245 results about "Short path algorithm" patented technology

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention relates to the technical field of fault positioning, and is used for solving the problem that a fault event positioning system in the prior art has limitation and cannot adapt to dynamic changes. The invention provides a fault event positioning method based on topological model tracking analysis. The method comprises the following steps: S1, constructing a topological model based on system information and setting an edge weight; s2, tracking a fault propagation path by using time sequence data, and updating a path weight; s3, a fault propagation path is optimized through a shortest path algorithm, the shortest path algorithm adopts an A * algorithm, and a fault source is verified in combination with machine learning; and S4, outputting fault source nodes, related path information and confidence evaluation. The method is suitable for various complex network systems, is high in universality and expansibility, can adapt to the dynamic change of the topological structure and the running state of the system by dynamically updating the edge weight of the topological model, tracking the path and the machine learning model in real time, and always maintains high positioning accuracy and efficiency.

The present invention discloses a bus route optimization design method and system based on resident travel big data, the method comprising the steps of: obtaining mobile phone signaling data of residents in a target area; obtaining the stop points of residents' travel trajectories; dividing the target area into a number of traffic areas; obtaining an OD matrix based on the stop points of residents' travel trajectories and traffic areas; constructing a bus route planning model; generating a number of bus alternative routes based on a shortest path algorithm; obtaining a target bus route from the alternative routes based on a genetic algorithm and in combination with an objective function and an OD matrix. Compared with the prior art, the present invention not only innovatively combines spatiotemporal big data and genetic algorithms in technical means, improving the accuracy and efficiency of bus route planning, but also demonstrates strong advantages in system architecture and data processing capabilities, and can provide a more scientific, dynamic and sophisticated optimization solution for the urban public transportation system, contributing to the efficient operation of urban transportation and the construction of smart cities.

The embodiment of the invention provides a routing method and device based on satellite processing, and the method employs a time slice division mechanism, and constructs a dynamic network topology structure according to a satellite constellation operation period and a link building rule. And the gateway station uploads the corresponding relation data of the time slice and the network topology to the satellite node for storage in advance. And after the satellite node receives the data, based on the network topology corresponding to the current time slice, the shortest path algorithm is adopted to realize autonomous routing calculation, and a next hop forwarding node is determined. And ensuring that the data is transmitted to the target node along the optimal path through a dynamic routing recalculation mechanism of the relay node. According to the method, the defects of the traditional technology in the aspects of routing real-time performance, topology management, on-satellite processing and the like are effectively overcome, and the performance of a satellite communication network is remarkably improved.

The invention discloses a load transfer path regulation and control method and system based on a maintenance topology rule base, and relates to the technical field, and the method comprises the following steps: constructing a graph model, obtaining a load node of a shutdown device in the graph model, and obtaining a load path switching node based on a connected component analysis method; correcting the graph model to obtain a power grid topological graph of load path switching nodes, and generating a load path switching scheme based on a shortest path algorithm; a pre-switching adjustment strategy is set, and a load transfer action instruction is generated and executed step by step based on a load path switching scheme; carrying out local control on load transfer path adjustment based on a load transfer action instruction and a distributed control technology; the optimal load switching path is identified through connected component analysis and the shortest path algorithm, and the load transfer path of the maintenance equipment is controlled and switched according to the load transfer action instruction in combination with the distributed control technology, so that the stability, the flexibility, the response speed and the regulation and control precision of load path regulation and control are improved.

The invention discloses a sea area dynamic communication method and system constructed based on a signal boat relay chain, and relates to the technical field of ocean mobile communication networks. The communication priority index matrix adopts a multi-target particle swarm optimization algorithm to carry out optimal configuration on the deployment position of the signal boat in the target sea area and output a signal boat network initial topology; based on the communication priority index matrix, constructing an optimal multi-hop communication path for each terminal task node, and adopting a bandwidth weighted shortest path algorithm to output a path stability score to perform path selection and updating; and evaluating the link health degree based on the path stability score, and continuously executing a link state monitoring task on all the running communication paths. According to the invention, intelligent deployment and path stable scheduling of the signal boat are realized, the coverage rate, fault tolerance and task adaptability of an ocean communication system are improved, and communication continuity and system flexibility are guaranteed.

The invention provides an expectoration auxiliary control method and device. The method comprises the steps that audio signals of breathing sounds and cough sounds at multiple positions of the chest of a patient are collected through an audio sensor array, and a standardized audio feature data set is obtained through noise reduction, segmentation and feature extraction; a dynamic weighted graph model is constructed, and a random edge sampling algorithm and a parallel batch processing dynamic algorithm are combined to analyze and obtain a sputum viscosity index and a sputum distribution position map; based on the result, mapping the vibration parameter space into an unweighted disk diagram, and obtaining a personalized vibration treatment scheme by using a shortest path algorithm; in combination with the real-time breathing cycle of the patient, working parameters of the sound wave vibrator and the negative pressure suction device are synchronously controlled, and a coordinated and consistent multi-mode treatment execution instruction is obtained; and collecting real-time feedback data in treatment, and dynamically adjusting working parameters through reinforcement learning to obtain an optimized expectoration adjuvant therapy scheme. Intelligent adjustment can be achieved based on the real-time breathing state and sputum characteristics of the patient, the expectoration efficiency is improved, and discomfort of the patient is reduced.

The invention relates to the technical field of artificial intelligence, and discloses an operation and maintenance automatic fault diagnosis and repair system based on artificial intelligence, and the system comprises the steps: obtaining equipment operation data, and carrying out the denoising and cleaning; calculating the deviation and trend change of the equipment operation data, and dividing the fault equipment into different abnormal levels according to the abnormal fluctuation degree; executing a shortest path algorithm strategy, and calculating path weights from the fault equipment to all possible fault sources; executing a deep learning algorithm strategy, and performing root cause analysis according to historical data and current fault information of the equipment; automatically generating a repair strategy according to the fault type, executing an AI self-learning repair strategy, automatically generating a corresponding repair scheme, and performing secondary repair on the fault equipment in combination with a distributed self-repair technology; operation data are analyzed in real time according to historical fault data of the equipment, and faults of the equipment are predicted and processed in time; the downtime of equipment can be effectively shortened, the operation and maintenance efficiency is improved, and the labor cost is reduced.

The invention discloses a document generation system and method based on multi-agent collaboration, and the method comprises the steps: understanding and extracting the high-level semantic information of a text input by a user, and dynamically generating a problem to guide the user to define a demand, so as to form a detailed document demand; recognizing the theme and key elements of the document from the detailed document requirements; the optimal sequence of task execution is determined based on a shortest path algorithm, potential conflicts in task execution are predicted and solved through parallel processing and a search algorithm, and optimized task planning is formed through feedback circulation; generating a document outline according to user requirements, retrieving associated contents of document chapters so as to write chapter contents, and performing grammar and format verification to form a preliminary document; defining a document quality evaluation index according to user requirements and industry standards, performing document quality evaluation on the preliminary document, and feeding back the document quality evaluation result to the user; and generating a complete final document and presenting the complete final document to the user. According to the invention, the document generation quality is improved.

The invention discloses a lecturer video automatic generation method based on AI speech synthesis and animation driving. The method comprises the following steps: performing structured analysis on a PPT or a text script through an improved interior point method and an incremental shortest path algorithm; performing semantic grouping by applying a full-dynamic parallel single-link clustering algorithm and generating an enhanced script with an expressive mark; a CosyVoice technology is combined with a low-rank approximation method to generate a high-quality voice data stream; establishing a mapping relation between contents and action expressions through semantic analysis, and generating a complete action expression instruction set; and driving the digital human model by using the msueTalk technology, and generating a final lecturer teaching video through a parallel rendering algorithm. According to the invention, the method achieves the efficient and automatic generation of the education video, remarkably improves the content production efficiency, reduces the production cost, and guarantees the specialty and expressive force of the teaching video.

The invention relates to the technical field of computer networks, and discloses a method for locating reasons for network dial test quality index abnormity, which comprises the following steps of: multi-source data acquisition and network topology modeling: generating state information of network equipment and links by acquiring performance data of each equipment and flow data of the links in a network, and establishing a network topology model; establishing a network topological graph model; shortest path calculation and abnormal path identification based on the graph theory: calculating a shortest path from source equipment to target equipment by using a shortest path algorithm in the graph theory, and identifying abnormal links and equipment in the path; and load evaluation and bottleneck analysis based on an information theory. Through combination of multi-source data acquisition, graph theory optimization, information theory evaluation, game theory resource allocation, deep reinforcement learning and multi-target optimization, network paths and loads are dynamically adjusted, abnormal sources are accurately positioned, and network performance, stability and adaptive capacity are improved.

The invention provides a knowledge base and business system collaboration method under an AI platform, and belongs to the technical field of AI platform digital data processing.The method includes the steps that triple semantic analysis is conducted on query by constructing a multi-level semantic vector representation module, a parallel data retrieval engine is started, vector retrieval, graph reasoning and real-time data pulling are executed at the same time, and the query efficiency is improved; establishing a dynamic confidence evaluation mechanism to evaluate the quality of the data source, executing a weight distribution algorithm based on reinforcement learning, dynamically calculating the weight of the data source according to a query type by adopting a graph convolutional network intelligent routing decision model, and implementing multi-source data fusion and consistency verification to solve data conflicts through a weighted voting mechanism. An intelligent result sorting and filtering system is established, a multi-dimensional evaluation strategy is adopted to output high-quality answers, a continuous learning and feedback optimization loop is constructed, system performance is continuously optimized through active learning and a graph shortest path algorithm, and the technical problem that knowledge base data and a service system cannot effectively and uniformly make decisions is solved.

The invention discloses a construction emergency early warning method and system, and the method comprises the following steps: collecting the three-dimensional coordinates of a constructor, combining a sliding time window with a wavelet packet energy entropy and other indexes, and generating a multi-scale movement disorder index through principal component analysis and fusion; continuously unstable persons are recognized according to the disorder index, after the trajectory of the persons is segmented, a weighted graph is constructed in combination with hidden Markov and building information model environment parameters, and the cognitive mismatch degree is calculated; mapping the cognitive mismatch degree to a space grid, calculating a local Moran index, fusing a density gradient and a mechanical operation sequence resonance result, and constructing a propagation weight matrix; constructing a heterogeneous graph based on weight matrix guidance, calculating risk influence propagation potential energy, combining historical disorder sequence analysis and relative entropy, and coupling to obtain a group-level instability pre-judgment value; and outputting a comprehensive critical level for the pre-judgment value over-limit individuals through motion trend prediction, spatial intersection and shortest path algorithms. The safety during building construction operation is improved.

The invention discloses a low earth orbit satellite network digital twin drive switching optimization method and system, and relates to the technical field of inter-satellite communication, and the method comprises the steps: building a digital twin system which is synchronously evolved with a physical LEO satellite network; predicting the link quality between each candidate satellite and the user terminal in a future time window based on a digital twin system; dividing a time window into a plurality of continuous time slices, constructing a directed weighted dynamic graph model which takes a satellite-time slice as a node and takes link comprehensive utility cost as an edge weight, and searching an optimal path in the directed weighted dynamic graph model by utilizing a shortest path algorithm so as to determine an optimal switching opportunity and a target satellite sequence; the optimal switching strategy is issued to a user terminal to be executed, and a link quality prediction result is corrected in real time through a feedback mechanism of the digital twin system to form closed-loop optimization; the method can effectively meet differentiated service quality requirements in a multi-service scene, and is suitable for a high-dynamic LEO satellite communication system.

The invention discloses a multi-source field-level blood relationship tracking method and system. The method comprises the following steps: extracting features from a multi-source heterogeneous system, matching templates, triggering a corresponding acquisition strategy, and outputting an original blood relationship log; reading an original blood relationship log to generate an abstract syntax tree, extracting a source / target field set to output a field-level mapping relation, further writing the field-level mapping relation into a graph database to form a blood relationship edge, writing metadata into a relational database, establishing a two-level index, and outputting a blood relationship graph capable of being quickly positioned; and taking the blood relationship map as input, returning a target path with the maximum sum of confidence coefficients by adopting a weighted shortest path algorithm, outputting a link-level traceability result, and displaying the link-level traceability result. According to the method, complete tracking of the field-level blood relationship is realized, the query response time is shortened to a millisecond level, rapid access to multiple types of data sources is supported, and the problems of insufficient blood relationship analysis precision, low query efficiency and weak multi-source adaptability in the prior art are solved.

The invention relates to the technical field of artificial intelligence and educational informatization, and discloses a teaching application system optimization method based on local knowledge base and AI association, comprising the following steps: constructing a dynamic knowledge graph, the graph comprising a plurality of nodes, each node representing a subject knowledge point, each edge representing a derivation relationship between the nodes, a reasoning path between the subject knowledge points is optimized through a graph theory shortest path algorithm; a space-time attention mechanism is introduced, weighted fusion is carried out on multi-modal data according to learning states of students, and different modal data including weighted weights of texts, images and audios are adjusted. According to the technical scheme, graph theory reasoning is combined with dynamic knowledge graph construction, the technical effects of automatically optimizing a subject knowledge point derivation path and improving the knowledge retrieval efficiency are achieved, and the problems that the reasoning path between knowledge points is lengthy and the retrieval efficiency is low are solved.

The invention discloses a data mining method and system serving a micro-grid control system, and relates to the technical field of micro-grids, and the method comprises the steps: collecting the related data of a micro-grid, constructing a micro-grid data set S, carrying out the feature extraction, obtaining a voltage stability factor Sv (t), a frequency deviation factor Sf (t) and a harmonic total distortion rate THD, and obtaining a power quality comprehensive score Qelec, the method comprises the following steps: judging whether a micro-grid has a fault or not, if the micro-grid has the fault, carrying out fault positioning, constructing an aging model of an energy storage battery and an inverter, and for a line where a fault point is located and the energy storage battery and the inverter of the fault point, obtaining the residual use time RULb of the energy storage battery and the residual use time RULinv of the inverter, and the Dijkstra shortest path algorithm is used to generate the optimal fault repair path Popt for fault repair, so that the efficiency of micro-grid fault detection and repair is improved, the service life of equipment is prolonged, the maintenance cost is reduced, and the stability of micro-grid operation and the reliability of power supply are improved.

The invention discloses a Text-to-SQL (Structured Query Language) generation method and device, a medium and a product, and relates to the field of geographic information technology application, the method comprises the following steps: analyzing a natural language query text input by a user to obtain a source field set and a target field set; determining corresponding nodes of each field in the source field set and the target field set in the river-lake long-system database mode knowledge graph to obtain a source node set and a target node set; the nodes in the source node set serve as starting points, the nodes in the target node set serve as terminal points, the knowledge graph is processed through the shortest path algorithm, and a path set is obtained; decomposing the natural language query text into a plurality of sub-problems by adopting a large language model and a thinking chain technology, and obtaining a dependency relationship among the sub-problems; according to the dependency relationship and the target field set, a public table expression is obtained, and then the SQL statement is obtained, and the accuracy of database query language generation under the river and lake long-term system scene can be improved.

The invention discloses an order logistics timeliness management system, and relates to the technical field of logistics intelligence, and the system comprises a risk prediction module which collects order logistics full-link data, carries out the preprocessing of the data, calculates the logistics node delay risk probability through a virtual intervention scene and a causal graph model, and obtains a delay risk prediction result; the visualization module is used for performing overlay analysis on the delay risk prediction result and the preprocessed order logistics full-link data to generate a delay risk thermodynamic diagram; the destroy-resistant module is used for carrying out vulnerability evaluation on the delay risk thermodynamic diagram to generate a vulnerability analysis report, and carrying out multi-objective optimization evaluation through a k-shortest path algorithm to generate a destroy-resistant strategy table; the scheduling module is used for carrying out transport capacity resource matching and scheduling scheme selection by utilizing a multi-target resource optimization model based on the survivability strategy table to obtain a scheduling instruction set; according to the invention, causal traceability and probability quantification of logistics delay risks are realized by constructing a collaborative prediction mechanism of a virtual intervention scene and a causal graph model.

The invention relates to the technical field of intelligent scheduling, and particularly discloses an intelligent scheduling decision control method and system for a distributed car washing robot, and the method comprises the steps: constructing a weighted graph model which comprises a vehicle position, obstacle distribution and path reachability, and dynamically adjusting the weight of an edge according to the path length, energy consumption and time; optimizing the graph structure by adopting an improved minimum spanning tree algorithm, and calculating a path efficiency index in combination with fuzzy logic and a neural network technology; on the basis of the optimized subgraph, applying a shortest path algorithm to obtain a local optimal path, and introducing a dynamic adaptation coefficient to evaluate the response capability of the path to environment change; fusing the path efficiency index and the dynamic adaptation coefficient into a comprehensive scheduling feature vector, inputting the comprehensive scheduling feature vector into a trained deep learning scheduling model, and predicting an optimal scheduling strategy in combination with a task priority and a robot resource state; and dynamically adjusting the moving path and the operation density of the robot according to a scheduling result, and realizing self-adaptive evolution and cooperative control of the system.

The invention discloses an irregular ship scheduling method and system considering uncertain harbor time, and a medium, and belongs to the technical field of shipping management. The method comprises the following steps: collecting ship, cargo, port and interference event data; generating an initial scheduling scheme by adopting a greedy strategy; generating multiple groups of random interference scenes based on Monte Carlo simulation; a two-stage stochastic programming model is constructed, in the first stage, income maximization serves as a target, and an initial plan is generated through damage and repair operator iterative optimization by means of a self-adaptive large neighborhood search algorithm; in the second stage, aiming at each interference scene, a recovery network is constructed for the influenced goods, and an optimal recovery strategy and adjustment cost are solved by utilizing a shortest path algorithm; and finally, outputting a scheduling scheme including a ship path, a navigational speed and a multi-scene recovery strategy through iterative optimization. According to the method, the earnings and the operation efficiency of the shipping companies are improved, the adjustment cost caused by uncertainty is reduced to the greatest extent, the benefits of the shipping companies and cargo owners can be balanced in a complex scheduling problem, and the service quality is improved.

The invention provides a structural engineering bearing capacity field detection method, and belongs to the technical field of structural engineering detection.The structural engineering bearing capacity field detection method comprises the steps that a multi-axial acceleration sensor and a strain gauge array are installed on the surface of a to-be-detected structural member, and an intelligent loading device is adopted to apply incremental loads; a mobile data acquisition terminal is utilized to acquire vibration response data and strain response data in real time, modal parameters are extracted based on a frequency domain analysis method to establish a load-modal parameter relation curve, the bearing capacity of a component is analyzed and calculated, a graph theory shortest path algorithm is adopted to optimize load transmission path analysis, and the load transmission path analysis is optimized. The bearing performance of the component is accurately evaluated through the structural response prediction model based on the Transform architecture, the technical integration of multi-dimensional detection data fusion, intelligent loading control, quantitative bearing capacity evaluation and accurate performance prediction is realized, and the technical problem of insufficient detection precision of the bearing capacity of the structural component is solved.

The invention discloses an automatic air rail scheduling method and system, and relates to air rail scheduling, and the method comprises the steps: obtaining air rail data; calculating the residual load of each vehicle according to the vehicle data set, and generating a to-be-distributed vehicle set V; performing priority ranking on the task data set to generate a task set U; generating a vehicle-task matching relationship according to the vehicle set V and the task set U; path planning is carried out on the vehicle-task matching relation through an improved Dijkstra algorithm, and an optimal scheduling scheme of vehicle scheduling is obtained; the improved shortest path algorithm Dijkstra is used for dynamically adjusting a path weight by introducing a track congestion degree coefficient; scheduling the sky rail vehicle according to the optimal scheduling scheme; aiming at the low path planning efficiency caused by the dynamic change of the track congestion of the sky track, the planning efficiency is improved by constructing a vehicle-task bipartite graph and combining a path planning strategy for dynamically adjusting the track congestion degree and the like.

The invention discloses a data-driven unmanned aerial vehicle cluster digital twin network binding routing method. According to the method, a tight coupling dependency relationship between data transmission and link control is relieved, and a management plane-control plane-data plane decoupling unmanned aerial vehicle cluster network architecture is constructed. The management plane decides a link weight with the help of a depth deterministic strategy gradient learning method by taking minimization of time delay and packet loss probability as optimization targets. And the control plane formulates a forwarding strategy according to the Floyd shortest path algorithm and the weighted topological graph and issues the forwarding strategy to the data plane. And the unmanned aerial vehicle nodes in the data plane concentrate on data forwarding to realize execution of binding routing. A simulation result shows that the link weight is adaptively adjusted by adopting the method, and compared with a traditional routing algorithm with a fixed link weight, the average end-to-end time delay and the packet loss rate can be effectively reduced, and the network service quality is improved.

The invention discloses a power network anomaly detection method and system, and relates to the technical field of intelligent power grid monitoring, and the method comprises the steps: constructing a neuron-like power network graph model, carrying out the node processing of equipment, introducing a multi-factor anomaly score function, and dynamically activating an abnormal node through combining with an improved Winner-Take-All mechanism; the connection weight is dynamically optimized through a topology-aware composite gradient descent method, and a multi-layer neural network structure is constructed to realize cross-layer forward propagation and feedback adjustment; and establishing an abnormal path model in combination with the propagation phase difference and the information entropy, identifying an abnormal path by using a propagation score function and a weighted shortest path algorithm, and carrying out fault tracing. According to the power network anomaly detection method provided by the invention, the neuron-like graph model is constructed, and a multi-factor anomaly activation mechanism is introduced, so that comprehensive perception of a power grid node state under a multi-dimensional time characteristic can be realized, and the anomaly recognition sensitivity and the multi-point simultaneous activation capability are effectively improved.

The invention discloses a carbon dioxide seepage dominant channel judgment and permeation enhancement effect evaluation method and system, and the method comprises the following steps: obtaining three-dimensional image data through the three-dimensional CT scanning of a rock sample before and after a CO2 mineralization storage experiment reaction, extracting a crack network as a topological structure center line skeleton, calculating the topological indexes of a network structure, and calculating the permeability enhancement effect of the network structure; a fracture network topological structure model constructed based on a graph theory is coupled with a discrete fracture network model for CO2 flow simulation, an equivalent permeability tensor is obtained, an improved Dijkstra shortest path algorithm is adopted, spatial distribution of CO2 seepage dominant channels is obtained, dominant channel characteristics before and after transformation are compared with the equivalent permeability tensor, the seepage enhancement effect is quantitatively evaluated, and the CO2 seepage enhancement effect is obtained. And changing injection conditions to perform multi-group simulation under different working conditions, calculating the seepage enhancement effect and the mineralization reaction rate under different injection conditions, establishing a quantitative evaluation model of the mineralization sealing permeation enhancement effect, and realizing sealing effect prediction and seepage enhancement effect evaluation.

The invention relates to the technical field of electric vehicle charging and discharging scheduling, in particular to an idle energy storage vehicle scheduling method and system, and the method comprises the steps: obtaining a charging and replacing task or a power grid power transmission task, and determining a target position; selecting idle energy storage vehicles according to vehicle states in a set range of an area where the target position is located; according to the current position, the vehicle speed, the battery state, the mileage information and the idle state information of the idle energy storage vehicles, the power supply capacity is determined, and the idle energy storage vehicle with the minimum distance from the target position is screened out; based on a shortest path algorithm, an optimal path between the idle energy storage vehicles and the target position is obtained, a scheduling instruction is sent to the corresponding idle energy storage vehicles, and the idle energy storage vehicles execute corresponding tasks; and after the task is finished, obtaining the actual charging and switching quantity, the service time and the user feedback score to form a reward function, storing the reward function in historical data, updating the state-action value function during the scheduling period based on the historical data, and enabling the scheduling algorithm to autonomously learn the optimal decision strategy through updating.

The invention discloses an electric power Internet of Things service scheduling method and device based on an SDN and an NFV, and a medium, and belongs to the field of virtual networks, and the method comprises the steps: receiving an electric power Internet of Things service request issued by a cloud network cooperative management platform through an SDN controller, and matching a corresponding VNF combination for the electric power Internet of Things service request; based on the control domain network topology structure, performing first path planning for the VNF combination through a weighted shortest path algorithm to obtain a first deployment path; constructing an optimization objective function of the first deployment path based on the dynamically adjusted time delay weight factor and load weight factor, and performing global optimization on the first deployment path based on the optimization objective function to obtain a second deployment path; and generating a deployment instruction based on the second deployment path, and issuing the deployment instruction to each corresponding physical node through the NFVO to perform VNF instance deployment so as to complete power Internet of Things service scheduling. Therefore, by implementing the method and the system, accurate scheduling of various services of the power internet of things can be completed according to the time delay requirement.

The invention discloses a fault identification and prediction method based on low-voltage distribution network topology modeling and an intelligent algorithm. Aiming at the problems of low fault positioning efficiency, insufficient intelligence and the like of the low-voltage power distribution network, the method realizes fault full-process processing through multi-module cooperation: firstly, collecting data such as voltage and current in real time through an Internet of Things terminal, and constructing a high-quality data set in combination with edge calculation and a four-step preprocessing technology; secondly, constructing a separated complex-valued neural network, and inputting a plurality of voltage and power vectors to realize accurate prediction of loop closing current and voltage deviation; reasoning fuzziness of equipment states and environmental factors through a fuzzy network, outputting TOP3 fault reasons, and tracking a fault propagation path for accurate positioning in combination with a shortest path algorithm and a D-S evidence theory; and finally, generating a switching operation sequence and a digital twinborn verification recovery scheme through an improved matrix algorithm. According to the method, the problems of dynamic modeling, uncertainty reasoning and the like are solved, and technical support is provided for intelligent operation and maintenance of the power distribution network.

The invention discloses a polar region ship navigation planning method based on sea ice prediction and multi-stage path optimization, and the method comprises the steps: obtaining a navigation risk map based on a to-be-navigated ship according to the updated single-day sea ice data in a time group; the method comprises the steps of obtaining a candidate path set based on a K shortest path algorithm, determining final U candidate paths according to path similarity of a result set of the candidate paths, further obtaining comprehensive scores of the paths in the result set, and finally determining a final driving path of a to-be-sailed ship according to the comprehensive score of each final candidate path. And completing the path planning of the polar region ship navigation. According to the method, the time, the risk and the path smoothness under the distance suboptimal constraint are taken as optimization targets, and similarity calculation is performed on the paths in the result set of the obtained candidate paths, so that diversified risk avoiding scheme paths can be provided. According to the method, the prediction time range is sliced, so that the high-frequency environment updating requirement in the prior art is met.