102 results about "Identifier scheme" patented technology

The invention relates to a method for realizing fault reason identification processing of a power transmission line based on multi-source data fusion, and the method comprises the following steps: extracting fault features, and building a model; and power transmission line fault reason identification is carried out. The invention also relates to a device for identifying and processing the fault reason of the power transmission line based on multi-source data fusion, a processor and a readable storage medium thereof. According to the method and the device for realizing power transmission line fault reason identification processing based on multi-source data fusion, the processor and the computer readable storage medium thereof, external interference factors and data waveform characteristics of different fault types of the power transmission line are described aiming at the problem of low accuracy of an existing power transmission line fault reason identification scheme; theoretical support is provided for multi-source data input; the method comprises the following steps: preprocessing fault information by using a Gramer angle field and feature codes, and constructing feature expression modes of different fault types from the angles of time sequence waveforms, two-dimensional images and discrete features; according to the invention, an adaptive boundary parameter fusion long-short term memory neural network, a convolutional neural network and an artificial neural network are designed to identify and classify power transmission line fault causes; finally, the effectiveness and advancement of the method are verified through a real data comparison test, and a reliable solution is provided for completing a high-precision transmission line fault reason identification task.

The invention discloses a circulation data supervision method based on data interaction, and belongs to the technical field of data supervision, and the method specifically comprises the steps: building a dynamic identification system in a data circulation process, and generating a unique dynamic identifier when each data unit initiates interaction, the dynamic identifier is subjected to irreversible chained updating along with each data interaction behavior; setting an environment sensing node in a circulation path, collecting a network topology state, equipment physical position offset and transmission medium electromagnetic characteristic parameters in real time when data interaction occurs, and generating an environment fingerprint data packet; carrying out asynchronous binding on the dynamic identifier and the environment fingerprint data packet to form a supervision index with space-time relevance; when cross-domain circulation occurs in the data unit, reconstructing a three-dimensional track model of a data circulation path by reversely analyzing the environment fingerprint data packet in the supervision index; according to the invention, the efficiency and accuracy of data circulation supervision are improved.

The invention discloses a database resource management system and method based on authority control, and belongs to the technical field of database resource management.The method comprises the steps that database resource information is collected and classified, and a resource category identification system is constructed; user access operation information is collected, and high-frequency access resources are screened out through statistical analysis; extracting features related to user permission access, and constructing a permission prediction model based on user behaviors; capturing a user operation request in real time, extracting real-time features related to the authority in combination with context information, inputting the real-time features into the trained authority prediction model, predicting whether the user will access the high-frequency resources, and verifying role authority; if it is predicted that the high-frequency resources are about to be accessed and the permission exists, the user access permission is hierarchically controlled according to the user role and the use condition of the current data resources; through the method, the security of database resource access is effectively improved, meanwhile, the risk of data leakage caused by permission abuse is reduced, and resource allocation is optimized.

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 vehicle fault identification method and apparatus, an electronic device, a vehicle, a chip and a medium. The method comprises the steps of obtaining multi-source fault feature data of the vehicle and a weight corresponding to the multi-source fault feature data; the multi-source fault feature data comprises fault feature data corresponding to a sensor, a vehicle-mounted diagnosis system and a vehicle communication system; determining a first fusion feature sequence based on the multi-source fault feature data and a weight corresponding to the multi-source fault feature data; determining a second fusion feature sequence within a preset duration, wherein the second fusion feature sequence is a feature matrix constructed based on the first fusion feature sequence; respectively inputting the second fusion feature sequence into a plurality of classifiers to obtain a plurality of classification results of the vehicle fault type; and identifying a fault type of the vehicle based on the plurality of classification results. According to the vehicle fault identification scheme provided by the invention, the accuracy and comprehensiveness of vehicle fault diagnosis 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 discloses a human and house data comprehensive treatment method, system and equipment based on a multi-level address model, and a medium, belongs to the technical field of data treatment, and aims to solve the technical problem of how to effectively integrate human and house data sources of multiple parties and improve the accuracy, consistency and availability of human and house data. The defects that in the prior art, people and house data are scattered, address description is not standard, the incidence relation is inaccurate, data quality is uneven and a unified identification system is lacked are overcome. According to the technical scheme, the method comprises the steps that a five-level core data table comprising a community table, a building table, a house table, a people and house relation table and a personnel information table is constructed; the method comprises the following steps: accessing original human and house data from a plurality of data sources, performing intelligent analysis and standardization processing on address information, and obtaining data after standardization processing; establishing a house unique identification system and a human-house multi-dimensional relation model based on the standardized data; evaluating and repairing the data quality; and standardized human and house data service is provided for the outside through the data service interface.

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 discloses an electric power system wiring detection method based on block parallel processing, and relates to the field of current detection, and the method comprises the steps: collecting real-time operation data through a sensor disposed at a key node, and constructing a basic data set after Kalman filtering denoising and normalization processing; establishing a sub-block identification system based on topological structure blocks, and extracting electrical and topological feature vectors; a master-slave parallel computing architecture is adopted, a master node allocates tasks according to priorities, and an improved random forest model is utilized to perform sub-block level anomaly preliminary screening; cross-sub-block collaborative verification is realized by verifying the boundary interconnection switch state, the branch current and the bus voltage; and finally, constructing a global wiring state map to perform difference analysis, accurately positioning abnormal equipment and types, and verifying a result through high-precision retest. The method has the advantages that the efficiency is improved through scientific partitioning and distributed parallel computing, the accuracy is guaranteed in combination with multi-dimensional detection, boundary verification and a retest mechanism, wiring abnormity is efficiently positioned, and reliable support is provided.

The invention discloses a self-adaptive link control method for a multi-dimensional identification system, and belongs to the technical field of link control. The method comprises the following steps: embedding an environment sensing module in a multi-dimensional identification system to obtain network state information; introducing a reinforcement learning algorithm to carry out adaptive coding; the self-adaptive waveform is realized through discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT); a self-adaptive waveform is realized through discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT); and establishing a link layer frame format of the multi-dimensional identification system to realize communication link management. According to the invention, through combination of a multi-dimensional identification system and an environment perception technology, a network state and channel characteristics can be perceived in real time, and a link control strategy is dynamically adjusted; the data transmission efficiency can be dynamically optimized according to the network state and the channel condition by introducing the adaptive coding modulation and adaptive waveform technology; the data transmission time delay is obviously reduced, and the data packet disorder and packet loss rate are reduced, so that the user experience is improved.

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 provides a smart financial product recommendation method and device, equipment and a storage medium, and the method comprises the steps: firstly generating a key pair and a digital ID according to a user certificate, and constructing a safety identity identification system; and obtaining user financial data through the digital ID, obtaining a credit value by combining a credit calculation intelligent contract, and screening selectable financial products from a product library. And then the data is stored in a distributed manner according to business departments to form a multi-node computing architecture. And each node adopts a K-means + + algorithm to perform initial clustering and calculate model parameters, and optimal model parameters are determined through target function evaluation and neighbor node parameter migration. And calculating the posterior probability of the user data point based on the optimal parameter, determining the category of the user data point, and outputting a clustering result. And finally, generating a grouping label according to a clustering result, and screening and recommending financial products from the selectable products. According to the method, through multi-technology fusion, accurate grouping and recommendation are realized while the data security is guaranteed, and the recommendation efficiency and reliability are improved.

The invention provides a data management method for forming a data element network by using data elements as digital objects, which comprises the following steps of: constructing the data element network which comprises a root node, a child node, a data element seller and the data elements; the seller generates a unique identifier for the data element as a custom object individual code of the MA identification system and registers metadata of the custom object individual code to the seller corresponding to the child node; calling a data element through an MA identification system; addressing to corresponding child nodes step by step and establishing DOIP connection; the child nodes are positioned to corresponding seller element libraries; reading corresponding data elements according to the corresponding element result codes; combining the element result and the element metadata information as a digital object, converting the digital object into a binary bit stream, and writing the binary bit stream into DOIP connection; searching the data element: issuing a search task to the child node by the root node through the DOIP connection; the child nodes perform fuzzy search on the data elements; and returning the search result to the digital object metadata information. According to the invention, efficient management and circulation of data elements in different transaction centers are realized.

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 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.

The application provides an AI-driven software project full life cycle intelligent management method and system, relates to the technical field of software project management, and comprises the following steps: realizing demand structural analysis by fusing NLP and a knowledge graph, automatically supplementing implicit demand and compliance requirements; completing task automatic disassembly, work hour estimation and resource dynamic scheduling based on reinforcement learning; identifying risks in real time by fusing multiple source data and generating a treatment scheme; completing online verification according to an optimal path and realizing data closed loop self-optimization; the system comprises five modules, namely, demand analysis, task management, risk control, online verification and data storage, and breaks data islands by means of a unified data platform, a distributed message queue and a globally unique identification system. The application effectively solves the problems of demand deviation, progress lag, risk lag and data island, significantly improves demand accuracy, management efficiency, risk response speed and delivery quality, and is suitable for intelligent management of software project full life cycle in multiple fields.

The invention provides a dynamic identification generation and updating method for a data cross-border service, a switch and equipment, which realize breakthrough in the aspects of structural design, state management, scheduling strategy, propagation mode and the like of a data identification mechanism. A complete dynamic identification system supporting multi-service isolation, state sensing updating, non-intrusive propagation and boundary analysis auditing is formed, the method is particularly suitable for cross-domain and cross-border data application scenes with high requirements for supervision intensity, data identification precision and real-time performance, and the safety of data propagation is guaranteed. On a critical path of data cross-border transmission, identifiability of service identities, updatability of state information, controllability of identification behaviors and traceability of propagation paths are realized, and capabilities of cross-domain data streams in aspects of compliance supervision, behavior auditing, abnormity traceability and the like are remarkably improved; the method is suitable for data security management requirements in multi-service complex scenes such as government affair cloud, financial science and technology, industrial internet and the like.

The present invention discloses a fire identification alarm method and a fire detector based on AI images, which belong to the field of fire identification technology. The method comprises: constructing a model library by a platform; identifying a target area according to a user's fire monitoring demand; matching a fire identification model that meets the fire monitoring demand from the model library and marking it as a main identification model; installing a monitoring device in the target area, performing a fire verification simulation in the target area according to the monitoring device, and obtaining various inaccurate data; performing temporary fire monitoring supplement according to various inaccurate data, and setting a corresponding fire comprehensive identification scheme; performing real-time monitoring of the target area to obtain monitoring data, processing the monitoring data according to the fire comprehensive identification scheme, and obtaining a fire identification result; adjusting the main identification model by the platform according to various inaccurate data, updating the adjusted main identification model at the user, and canceling the temporary fire monitoring supplement.

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.

The invention relates to the technical field of high-altitude platform station operation and maintenance management, and provides a multi-load airship platform OMC system equipment identification and management method, which comprises the following steps of: distributing an equipment identification number for each managed equipment on an airship platform, pre-defining a unified management model for each equipment type, and adopting a layered micro-service distributed system architecture, an equipment management function is realized through cooperation among layers; and in the equipment access adaptation layer, a JavaScript-based plug-and-play plug-in framework is adopted, and a JS script corresponding to an equipment type is dynamically loaded and executed, so that protocol adaptation and format conversion of a management command and reported data are realized. The core problem of unified management of heterogeneous equipment in a multi-load airship platform is solved by introducing a hierarchical equipment identification system, a model-driven unified management framework, a micro-service architecture and a JS plug-in adaptation technology.

The invention discloses a power transformer parallel operation multi-modal data optimization storage method, which relates to the technical field of power data storage, and comprises the following steps: constructing an association identification system based on power transformer parallel operation characteristics; establishing an event-triggered time sequence document collaborative storage model which comprises three layers of data models; scene event driven synchronization is realized based on power grid event triggering, and a parallel operation characteristic parameter out-of-limit event is monitored as a synchronous triggering condition; when an event is triggered, scene identification change information in the event is analyzed through an Flink engine, the time sequence layer and the document layer are linked to execute dynamic storage strategy recombination, and cross-library associated corresponding data is injected. According to the invention, cross-device data association can be realized, traceable linkage of high-frequency measurement and scene parameters of all transformers in a parallel operation group is realized, and meanwhile, the data storage efficiency is improved through an event-driven synchronization mechanism.

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 invention relates to a scientific research data modeling method and device, electronic equipment and a storage medium. The method comprises the following steps: uniformly describing a data structure of multi-modal scientific research data according to metadata by adopting a scientific research data modeling algorithm based on a DO-I RP protocol under a digital object architecture, and establishing a digital object class; a unique digital object identifier of the whole network is allocated to each multi-mode scientific research data resource through the established identification system; defining a digital object model by adopting metadata of a digital object class, establishing a relation type of a digital object, and generating a scientific research data model; and analyzing and accessing the scientific research data model through the scientific research data model operation support platform. According to the method, data line granularity identification is carried out on the scientific research data through data description and identification, and high-granularity safety control and quality management of the scientific research data can be supported. The scientific research data model is analyzed and stored through the scientific research data model operation supporting platform, and unified data packaging is achieved.