126 results about "Distributed collaboration" patented technology

A composite AI system and method for advanced reasoning and automation that integrates symbolic knowledge graphs and algorithms with non-symbolic, or connectionist, models such as neural embeddings. A hierarchical architecture enables dynamically distributed, cooperative reasoning through layperson and expert-led challenge-based verification, model blending, model fitness and retraining and selection, comprehensive feedback loops at individual model or model blend or process flow with or without supervision, and specialized routing of processing to account for various operational risk, regulatory, legal, privacy, or economic considerations. Models, datasets, knowledge bases, simulations and simulation components, and embeddings are iteratively refined using knowledge graph elements and model, process, simulation or flow / process optimal hyperparameters which are recorded and tracked. Extraction of symbolic representations from connectionist models links them to curated ontologies of facts and principles.

The invention discloses a remote centralized control distributed data processing and analysis method based on a JMS, and the method comprises the steps: achieving the message routing and resource scheduling through the real-time monitoring of the state of a system, reinforcement of learning decision, deep learning optimization and multi-dimensional message distribution; dynamically adjusting message flow rate and queue length based on real-time network conditions, node load and flow fluctuation; through edge node intelligent task scheduling, distributed collaboration of calculation and storage is realized; adaptive compression algorithm selection is carried out by using deep learning, and the consumption of message transmission bandwidth is reduced; non-tampering storage of messages is realized through a block chain technology; based on a container arrangement technology, dynamic expansion and contraction of computing resources are realized; and dynamic resource scheduling and optimization decision are realized by utilizing reinforcement learning and multi-dimensional monitoring data. According to the invention, the bottleneck problem of the traditional message transmission system is overcome, and the system can efficiently and reliably process mass data in a high-concurrency and high-throughput scene.

The invention relates to the technical field of lithium battery safety management, and discloses a lithium battery fault diagnosis method based on a BMS, and the method comprises the steps: obtaining multi-dimensional battery operation data and real-time data, firstly extracting a local feature vector, generating a preliminary fault signal indication, then extracting an abnormal feature vector according to the preliminary fault signal indication, and carrying out the fault diagnosis according to the abnormal feature vector; and if the preset threshold is exceeded, compressing and transmitting to the adjacent management unit to form a shared data packet. According to the local feature vector and the shared data packet, updating a diagnosis model parameter to obtain an optimized fault recognition model for analyzing real-time data and calculating a fault matching degree, and determining a potential fault type if a threshold value is exceeded; and generating a collaborative query request to the distributed network to obtain a historical fault empirical data set, integrating the historical fault empirical data set, refining parameters to obtain an accurate fault probability, activating an alarm and recording a log if a warning threshold is exceeded, and finally updating the global shared knowledge base. According to the method, the problem of insufficient lithium battery fault diagnosis accuracy in a distributed scene is solved, and collaborative optimization of diagnosis accuracy and distributed collaboration is realized.

The invention discloses a power equipment monitoring and early warning system based on intelligent patrol, and relates to the technical field of power system intelligent monitoring and safety early warning, the system fuses visible light, infrared, ultraviolet, acoustics, vibration and other multi-source signals, innovatively encodes multi-modal features in a complex manner, realizes high-speed and high-dimensional feature extraction through an optical convolution chip, and realizes high-precision and high-dimensional feature extraction. And the early perception capability of hidden dangers is greatly improved. The adaptive complex neural network is introduced, the model structure and parameter dynamic optimization, self-learning and self-evolution functions are achieved, the fault recognition accuracy is remarkably improved, and false alarm and missing alarm are reduced. The system supports cloud side end distributed collaboration, data real-time convergence and multi-scene visualization, has hierarchical deployment and online adaptive capabilities, and adapts to intelligent operation and maintenance requirements of multiple types of power stations. The system has the advantages of high performance, high intelligence, easiness in integration, expandability and the like, is prominent in innovativeness, and can be widely applied to intelligent monitoring and operation and maintenance in electric power and related fields.

The invention discloses an efficient task scheduling method based on distributed collaboration, and belongs to the technical field of distributed computing. Aiming at the problems of non-uniform resource allocation, insufficient task type difference adaptation, lack of multi-task dependence global optimization and the like existing in the existing scheduling strategy, the invention provides the following technical scheme: firstly, classifying tasks based on calculation characteristics and establishing a multi-dimensional resource index; secondly, performing dynamic weight evaluation on the heterogeneous resources by adopting an optimal worst weighting method (BWM), and constructing a task-resource matching matrix; a task dependency relationship is modeled through a directed acyclic graph (DAG), and a global priority sequence is generated in combination with a list scheduling algorithm; and finally, single-task optimal node matching is realized by adopting an elimination selection method, and cooperative scheduling is performed on multiple tasks by applying an arithmetic optimization algorithm (AOA). According to the method, accurate resource matching of a calculation-intensive task and a data-intensive task is realized through three technical dimensions of task feature perception, resource dynamic adaptation and dependency relationship collaborative optimization.

A composite AI system and method for advanced reasoning and automation that integrates symbolic knowledge graphs and algorithms with non-symbolic, or connectionist, models such as neural embeddings. A hierarchical architecture enables dynamically distributed, cooperative reasoning through layperson and expert-led challenge-based verification, model blending, model fitness and retraining and selection, comprehensive feedback loops at individual model or model blend or process flow with or without supervision, and specialized routing of processing to account for various operational risk, regulatory, legal, privacy, or economic considerations. Models, datasets, knowledge bases, simulations and simulation components, and embeddings are iteratively refined using knowledge graph elements and model, process, simulation or flow / process optimal hyperparameters which are recorded and tracked. Extraction of symbolic representations from connectionist models links them to curated ontologies of facts and principles.

The invention provides a new energy automobile in-vehicle network real-time intrusion detection system based on edge computing, which relates to the field of new energy automobile network security and intrusion detection and comprises an in-vehicle edge computing node, a vehicle-mounted communication interface and a regional collaboration module. The in-vehicle edge computing node, the vehicle-mounted communication interface and the regional collaboration module form a distributed collaboration architecture to realize in-vehicle network real-time intrusion detection and dynamic defense, and the in-vehicle edge computing node is deployed in a vehicle electronic control unit and a gateway, collects and analyzes network traffic and sensor data, executes a local defense decision, and sends the local defense decision to the regional collaboration module. The system monitors the controller local area network flow, the electronic control unit state and the sensor signal of the in-vehicle network in real time through edge calculation, network abnormity and physical attack signs can be found in time, the safety risk caused by delay detection is effectively avoided, and the efficiency and timeliness of in-vehicle network monitoring are improved. The system adopts intelligent analysis methods such as a topological immune reconstruction algorithm and a photon trajectory aggregation algorithm.

The invention provides a load balancing algorithm system of a server cluster, which relates to the field of computer server clusters and comprises a priority management module, a resource monitoring module, a load distribution module, a cooperative communication module, a cost evaluation module and a large flow algorithm splitting and sequential execution module. The large-scale process algorithm splitting and sequential execution module comprises a process splitting unit, a sequential arrangement unit and a load balancing execution unit. The method has the beneficial effects that a large-scale process algorithm is split and sequenced, so that the method runs according to a specified sequence under the balance of a plurality of loads, and the complex service processing capability of the system is improved; and meanwhile, algorithms such as service priority, resource adaptation, distributed cooperation and cost benefit optimization are comprehensively applied, the resource utilization rate, the system performance and the cost benefit are improved, the load balancing effect of the server cluster is enhanced, a distributed cooperation mechanism avoids a single-point fault, local load changes can be quickly responded, and the stability and availability of the cluster are improved.

The present invention relates to the field of avionics equipment. Provided is an avionics fault knowledge fusion method oriented to distributed collaboration. The method comprises the following steps: S1, acquiring multi-source fault knowledge data of avionics equipment, and performing analysis processing and screening on the acquired multi-source fault knowledge data of the avionics equipment; S2, for the problem of knowledge conflicts across different timelines, comprehensively calculating machining fault knowledge on the basis of attribute similarity, in order to complete fault knowledge fusion; S3, for the problem of fault knowledge conflicts from different sources, completing fault knowledge fusion by means of fault element annotation and identification; and S4, constructing a fault knowledge graph, and storing fused fault knowledge. The present invention can mitigate knowledge conflicts during both the initial construction of a fault knowledge model and the expansion of existing fault knowledge models, thereby improving the accuracy, reliability and integrity of knowledge model construction for avionics information equipment, and thus providing better support for fault localization and maintenance of the avionics equipment.

The invention relates to the technical field of gateway performance optimization, and discloses a multi-terminal gateway connection performance optimization method and system.The method comprises the steps that SPI bus parameters of gateway hardware are set, SPI high-speed communication driving configuration is obtained, multi-channel frequency resource allocation is executed, and a plurality of channel configuration items are obtained; performing parallel spreading factor monitoring on the plurality of channel configuration items to obtain spreading factor dynamic adjustment data; monitoring the gateway network connection state according to the spreading factor dynamic adjustment data to obtain a network state monitoring result; and inputting the network state monitoring result into a distributed cooperation system for gateway cooperation group division to obtain a gateway resource allocation scheme, thereby realizing dynamic optimal allocation of resources between gateways, avoiding overload operation of a single gateway, and improving the signal coverage range and the transmission quality.

The invention discloses a digital twin control platform. The platform comprises a multi-protocol adaptive edge intelligent gateway; the dynamic twin modeling layer comprises an online transfer learning engine and a federal learning coordinator; the intelligent decision control layer is integrated with an MPC-DRL hybrid algorithm and an elastic resource scheduler; and the security interaction layer supports RLWE homomorphic encryption and block chain traceability. According to the edge intelligent gateway, through the wavelet denoising algorithm and the improved Huffman encoder, the 5G and TSN networks are combined, the multi-modal data acquisition and transmission efficiency is remarkably improved, the problem of processing lag of a traditional platform is solved, and the industrial real-time requirement is met. The dynamic twin modeling layer adopts a physical constraint loss function of an online transfer learning engine and a differential privacy mechanism of a federated learning coordinator to realize real-time updating of the model and distributed cooperative privacy guarantee, and overcomes the defects of static modeling and data leakage.

The invention discloses a multi-agent cooperative electric power inspection method and system, which realizes intelligent and efficient inspection task distribution and operation inspection task processing by introducing a task coordinator middleware to optimize a task distribution process of a contract network protocol and combining a priority task queue and a dynamic task scheduling strategy. The method provides help for distributed cooperation and efficient operation inspection application of a panoramic inspection intelligent agent and an equipment operation inspection intelligent agent of an electric power station, and solves the problems of low inspection efficiency of an existing electric power industrial station and low task allocation and resource utilization rate in a multi-agent electric power inspection method.

The invention discloses a heterogeneous federated learning method based on attention guidance aggregation and prototype enhancement, and relates to a heterogeneous federated learning method. The invention aims to solve the problem that the existing heterogeneous federal learning method cannot improve the granularity of client feature representation learning and cannot solve class imbalance in global head training. The method comprises the following steps: step 1, constructing a feature aggregation mechanism based on attention guidance; 2, designing a contrast learning objective function, and explicitly enhancing inter-class separability and intra-class compactness of feature representation in a local training process; 3, designing a self-adaptive prototype enhancement strategy, and relieving a class imbalance problem; and 4, constructing a collaborative optimization framework of the global model and the local model. The invention belongs to the technical field of distributed collaborative learning.

The invention belongs to the technical field of urban power grid safety control, and particularly relates to a source-load-storage modeling method and a power grid voltage control method and system. The method comprises the following steps: carrying out fine modeling of photovoltaic, energy storage and classified loads, collecting equipment operation state data and identifying parameters; building a centralized coordination-distributed coordination double-layer control architecture, deploying a centralized coordinator and a distributed coordinator, and defining functions; measuring topology, source load real-time and historical data of the power distribution network under the control architecture, and executing dynamic cluster division; based on initialization of a historical optimal solution database, an ADMM distributed optimization algorithm is adopted to calculate the cluster total power adjustment amount of each cluster; and based on the cluster total power adjustment amount, the voltage deviation factor and the resource margin factor, power distribution of the photovoltaic reactive adjustment amount and the energy storage active adjustment amount is carried out on equipment connected with the nodes in combination with the model and the model parameters. And due to refined modeling, a basis is provided for voltage control, and the accuracy and reliability of control can be improved.

The invention discloses a distributed collaborative waveform switching method and system oriented to networked computing, and mainly solves the problems of overlarge time delay and overhigh complexity when distributed collaborative waveform switching achieves the consistency of the whole network in a multi-hop ad hoc network environment in the prior art. The implementation scheme comprises a distributed collaboration stage and a distributed decision consistency stage. Wherein in the distributed collaboration stage, each node periodically exchanges a state packet and a collaboration data packet, and in the exchange process, distributed waveform decision making and time delay information updating are respectively executed in the nodes; in the distributed decision consistency stage, a unique central node is selected through the collaborative data packet stored in the distributed collaborative stage, and the central node performs majority decision through the internally stored waveform decision information of each node to make an optimal waveform switching decision. According to the method, the calculation complexity can be reduced, the decision timeliness and accuracy can be improved, the distributed consistency of the whole network can be ensured, the communication time delay can be remarkably reduced, the system performance can be improved, and the method can be used for networked calculation in a wireless ad hoc network.

The present invention relates to an active-standby switching method for clock asynchronization and a weak-network environment, and relates to the technical field of distributed systems. The method comprises: providing a distributed lock service of FIFO, and in each process, determining, on the basis of the distributed lock service, whether a lock has been obtained; after startup, a management node first waiting, by means of the distributed lock service, to obtain the lock, creating a local lease after obtaining the lock, loading lease information from a parent node of a temporary node in a distributed cooperative system, and writing the lease information into the parent node of the temporary node after waiting for a preset loading time interval, wherein after the lease information is successfully written, the management node is considered to become an active node; and before data is written, the management node first determining, by means of a data writing fence, whether a data writing condition is met, and generating a denial of service anomaly if the data writing condition is not met. The problem of active-standby switching only being completed depending on clock synchronization and a smooth network is solved.

To provide a data compression system that combines large-scale language models (LLMs) with distributed computing to solve the problems that conventional compression techniques only consider statistical redundancy and are unable to exploit semantic structure or contextual information, and furthermore, large-scale model-based compression requires high resources and fails to fully exploit the interrelationships in multimodal data.SOLUTION: The present invention achieves highly efficient compression even in resource-constrained environments, by utilizing the interrelationships between different data formats using a multimodal understanding module, selecting an optimal model according to the data characteristics and communication environment using an adaptive model selection module, utilizing shared knowledge between terminals via a distributed collaborative compression module, transferring the compression capabilities of large-scale models using a knowledge distillation module to lightweight models, and integrating efficient coding using an adaptive arithmetic coding module.SELECTED DRAWING: None

The invention discloses an end network information fusion detection method based on AI assistance. The end network information fusion detection method comprises the steps that S01, terminal data are collected, and equipment fingerprints are constructed; S02, a unique equipment ID is generated through a Hash algorithm, and ID preposition marks are used for distinguishing terminal types and establishing virtual identities for equipment; S03, multi-source information fusion is carried out; s04, carrying out abnormal feature evaluation through a multi-source data cross validation model, and constructing a dynamic defense network through a bee colony distributed cooperation mechanism, S06, carrying out information integration traceability tracking, and S07, carrying out causal chain reverse deduction; the method has the advantages that the updated multi-source data cross validation model is higher in application list, behavior log and flow feature anomaly detection capacity and wider in range, the updated multi-source data cross validation model is further applied to dynamic behavior modeling, known dangerous behavior data can be removed, and the method is suitable for dynamic behavior modeling. And repeated data accumulation is avoided, the data stock is reduced, and the problem of high maintenance and updating cost is solved.

The invention discloses an intelligent MIMO signal transmission method and system based on distributed cooperation, and the detection method comprises the steps: continuously employing different distributed units in an MIMO system to execute preset detection steps until the number of iterations reaches a preset number-of-iterations threshold L; sending the estimation vector subjected to the L times of iteration to a central processing unit, and quantizing the estimation vector by the central processing unit to obtain a final detection signal; the preset detection step comprises the following steps: processing a received radio frequency signal into a baseband signal; performing channel estimation by using the pilot signal to obtain a local channel matrix; based on a multi-step conditional random block Kaczmarz algorithm, updating an estimation vector by using a baseband signal and a local channel matrix, and selecting a next distributed unit based on a multi-step conditional sampling probability; and sending the updated estimation vector to the selected next distributed unit based on the topological structure of the MIMO system. According to the invention, not only can higher performance and lower complexity be realized, but also the required data transmission bandwidth is smaller.

This invention provides a method for reconstructing actuator faults in a heterogeneous multi-agent integrated energy system, relating to the field of fault diagnosis. The invention establishes a unified dynamic model for the agents; defines intermediate variables and constructs their dynamic equations; builds a global error dynamic system for distributed intermediate observers and agents; derives the stability criterion for the global error system based on Lyapunov stability theory, obtaining the state observer gain matrix for state estimation; loads the state observer gain matrix and the gain matrix for state estimation into each agent; and during the real-time operation of the agents, obtains the current state vector estimate and intermediate variable estimate, and calculates the reconstructed actuator fault value. This invention utilizes an output space coupling mechanism, enabling distributed collaboration between different energy networks without requiring a unified model dimension, thus breaking down the physical barriers to actuator fault reconstruction in integrated energy systems.

The application relates to a communication-optimized distributed cooperation method, and belongs to the field of machine learning. Firstly, the relative entropy is calculated based on the difference between the global model of the last round and the local model of the current round, and whether the current model layer is transmitted is judged according to the result of the relative entropy; then, different quantization levels are specified by the server under different rounds to realize time-adaptive quantization, and different quantization levels are continuously specified for different clients under the same round to realize client-adaptive quantization, so that the problem of excessively high communication cost in a bandwidth-limited environment is solved. In addition, on the basis of normal local training of the client, the situation of frequent accidental interruption in the transmission process is considered, and when the geographical position of the client continuously changes, the federated learning can still be normally trained.

This invention discloses a hardware-in-the-loop (HIL) simulation platform for load cluster tracking control. The master station simulation module simulates the issuance of load cluster control aggregation commands. Any smart energy unit module decomposes the load cluster control aggregation commands according to a distributed collaborative control algorithm, achieving distributed collaboration among each smart energy unit. Any adjustable load in each adjustable load cluster decomposes the load cluster control commands according to the distributed collaborative control algorithm, achieving distributed clustering of each adjustable load in the adjustable load cluster. Each adjustable load calculates its current adjustment amount based on the received load control commands and adjusts its operating state in real time according to this adjustment amount. This invention employs a distributed collaborative control strategy on the HIL platform and uses a synchronization mechanism to ensure the continuity of the distributed strategy, improving the system's stability and robustness.

The present invention discloses a data transmission method for smart agricultural mobile scenarios in the field of wireless communication technology, comprising a ground base station at a base station transmitting a signal to a drone cluster relay communication system, the drone cluster relay communication system receiving the signal and forming a virtual antenna array, the drone cluster relay communication system reconstructing and optimizing the beam, and using the APSSA-PSO algorithm to optimize the parameters of the communication system to ensure a stable connection between smart agricultural machinery in agricultural operation scenarios. The drone cluster relay communication system performs beam reconstruction based on the optimization results, determines the number of nodes and the optimal position of the drone cluster, and then generates distributed collaborative beamforming through the virtual antenna array to transmit data to ground agricultural equipment. The present invention solves the problems of insufficient long-distance signal coverage and unstable transmission when nodes move in traditional wireless communication technologies in smart agricultural scenarios by using drones as mobile relay nodes.

This invention discloses a distributed intelligent lighting control method and system based on edge computing, belonging to the field of intelligent lighting technology. The system includes a distributed lighting node module, an edge computing node module, a cloud collaboration module, a multimodal sensing module, a communication module, and a user interaction module. These modules are deployed in a distributed manner, achieving an organic combination of localized control and cloud-based collaborative management. The multimodal sensing module collects multi-dimensional data such as illumination, personnel, temperature, and humidity within the area. The edge computing node module completes lighting control decisions through localized computation. This invention also discloses the corresponding control method, including steps such as initialization, data acquisition and preprocessing, localized control, distributed collaboration, and cloud optimization. This invention solves the shortcomings of existing systems, such as high response latency, poor fault tolerance, low control accuracy, and high energy consumption, achieving precise, rapid, and energy-saving lighting control, improving system stability and scalability, and adapting to various application scenarios.

The application provides an intelligent summary automatic processing method for bad asset related cases, and relates to the technical field of data processing, which comprises the following steps: Sp1: collecting original case records of bad asset related cases, wherein the original case records comprise judicial ruling texts, financial reconciliation data and protocol scan images; Sp2: constructing a distributed collaborative analysis unit and receiving the original case records; in the distributed collaborative analysis unit, the original case records are converted into a weighted characteristic matrix through a case characteristic clustering algorithm, wherein the weighted characteristic matrix comprises a core characteristic vector, an auxiliary characteristic vector and a stability factor vector; by introducing the distributed collaborative analysis unit and the k-means clustering algorithm, the technical solution realizes automatic characteristic extraction and clustering processing of large-scale case data. Compared with the inefficiency of manual review, the present application can quickly process hundreds to thousands of cases, significantly shortening the processing time.

The invention relates to a multi-hydrogen energy industrial park hierarchical game operation method, which belongs to the field of hydrogen energy industrial park comprehensive energy system optimization scheduling, and comprises the following steps: firstly, combining a carbon transaction mechanism to construct an energy aggregator economic operation model based on energy hub optimization and a user energy consumption cost model based on demand response; a price-demand response collaborative optimization operation strategy of a single hydrogen energy industrial park based on a master-slave game is proposed by taking the maximum net income of an energy aggregator and the minimum energy consumption cost of a user as targets, and a multi-EA collaborative operation model based on hydrogen energy transaction is established based on a cooperative game and considering the operation privacy of multiple HIPs. Distributed optimization with the goal of minimizing alliance cost is realized by adopting an alternating direction multiplier method, and a benefit distribution mechanism is designed by utilizing a Shapley value. According to the method, modeling is carried out on interests and interaction mechanisms of different subjects, a scheduling method based on distributed collaboration is adopted, the collaboration and game problems of the subjects in a multi-HIP operation scene are solved, and multi-HIP economical and low-carbon operation is achieved.

The invention provides a power supply bus monitoring method and device, and relates to the technical field of power supply bus monitoring. The method comprises the following steps: carrying out standardization processing on collected operation data to obtain local storage data in a unified format; performing real-time monitoring and state judgment on the corresponding bus system based on the local storage data, and generating capacity information of the corresponding bus system; the edge data processing servers communicate with each other through a lightweight communication protocol to share respective capacity information; generating a load transfer instruction based on a preset distributed capacity coordination rule set; and the edge data processing server sends a load transfer instruction to the bus system of the edge data processing server and the opposite bus system. According to the method, the limitations of data islands, data isomerism, communication limitation and difficulty in efficient utilization of dispersed historical data are broken through, a light-weight and high-robustness distributed coordination mechanism is constructed, and dynamic and rapid mutual assistance and distribution of cross-regional bus capacity based on a real-time state are realized.

The application provides a smart logistics and warehouse control processing method and system. The method detects the power grid and communication state in real time through an independent monitoring device, and immediately broadcasts an emergency activation signal once an anomaly occurs; an edge computing node receiving the signal switches to emergency power supply and activates a low-power communication module to form a regionalized emergency collaborative subnetwork; each AGV reports its own state to the subnetwork master node, and the master node generates a candidate set containing multiple emergency tasks for each AGV based on global information; and the AGV selects and executes the optimal task according to the preset decision model and its own state. The method realizes seamless switching from centralized control to distributed collaboration, ensures that the system can still operate in an orderly manner under sudden failure, and avoids system-wide paralysis; through regional networking, the state sharing and task collaboration among AGVs are realized, and the emergency efficiency is improved.

The invention discloses a phase synchronization method and system for distributed cooperative nodes, and relates to the technical field of wireless communication. The method comprises the following steps: each distributed cooperative node generates a transmitting phase weight of a current time slot and cooperatively transmits a signal to a target node, wherein the transmitting phase weight is only composed of a reference phase and a disturbance phase; when each distributed cooperative node generates a next time slot disturbance phase, a deterministic inversion mechanism based on a reverse state mark is introduced, a phase disturbance step length is adaptively and dynamically adjusted by using a hysteresis type double counter, iteration is carried out until each node phase converges, and the next time slot disturbance phase is generated; the problems of reverse response delay, high calculation overhead, poor step length stability and the like in the prior art are solved. The reverse operation can be triggered without waiting for continuous failure accumulation, the phase calculation architecture is simplified, the node storage and calculation burden is reduced, meanwhile, the stability and convergence precision of phase disturbance step length adjustment are improved, and the phase synchronization requirement of the distributed cooperative beam forming system can be effectively met.

The present invention discloses a method for collecting and locating traveling wave characteristics of distributed cable faults, which relates to the field of cable fault technology. The method includes collecting traveling wave characteristic information through branch point sensing units and sharing the collected traveling wave characteristic information through a communication network; constructing a dynamic topology perception and network reconstruction framework, wherein weighted averaging includes assigning weights to data sources based on signal-to-noise ratio, topological distance, and time consistency; establishing a propagation path model of traveling wave signals in distributed cables, and using hyperbolic positioning rules in combination with the propagation path model to define the cable fault location coordinates. The present invention enhances the distributed collaboration capability of the system. While improving positioning accuracy, the overall solution has good scalability and adaptability. It can be widely used in fault monitoring and management of large-scale distributed cable networks, effectively reducing troubleshooting time and cost, and providing strong support for the intelligent operation and maintenance of power systems.