1130 results about "Graph theoretic" patented technology

The invention relates to the technical field of network time delay scheduling, and discloses a deterministic network time delay scheduling method and device based on a TSN and a storage medium. The method comprises the following steps: carrying out graph theory analysis on a TSN network topology structure to obtain a network node connection relationship and link bandwidth information; extracting time delay requirements and bandwidth requirement characteristics of the data streams, and generating a data stream priority queue and a resource requirement index matrix; inputting a deep Q network, establishing a state-action mapping relation including a network load state and a time slice distribution action, and generating an initial time delay scheduling strategy model; calculating queuing time delay, transmission time delay and processing time delay of each data stream, and generating a time slice resource redistribution scheme; generating a global resource allocation matrix based on the time slice resource reallocation scheme; and parameter feedback updating is carried out on the initial time delay scheduling strategy model to obtain a target time delay scheduling strategy model, and the utilization efficiency of network resources is remarkably improved through bandwidth borrowing and time slice reallocation.

The invention discloses a DDGS three-dimensional storage device based on PLC control and a management method of the DDGS three-dimensional storage device, relates to the technical field of intelligent storage management, and solves the problems that in traditional DDGS feed storage, the storage space utilization rate is low, the storage turning cost is high, risk control is not timely, and a quick response strategy is lacked. According to the method, the dynamic perception metadata warehouse is constructed, and DDGS material characteristic key data is monitored in real time to support anomaly detection; a storage control topology model is generated by using a graph theory modeling and dynamic attribute injection method to map an exception type; periodically collecting equipment data and fusing and updating a storage state; state diagram nodes are enhanced, risk response priorities are calculated, and grading operation is triggered; pLC control logic is dynamically adjusted based on historical data, and real-time monitoring and multi-device linkage control are achieved; the intelligent level of DDGS three-dimensional warehouse management is remarkably improved, the warehouse space utilization rate is increased, and the risk control capability is enhanced.

The invention provides a large-scale network node scene construction method and system based on a network target range, and relates to the technical field of network security, and the method comprises the steps: dividing network nodes into a plurality of attack and defense levels through calculating the vulnerability association degree between the nodes, and screening a target network node template based on a vulnerability chain-type propagation coefficient. Secondly, optimizing a network topology structure by adopting a graph theory clustering algorithm, and constructing a connection relation between nodes based on service affinity; and finally, according to the resource utilization rate index, constructing a node operation environment, deploying network services, implanting a vulnerability program and carrying out anomaly detection and repair, and finally generating a large-scale network node scene meeting resource optimization scheme constraints. According to the method, the large-scale network target range with a complex topological structure and a real vulnerability environment can be automatically constructed, and the efficiency and authenticity of network security attack and defense drilling are improved.

The invention relates to the technical field of power grid fault repair, in particular to a power grid fault emergency rescue method. The method comprises the following steps: acquiring topological structure data, key equipment parameters and real-time operation state data of a power grid; constructing a power grid digital twin model according to the power grid topological structure data, the key equipment parameters and the real-time operation state data, and performing real-time state evaluation to obtain fault risk early warning data and real-time fault condition data; when the real-time fault condition data shows that a fault occurs, performing fault point positioning and fault type determination based on deep learning according to the fault risk early warning data and the real-time operation state data to obtain fault diagnosis data; and constructing a fault influence range model according to the fault diagnosis data and a preset FACTS and HVDC characteristic model. According to the method, deep learning, a graph theory and a multi-objective optimization technology are fused, and intelligent fault identification, influence range modeling and accurate emergency disposal scheme optimization are realized.

The invention discloses a transmission line three-dimensional reconstruction method and system based on a monocular channel monitoring camera, and the method comprises the steps: obtaining compensation image data through adaptive illumination compensation processing, obtaining direction feature data based on frequency domain feature analysis, constructing a texture enhancement model through a graph theory, generating enhanced image data, and carrying out the reconstruction of a transmission line. Outputting feature aggregation data through time sequence feature aggregation; generating three-dimensional contour data in combination with multi-wire topology analysis and geometric constraint optimization; depth probability modeling and multi-scale consistency processing are adopted to obtain optimized depth data; and outputting system state data, three-dimensional point cloud data and deformation parameter data through physical model construction and space-time constraint fusion. According to the method, high-precision three-dimensional reconstruction of the power transmission conductor is realized, and the method has relatively high environmental adaptability and calculation efficiency.

The invention relates to the technical field of intrusion detection, in particular to a network threat detection method and system, and the method comprises the following steps: building a threat path logic diagram through collecting field dependency items, action trigger timestamp items and action propagation hop count items of an attack behavior chain, and matching field dependency items among nodes based on a graph theory algorithm to obtain a threat path logic diagram; and detecting a mutual exclusion logic field combination, and generating a logic diagram structure with a connecting edge and a mutual exclusion mark. In the method, a threat path logic diagram is constructed by fusing field dependence, action timestamps and propagation hops, graph theory identification field mutual exclusion combination enhances cross-protocol attack chain analysis, and hidden Markov modeling state transition probability verifies time sequence continuity and path length. And performing dynamic time warping alignment on forward and reverse instruction sequences to extract semantic offset, overlapping rate and time sequence entropy, and performing non-linear score classification based on an isolated forest to detect an adversarial sample, topological structure analysis, time sequence verification, instruction alignment and non-linear classification to cooperatively identify a composite attack with field mutual exclusion and time sequence confusion.

The patent application describes an adaptable neuromodulation device for transcranial electrical brain stimulation. The system can generate complex poly-modulated waveforms by combining multiple pulse parameters in a randomized manner across dimensions like amplitude, frequency, phase, timing, and polarity. It leverages both digital and analog modulation techniques for waveform versatility. The device features pathway optimization algorithms that tune stimulation using neural feedback data to target specific brain region shapes with greater precision. It also enables closed-loop operation for responsiveness to physiological changes. The application details the mathematical and computational foundations, spanning graph theory to generate digital twins. It highlights applications from cognitive enhancement to seizure control. Overall, the innovation promises versatility and precision in non-invasive neuromodulation through adaptable, optimized waveforms grounded in sophisticated modeling.

The invention provides a method and a system for detecting and defending cross-domain threats of a power system. The method comprises the following steps: after carrying out anomaly identification on operation monitoring data of a physical domain node in a target power grid region to obtain an anomaly identification result and carrying out denial of service attack identification according to network flow data of an information domain node to obtain an attack identification result, carrying out abnormal event association analysis on the anomaly identification result and the attack identification result to obtain an attack cross-domain anomaly identification result; according to key nodes and key risk propagation paths in a cross-domain attack chain generated based on a graph theory algorithm, generating an attack tracing atlas, and according to vulnerability information of the key nodes in the atlas, obtaining a corresponding power system topological graph and a corresponding communication network topological graph; and iteratively generating an active defense rule based on a game theory algorithm and a reinforcement learning algorithm, and issuing the active defense rule to the node. According to the method, the cross-domain attack risk is accurately perceived in real time and adaptive security defense is executed through cross-domain abnormal event association analysis, so that the comprehensiveness and reliability of security protection of the power system are improved.

The invention discloses an unmanned aerial vehicle group cooperative inspection control method for power station inspection, and the method comprises the steps: determining an unmanned aerial vehicle group according to a power station inspection task; performing task allocation of the heterogeneous unmanned aerial vehicle cluster by using a multi-objective optimization algorithm; performing unmanned aerial vehicle path planning by adopting an improved dynamic multi-population particle swarm algorithm; based on a graph theory and master-slave control, unmanned aerial vehicle group attitude consistency control is realized; integrating a target detection technology based on deep learning, and constructing an inspection anomaly detection model; performing remote fault diagnosis analysis on the real-time inspection data through an inspection anomaly detection model to obtain an inspection anomaly result; optimizing the inspection anomaly detection model according to the target condition of the real-time inspection data; and dynamically controlling task allocation and path planning of the unmanned aerial vehicle group according to an abnormal inspection result. Through the scheme, potential problems can be found and processed in time, the intelligent control level of power station inspection is improved, and the efficiency and accuracy of unmanned aerial vehicle group cooperative inspection are improved.

The invention discloses a comprehensive regulation and control method and system based on multi-mode electromagnetic and ultrasonic stimulation, and the method comprises the steps: collecting brain wave signals in real time through a multi-electrode EEG system, synchronously collecting skin electroreaction and heart rate variability signals, preprocessing the collected signals, and carrying out the feature extraction; the extracted signal features are subjected to multi-modal feature fusion through a CNN, and the multi-modal features are subjected to time sequence dependency analysis through a long-short-term memory network to generate a real-time emotional state quantization vector and identify a current emotional state category. Through synchronous acquisition and feature fusion of multi-electrode EEG, EDA and HRV signals, a real-time brain function connection network is constructed, accurate recognition and stimulation strategy making are performed on emotion regulation and control nodes based on graph theory analysis, the accuracy and reliability of emotion recognition are improved, personalized regulation and control of individual emotion states are realized, and the emotion regulation and control efficiency is improved. The cognitive function and language expression ability of an individual can be improved, and movement coordination disorder caused by emotional fluctuation is reduced.

The invention discloses an intelligent inspection monitoring method and system based on electronic vision, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: collecting multi-mode inspection data, and carrying out the preprocessing of the data; target identification classification and anomaly detection are carried out based on a convolutional neural network; and optimizing the inspection path by adopting a graph theory model and a shortest path algorithm. According to the intelligent inspection monitoring method based on electronic vision, the image obtained in the inspection process is analyzed through the convolutional neural network, the fault detection precision and the equipment classification accuracy are improved, the flexibility and the accuracy of anomaly detection are greatly improved through the auto-encoder model, and the detection accuracy is improved. The equipment position is regarded as the node of the graph, and the optimal inspection path is calculated by adopting the graph theory model, so that the inspection efficiency is remarkably improved, the inspection scheduling is optimized, the inspection coverage rate is improved, and better effects are achieved in the aspects of fault identification accuracy, inspection efficiency and adaptability.

The invention relates to the technical field of communication control, and discloses an interconnection test method and system for a wireless communication module, and the method comprises the steps: collecting real-time communication data through a multi-source communication interface, extracting topological features through a graph theory model to generate a matrix, and obtaining a signal optimization parameter through a self-adaptive filtering model; and constructing a multi-target test path planning model and outputting optimal path data, and establishing a layered test execution model to complete the test. The system comprises a communication topology analysis module, a path dynamic planning module and a hierarchical test execution module. The method overcomes the defects of a traditional testing method, can comprehensively collect data, accurately analyze topology, effectively process signals, scientifically plan a testing path and perform layered collaborative testing, has remarkable effects in improving testing accuracy, efficiency and comprehensiveness, can adapt to the development requirements of a wireless communication technology, and ensures stable and reliable work of a wireless communication module.

The invention discloses a disaster emergency rescue command system and method, and relates to the technical field of rescue command systems. Comprising an information collection module which collects information, including disaster situations, personnel distribution and environmental conditions, of a disaster site through sensors and communication equipment; the collected information is input into the system, so that accurate disaster assessment can be conveniently obtained; the decision support module determines optimal allocation of rescue resources by using a linear programming model according to disaster assessment provided by the information collection module, and plans a rescue path by using a graph theory algorithm; and the command control module is used for converting the rescue decision of the decision support module into a specific rescue instruction. According to the system, the disaster assessment time can be shortened through real-time monitoring and rapid data processing, and the rescue decision-making speed is increased; optimal configuration of resources is realized through an algorithm, resource waste is avoided, and resource availability at a critical moment is ensured.

The invention discloses a V2X-based global path planning method for a mine unmanned driving cloud control platform, and relates to unmanned driving. The V2X-based global path planning method comprises the following steps: constructing a traffic semantic map data structure # imgabs0 #; according to task issuing or operation plan adjustment, generating path request data R, and performing time constraint, resource constraint and path optimality constraint verification on the path request data R; according to the R and # imgabs1 #, adopting a heuristic search algorithm based on a graph theory to carry out optimal path search on the road topological structure, and generating a global path P containing a node sequence and driving parameters; and acquiring obstacle data detected by the vehicle end through a local sensor, performing obstacle avoidance correction through a local path optimization algorithm according to the obstacle data and the global path P, and generating a local path meeting the safety distance constraint and the path deviation constraint. According to the method, on the premise of meeting multi-dimensional coupling constraints such as time-space, priority-resource, safety-efficiency and the like, the optimal driving path dynamically adapting to the complex environment of the well industry and mining is generated.

The invention relates to the technical field of three-dimensional modeling in engineering surveying, in particular to a point cloud building component modeling method and system based on feature extraction. Three-dimensional point cloud data of building components are obtained, a multi-scale local neighborhood calculation covariance matrix is constructed with each point as the center, eigenvalues are decomposed, an edge probability graph is generated, corresponding component categories are semantically output, and corresponding key feature point sets are screened; taking the feature point cloud as a control point, constructing a three-level B-spline surface model, and adjusting the corresponding spatial distribution density; a weighted complete graph is formed through a key feature point set, a non-planar area is identified and processed through a Kurtowski theorem, a QEM algorithm is applied to carry out lightweight processing on a three-dimensional model, screening and optimization are carried out through automatically extracting key feature points of building components, and a concise and accurate lightweight model is constructed based on feature point topological optimization and a graph theory algorithm. The automation degree and efficiency of modeling are improved, and the contradiction between model lightweight and precision is effectively solved.

The invention discloses a low-voltage power distribution area topology identification method and system based on clustering analysis and a graph theory, and relates to the technical field of low-voltage power distribution area topology classification. The system can accurately identify the topological structure of each partition through the combination of a clustering analysis model and a graph theory algorithm, and automatically calculate a topological characteristic coefficient Tp; therefore, the complex topological structure of the power distribution area can be classified and analyzed more carefully and comprehensively. According to a traditional method, accurate classification is often difficult when a complex branch structure is faced, and a topology recognition module ensures the accuracy of a classification result through a calculation mode of a graph theory. Through the feedback management module, when the power performance risk coefficient Lpjx of the partition exceeds a safety threshold R, a fault early warning signal can be sent out in time, abnormal nodes are automatically identified, faults are isolated, meanwhile, load transfer is executed, the power demand is rapidly transferred to the normally operating partition, and the power failure range and time are reduced.

The invention discloses a self-adaptive event triggering safety control method of a multi-agent system under spoofing attack, and relates to event triggering control of the multi-agent system. A communication topological graph, an adjacent matrix and a Laplacian matrix of the system are defined based on a graph theory, it is assumed that communication topology between multi-agent systems is randomly switched, and the switching process follows a continuous time Markov process; assumptions on the communication topological graphs are relaxed, it is required that the combined graph of all possible communication topological graphs is undirected connected, and it is not required that each switching sub-graph is connected; modeling a spoofing attack, setting an attack signal upper bound, and defining a system security control objective function under the spoofing attack; an event-triggered distributed adaptive security control strategy is designed, the state of a neighbor agent is estimated by using an observer technology, it is verified that the control strategy can ensure the system security under the Markov random switching communication topology, and the effectiveness of the method is verified by using a Lyapunov stability theory, a stochastic analysis and linear matrix inequality method and simulation.

The invention discloses an automatic river network grading and river topological relation identification method based on a graph theory, and relates to the technical field of hydrological geographic information. The method comprises the following steps: acquiring and cleaning a vector river network, a key point location and DEM data of a target drainage basin; constructing an initial river network graph model based on the line element connection relationship; integrating DEM topographic evidence and graph theory connection features, constructing and solving a global potential energy field equation containing topographic driving and boundary constraint, and calculating flow potential energy attributes of nodes of the whole network to determine a flow relationship; based on the flow direction relation, identifying topology abnormal structures such as strong connectivity components in the network, and performing ring breaking processing by using direction confidence to generate a ring-free directed network structure; and performing river grade division based on a topology transfer rule, and associating the key point location to a river network skeleton. According to the method, through global potential energy field solving and topological optimization, the problems that the flow direction of the plain micro-geomorphic area is difficult to recognize and complex loops cannot be graded are solved, and automatic construction of the river network topology is achieved.

The invention belongs to the technical field of power distribution network fault recovery, and provides a power distribution network fault recovery method based on graph reinforcement learning and a related device, and the method comprises the steps: constructing a power distribution network fault recovery problem graph model, and carrying out the embedding of a power distribution network model graph based on a graph attention neural network; based on the power distribution network fault recovery problem graph model, an agent set of multi-agent reinforcement learning is constructed, and the power distribution network fault recovery problem is modeled as a Markov decision process; and solving the Markov decision process, carrying out multi-agent training, and generating a fault recovery strategy by the trained agents according to the current state of the power distribution network. According to the method, the technologies of graph theory, deep learning and reinforcement learning are deeply fused, and efficient and accurate power distribution network fault recovery strategy generation is realized.

The invention provides an intention guide pushing method and system based on geographic position information and multimedia interaction. The method comprises the following steps: constructing an interest model based on a geofence technology and a deep neural network; generating a user activity intention prediction result based on the interest model, obtaining a behavioral rhythm analysis result by using a Bayesian network and a time sequence prediction algorithm based on the user activity intention prediction result, and constructing a user and place relation graph by using a graph theory algorithm based on determining the optimal pushing opportunity of the behavioral rhythm analysis result. Generating a time sensitivity optimization scheme; generating customized content based on a time sensitivity optimization scheme, processing the customized content by adopting an emotion analysis method, a collaborative filtering algorithm, an auto-encoder and a multi-head attention mechanism to obtain final content, adjusting the final content through an instant feedback mechanism, and generating a multimedia push packet; according to the invention, the performance and service quality of the intelligent pushing system are improved, and more accurate and personalized recommendation experience is provided for the user.

The invention discloses a power grid fault online identification system and method based on big data, and relates to the technical field of power systems, and the method comprises the steps: collecting multi-source data of a power grid system through a sensor; data filtering is performed through a hierarchical storage strategy, a data lake is constructed in combination with multi-dimensional data indexes, and multi-source data is stored in a classified manner; aggregating and converting data in a data lake by adopting a real-time stream processing framework, and extracting high-dimensional features related to a fault by combining wavelet transform with principal component analysis; based on the high-dimensional features, identifying the real-time state of the power grid by using a graph neural network in combination with a deep learning model; and according to the identification result of the real-time state, combining a power grid topological structure, and carrying out fault positioning through a time difference method and a graph theory algorithm. The power grid fault on-line identification system based on big data provided by the invention can efficiently manage and store a large amount of multi-source data in a power grid, and improves the data processing and access speed.

The invention relates to the technical field of fracture network fracture water channel identification, and discloses an identification method for analyzing a dominant flow channel in a three-dimensional fracture network based on a topological network. According to the method, probability distribution parameters such as geometric occurrence, gap width and density of a rock mass multi-scale fracture system in a target area are obtained through field surveying and mapping and three-dimensional scanning, and a spatial topological structure of the three-dimensional fracture system is reconstructed by adopting Monte Carlo method simulation and discrete fracture network modeling technology; secondly, abstracting the fracture network into a three-dimensional topological graph model based on a graph theory principle, defining fracture center points and cross points as graph nodes, and converting fracture sections into weighted edges; the method breaks through the continuous medium hypothesis limitation of traditional seepage analysis, and has important engineering application value in the fields of deep geological energy storage reservoir seepage risk assessment, shale gas fracture network optimization design, rock slope stability analysis and the like; compared with a traditional numerical simulation method, the method has the technical advantages of being high in calculation efficiency, good in prediction precision, high in multi-scale applicability and the like.

The invention discloses an intelligent contract vulnerability detection and repair system based on a heterogeneous graph neural network, and belongs to the technical field of block chain security, and the system comprises a contract analysis module, a multilayer graph construction module, a heterogeneous graph neural network module, a vulnerability feature library, a vulnerability recognition engine, an automatic repair module and a visual interface. After the source code of the intelligent contract is input, code analysis and standardization are completed by a contract analysis module; the multi-layer graph construction module constructs a contract internal heterogeneous graph, an inter-contract interaction graph and an ecosystem relation graph based on a graph theory; the heterogeneous graph neural network module learns a vulnerability feature mode; the vulnerability recognition engine combines the vulnerability feature library to realize vulnerability classification and risk assessment; the automatic repairing module generates a repairing scheme; and the visual interface realizes detection progress monitoring, result display and encrypted report export. The intelligent contract vulnerability detection and restoration system based on the heterogeneous graph neural network provided by the invention provides technical support for block chain digital asset security and ecological stability.

The invention provides an intelligent actual measurement and quantity management method and system for a building. The method comprises the following steps: capturing environmental parameters, material circulation paths and spatial geometric information of key parts of a building in a construction process in real time to obtain a measurement data set; a distributed ledger technology is combined with a homomorphic encryption algorithm, a graph theory analysis technology is applied to evaluate a dependency relationship among operation points of a construction site, and a data log is generated; according to the data log, using an augmented reality technology to superpose a BIM model, introducing a support vector machine algorithm to carry out automatic classification and risk assessment processing on deviation data between measured data and preset standard data, and generating a rectification suggestion report; and a closed-loop quality management process is formed by continuously collecting rectification effect feedback. According to the technical scheme, high transparency and efficient quality control in the construction process are ensured.

The invention discloses an energy storage method and system for base station management, and the method comprises the steps: carrying out the multi-modal data fusion processing according to the historical business volume data, real-time user movement mode data and meteorological data of each virtual base station in a target region, and obtaining a dynamic load prediction result of each virtual base station in a target time period; based on a dynamic load prediction result, adopting a distributed constraint optimization algorithm to dynamically allocate energy storage resources, and generating an optimal allocation strategy containing a cross-base-station energy scheduling path; and according to the wireless network topology structure characteristics of the target area, constructing an energy scheduling decision model based on a graph theory, optimizing an energy storage scheduling time sequence, and generating a dynamic energy storage scheduling scheme with a fault-tolerant mechanism so as to realize energy storage for base station management. By utilizing the embodiment of the invention, the energy efficiency of the base station can be improved, energy waste is reduced, and efficient configuration and real-time scheduling of energy storage resources are realized.

The invention relates to the field of surveying and mapping and geographic information systems, and discloses a high-precision surveying and mapping method based on multi-source data fusion, and the method comprises the following steps: S1, obtaining data sources from a plurality of sensors, and carrying out the preprocessing of the data sources; s2, constructing a sparse representation model for each preprocessed data source, and obtaining a sparse coefficient and a dictionary through an optimization method; s3, estimating posterior distribution of the sparse representation model by using a variational inference technology; s4, fusing the sparse coefficients of the plurality of data sources based on a multi-task sparse coding method; s5, enhancing the relevance among the data sources through a graph theory regularization method; s6, optimizing the fusion result by using a semi-definite programming method to obtain a globally optimal solution; and S7, outputting an optimized surveying and mapping data result. Through efficient fusion and optimization of multi-source data, high precision, consistency and robustness improvement of surveying and mapping data are realized, and the method is suitable for wide surveying and mapping application scenes.

The invention provides a power distribution network multi-time scale voltage regulation and control method adapting to topological changes, and aims to solve many problems of traditional power distribution network voltage regulation and control. According to the method, firstly, key parameters of the power distribution network are monitored in real time, topological changes are recognized through a clustering analysis algorithm, and a node-branch model based on the graph theory is dynamically updated; secondly, designing long-time and short-time scale strategies, adjusting equipment based on load prediction and a power generation plan for a long time, and rapidly adjusting load and distributed power supply changes for a short time; and then sensitivity analysis is carried out, and a regulation and control strategy is optimized according to the influence weight of the equipment on the voltage. A distributed cooperative control framework is constructed, and a regional controller coordinates a photovoltaic inverter and an energy storage device according to a selected protocol and mechanism. Finally, deviation is calculated through real-time data collection, and a control strategy is adjusted in a self-adaptive mode. The method effectively deals with topological changes, integrates the advantages of multiple strategies, improves the voltage regulation and control precision, enhances the power supply reliability of the power distribution network and the distributed energy consumption capability, and guarantees the stable operation of the power grid.

The invention provides a fire-fighting escape guiding method based on a sensor, and the method comprises the steps: calculating a safe evacuation path and evacuation facilities through a graph theory algorithm according to a building structure model and the position information of escapers in combination with a fire spreading trend, a fire spreading speed and a fire spreading direction; according to the fire development situation and a preset trigger threshold value, the updating frequency of fire induction and evacuation alarms is dynamically adjusted, and it is ensured that real-time alarms transmitted to escapers have perspectiveness and accuracy; fire induction information and evacuation alarms are dynamically pushed to escapers at different positions through electronic display equipment or sound prompts, and evacuation paths and facilities which should not go to or be used at present are informed; according to the real-time position of the escaper and the fire development condition, the induction direction and the evacuation path are continuously optimized, and the accuracy and the real-time performance of user safety induction and evacuation alarm are ensured.

The invention relates to the technical field of ecological networks, and discloses a method and device for evaluating the influence of a road on an ecological network, computer equipment and a medium, and the method comprises the steps: obtaining basic geographic information data of a research region, and carrying out the preprocessing of the basic geographic information data; based on the preprocessed basic geographic information data, setting a plurality of scenes according to resistance surface superposition conditions of different grades of roads; constructing an ecological network corresponding to a plurality of different diffusion distance thresholds for each scene; evaluating the connectivity of the ecological network from a landscape scale, a plaque scale and a corridor scale by using a graph theory index and a circuit theory index to obtain a multi-scale ecological network connectivity evaluation result; based on a multi-scale ecological network connectivity assessment result, the influence degree of the road grade on the ecological network is compared and analyzed, and the problem that the influence of comprehensive assessment of the road grade on the landscape connectivity of the ecological network on multiple scales is not considered in the prior art is solved.

The invention discloses an electric vehicle charging path planning method based on a dynamic energy consumption model and road network information. According to the method, the influence of different temperatures on the battery capacity is analyzed according to the charging and discharging experimental data of the lithium battery, the power consumption generated in the driving process of the electric vehicle is analyzed, and a refined air conditioner energy consumption model and a mileage energy consumption model are established. Based on an improved A * algorithm, a method for searching a reachable path with the minimum energy consumption according to the residual electric quantity of a vehicle-mounted battery, the position of a charging station and the real-time road condition is established. The A * algorithm of the model solving algorithm serves as an optimal priority search algorithm in graph theory search methods, and the optimality requirement can be met by reasonably designing a function; and meanwhile, aiming at an urban road network, a heuristic A * algorithm is adopted to be matched with the path optimization solution of a large-scale traffic network in a road-electricity coupling scene. The effectiveness of the provided model in the charging path decision is verified by respectively comparing three targets of time optimization, path optimization and comprehensive energy consumption optimization provided by the invention.