192 results about "Topology identification" patented technology

The invention discloses a power distribution area topology identification method and system based on an intelligent fusion terminal and correlation coefficients, and the method comprises the steps: collecting the multi-source data of a power distribution area through an edge fusion terminal, and carrying out the data preprocessing and feature extraction at a terminal side; carrying out parallel calculation on a voltage fluctuation Pearson's correlation coefficient and a load change Kendall rank correlation coefficient, and constructing a correlation coefficient matrix in different time periods; calculating an adaptive weight based on the load fluctuation entropy, and fusing a dual-mode correlation coefficient; carrying out topology generation by using a graph neural network, and outputting an edge existence probability and a node hierarchy; performing physical constraint optimization on the initial topology by adopting a genetic algorithm; the system comprises an edge fusion terminal cluster, a cloud analysis platform, a topology verification module and a terminal management platform. According to the method, topology recognition precision and dynamic adaptability are remarkably improved, bimodal correlation coefficients and graph neural network space modeling are creatively fused, and a complex topological structure is precisely restored.

The invention relates to the technical field of power system power distribution network fault detection, in particular to an arc fault diagnosis and analysis method based on an artificial intelligence algorithm, and the method comprises the four steps: synchronous data collection and topological excitation, deployment of terminals at transformer area nodes, injection of characteristic current, and synchronous collection of response waveforms; signal preprocessing: separating a key frequency band through double-digital band-pass filtering, and calculating energy ratio and other enhanced fault features; performing multi-source feature fusion and intelligent diagnosis, constructing a vector containing statistical features, time domain distortion features and topology identification, and inputting a gradient boosting decision tree and deep neural network hybrid model to obtain fault confidence and type; and based on fault positioning and verification of topology, scheduling multi-node cooperative monitoring, and determining a fault point in combination with a topological relation. The method improves the data reliability and diagnosis precision, achieves the precise positioning of a fault, and guarantees the safe operation of a power distribution network.

The invention discloses a distribution network dynamic topology identification and self-healing control method and device, and the method comprises the steps: collecting operation data of a distribution network, carrying out the time synchronization processing, data quality evaluation and data restoration, and obtaining a high-quality network state data set; based on the data set, performing dynamic topology identification by adopting a path improved by a relaxation method and a forest enhancement algorithm to obtain a dynamic network topology model; constructing a backpack network model by adopting a cyclic data generation method, and evaluating distributed photovoltaic output and load conditions; network abnormity is monitored in real time, and a machine learning algorithm is adopted to assist in judging fault types, positions and influence ranges; power supply reliability and economy are comprehensively considered, and a multi-objective optimization algorithm is adopted to generate a switch operation sequence; switching operation is executed, a temporary micro-grid is formed or a power supply path is adjusted, and the execution effect is monitored and evaluated in real time. According to the method, the dynamic topology of the high-proportion distributed photovoltaic power distribution network can be accurately identified, and fault positioning and efficient self-healing control are realized.

The invention discloses a graph contrast learning identification method for the low-voltage transformer area topology of a power distribution network. The method comprises the following steps: data acquisition and standardization; constructing a feature extraction module; constructing a time sequence encoder, a voltage correlation module, a time alignment module and a power mode module; semi-supervision and measurement combined training: carrying out graph contrast learning based on attributes, and carrying out supervision training on transformer attribution and meter box attribution by adopting cross entropy loss; and topology identification and reasoning output: inputting time sequence data of a user and a transformer under the condition of zero sample of a new transformer area, and outputting a user-transformer attribution probability and a meter box attribution probability through a feature extraction and classifier. According to the method, physical interpretable features such as voltage correlation and time alignment are introduced and are combined with time sequence coding and metric learning, so that the accuracy and stability of topology identification of the low-voltage transformer area are remarkably improved under the condition of small samples.

The invention relates to the technical field of power distribution network bidirectional power flow coordination control, and discloses a power distribution network bidirectional power flow coordination control method based on V2G aggregation, and the method comprises the following steps: S1, constructing a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharge; s2, aggregating the logic energy storage units into virtual power plant nodes which can be dispatched by a power grid through an information communication technology (ICT); and S3, bidirectional power flow sensing and real-time state estimation: utilizing an intelligent electric meter, a sensor and a topology identification algorithm. According to the method, a logic energy storage unit composed of a plurality of V2G charging piles and vehicles supporting reverse discharging is constructed, the logic energy storage unit is aggregated into a virtual power plant node capable of being dispatched by a power grid through an information communication technology (ICT), and specific charging and discharging actions are decomposed and executed, so that dispersed V2G resources can be effectively integrated, and the V2G resources can be effectively integrated. And collaborative optimization with operation states such as voltage and power flow of the power distribution network is carried out.

The invention discloses a structure three-dimensional displacement monitoring method and device based on a monocular camera and a concentric annulus, and relates to the field of computer vision and structure health monitoring, a concentric annulus target is fixed at a to-be-monitored structure monitoring point, and the monocular camera carries out positioning and lens parameter adjustment; shooting a checkerboard image and calibrating the checkerboard image by adopting a camera calibration algorithm to obtain a distortion parameter and an internal reference matrix; collecting a video sequence containing a structure displacement process of the target; image distortion correction is carried out based on the distortion parameters and the internal reference matrix, a target is identified through a target detection algorithm, and target parameters are calculated through least square circle fitting; realizing multi-target tracking by a target point topology identification algorithm based on Y-X threshold sorting; calculating scale factors according to target parameters and actual physical sizes, and calculating in-plane orthogonal direction displacement and out-of-plane displacement to form three-dimensional displacement information of the structure; according to the invention, the monocular camera is adopted to realize the accurate measurement of the three-dimensional displacement of the structure and reduce the complexity and calibration difficulty of the system.

The invention discloses a bidirectional reversible conversion method and system between a peptide molecule SMILES and a sequence expression. The core innovation lies in that a new sequence description syntax is defined to retain information of a polypeptide special bond and specific modification of amino acid; a main chain atom index and adjacency traversal topology identification algorithm is adopted, and end group and topology integrated detection and coding are carried out; a residue recognition algorithm for main chain cutting and template library matching is compatible with any standard or non-standard amino acid residues, an extensible end group library / monomer template library and an automatic increment mechanism, and automatic recognition and sequence annotation of S-S disulfide bonds; the invention relates to a high-fidelity assembly algorithm of HELM anchor points and topology aware cyclic peptide processing. The method solves the problems of incapability of supporting a complex polypeptide topological structure, poor reversibility, insufficient expansibility of a monomer library and the like in the prior art, can be widely applied to scenes of quantitative structure-activity relationship model construction, large-scale polypeptide data cleaning and the like, and has remarkable practicability and innovativeness.

The invention discloses a topology identification method based on intelligent measurement data of a low-voltage transformer area. According to the technical scheme, the topology identification method comprises the steps that 1, a master station issues a timing command to an intelligent measurement terminal and an electric meter; step 2, the master station issues a transformer area electric meter file to the intelligent measurement terminal, configures an acquisition task and issues the acquisition task to the intelligent measurement terminal; step 3, establishing an electric quantity sum linear regression model based on a Lasso algorithm by using daily frozen electric quantity data, and converting the identification problem of the transformer area user-transformer relation into regression coefficient solving; step 4, on the basis of user change relation identification, performing dimension reduction on historical voltage data by using a t-SNE algorithm to improve the identification efficiency of the model; and step 5, clustering the voltage data after dimension reduction by using agglomerated hierarchical clustering, identifying a user phase and a meter box to which the user belongs, and completing identification of a topological structure. According to the method, the power grid topology information of the transformer area can be automatically identified, the efficiency and the accuracy of topology identification are improved, and the method has far-reaching significance for construction of an intelligent power grid.

The invention is suitable for the technical field of smart power grids, and relates to a low-voltage transformer area undisturbed topology identification method and system, a medium and a terminal, and the method comprises the steps: S10, cleaning and screening original communication and measurement data; s20, the RSSI between the devices is converted into the distance between the nodes, and an undirected weighted graph is formed; s30, determining an affiliation relationship between the meter box and the electric meter by using a clustering algorithm; s40, performing constraint matching on the transformer, the line and the meter box node according to the power utilization information, and optimizing the topological structure by using the law of conservation of energy and the Kirchhoff's current law; and S50, evaluating the accuracy of the identification result by using the adjusted Lam index and the normalized mutual information. The method is simple in process and convenient to operate, a complete topological structure is constructed step by step on the premise of not adding extra hardware, uninterruptible power and not injecting characteristic signals by relying on electric power data of an existing intelligent electric meter, a communication signal and an electricity utilization information acquisition system, and high-precision and automatic identification of the topological structure of the transformer area is achieved.

The invention relates to the technical field of building block toy programming, and discloses an extensible modular building block toy intelligent programming control system and method. A building block topology identification module of the system collects the physical connection state of modular building blocks through a multi-modal sensor, and extracts module identity features, a connection relation matrix and combined configuration parameters; the programming logic conversion engine establishes a mapping rule of the module identity characteristics and the connection relation matrix, and analyzes a correction rule of the combination configuration parameters on the two; the instruction sequence generator constructs an instruction conversion model based on the mapping rule and the correction rule, and generates a target control instruction queue; and the actuating mechanism driver analyzes the instruction queue and outputs a driving control signal matched with the current building block combination. The system can accurately identify the building block combination state, flexibly convert the programming logic, dynamically generate the adaptive instruction and accurately execute the driving action, the expansibility and adaptability of the system are improved, and the use experience of a user is improved.

The invention discloses a topological structure rapid identification method for real-time operation of a power distribution network, and the method comprises the steps: obtaining multi-source measurement data of a plurality of data sources in the power distribution network, and carrying out the preprocessing of the multi-source measurement data, and obtaining effective measurement data; generating a candidate topology set based on the on-off state of the power distribution network switch equipment; performing state estimation on each candidate topology in the candidate topology set to obtain a state estimation value; calculating a topology error between the state estimation value of each candidate topology and the corresponding measurement value in the effective measurement data; and screening out an effective topology from the candidate topology set based on the topology error, and taking the effective topology as the operation topology of the power distribution network at the current moment. According to the scheme of the invention, the accuracy and efficiency of topology identification of the power distribution network are effectively improved.

The invention discloses a radial power distribution network topology identification method, system and device and a medium. The method comprises the following steps: acquiring node voltage amplitude measurement data; calculating a sample covariance matrix based on the voltage amplitude measurement data to obtain the covariance of the voltage amplitude between the nodes; estimating a precision matrix of the covariance matrix through a graph lasso model; calculating a partial correlation matrix based on the precision matrix, wherein the partial correlation matrix is used for representing a direct correlation coefficient between nodes in the radial power distribution network; and deriving an adjacent matrix through a maximum spanning tree based on the partial correlation matrix to obtain a radial power distribution network topological structure. According to the method, sparse learning and partial correlation analysis are combined to deal with the problem of limited data volume; the key condition dependency relationship between the nodes can be quickly and accurately identified only through voltage amplitude measurement data without a large number of samples and phase angle information, and the identification precision is high.

The invention relates to a distribution network maintenance topology identification system based on multi-source data fusion, and the system achieves the effective integration and precise processing of multi-source power distribution network data through the cooperative work of a multi-source data collection module, a data preprocessing module and a measurement generation module, and remarkably improves the temporal-spatial resolution of the state quantity of a distribution network. According to the method, a confidence evaluation system is constructed, and an algorithm confidence evaluation system is used for carrying out confidence scoring on an output identification structure, so that the uncertainty of an identification result can be quantified, and the availability and reliability of the system in an actual complex environment are greatly improved; through seamless connection and data interaction between a database and a marketing system and a scheduling system, the applicability and effectiveness of the system in an actual maintenance scene are ensured, accurate and reliable topology identification support can be provided for power distribution network maintenance, and the maintenance efficiency and the power supply reliability are improved; the method has the advantages of being high in self-adaption, accurate and reliable.

The invention discloses a block identification-based low-voltage transformer area topology modeling method and device and a storage medium, and the method comprises the steps: carrying out the correlation analysis of a target voltage time sequence of a branch box and a user based on the target voltage time sequence of the branch box and the user, obtaining the affiliation of the user and the branch box, and completing the preliminary blocking; constructing a data matrix used for branch box-electric meter box topology identification, and based on the target power time sequence of the branch box and the electric meter box, calculating to obtain a relation matrix representing a topological relation between branch box nodes and electric meter box nodes; a data matrix used for electricity meter box-user topology identification is constructed, and a relation matrix representing the topological relation between electricity meter box nodes and user nodes is obtained through calculation based on the power time sequences of electricity meter boxes and users in all branches. According to the method, the power sequences serve as main data, the data matrix is constructed, the linear relation of the power sequences of the adjacent hierarchical nodes is solved integrally, node-by-node similarity comparison is not needed, and the recognition convenience and the calculation efficiency are improved.

The invention discloses a digital power grid power line anti-theft alarm system, which relates to the technical field of electric power safety protection and comprises a coupling feature extraction module, an interference mode analysis module, a cross-modal feature fusion module, a time sequence calibration regulation and control module and a closed-loop dynamic suppression module, the method comprises the following steps: constructing a time-frequency phase-locked observation baseline of a continuous sampling signal, inverting coupling spectrum characteristics of a monitoring link and a communication link by using multi-channel phase reconstruction processing, generating a phase and amplitude residual fingerprint map, and calibrating a sudden change threshold domain as a dynamic reference of topology identification. Through technologies of phase reconstruction, causal replay, multi-modal fusion, time calibration and the like, electromagnetic coupling disturbance is accurately identified and suppressed, effective distinguishing between disturbance and intrusion signals is realized, a closed-loop steady-state control mechanism is constructed, and the accuracy, intelligence and robustness of power grid protection are improved.

The invention relates to the technical field of transformer substation monitoring, in particular to a transformer substation monitoring method and system based on multi-terminal fusion. A three-dimensional digital model is constructed based on transformer substation structure parameters and equipment point location information, and unique topology identification and space coordinates are distributed to each equipment node; accurate modeling and unified identification management of the equipment space relation are realized. And images and point cloud data acquired by the inspection vehicle, the unmanned aerial vehicle and the fixed monitoring equipment are mapped to the three-dimensional model. And performing anomaly recognition and state judgment on the fused three-dimensional digital model through a deep learning model, updating the fused three-dimensional digital model into a digital twin model in an attribute tag form, and further automatically positioning and monitoring incomplete or abnormal to-be-confirmed nodes based on traversal calculation of topological adjacency relation and time sequence consistency detection. And finally, a supplementary inspection task is generated through node distribution characteristics and terminal capability constraints, scheduling and data acquisition are executed in combination with task accessibility of each terminal, and the integrity and real-time performance of system monitoring are effectively improved.

According to the CANopen-based remote I / O node dynamic topology identification and adaptive configuration method, key configuration data of equipment is stored in Flash, and power-on automatic loading is supported. A bus is scanned in real time through an RS485 interface, the number, types and topological positions of local newly-added I / O cards are automatically recognized, an object dictionary and a PDO mapping table are dynamically updated based on a recognition result, and hot plugging, dynamic adding and online expansion of the I / O cards are achieved. The object dictionary is provided with 0 * 2000 as an equipment state area and 0 * 2001-0 * 2010 as 16 independent I / O card configuration areas, and a master station can remotely modify and dynamically add parameters of I / O cards through an SDO interface to realize online parameter configuration and hierarchical management; the system expands multiple groups of PDO and combines with a master station synchronization signal, and can adapt to the data flow change after the I / O card is dynamically added. Automatic configuration, SDO online maintenance, dynamic identification and hot plug expansion are supported, the I / O card can be dynamically added without interrupting system operation, and topology expansibility, dynamics and real-time performance of the remote I / O node are improved.

The invention is applicable to the technical field of low-voltage power distribution networks, and relates to a transformer area topology identification method based on multi-source time sequence fusion and sparse optimization, which comprises the following steps: S10, acquiring and processing node data in a low-voltage power distribution network, finding out a common time point set of all nodes, and eliminating initial state differences; s20, based on the energy and voltage relationship between the nodes, creating an initial network topology structure, generating an initial adjacency matrix, screening out a reasonable node relationship, and reordering the adjacency matrix; s30, constructing an optimization problem P4, setting an optimization objective function containing a regularization item, iteratively solving the optimization problem P4, and updating an adjacent matrix; s40, initializing optimization parameters, iteratively executing the step S30 to optimize the topological structure, and updating the adjacent matrix; and S50, outputting a final optimized topological structure. The method is simple in process, convenient to operate and high in topological structure recognition accuracy and stability.

The invention discloses a power distribution network sensing and topology dynamic identification method, system and device and a storage medium, and relates to the technical field of power distribution network monitoring and management, and the method comprises the steps: carrying out the real-time data collection and processing of each transformer, line and user side equipment in a power distribution network through edge equipment; based on the data processed by the edge device, system parameter identification of time-varying parameters is carried out, and a resistance value and a reactance value on a feeder line between a transformer outlet and any user ammeter are calculated; uploading a system parameter identification result of the time-varying parameter to a cloud, performing identification and collaborative optimization on a topological structure and line parameters of the whole transformer area power distribution network, and further processing and transmitting to a dispatching center; real-time data acquisition and parameter identification of the power distribution network are realized, line resistance and reactance are accurately mastered, power supply reliability and efficiency are improved, energy consumption and power failure loss are reduced, and safe and stable operation of the power distribution network is guaranteed.

The invention discloses a whole box metering misalignment analysis system and method based on carrier topology identification and a chip, and belongs to the technical field of electrics. The system comprises a guide rail type electric energy meter; a user electric energy meter; the concentrator is configured to establish a topology model representing a power supply structure of the power distribution area; checking data uploaded by the guide rail type electric energy meter and the user electric energy meter based on the topological model to determine a target metering box with metering misalignment of the whole box; and respectively carrying out trend consistency analysis and form similarity analysis on the time-sharing power curves of the guide rail type electric energy meter of the target metering box and the corresponding user electric energy meter so as to determine a suspected misalignment user electric energy meter in the target metering box. According to the method, whole box metering misalignment detection is identified based on topology, the suspected misalignment user table is positioned through trend and form double analysis, a cloud-side-end collaborative diagnosis system is formed, and automatic, real-time and high-precision hierarchical metering anomaly detection can be realized.

The invention discloses a low-voltage transformer area topology identification method and system based on node mutual impedance characteristic identification, and relates to the technical field of power supply and distribution. The identification method comprises the following steps: firstly, synchronously acquiring voltage and current time sequence data of the head end of a transformer area and each user node; constructing and solving a mathematical model based on a circuit superposition principle to obtain a user node mutual impedance coefficient matrix; the impedance feature vector of each user node is extracted, the difference value between the self-impedance and the mutual impedance is calculated, and the difference value is compared with a preset judgment threshold value based on physical parameters, so that the relationship between the upstream and the downstream of the user nodes and the same access point is accurately judged; and finally, reconstructing a complete tree-shaped physical topology of the transformer area. According to the method, only existing measurement data is utilized, extra hardware is not depended on, efficient and accurate automatic identification of the topological structure of the low-voltage transformer area is achieved, and reliable support is provided for refined operation and maintenance such as power distribution network line loss treatment and fault positioning.

The invention relates to the technical field of power systems, in particular to a graph attention neural network-based power distribution network topology identification method, system and device and a medium, and the method comprises the steps: collecting the voltage, active power, reactive power and other operation state quantities of a power distribution network node, and constructing a topology identification sample; performing standardization processing on the collected data, and unifying node features; after standardizing the data, generating an adversarial sample by adopting an adversarial strategy, and taking the adversarial sample and the topology identification sample as an input training network model; acquiring node data in real time, inputting the node data into the trained graph neural network model, and outputting a topological state result; through combination of sample construction with topological labels, data standardization processing, adversarial sample generation and model training, real-time reasoning output is carried out. According to the method, a complete processing flow is formed, limitation caused by dependence on single simulation data and measurement difference is solved, it is guaranteed that the topological state of the power distribution network can still be recognized under complex working conditions, and a result capable of being used for operation scheduling and analysis is output.

Aspects of the subject disclosure may include, for example, determining a topology of a network, wherein the network includes a plurality of network elements (NEs) and at least one optical network-based fronthaul, at least one optical network-based midhaul, or combination thereof, for one or more routes associated with one or more of the plurality of NEs identified based on the topology, applying one or more sets of parameters for that route, obtaining data relating to the network based on the applying the one or more sets of parameters, training one or more AI models using the data, resulting in one or more trained AI models, and utilizing the one or more trained AI models to generate one or more predictions regarding spectral efficiency relating to the at least one optical network based fronthaul, the at least one optical network-based midhaul, or the combination thereof. Other embodiments are disclosed.

The invention provides a transformer area topology identification method and system based on a graph neural network, and relates to the technical field of power system power distribution networks, and the method comprises the steps: taking an HPLC concentrator as an edge module, carrying out the modeling of a transformer area into a graph structure, taking a transformer and an intelligent electric meter as the nodes of the graph structure, and taking the potential electrical connection between the nodes as the edge of the graph structure; the edge module collects multi-source data of each intelligent electric meter in a transformer area, and after the multi-source data is preprocessed, node feature vectors and edge feature matrixes are obtained according to the preprocessed multi-source data; the edge module learns node features and edge features by using a graph neural network based on the obtained node feature vectors and edge feature matrixes, and calculates the probability of electrical connection between any two nodes; and the edge module adopts a minimum spanning tree algorithm to generate a transformer area topological structure according with the set physical constraint according to the obtained result. According to the invention, high-precision automatic identification of the transformer area topology can be realized.

This invention discloses a fault handling method and apparatus based on main and distribution network coordination, belonging to the field of power system automation technology. The method includes: using a graph neural network to perform topology identification and fault location of the power grid, generating fault impact features; based on the spatial range and electrical connection relationships of the fault impact features, obtaining the switches at both ends of the faulty section of the power grid, and performing fault isolation through remote control tripping; analyzing the fault isolation information using topology impact analysis; and, combined with changes in the current topology of the power grid, generating a power grid protection strategy migration mechanism and initiating a fault recovery process to restore power supply. This invention achieves dynamic modeling of the power grid topology and accurate identification of faulty sections by using a graph neural network to embed the power grid graph and combining multi-channel time series information to generate node potential state vectors, thus improving the real-time performance and accuracy of fault location.

The invention belongs to the technical field of low-voltage transformer area topology identification, and particularly relates to an electricity-obtaining data-based electricity meter topology automatic hitching method and system. Comprising the following steps: acquiring an opening event of an intelligent tail-end circuit breaker through an intelligent fusion terminal of a low-voltage transformer area; the intelligent fusion terminal issues a closing instruction to the plurality of intelligent tail end circuit breakers in sequence according to a set time interval T; the intelligent fusion terminal receives the electricity obtaining time of the household meters reported after the household meters are electrified, and collects the closing time recorded by the intelligent tail end circuit breakers or the measuring switches; and carrying out clustering comparison on the electricity obtaining time of the household meters and the closing time recorded by the intelligent end circuit breakers or the measuring switches, and determining the hooking object of each household meter. According to the method, the electricity obtaining time of the household meters and the closing time recorded by the intelligent tail end circuit breakers or the measuring switches are subjected to clustering comparison, the hooking object of each household meter is determined, automatic hooking of the household meters is achieved based on the determined hooking objects, and the intelligence and automation degree of precise topology construction is improved.

The invention provides a method and a related device for identifying and analyzing line loss abnormity based on dynamic topology of a novel electric red-hong area, and the method comprises the steps: injecting an excitation signal into a line through an electric red-hong measuring terminal of the electric red-hong area, synchronously monitoring the electrical characteristics of the electric red-hong area, and obtaining monitoring data; performing correlation analysis based on the data, deducing a node connection relationship and a hierarchy, and generating a transformer area dynamic topological structure; extracting electrical quantity data of each node in the same time period based on the topology, and calculating an actual line loss rate; comparing the deviation with a theoretical line loss rate, and judging that the line loss is abnormal if the deviation exceeds a set threshold value; and for the abnormal transformer area, comparing the current topology with the dynamic topology, and tracing and positioning the abnormal user of the power supply point according to the node hierarchy. According to the invention, the excitation signal is injected through the electric red-chilled measurement terminal, the dynamic topology is generated by monitoring data, line loss calculation and abnormity tracing are carried out according to the dynamic topology, and real-time monitoring of line loss abnormity and timely positioning of abnormal users of power supply points can be realized.

The invention discloses a multi-terminal differential protection control method and device for automatic identification of a topological structure and a storage medium, and belongs to the technical field of electricians, the method comprises the following steps: enabling each single-terminal device in a multi-terminal differential protection device to communicate through a multi-terminal differential channel, and enabling a data frame transmitted in the communication process to be used for identifying a communication state; recording a communication state between each single-ended device and other single-ended devices through the single-ended device, and sending the communication state to the other single-ended devices; judging a topological structure of a multi-terminal differential channel according to the communication state of each single-terminal device and the received communication states of other single-terminal devices; according to the differential protection method and device, the corresponding differential logic calculation is carried out according to the topological structure judgment result, the corresponding differential protection is executed according to the calculated differential logic, and the differential protection is locked when the topological structure is invalid, so that the risk that the differential protection quits and has no protection due to the fact that the differential channel is abnormal is avoided or reduced, and the utilization rate of the differential protection is improved.

The invention provides a method for detecting vehicles retained in a highway section, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source vehicle passing data in a target space-time scene, and enabling the multi-source vehicle passing data to be fused with each other through a vehicle identifier, and obtaining vehicle track data; constructing a candidate road network topology according to the vehicle trajectory data, and identifying abnormal road sections and abnormal events in the candidate road network topology; optimizing the candidate road network topology according to an identification result to obtain a local road network topology; selecting a target detection time window according to the to-be-detected time, and determining candidate remaining vehicles, missing detection vehicles and accident remaining vehicles of the to-be-detected road section in the target detection time window; and according to the candidate reserved vehicles, the missed vehicles and the accident retained vehicles, determining final reserved vehicles. By adopting the method for detecting the retained vehicles on the highway section, the road network topology can be dynamically updated, the retained vehicles are detected under the updated road network topology, and the accuracy of detecting the retained vehicles on the highway section is improved.

The invention relates to the technical field of power distribution network area topology identification, in particular to a combined topology identification method and system based on HPLC (High Performance Liquid Chromatography) and characteristic current injection, comprising the following steps: a control node broadcasts and sends a central beacon signal carrying a characteristic current sending instruction to a broadband power line carrier communication network; the characteristic current sending node sends a corresponding characteristic current signal by taking the initial position of a CSMA time slot area of a beacon period in which a central beacon signal is located as the initial time of sending the characteristic current signal, and the characteristic current identification node detects whether the characteristic current signal exists in a characteristic current signal detection window of the beacon period in which the central beacon signal is located; a detection result is reported to the control node, and the control node obtains a topology identification result of the power distribution area according to the detection result; according to the method, the beacon signaling and the whole network broadcasting system of the existing HPLC communication network are reused, so that the topology identification efficiency of the power distribution area is comprehensively improved, and the reliability and the accuracy of identification are enhanced.