440 results about "Mathematical Graph" patented technology

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 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 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 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 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 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 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 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.

A honeycomb-shaped active power distribution network topological structure selection and optimization method based on a graph theory comprises the following steps: taking line impedance and rated apparent power as constraints, constructing a multi-target weight factor, adopting an improved spectral clustering algorithm to cluster and distribute power grid nodes, and transiting a micro-grid group after cluster division into a honeycomb-shaped active power distribution network; abstracting each micro-grid as a honeycomb distribution network single node, constructing a connection relation mathematical representation of the HSPH and the micro-grid, and realizing sparse matrix method modeling of the honeycomb active power distribution network; optimizing a site selection strategy of the HSPH by taking a maximized HSPH return on investment index and a system stability index as a target function; and optimizing the energy storage capacity of the HSPH based on the net load time sequence data of the micro-grid group, and finally generating an optimal topological structure of the honeycomb-shaped active power distribution network. And through honeycomb topology and HSPH addressing and sizing optimization, the new energy consumption capability of the power distribution network is improved, and the economy, reliability and flexibility of the power distribution network are 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 provides a method for constructing an arch-shaped large-span shell steel roof, and belongs to the technical field of arch-shaped large-span shell steel roof construction. A temporary supporting system is built, and distributed optical fiber sensors are arranged to monitor the lifting process of a main arch; a cable force distribution scheme of a cable supporting system is calculated by adopting a double-layer game optimization model to realize cooperative control of stress uniformity and form precision, tensioning force is applied by using an intelligent tensioning system in stages, and a crane moving path is planned in combination with a path optimization algorithm based on a graph theory to realize graded unloading of a temporary supporting system. The technical problem that it is difficult to achieve structural stress uniform distribution and form accurate control of an arched large-span shell steel roof at the same time in the tensioning process of a cable supporting system is solved.

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 belongs to the field of construction of a protein structure on a cryoelectron microscope density map, and relates to a method for constructing a protein structure from a cryoelectron microscope density map by combining de novo modeling and structure prediction, a computer device, a readable storage medium and a program product. According to the method, the atomic probability and the amino acid type are predicted through the three deep neural networks respectively, full-atom optimization is carried out, the output result is combined with the graph theory, optimization and geometric algorithms to jointly assist protein structure modeling, and protein structure information can be mined to the maximum extent. According to the method, de novo modeling can be carried out in the absence of full-length structure data of the protein monomer, integrated modeling can also be carried out under the condition of inputting the full-length structure data of the protein monomer, the application scene is wide, the structure of the protein compound can be automatically constructed in a cryoelectron microscope density map with middle and high resolution, and the method is suitable for popularization and application. And for a region with poor local resolution in a traditional method, the modeling precision can be remarkably improved.

The invention discloses a cascade power station connectivity fish monitoring method and system, and the method comprises the steps: collecting the water flow speed, water temperature, dissolved oxygen and other hydrological parameters of a water area cross section, the fish activity frequency data, the gate dam opening and closing state, and the pump set operation parameters through a sensing assembly; dividing a potential migration area; abstracting a water area node as a graph theory vertex, abstracting a connected state as an edge weight, and constructing a model to calculate node connectivity; according to the pump set parameters and connectivity indexes, calculating water area node connectivity probabilities under different pump set operation conditions; the number, body type distribution and motion trail of fishes are obtained through an image and acoustic module and are associated and matched with a migration area and a connectivity probability; and integrating multi-link data to generate a connectivity and fish activity relevance monitoring result. Functional units are arranged in all links of the corresponding method of the system, all the units operate cooperatively, cascade power station water area connectivity and fish activity association monitoring precision are improved, and data support is provided for drainage basin ecological protection.

The invention discloses a cooperative control and disturbance suppression method for a multi-axis servo system, and relates to the technical field of cooperative and disturbance control of servo systems, position and speed signals of each servo axis are acquired in real time, a cooperative error vector is calculated based on a graph theory model, the vector reflects the deviation between the state of each axis and an expected cooperative trajectory, and the cooperative control and disturbance suppression of the multi-axis servo system are realized. And synchronous movement is ensured. The collaborative error is used for generating a preliminary control signal, and a proportional, differential and integral term combination strategy is adopted to quickly correct the error and eliminate the steady-state error. Meanwhile, disturbance vectors are estimated through an observer, and external interference or internal changes are compensated. And after the preliminary control signal and the disturbance compensation are added, amplitude limiting processing is carried out to prevent saturation of the driver. The control parameters are adjusted through an off-line optimization algorithm to minimize errors and disturbance influences, so that the tracking precision, the anti-interference capability and the adaptability of the multi-axis servo system are improved, and the method is suitable for the fields of high-precision manufacturing, robot control and the like.

The invention discloses a control valve drawing part intelligent identification system, and belongs to the technical field of intelligent manufacturing and drawing identification, the system obtains a direction control valve drawing sample, combines a QATM algorithm, a YOLO model, EAST and TesseractOCR, and U-Net and a graph theory algorithm to carry out multi-modal feature extraction and joint identification, constructs a three-level classification matching system, and outputs an initial identification result; through label data conversion, manual sampling auditing, initial recognition standard construction, extended label data generation and comprehensive precision label data fusion, multiple times of iterative optimization of an initial model is achieved, and finally a high-precision detection model is output. Automatic and intelligent recognition of part structures, parameters and topological relations in control valve drawings is achieved; the drawing recognition efficiency and accuracy are remarkably improved, and the method is suitable for industrial drawing digitization and intelligent assembly application.

The invention discloses an assembly type machine room sub-module design system based on a BIM model and a three-constant system, and high-precision modular design of a machine room is realized through BIM parametric modeling, three-constant system performance analysis, graph theory module division, prefabricated member design, assembly process planning and CFD simulation verification. The system integrates the block chain and the knowledge graph technology, supports the prefabrication rate being greater than or equal to 85%, reduces the energy consumption by 25%, solves the problems of low construction efficiency and poor precision of a traditional machine room, and provides technical support for a smart building.

The invention discloses a method for exploring a heterogeneous catalytic reaction network based on a deep potential energy surface model, and the method comprises the steps: carrying out the accurate prediction of the free energy change of all possible element reactions on a catalyst through the combination of transfer learning and a density functional theory, and constructing a reaction network diagram based on a graph theory method; an improved Dijkstra method is used for searching a reaction network diagram to determine a reaction mechanism, and the selectivity of the catalyst is explored based on a Markov chain method. According to the technical scheme provided by the invention, when the CO2 reduction reaction mechanism on the monatomic alloy is explored, the prediction error MAE of the electron energy of each intermediate is only 0.16 eV, the calculated amount is reduced by 90%, the dominant path of the CO2 reduction reaction is visually displayed, and a new normal form is provided for complex mechanism exploration and catalyst screening.

The invention provides a power distribution network fault self-healing control method based on artificial intelligence, and relates to the field of power system intelligent control, in particular to a power distribution network fault self-healing technology, which comprises the steps of acquiring real-time operation state data of a power distribution network, extracting features to establish a fault prediction rule base, monitoring faults in real time and determining positions, and constructing a topological structure based on a graph theory algorithm. And calculating the load transfer capacity of the selectable reconstruction path, solving the multi-objective optimization function by adopting an improved particle swarm algorithm to obtain an optimal reconstruction scheme, generating a control instruction sequence to isolate a fault area, and recovering power supply of a non-fault area. The intelligent level of power distribution network fault processing is improved, the fault recovery time is shortened, and the power supply reliability is improved.

The invention relates to a GPU scheduling optimization method for task and node bidirectional modeling, which comprises the following steps: step 1, task modeling and classification, step 2, node feature modeling and scoring mechanism, step 3, priority scheduling of strong dependency tasks, and step 4, optimal matching scheduling of weak dependency tasks. And constructing an adaptive scoring matrix between the task and the node, and realizing optimal matching between the task and the node by means of a graph theory matching model. The method comprises the following steps: constructing a multi-dimensional feature modeling system, and abstractly expressing resource demand features of a computing task and computing power features of a computing node; and then, based on a multi-index comprehensive weighting mechanism, evaluating a matching relationship between the task and the node, fusing factors such as a task dependence structure and a task emergency degree, and executing fine distribution by adopting a hierarchical scheduling mechanism, so that efficient utilization of GPU resources and interpretable optimization of a scheduling result are realized.

The invention relates to the technical field of electronic design automation, and discloses a standard cell layout legalization method and system based on a graph theory constraint graph, and the method comprises the steps: building a bidirectional directed constraint graph based on an existing layout and a design rule, and building a directed edge between adjacent graph edges according to the design rule; traversing the constraint graph, detecting whether a directed edge which does not meet a constraint condition exists, and if so, identifying the directed edge as a constraint violation edge; for two end points of each constraint violation edge, searching a key path based on a depth-first search algorithm, and determining a maximum movable distance in combination with a greedy strategy; moving the endpoint to a legal position according to the critical path and the maximum movable distance, and updating the position of a related node in the constraint graph; and mapping the position changes of the nodes in the constraint graph back to the corresponding graph edges in the layout, and outputting the legalized layout, so that efficient and automatic repair of design rule violation is realized through the method, and the layout compliance rate and the area optimization effect are remarkably improved.

The invention provides a grounding grid corrosion state diagnosis method based on plate reduction, and belongs to the technical field of grounding grid corrosion state diagnos.The grounding grid corrosion state diagnosis method comprises the steps that a self-adaptive clustering algorithm based on the graph theory is adopted to decompose a grounding grid into meta-plates with the same topological structure, and a meta-plate corrosion working condition database is established; the method comprises the following steps: carrying out sweep frequency impedance measurement in a frequency range of 1000Hz to 10000Hz to obtain a frequency response characteristic curve, calculating an impedance modulus increase rate as a characteristic vector, carrying out sample expansion by using an SMOTE technology to establish a training data set, constructing a multi-scale frequency response characteristic fusion model based on a ResNet architecture to carry out training, dynamically adjusting a hierarchical fusion weight through a frequency response parameter adaptive adjustment function, and carrying out multi-scale frequency response characteristic fusion based on the ResNet architecture. Accurate identification of the corrosion branch position and the corrosion degree of the grounding grid to be diagnosed is achieved, and the technical problems that the grounding grid corrosion state diagnosis precision is low and the calculation complexity is high are solved.

The invention relates to a motorcade consistency formation control method and device based on a dynamic event triggering mechanism, electronic equipment and a storage medium. The method comprises the steps of generating a communication topological graph of a multi-agent system composed of a preset number of unmanned vehicles according to the theoretical basis of a graph theory, and building a mathematical model of each unmanned vehicle; based on the mathematical model, designing a combined measurement function, a measurement error and a consistency controller of the multi-agent system; distributed event triggering conditions and self-triggering conditions are designed based on Lyapunov parameters; according to a dynamic event triggering condition and a self-triggering condition, a triggering time interval expression of the intelligent agent is obtained through calculation; by constructing a multi-agent system consistency controller and combining distributed event triggering conditions and self-triggering conditions, multi-vehicle formation consistency control is completed. According to the fleet consistency formation control method disclosed by the invention, accurate and efficient control of a fleet formed by multiple unmanned vehicles can be realized.

The invention discloses a global positioning method and system based on multi-source information fusion and a graph theory, and the method comprises the steps: fusing a laser radar point cloud, an image sequence and IMU data in a prior map construction stage, segmenting an image, extracting a bounding rectangle, and generating an object-level map; in the online mapping stage, three-dimensional reconstruction of a dynamic object is realized through a monocular depth estimation network and pose information; in the global data association stage, a soft association mechanism is innovatively proposed, an affinity matrix is constructed, and an optimal matching relation is solved through graph optimization; and finally, global positioning is realized through pose transformation calculation. According to the method, the data processing flow of the multi-modal sensor is uniformed in a breakthrough mode, the matching robustness in a cross-modal scene is remarkably improved by adopting an object-level map expression and subgraph division strategy and combining two-way K-nearest neighbor matching and shape similarity evaluation, and compared with a traditional geometric method, the method has the advantages that the registration precision is improved, and the matching robustness is improved. The problem of association ambiguity caused by depth estimation difference in a dynamic environment is effectively solved.

The invention discloses a power system carbon emission dynamic traceability and evaluation method, system and device based on life cycle and topological entropy fusion and a medium, and belongs to the technical field of power system low-carbon operation and carbon emission evaluation, and the method comprises the steps: collecting carbon emission data of different stages; establishing a stage set, calculating the emission of each stage, and solving a life cycle carbon entropy; abstracting a power system into a weighted directed graph, calculating a branch carbon flow rate based on the weighted directed graph, and calculating a graph theory carbon entropy based on node inflow; introducing a weight parameter, fusing the life cycle carbon entropy and the graph theory carbon entropy, and defining a fusion entropy; under a multi-period operation condition, calculating a time evolution sequence based on the fusion entropy, and defining a sensitivity coefficient; and generating a visual carbon flow map containing node carbon potential distribution, a branch carbon flow rate, a carbon entropy evolution curve and a responsibility allocation matrix. According to the method, dynamic conduction and node-level tracking of carbon emission along the electric power flow are realized, and the complexity and balance of carbon emission space distribution are quantified through an entropy theory.