388 results about "Topology identification" patented technology

The present disclosure relates to the technical field of power systems. Disclosed are a distributed fault self-healing method and system for an active distribution network, and a device and a medium. In the present disclosure, using a two-terminal waveform difference as a faulty section locating principle is applicable to an active distribution network, which contains various types of DGs and different DG permeabilities. The present disclosure has a relatively low requirement for data computation and storage regarding an STU, and topology identification and update can be automatically completed during operation by simply configuring basic static network topology information for the STU, thereby adapting to a change in the operation mode of the network. By means of the present disclosure, a rational fault recovery rule is set, and important load nodes can be recovered as much as possible while meeting a network power constraint. In a recovery solution, the presence of distributed generation is taken into consideration, and different processing methods are designed according to whether the distributed generation can operate in an island manner for a long period of time.

The invention relates to the technical field of power system topology identification, in particular to a power distribution network frame topology identification method based on an improved graph neural network, and the method comprises the steps: collecting the real-time electric quantity data of nodes and edges of a power distribution network, and carrying out the modeling of a power distribution network graph structure; the method comprises the following steps: constructing a node, edge and hyperedge feature matrix by using real-time collected data, inputting an improved graph neural network topology identification model, dynamically weighting and adjusting an edge weight through a graph attention network, splicing and fusing local topological features and global topological features, and generating a prediction adjacency matrix; when the power distribution network is dynamically changed, a change area is positioned through adjacency matrix difference, and a sub-graph is extracted for incremental updating; and constructing a topological structure of the power distribution network based on the final adjacent matrix, and outputting a connection relationship between the physical positions of the nodes and the edges. Compared with the prior art, the real-time performance, generalization ability and applicability of the model are remarkably improved, and the problems of accuracy and efficiency of power distribution network topology identification in the dynamic environment are solved.

The invention belongs to the technical field of supply chain management, and discloses an intelligent purchasing system and method of a supply chain. Comprising the steps of collecting demand prediction data and supplier performance data; carrying out deep mining on the demand prediction data, and predicting future purchase demands; generating a capability portrait based on the performance data of the suppliers, and performing quantitative evaluation on performance scores of different suppliers according to a pre-constructed evaluation system; historical order data is acquired, a supplier relation network topology is constructed in combination with the capability portraits, and a synergistic effect between suppliers is identified; fusing the performance score, the future purchase demand and the synergistic effect, and recommending an optimal supplier combination; acquiring purchase management data, and intelligently formulating a purchase strategy in combination with the supplier performance data of the optimal supplier combination; according to the invention, the purchase efficiency can be improved, and the flexibility and toughness of the supply chain are enhanced, so that the overall response speed, cost control capability and operation quality of the supply chain are effectively improved.

The invention relates to the technical field of electric power communication, and discloses a district physical topology identification method based on dual-mode communication signal strength, comprising the following steps: S1, building a district network; s2, topology identification is started, self initialization is completed, and necessary environment preparation before topology identification is carried out; s3, adding STA operation participation data one by one until all STA data are added; s4, identification calculation is started, branch calculation is carried out through HPLC signals of neighbor STAs by adopting a mode of selecting a rated threshold value, meter box calculation is carried out through HRF signals of the neighbor STAs, and relevancy ranking is determined; and S5, obtaining a topology result, and ending identification. According to the method, identification of the physical topology of the transformer area can be completed without additional equipment and manual intervention, and the method is safe, convenient and high in practicability. In the recognition process, the method is not affected by load voltage and current changes and the length of a power line, and compared with an existing impedance calculation and time delay calculation scheme, the recognition accuracy is high.

The invention discloses an equipment management system and a server, and relates to the technical field of servers, and the equipment management system is characterized in that a topology identifier storage assembly for storing slot position signal source identifiers is moved to a server mainboard end, so that the occupation of mainboard connector pins by information transmission is reduced; and meanwhile, the management control component establishes and stores a corresponding relation between the address of the arbitration component and the screen printing area of the case. The adapter card does not need to be pre-bound and configured, can automatically identify the screen printing area of the case through the relationship by inserting any communication interface of the mainboard, and generates slot screen printing information in combination with the pre-stored signal source identifier. The problems that a large number of pins are occupied due to the fact that the adapter card hardware address signal identification configuration needs to be transmitted through a connector, and the adapter card needs to be bound with the fixed configuration and lacks flexibility in the prior art are solved. The technical effects of reducing pin occupation, realizing flexible plugging of the adapter card and automatic identification of screen printing, reducing hardware development cost and meeting the requirement of the server for reserving more bus interfaces for the connector are achieved.

The invention discloses a power distribution area line topology identification method and system based on an embedded system. The method comprises the following steps: acquiring and preprocessing electrical operation data and equipment state data in a power distribution area line; constructing a graph neural network model based on the preprocessed electrical operation data, and constructing a static graph structure in combination with the preprocessed equipment state data to train the graph neural network model; monitoring the real-time load change degree of the distribution line, and judging whether to trigger a dynamic update mechanism based on the load change degree, including dynamically updating the parameters of the static graph structure according to the load change degree to obtain an optimized model; and obtaining a topology recognition result according to the optimized graph neural network model, and regularly evaluating and optimizing the topology recognition result. The dynamic updating mechanism is triggered by monitoring the load change in real time, and the model can be adjusted in real time according to the actual operation condition of the power distribution area, so that the topology identification can accurately reflect the real-time state of the power distribution area.

The invention discloses a metering processing method for an electric energy meter topology application and an electric energy meter of a transformer area topology. The metering processing method comprises the following steps: S1, constructing a measurement data set based on an equipment identifier, a power factor and a sampling timestamp of the electric energy meter; s2, generating an initial graph model containing user nodes and electric energy transmission edges based on the data set; s3, inputting a dynamic coupling backflow enhancement graph diffusion network to execute topology identification; s4, constructing a fusion path diagram and initializing an edge weight; s5, calculating an edge weight regulation factor, and updating the edge weight; s6, asynchronous propagation is carried out to output a transformer area power supply topological graph; s7, constructing a metering path based on the topological graph and identifying an abnormal path segment; and S8, correcting the abnormal path, and generating an optimization topology and evaluation report. According to the method, the precise identification of the power supply topology of the transformer area and the self-adaptive correction of the abnormal metering path are realized.

The invention relates to the technical field of electric power topology identification, and discloses an electric power topology rapid identification method based on depth map matching, and the method comprises the following steps: S1, carrying out the multi-source heterogeneous data fusion and adaptive noise reduction, and employing an adaptive threshold noise reduction algorithm; s2, modeling a dynamic space-time diagram; s3, attention-driven graph matching is carried out, and a two-channel graph neural network is used; and S4, updating the incremental topology library. For the condition that the real-time data of the low-voltage power distribution network generates significant noise due to equipment aging and acquisition errors, a self-adaptive threshold noise reduction algorithm is adopted, high-frequency noise components are accurately filtered out according to a threshold value, missing data are intelligently repaired in combination with a space-time Kriging interpolation method, the purity and integrity of the data are ensured, and a data basis is provided for subsequent topology recognition.

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 power distribution network topology identification method and device, and the method comprises the steps: obtaining the original measurement information of a power distribution network; based on the original measurement information, constructing a mixed integer nonlinear programming topology measurement identification model based on a branch current method; utilizing the target data physical fusion driving linearization model to perform linearization processing on a nonlinear constraint and measurement equation to be used in the topology measurement identification model of the mixed integer nonlinear programming based on the branch current method to obtain a processing result; and constructing a topology measurement identification model of mixed integer linearization programming based on a branch current method by using a processing result so as to output a topology identification result of the power distribution network. Therefore, the problems that in the related technology, the efficiency of maintaining the topology file based on a manual and data statistics method is low, a topology error identification model based on an optimization class method is poor in adaptability and low in accuracy, the accuracy of topology identification is reduced, and the operation stability of a power distribution network is reduced are solved.

The invention relates to a transformer area topology identification method and device based on a carrier communication network, and a concentrator. The method comprises the following steps: determining electric energy meter nodes in the same phase line and the same court and electric energy meter nodes in a cluster unit of the lowest level in a plurality of cluster units; performing hierarchical identification on the plurality of cluster units by adopting a carrier communication network to obtain hierarchies of the plurality of cluster units; according to the electric quantity data sent by the monitoring device corresponding to each branch box cluster unit in the plurality of cluster units and the electric quantity data sent by the monitoring devices corresponding to the superior power distribution room unit and the subordinate plurality of meter box units of each branch box cluster unit, determining an outgoing line and incoming line corresponding relation of the branch box cluster units in the plurality of cluster units, and forming a transformer area topological graph. By adopting the method, the transformer area topology identification can be carried out based on the carrier communication network, so that the situation that signal receiving devices are additionally arranged on branch boxes and electric meter boxes in a power distribution network is avoided, and the cost of transformer area topology identification is further reduced.

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 relates to the technical field of power distribution networks, in particular to a low-voltage transformer area topology recognition method and system adopting a graph diffusion model, equipment and a medium, and the method mainly comprises the steps that a topology recognition model is established, target time sequence characteristics are input into the topology recognition model, and the target time sequence characteristics are input into the low-voltage transformer area topology recognition model. And implicitly identifying node pairs with synchronous fluctuation through a topology identification model, outputting node features fusing neighbor information, predicting a connection probability between nodes based on the node features, and outputting a new transformer area topology structure. Different from a traditional method which depends on a specific transformer area rule or needs a large amount of manual adjustment, the graph neural network trained by the scheme aims at learning a general electrical characteristic-topological structure mapping rule and denoising logic. The model is not limited to a specific structure of training data, can be effectively generalized and applied to new courts with large topological structure differences, and solves the problems that a traditional method is poor in adaptability and weak in expansibility.

The invention discloses a transformer area topology identification method and system capable of being deployed in a concentrator, equipment and a medium, and the method comprises the steps: solving an initial virtual impedance matrix based on historical electrical quantity data of a transformer area, and taking the minimum deviation between an optimization result and the initial matrix as an optimization target; and taking symmetry constraint, boundary constraint and collinear constraint as constraint conditions, and performing iterative solution to obtain an optimized virtual impedance matrix, so that the optimized virtual impedance matrix is closest to an original matrix on the basis of conforming to the actual topology, noise interference can be eliminated, the topology identification precision is improved, the method can dynamically adapt to a transformer area change scene, and the method is suitable for being applied to a transformer area. The method has high robustness, the unstructured virtual impedance matrix can be fitted into a unique solution conforming to physical topology, fuzziness of a traditional method is avoided, the whole recognition process only depends on historical electrical quantity data of the transformer area, no initial topology information is needed, and the high-precision transformer area topology can be constructed from zero.

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 low-frequency load shedding cooperative control method, system and device based on GOOSE communication and a readable storage medium, and the method specifically comprises the steps: calling a topology analysis service to confirm that a home terminal is in a receiving end power grid, sending a state query request to an adjacent terminal, and receiving an action signal of the adjacent terminal through GOOSE communication; based on the dynamic load shedding sequence, executing load shedding operation; a marginal cost function of each terminal is calculated, distributed consensus verification is carried out in combination with a block chain technology, and then load shedding parameters are optimized, issued and updated; and after load shedding is completed, a fault section is positioned through residual voltage detection and direction discrimination, a personalized reconstruction scheme is generated by combining topology identification and fault type matching, and power supply of a non-fault section is recovered within a preset distance. According to the method, quick response, cooperative control, accurate load shedding, intelligent optimization and quick fault recovery of low-frequency load shedding are realized, and the frequency stability and the power supply reliability of a power system in a new energy high-proportion access scene are remarkably improved.

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 provides a one-master multi-slave type high-speed serial communication and synchronous control method and system and a medium, and the method comprises the steps: firstly, resetting slave stations based on an initialization instruction, and sequentially transmitting equipment type query instructions based on a physical connection sequence, thereby completing the topology recognition of the slave stations; then, aggregated data frames are periodically constructed, a frame header is written into a master station status word, a frame body is formed by splicing slave station data packets according to a station number sequence, and a frame tail is a check code; and finally, the slave station analyzes the aggregated data frame based on the station number, executes the task, and feeds back a task execution result according to a physical inverted sequence. In addition, when a new slave station is accessed, the master station switches a status word and triggers the whole system to be reinitialized, and the new slave station is dynamically incorporated into the topology; according to the invention, the expansibility, the real-time performance and the reliability of multi-node synchronous control in an industrial scene are comprehensively improved.

The invention relates to a topological structure identification method, device and equipment, and is applied to a target GPU node connected with a management node through a plurality of connectors, and the method comprises the steps: obtaining electric signal combinations corresponding to the plurality of connectors when it is detected that a topological structure between the target GPU node and the management node is switched, each electric signal in the electric signal combination corresponds to a plurality of pins in the corresponding connector; for each connector, determining port information corresponding to the connector based on the first truth table and the electric signal combination corresponding to the connector; based on the port information corresponding to the plurality of connectors, determining a target topology type corresponding to the switched topology structure; and the target configuration file corresponding to the target topology type is obtained, and the target configuration file is loaded in the PCIE Switch in the target GPU node, so that the influence of topological structure switching on task execution on the server and the maintenance cost are reduced.

The invention discloses a low-voltage distribution area topology identification method, system and device and a storage medium, and the method comprises the following steps: deploying a synchronous measurement unit in a low-voltage distribution area, and obtaining the outgoing line current phasor and voltage phasor data with a synchronous time scale of a whole network; constructing an incoming line and outgoing line current phasor set, and performing adaptive descending sorting according to amplitudes; determining that the nodes corresponding to the incoming line current phasors and the found nodes corresponding to the one or more outgoing line current phasors are in a father-son relationship, and iterating the process until all sets are empty, thereby completing the identification of the topological relationship of the power distribution side; and according to the equipment number contained in the obtained voltage phasor data, determining a meter box connected with the user electricity meters, and then clustering the user electricity meters connected to the same meter box according to the voltage phase angle to obtain the connection phase sequence of each user electricity meter. The problem of key information missing is solved from a data source, and accurate and efficient identification of topology is realized by fusing physical constraints.

The invention relates to a low-voltage power distribution network topology identification method and device, electronic equipment and a storage medium, and the method comprises the steps: complementing different measurement features in a preset training data set through employing a pre-constructed measurement complementation network model, so as to obtain the complemented measurement data; simulating the complemented measurement data to obtain a historical data set meeting a preset integrity condition under different topology conditions, and constructing an identification model training set by using the historical data set; and training a pre-constructed multi-topology identification network model by using the identification model training set to obtain a target multi-topology identification network model meeting a preset convergence condition, and performing topology identification of the target low-voltage power distribution network by using the target multi-topology identification network model to obtain a corresponding low-voltage topology identification result. Therefore, the technical problems that in the related technology, the efficiency is low and the real-time performance is poor when a large amount of calculation is carried out in a traditional method, and a topology identification method based on artificial intelligence depends on complete intelligent electric meter measurement data greatly and is poor in economy and practicability are solved.

The embodiment of the invention provides a road topology identification method and device based on hierarchical fusion of map priori and electronic equipment. The method comprises the following steps: respectively carrying out feature processing on road data in a first representation format and a second representation format to obtain a first top view feature and a second top view feature; performing feature fusion on the first top view feature and the second top view feature to obtain a third top view feature, the first top view feature and the second top view feature being top view features; performing context fusion processing on query features obtained by performing feature extraction on vectorized map prior data and third top view features to obtain lane line information; analyzing the lane line information to obtain a first topological relation between the lane lines, and analyzing the lane line information and the traffic element information to obtain a second topological relation between the lane lines and the traffic elements; and summarizing the first topological relation and the second topological relation to obtain a road topology recognition result.

The invention discloses a transformer area topology identification method and device based on multi-feature fusion, and the method comprises the steps: S1, distributing an initial branch node for each user, constructing an initial topological structure identification model, and obtaining a preliminary branch node-user association relation adjacency matrix; s2, constructing a topological optimization model based on difference characteristics, and enabling the error between branch node power difference and user power difference combinations to be minimum; s3, constructing a topological optimization model based on peak-valley feature matching, and enabling the matching degree of the peak-valley feature combination of the branch node and the peak-valley feature combination of the user to be maximum; s4, constructing an optimization model based on total fitting error correction, and enabling the total error between the actual power and the fitting power of the branch node to be minimum; s5, judging whether undistributed users exist or not, if not, obtaining a final topology identification result, and otherwise, performing S1 to S4 again; according to the method, the area topology connection relation is effectively identified.

The invention discloses a memory processing system, method and device, a medium and a program product in the technical field of computers. In the application, the plurality of memory devices are used as extended memories of the plurality of computing devices in the host. The switching devices are connected between the computing devices and the memory devices and can determine the memory devices connected with downstream ports, record corresponding memory connection relations and send the memory connection relations to a main management controller in a host; when any computing device reads and writes the memory abnormally, the main management controller locates the abnormal memory device based on all the received memory connection relationships, and sends a hot removal command to the switching device connected with the abnormal memory device through the network device, so that the corresponding switching device responds to the hot removal command and performs hot removal on the abnormal memory device. Therefore, global memory connection topology identification, fault detection and hot removal are realized for the memory device, and normal operation of host services is not affected.

The invention relates to a low-voltage distribution network topology identification method based on voltage-current correlation feature mining, and the method comprises the steps: carrying out the time synchronization processing of user-side multi-source electric parameter data after obtaining the user-side multi-source electric parameter data, and forming a unified electric parameter data set; after further preprocessing the integrated electric parameter data, extracting a voltage feature sequence based on the preprocessed point parameter data; the method comprises the following steps: establishing a user group, calculating voltage curve similarity among users, establishing a feature matrix and performing clustering analysis to preliminarily identify the user group, then establishing a mixed integer programming model by combining a Kirchhoff's current law, optimizing a phase-line-user relationship, and finally outputting a topological structure result. The problems that the low-voltage power distribution network topological structure is insufficient in recognition precision, poor in data consistency and low in dynamic adaptive capacity are solved, so that efficient and reliable network topology recognition is achieved, and monitoring and fault management of the power distribution network are effectively supported.

The invention relates to the technical field of distributed node identification and meter recombination, and discloses a distributed node identification and meter recombination strategy generation method, which comprises the following steps: collector end self-discovery: a collector broadcasts a discovery signal containing a unique identifier of the collector and a discovery request according to a preset period; node information collection and analysis: the collector receives and stores response information of the nodes; node topology identification: constructing a node topology structure model based on the analyzed node information; automatically grouping according to topological structures and functional attributes of the nodes; and generating a meter recombination strategy: detecting that a node topological structure changes, realizing automatic identification of the node topology and automatic grouping of equipment through a self-discovery mechanism, quickly and accurately detecting existence of each node by a collector in a deployment site, automatically constructing the topological structure according to a preset rule or an intelligent algorithm, and automatically establishing a reorganization strategy according to the preset rule or the intelligent algorithm. And the nodes are reasonably grouped, so that the workload of manual intervention is reduced.