derivation of the mechanical response of a semi-infinite elastic body surface subjected to a concentrated normal force with experimental test data analysis. Secondly, the invention solves the problem of measuring earth pressure behind retaining walls / piles under localized overload. The optimized scheme provides identification schemes for eight measurement conditions under five types of rotational displacement modes of retaining walls / piles, as well as non-limit active earth pressure measurement schemes under each measurement condition. The measurement method of this invention has a clear calculation model, easily obtainable parameters, and high engineering applicability. This invention also provides a force measurement system based on the measurement method and a medium for carrying the measurement method.

The invention discloses an aquatic organism resource identification and traceability method and system based on image identification and deep learning, and is applied to the technical field of aquatic organism resource traceability. The method comprises the following steps: obtaining an image of an aquatic organism resource, sequentially carrying out ROI region extraction, attitude standardization and key point coordinate extraction and identification, establishing a data file of the whole life cycle of the aquatic organism resource, and storing traceability information to a cloud platform; and assigning a unique identifier to each batch of aquatic organism resources and associating the unique identifier with the traceability information, reading all the traceability information of the corresponding batch of aquatic organism resources through the identification identifier, performing identification through the shot image, and determining whether the identification information is consistent with the traceability information recorded in the traceability information. According to the method, the overall calculation amount is greatly reduced through a staged identification scheme, so that real-time processing in the aquatic organism resource transaction process is easier to realize, and the traceability information is ensured to be real and reliable in combination with a data verification technology.

The invention provides a verification code recognition method, system and device and a storage medium, and the method comprises the steps: obtaining a verification code type based on a verification code recognition request, and recognizing a layout parameter of a to-be-recognized region in an image verification code if the verification code type comprises the image verification code; and if the layout parameter satisfies a first parameter condition, performing target object identification on the image verification code by using a vision-based image identification model to obtain a target object identification result, and if the layout parameter satisfies a second parameter condition, performing target object identification on the image verification code by using a target detection algorithm to obtain a target object identification result. And if the verification code type comprises a voice verification code, recognizing the voice verification code by using a localized voice recognition model, and then simulating a corresponding user operation as a response verification code recognition request. According to the verification code identification scheme provided by the embodiment of the invention, the appropriate verification code identification strategy can be automatically selected according to the verification code type, so that the verification code identification accuracy is improved.

The invention discloses a large language model intellectual property protection method and device based on domain-specific prejudice feature fingerprints, and belongs to the cross technical field of artificial intelligence safety and intellectual property protection. The method specifically comprises the steps that a three-dimensional orthogonal prejudice data set is constructed, and the data set depicts model prejudice features from the three dimensions of the field, the prejudice type and the response mode; screening the three-dimensional orthogonal prejudice data set based on a two-stage screening mechanism so as to extract core sample data with intra-family high stability and inter-family high discrimination; performing domain-specific prejudice feature fingerprint extraction based on the core sample data to obtain a standardized reference fingerprint; and performing ownership judgment on the to-be-tested model by adopting a confrontation verification mechanism based on the standardized reference fingerprint. According to the method, a quantifiable and high-robustness ownership identification scheme can be provided for the closed-source large language model, and the method has important application value for solving the safety problems of infringement and embezzlement of intellectual property of the large language model and the like.

The invention discloses an XGBoost algorithm-based direct-current source load-containing low-voltage system selective protection method. The method comprises the following steps of: extracting a fifth-scale detail component dd5 of a current signal in real time; comparing the value of the fifth scale detail component with a preset threshold, and only uploading threshold exceeding data; constructing a feature vector based on the uploaded data, and inputting the feature vector into an XGBoost model to locate an alternating current side fault; and the direct current side directly triggers the circuit breaker to open through local threshold comparison. An alternating current system fault position identification scheme based on an XGBoost algorithm is combined with a direct current system in-situ protection scheme based on short circuit fault early detection to form a low-voltage alternating current and direct current system selective protection method. The method is beneficial to improving the rapidity, sensitivity and selectivity of low-voltage AC / DC system protection.

The utility model provides a light source group, an image receiver, an image acquisition device and electronic equipment, and relates to the technical field of biological recognition, the light source group comprises at least two light sources, each light source is used for emitting polarized light with the polarization degree not equal to 0, and the polarized light emitted by the light source group has at least two polarization directions; the polarized light in at least two polarization directions is reflected by the target object and then carries polarization information corresponding to the contour surface of the target object to sequentially enter the image sensor. The polarized light emitted by each light source in the light source group is optimized, so that the structure of the light source group is simplified, when the light source group is applied to three-dimensional imaging, target object recognition or anti-counterfeiting recognition and other schemes, the schemes can be simplified, and accurate face contour reconstruction and recognition precision improvement are realized with a relatively simple scheme and relatively low cost.

The embodiment of the invention discloses an induction illumination recognition system. The induction illumination identification system comprises a passive label and an illumination end. The lighting end is configured to transmit an identification signal to a target lighting area in real time; the passive tag is pasted on a person or an object needing to be recognized, and the passive tag is configured to be activated after receiving a recognition signal sent by the lighting end and send a response signal to the lighting end; the lighting end is further configured to provide a lighting function after receiving the response signal sent by the passive tag. According to the embodiment of the invention, the problems that the existing induction illumination system is easily interfered by the outside and is poor in stability are solved, an illumination induction identification scheme without an active transmitting device is provided, the energy consumption is effectively reduced, and the use convenience is improved.

The invention provides a load scheduling processing method and system for a zero-carbon park, and belongs to the technical field of zero-carbon parks, and the method specifically comprises the steps: taking interaction types under different weather types, adjustment matching conditions of a user load in different time periods, and interval time period data of an internet surfing time period as a basis; the method comprises the following steps: determining an update identification scheme of a load scheduling strategy under a weather type when a settled enterprise in a park changes, determining an update processing scheme of the load scheduling strategy under different weather types based on the update identification scheme and a coincidence deviation condition of load data of a newly settled enterprise and an internet surfing time period, and updating the load scheduling strategy under the load scheduling strategy under the different weather types based on the update processing scheme and the coincidence deviation condition of the load data and the internet surfing time period of the newly settled enterprise. And the carbon emission of the park is reduced.

The application belongs to the technical field of image processing, and particularly relates to a crack image segmentation method and system. The robustness of a model to noise is improved by introducing Gaussian noise into image data for enhancement processing. An encoder composed of three layers of progressive stacked residual units is used to extract crack features, and a DropBlock regularization module layer is used to reduce feature redundancy and enhance feature diversity. The image space resolution of the crack features is restored by a decoder symmetrical to the encoder, and the fine structure of the crack boundary is restored. Finally, a dense conditional random field is used to optimize the crack probability distribution map, and the final crack segmentation map is output. The application effectively alleviates the foreground-background class imbalance problem, reduces false positives and false negatives, improves the segmentation performance of the model, and is particularly suitable for deployment on edge devices such as unmanned aerial vehicles and mobile phones, providing a high-precision, low-complexity and strong-robustness crack identification scheme for infrastructure health monitoring.

The invention discloses an aircraft assembly drilling process verification method based on product structural characteristics, and the method comprises the steps: presetting a preassembling scheme based on the product structural characteristics, and adjusting the connection mode and connection spacing of a pre-connection fastener to guarantee the overall stability after preassembling; based on the component assembling scheme, an identification scheme of the pre-assembled fastening nails is reasonably selected and matched; an identification track of the preassembled fastening nail is formulated based on a path optimal principle, and positioning of a product structure form under a global coordinate system is completed; manufacturing indexes of different working condition differences of all hole sites are determined based on aircraft assembly hole manufacturing process correlation parameters; presetting a test working condition based on the manufacturing index of a certain hole site, and enabling the current test working condition to be equivalent to the product working condition; step-by-step optimization is carried out on a hole making process parameter domain based on a test panel test, an optimization result is directionally migrated to product hole making, and process parameter making is completed; and a process strategy is called according to the accumulated information, and hole making of the complete hole making molded surface is completed, so that the correctness of equipment after wire entering is ensured, and the suitability of the process strategy is improved.

The present application provides a defense method, device, and processing equipment for intranet segment detection, which is used to provide a more refined identification scheme for intranet segment detection, thereby achieving an effective and stable defense effect and ensuring network security. The defense method for intranet segment detection provided by the present application includes: obtaining target network traffic with a traffic direction of a preset intranet segment from different network traffic; for the target network traffic, based on the three dimensions of the number of session packets, the load length, and the number of target segments, analyzing whether the target network traffic has intranet segment detection behavior; if so, responding to the target network traffic.

This invention defines a symbolic identity and trust protocol for wearable AGI agents, establishing a real-time, consent-anchored passport system that replaces centralized biometric or crypto-based identification schemes. Each passport instance contains a symbolic identity graph linked to cognitive signatures, ethics verifications, and mission context. Consent tokens are issued and revoked dynamically, with real-time swarm synchronization across agents and devices. All interactions—including memory access, network sync, and sensor control—require verification via these passports, enforced through zero-knowledge proofs and treaty-aligned arbitration. The system fully prevents the emergence of exploitative, opaque, or surveillance-based identity regimes like Worldcoin or Meta-linked AGI tokens. Identity becomes sovereign, ethical, and cognitively validated.