551 results about "Mathematical Graph" patented technology

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 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 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 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 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 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 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 relates to the field of clean energy, and provides a distributed secondary control method based on deep reinforcement learning, and the method comprises the steps: S1, deploying a DDPG intelligent agent in a distributed micro-grid, and enabling the DDPG intelligent agent to be used for secondary control; s2, establishing a communication graph model of the distributed micro-grid based on a graph theory, so that each distributed power supply in the distributed micro-grid only exchanges control signals and voltage information with the adjacent distributed power supplies; and S4, regulating and controlling the output power of the distributed power supply by using the intelligent agent, and dynamically regulating the frequency and voltage secondary control signal according to the decision of the intelligent agent. Therefore, the dynamic adaptability and the system-level reliability of the distributed power system are improved.

The invention discloses a space-ground integrated dynamic traffic flow analysis and road topology reasoning method and device. A cloud server generates and issues real-time traffic information through multi-level processing such as map matching, traffic flow analysis, prediction and early warning and road topology analysis and updating; specifically, firstly, the high-precision positioning and communication capability of a low-orbit satellite is utilized, data fusion of an inertial measurement unit and a wheel speed sensor is combined, and a multi-source fusion algorithm is constructed to improve the positioning precision; secondly, traffic flow dynamic analysis and prediction are carried out based on a Transform interaction analysis model, and real-time updating of a road topological structure is realized at a cloud end by using a clustering algorithm and a graph theory model; finally, the traffic flow and road topology information is sent to related vehicles and a traffic management system, and real-time monitoring, early warning and optimization of the traffic state are achieved. According to the invention, the accuracy and real-time performance of traffic information acquisition, processing and issuing can be effectively improved, and the intelligent level of traffic management and decision making is enhanced.

The invention discloses a truck-mounted crane group operation safety monitoring system and method based on multi-source perception and collaborative decision, and particularly relates to the field of safety monitoring, the truck-mounted crane group operation safety monitoring system comprises a distributed perception module, an edge calculation module, a graded early warning and execution module, a digital twin platform module and a man-machine interaction module; collected multi-source data are fused through Kalman filtering and then transmitted to an edge computing node; generating a dynamic safety envelope region based on an adaptive kinematics model, predicting a group conflict risk through a graph theory algorithm, and calculating collision time; the digital twin platform fuses a BIM model and real-time data, hoisting path rehearsal, load swing prediction and foundation bearing capacity visualization are carried out, an improved RRT * algorithm is adopted to plan a collision-free path, and a Lagrange dynamics model is adopted to predict a swing track; the grading early warning module triggers differential response according to the risk grade; and risk warning and path guidance are provided through the AR-HUD and the three-dimensional situation billboard.

The invention provides a method for optimizing the mechanical property of a bio-based composite material based on nano modification, and belongs to the technical field of high-molecular compound compounds.The method includes the steps that wood flour is subjected to silane coupling agent modification pretreatment, a functionalized nano particle dispersion liquid is prepared, the dispersity is evaluated through a graph theory model, and the mechanical property of the bio-based composite material is optimized. The modified wood flour and polyethylene are subjected to melt blending in a twin-screw extruder in proportion, the functionalized nanoparticle dispersion liquid is synchronously added, real-time regulation and control are performed by using an intelligent dispersion optimization model, and the network structure integrity is evaluated through gridding performance indexes. The tensile strength, the bending strength and the impact strength are comprehensively optimized by adopting a multi-objective optimization game model, the process parameters such as the nano-particle addition amount, the mixing temperature and the mixing time are adjusted according to the optimization result, and the optimization is repeated until the preset target values are reached, so that the problems that the nano-particles are non-uniformly dispersed, local stress concentration points are formed, and the stability of the nano-particles is improved are solved. The overall mechanical property is influenced.

The embodiment of the invention provides a water supply network pollution tracing and positioning method and device based on a graph theory. The method comprises the following steps: S1, abstracting a topological structure of the water supply network based on a hydraulic model of the water supply network, constructing an adjacent matrix of the water supply network based on the topological structure, and determining an initial dynamic detection point candidate range by combining the adjacent matrix of the water supply network and utilizing a breadth-first search algorithm in a graph theory; s2, according to the dynamic detection point candidate range, dynamic detection point candidate schemes are generated, the information gain of each dynamic detection point candidate scheme is evaluated, and a dynamic detection point most beneficial to pollution source positioning is selected based on the information gain; s3, repeatedly executing the step S2 to obtain water quality pollution information, and continuously narrowing the candidate range of the dynamic detection points; and S4, when the calculated node pollution source result meets the positioning success condition or the dynamic detection point candidate range is empty, determining the position of the pollution source. In this way, the position of the pollution source can be quickly positioned.

The invention discloses an electric power engineering cost simulation analysis method and system based on digital twinning, and relates to the technical field of simulation analysis, and the method comprises the steps: obtaining multiple engineering task multi-modal data and task dependency relationships for electric power engineering; processing the multi-modal data of the plurality of engineering tasks and the task dependency relationship based on a digital twinning technology so as to construct an electric power engineering virtual model; inputting the electric power engineering virtual model into a graph neural network so as to extract task node features corresponding to each engineering task virtual module through a message passing mechanism; and defining an input state of a reinforcement learning model according to each extracted task node feature so as to generate a task resource allocation action for each engineering task through reinforcement learning. Therefore, the digital twinborn technology, the graph theory feature extraction technology and the reinforcement learning technology are comprehensively applied, real-time monitoring and simulation analysis are carried out on cost and resource consumption of different links in the electric power engineering, and resource allocation is intelligently optimized.

The invention discloses a graph theory-based industrial internet identification system full-process tracing method, which realizes full-process tracing in a supply chain by introducing identifier fields into metadata and constructing a directed graph structure. The identifier fields include fields with similar representations for representing associations of the product upstream and downstream of the supply chain. The system queries across enterprises through a graph traversal algorithm (such as depth-first search (DFS) or breadth-first search (BFS)), and tracks the full life cycle of a product. The method supports attribute pointing code management, is used for tracing multi-attribute information (such as allocation weight, time and the like) of a product, solves cross-enterprise and cross-system coding differences through an alias mechanism, and simplifies a tracing process. The method has a flexible traceability path setting capability, can monitor the product circulation process in real time, gives out an early warning when an abnormality is found, and improves the transparency and safety of a supply chain.

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 splicing curved surface reinforcement method fusing a graph theory and UV parameterization. The splicing curved surface reinforcement method comprises the steps of creating a component BIM three-dimensional model, inputting reinforcement parameters, searching an arrangement path and generating reinforcing steel bars. In order to solve the technical problem of splicing curved surface reinforcement in the BIM three-dimensional design process of complex structure engineering, a graph theory is introduced to construct an edge and surface topological connected graph model, so that the problem of steel bar path planning in disordered splicing surfaces is effectively solved, and the construction efficiency is improved. The rapid routing of the steel bar arrangement path in the curved surface topology network and the rapid positioning of the steel bar arrangement path in a plurality of sampling points on the topology edge are realized. Meanwhile, by combining a UV bidirectional parameterization mapping mechanism, the problem of self-adaptive arrangement of the reinforcing steel bars in the three-dimensional curved surface is solved, so that efficient solving and accurate control of the reinforcing steel bars of the complex spliced curved surface are achieved, a traditional reinforcing steel bar mode dominated by manual experience is broken through, the automation degree and engineering reliability of the reinforcing steel bar design of the complex spliced curved surface are remarkably improved, and the construction cost is reduced. Good application prospects are realized.

The invention discloses a data communication method, system and device under a disaster condition, and belongs to the field of power systems, and the method comprises the steps: determining a graph structure model based on a current network topology structure and network topology change information under the disaster condition, carrying out the calculation through a graph theory algorithm, and determining a transmission node and a standby path; if the node connection is abnormal, performing multi-path planning calculation on the standby path to obtain a planning result; splitting the power grid information packet into a plurality of data packets, performing data transmission through a multi-path planning result, and monitoring a transmission progress and arrival confirmation information in real time; and if it is monitored that the transmission is overtime or lost, updating the planning result based on a preset path switching rule to obtain a target planning result, and performing rerouting processing on the remaining data packets according to the target planning result to realize data communication under the disaster condition. And efficient, reliable and stable operation of data communication of the power grid under disaster conditions can be realized.

The invention discloses a hybrid model driving-based space-time consistency regulation and control method for a central heating system, which comprises the following steps of: performing hydraulic equilibrium analysis on the central heating system, and performing hydraulic analysis on a space structure of a heating pipe network by using a graph theory method based on an energy conservation law, simulating the hydraulic working condition of the official website by using a basic loop analysis method; based on an energy conservation and mass conservation heat balance equation, a heat source and a heat user are indirectly connected through heat exchanger transition, a dynamic physical model of each subsystem is established based on a lumped heat capacity method, a refined model of the heat supply system is established, and the thermal dynamic characteristics of the system are researched and analyzed; based on a Pearson's correlation function, considering thermal inertia of equipment such as a pipe network of the central heating system and a building fence structure, and determining a regulation and control period and time of the regulation and control model; according to the method, by controlling the opening degree of the regulating valve of the heat exchange station, the suitability degree of the room temperature of a heat user is guaranteed.

The invention provides a zero-sum game-based distributed elastic consensus optimal control method under denial of service attack of a multi-agent system, and the method specifically comprises the steps: firstly, constructing a multi-agent formation model in which a leader and a plurality of followers cooperatively move through graph theory knowledge and a multi-agent second-order state equation; secondly, in order to reduce the influence of denial of service attack on communication topology, a time-varying weight distributed elastic observer is provided to estimate the state of a leader, and the attacked condition of the leader is considered; then, by constructing an augmentation system, a distributed consistency tracking problem with a leader is converted into a local tracking problem between each follower and a virtual leader thereof; and finally, in order to solve the zero-sum game problem, introducing a Hamiltonian-Jacobian-Ansaxophone equation to realize optimal control input under maximum external disturbance, and realizing algorithm design by using single-evaluation reinforcement learning with experience playback and combining a gradient descent method.

The invention relates to a river network grading system and method based on a graph theory and confluence cumulant, and belongs to the technical field of water conservancy projects and geographic information. Comprising the following steps: acquiring original DEM data and water system data, and converting the water system data to a preset projection coordinate system; extracting a primary river network based on the DEM data, and screening the primary river network in combination with the length of the river network and the area of the drainage basin to obtain a simplified river network; river network flow calculation: carrying out flow assignment on each river network element in the simplified river network based on the confluence cumulant; graph theory modeling: abstracting the simplified river network into a directed graph model; and river network grading: reverse tracing is carried out by taking the water outlet as a starting point, and the hierarchical relationship between the trunk and the branch is determined by combining the in-degree / out-degree and the edge weight of the node, so that river network grading is realized. According to the method, river network grading is upgraded from morphological description to hydrological function description, and the method is suitable for high-precision river network refined analysis, intelligent water conservancy emergency management and other scenes.

The invention provides a mutual supply and mutual aid hybrid control method based on a honeycomb-shaped transverse topological framework, and the method comprises the following steps: carrying out the abstract representation of the transverse topological framework of a honeycomb-shaped power distribution network through a graph theory, and enabling each vertex to represent a main transformer substation of each regional grid in the honeycomb-shaped power distribution network, the regional grids are connected through a flexible interconnection device; constructing an effective node set through a screening method based on honeycomb-shaped effective ring topology; and representing the hybrid discrete-continuous action space model through a conditional variation auto-encoder and an embedded table, and embedding an approximate tidal current diagram neural network into a reward function of a parameterized action Markov decision process to realize optimal cooperative mutual supply and mutual aid of the honeycomb transverse topology. According to the method, the overall load balancing rate of the honeycomb-shaped power distribution network can be greatly improved on the basis of meeting the power flow constraint, the safe and optimized operation of the power grid is ensured, and the application of the deep reinforcement learning method in the power grid mixing problem is expanded.

The invention discloses a coal rock instability precursor identification method and system based on fracture topological parameters, and the method comprises the steps: obtaining sound wave data through micro-seismic monitoring, and extracting coal rock fracture core parameters in combination with seismic source positioning and seismic source mechanism inversion; building a fracture network topology model based on a graph theory method by considering the geometric dimension and direction characteristics of the fracture, and obtaining fracture space-time topology evolution parameters to build a multivariable time sequence topology characteristic data set; introducing a multivariable time sequence graph model MTAD-GAT, dynamically capturing a structural dependency relationship among topological parameters by using a graph attention mechanism, mining collaborative anomaly characteristics of the structural dependency relationship, and capturing an evolution trend of the structural dependency relationship in combination with a time sequence modeling module; and fusing the prediction error and the reconstruction error to form an abnormal score as an instability precursor index, extracting covariant features and evolution trends of a plurality of key topological indexes in the precursor stage, and assisting in distinguishing an instability critical state, thereby realizing unsupervised automatic identification and intelligent early warning of the topological precursor.

The invention provides an unmanned aerial vehicle-unmanned vehicle combined formation cooperative control method and system, and relates to the technical field of vehicle-vehicle cooperation, and the method comprises the steps: taking a virtual unmanned aerial vehicle as a leader, taking the unmanned aerial vehicle and the unmanned vehicle as followers in a unified manner, setting a fixed formation offset for each follower, and defining a communication topological relation based on a graph theory; the method comprises the following steps: acquiring an actual measurement state vector of a sensor in real time, processing a current estimation state through a constructed radial basis function neural network observer, outputting a disturbance estimation value in combination with a weight matrix, updating the estimation state according to a core equation, and judging whether to update the weight matrix or not according to an observation error; calculating the expected state of the follower according to the reference trajectory of the leader and the fixed formation offset, calculating the formation error, constructing an event trigger communication condition, judging whether the condition is met or not, enabling the follower to interact the state and the error according to the communication topology only when the condition is met, or else, continuing to use the previous trigger data; and finally, current control input of the follower is calculated through a distributed control law.

The invention provides a multi-unmanned aerial vehicle communication sensing cooperation method in a power grid fault scene, and the method combines unmanned aerial vehicle dynamic topology reconstruction and a multi-stage optimization strategy, and achieves efficient information acquisition for emergency scenes of damaged ground base stations and large-area power failure in disaster areas after a power grid fault. Power flow entropy is introduced to analyze power grid node vulnerability, communication unmanned aerial vehicle deployment is optimized, and an optimal communication coverage strategy is solved based on a multi-target grey wolf optimization algorithm; meanwhile, a graph theory method and a shortest path search algorithm Dijkstra are used for optimizing the inspection path of the unmanned aerial vehicle, so that the fault diagnosis efficiency is improved; under a two-stage optimization framework, in the first stage, important load nodes are preferentially covered and routing inspection is executed, in the second stage, supplementary optimization is carried out on an uncovered area, and finally a global optimal solution considering unmanned aerial vehicle resource efficiency, communication quality and information acquisition is obtained. The method improves the recovery efficiency of a post-disaster power grid, and is suitable for the emergency response of a power system in an extreme disaster environment.

The invention discloses a climate risk prevention and control method and device based on a graph theory and a knowledge graph. The method comprises the following steps: collecting climate data, traffic facility data and geographic topology data from multi-source heterogeneous data; constructing a knowledge graph by utilizing the entities extracted from the data and the relationship between the entities, representing knowledge by adopting triads in the knowledge graph, and updating by adopting a dynamic updating mechanism; a multi-temporal-spatial dynamic network is constructed based on a graph theory technology, a time dimension is introduced into the multi-temporal-spatial dynamic network, and a dynamic propagation process of climate risks is described by using a nonlinear dynamic model; describing a dynamic propagation process of climate risks in the multiple spatial-temporal dynamic networks by using a nonlinear dynamic model; by analyzing the multiple time-space dynamic networks, key nodes and risk propagation paths are identified and optimized. The technical problem that in the prior art, key risk nodes and propagation paths are difficult to accurately recognize, and consequently corresponding optimization strategies are lacked is solved.

The invention relates to the technical field of artificial intelligence, in particular to a personalized soft skill training method and system based on artificial intelligence. The method comprises the following steps of performing evaluation by using a multi-dimensional questionnaire, and analyzing semantics of user answers in combination with a large language model to generate an initial user portrait; updating the initial user portrait; constructing a skill tree by using a word embedding technology and an improved Woltzz method; constructing and optimizing a global model based on the skill tree; and dynamically generating a personalized learning path according to the global model. By introducing an artificial intelligence large language model technology, a neural network word embedding algorithm and a graph theory algorithm and combining a'questionnaire-learning-practice 'closed-loop training mode, individuation, systematization and intelligentization of soft skill training are realized, the technical bottleneck of a traditional method in the aspect of subjective question type intelligent feedback is broken through, and the intelligent feedback of the subjective question type is realized. And the soft skill training effect and the user experience are obviously improved.

The invention provides a park integrated energy system low-carbon scheduling method combining demand response and carbon emission flow, and belongs to the technical field of energy system low-carbon scheduling. Establishing a directed weighted carbon flow network model based on a graph theory, tracking a carbon emission transmission path by adopting a maximum flow minimum cut theorem and a proportional allocation principle, constructing a space-time coupled dynamic carbon flow state space model, and performing state estimation by adopting a Kalman filtering algorithm; the carbon emission responsibilities are distributed based on a Shapley value method, a stepped carbon transaction cost function is established, an optimal scheduling strategy is solved through a double-layer iterative optimization framework, and the technical problem that the carbon emission responsibilities are difficult to distribute reasonably due to the fact that a park integrated energy system cannot accurately track a carbon emission transmission path when electric heat gas multi-energy flow coupling is considered is solved.