89 results about "Cellular automation" patented technology

The invention discloses a low-altitude airway flow field sensitive area dynamic identification optimization method based on set simulation, and the method comprises the steps: building a low-altitude flow field preprocessing data base with consistent time and space based on Beidou subdivision grids and multi-source heterogeneous data fusion; constructing a low-altitude airspace digital twinning environment based on the data; based on the low-altitude airspace digital twin environment and the cellular automaton-fluid coupling model, generating a diversified flow field evolution scene covering extreme weather and equipment faults; based on a set simulation result, extracting a high-conflict probability region through a spatio-temporal clustering algorithm and quantifying region risk features; generating an air route planning scheme meeting security constraints through a multi-objective evolutionary algorithm based on the quantitative regional risk features; on the basis of a low-altitude airspace digital twin environment and an air route planning scheme, verifying the feasibility of the air route planning scheme through historical data playback and virtual-real fusion test; and according to a verification feedback result, carrying out dynamic feedback optimization on the low-altitude air route flow field sensitive area identification and air route planning scheme.

The invention relates to an AI-based planned land site selection multi-objective optimization method and system. The method comprises the following steps: constructing a city data cube; constructing a cellular automaton model and generating a land utilization potential map; constructing a game decision model, and generating a preliminary site selection scheme set; inputting the preliminary site selection scheme set into a cellular automaton model to generate an evolution simulation data set; constructing a multi-target evaluation model, and calculating the relative closeness between each scheme in the preliminary site selection scheme set and an ideal solution; generating a site selection decision report according to the relative close degree; in conclusion, the urban data cube is constructed by integrating multi-source real-time and historical data, the dynamic land potential map is generated by applying the cellular automaton model, and the multi-target scientific balance is realized by introducing the multi-agent game decision. Automatic processing and consistency guarantee of planning rules are realized, a scientific quantitative tradeoff mechanism in a multi-target conflict scene is provided, and the method has the effect of improving data timeliness and dynamic response capability of site selection decision.

The invention discloses an encrypted communication system and method based on cognitive multi-carrier spread spectrum, and belongs to the field of covert communication. The encryption communication system comprises a transmitting device and a receiving device. The transmitting device comprises a data encryption module, a constellation encryption modulation module, a multi-carrier spread spectrum module, a frequency hopping module and a cognitive power distribution module. The receiving device comprises a band-pass filtering module, a de-hopping frequency module, a de-spreading module, a constellation decryption demodulation module, a data decryption module and a coherent combination module. The data encryption module is divided into an interleaving coding unit and a double-rule reversible cellular automaton encryption unit. The cognitive power distribution module continuously scans a wireless electromagnetic environment and carries out real-time spectrum analysis to give power spectrum density results sensed under all frequency hopping frequency sets. According to the invention, continuous detection and dynamic response to the spatial frequency spectrum state can be realized, and the communication concealment, the anti-interference performance and the resource utilization efficiency in a complex electromagnetic environment are remarkably improved by utilizing the frequency spectrum hole.

The invention provides a cable path intelligent optimization method fusing a cellular automaton and an ant colony algorithm, and the method comprises the steps: obtaining a three-dimensional boundary range of a target region, constructing a three-dimensional underground space voxel model, and dividing the target region into voxel units containing five-dimensional attribute tensors; constructing and inputting the five-dimensional attribute tensor into a physical constructable potential field model, and generating a corresponding three-dimensional physical constructable potential field; performing construction scoring according to the three-dimensional physical constructable potential field and the five-dimensional attribute tensor; constructing a composite cost function in combination with the three-dimensional physical constructable potential field and the construction score, generating a composite passing cost of each voxel, and generating an optimal cable path in combination with a path search algorithm which takes an improved ant colony algorithm as a control mechanism and introduces a rule of a cellular automaton on a path state propagation structure; and performing three-dimensional geographic mapping, structure reconstruction and geometric fairing on the optimal cable path, and outputting a standardized path result.

The invention relates to the field of distributed storage, and discloses a distributed storage system, method and equipment for wind power plant data and a medium, which are used for realizing flexible adjustment of a storage strategy and meeting the storage requirement of dynamic change of a wind power plant data value. Comprising the following steps: collecting SCADA system data at edge nodes of a wind power plant, inputting an information entropy module and an access heat analysis module in parallel, fusing to generate a data value evaluation result, inputting the evaluation result into a cellular automaton engine, outputting a cellular state change instruction, summarizing collaborative strategy requests of at least two edge nodes to a regional center node, and carrying out collaborative strategy analysis on the regional center node. And a fair resource allocation scheme is output through the Sharpley value calculation module. And each edge node synchronously receives the scheme, the time sequence database controller generates a final storage instruction, and data distributed persistent storage is completed. According to the method, on the basis of the correlation between the long-term data value evaluation result and the actual use value, the entropy value and the popularity weight coefficient are optimized, and self-adaptive optimization of the system is achieved.

The invention relates to the technical field of land data planning, and discloses a reasonable land planning system based on machine learning carbon storage, and the system comprises a multi-source data fusion module, a carbon storage prediction module, a three-generation coordination discrimination module, a land use intelligent optimization module, a scene interaction deduction module, and a planning result generation module. By integrating data such as standardized land utilization and carbon density, a machine learning model is utilized to predict the dynamic state of the carbon density in real time, and the functional coordination degree of the three plants is quantitatively evaluated; and then, by taking the improvement of the carbon reserves and the coordination of the three plants as targets, carrying out spatial simulation and iterative optimization in combination with multi-objective optimization and a cellular automaton, comparing different policy carbon sink effects through scene deduction, and finally outputting an optimization scheme, thereby realizing the real-time closed loop of evaluation and optimization, and synergistically improving the carbon sink benefits and the three-plant function.

The invention provides a heterogeneous unmanned aerial vehicle forest fire rescue cooperative scheduling method based on reinforcement learning and quantum particle swarm optimization, and relates to the field of cooperative scheduling, and the method comprises the steps: constructing a cellular automaton fire spread model based on wind speed, vegetation and gradient, and generating a combustion cell priority sequence; establishing a three-state conversion model of the multi-type unmanned aerial vehicle, and combining the combustion loss and the flight time to construct a comprehensive cost; task allocation and track solving are realized through a quantum particle swarm optimization algorithm enhanced by reinforcement learning; and a scheduling scheme is corrected in real time by adopting a rolling updating mechanism, and fault-tolerant re-planning is triggered under an abnormal condition, so that the cooperative fire extinguishing efficiency of the heterogeneous unmanned aerial vehicle group is improved.

The invention discloses a land utilization planning method based on AI driving, relates to the technical field of land planning, and solves the technical problems that it is difficult to construct a regional heterogeneous graph and calculate spatial dependence intensity by using a heterogeneous graph attention network, and it is also difficult to construct a multi-agent reinforcement learning model with a planned region as an environment. Comprising the following steps: firstly, constructing a land feature tensor containing land configuration affinity and a development pressure evaluation value; constructing a regional heterogeneous graph by taking the land feature tensor as an initial feature of each node in the graph, calculating spatial dependence intensity among the nodes by using a heterogeneous graph attention network, and generating a land unit enhanced feature vector; the method comprises the following steps: firstly, obtaining feature vectors of different planning target agents, constructing a multi-agent reinforcement learning model based on the feature vectors, simulating games among different planning target agents, generating a Pareto optimal land utilization scheme set, and finally, integrating a cellular automaton to carry out multi-scene dynamic deduction, and receiving a user interaction instruction to generate a final planning scheme.

This invention relates to the field of wetland ecological regulation technology, specifically a method for optimizing the ecological restoration of small wetlands based on multimodal data. The method includes: collecting multimodal monitoring information such as remote sensing image sequences, time-series data of physical and chemical parameters, and ecological sample data of the target area; constructing a dynamic three-dimensional ecological field; assimilating and fusing multi-source data using a spatial grid as a basis to form a comprehensive ecological state attribute value with timestamps; utilizing an improved ecological cellular automata algorithm, combined with an ecological restoration intervention library and the attribute differences of adjacent grids, completing the synchronous evolution calculation of the ecological state of the entire grid, and generating a spatialized ecological evolution field; selecting spatially connected stable grid clusters and implementing connectivity enhancement and structural optimization; and outputting restoration layout, implementation timeline, and engineering quantity configuration content that matches site conditions. This method achieves spatiotemporal integration of multi-source heterogeneous ecological data, refines the accuracy of regional ecological evolution prediction, and enhances the ability to regulate the spatial structure of wetlands.

An artificial intelligence system can be implemented to identify relationships through the propagation of ripple patterns through a grid. In such a system, the grid may comprise cells which operate as cellular automata. Relationships may be identified based on collisions of signals detected by the cells in the grid, and, when a relationship is identified, it may be used to create high speed connections between cells.

The application relates to an AI-based land planning site selection multi-objective optimization method and system, which comprises the following steps: constructing a city data cube; constructing a cellular automaton model and generating a land use potential map; constructing a game decision model and generating a preliminary site selection scheme set; inputting the preliminary site selection scheme set into the cellular automaton model to generate an evolution simulation data set; constructing a multi-objective evaluation model and calculating the relative closeness of each scheme in the preliminary site selection scheme set to an ideal solution; generating a site selection decision report according to the relative closeness; and in summary, the application integrates multi-source real-time and historical data to construct a city data cube, applies a cellular automaton model to generate a dynamic land use potential map, introduces multi-agent game decision to realize multi-objective scientific trade-off, realizes automatic processing and consistency guarantee of planning rules, and provides a scientific and quantitative trade-off mechanism in a multi-objective conflict scenario, and has the effect of improving the data timeliness and dynamic response capability of site selection decision.

The invention discloses a chloride ion concentration driven corrosion evolution probability type cellular automaton simulation method, and relates to the field of metal corrosion evolution simulation, and the method comprises the following steps: S1, setting a cellular space size and a cellular size, and constructing a three-dimensional cellular space; s2, cell types are defined, and initial distribution of various types of cells is completed in a three-dimensional cell space; s3, setting a reaction mechanism of a model iteration process, and judging a conversion rule among various types of cells according to a corrosion probability Pa, a passivation probability Pb and a passivation film fracture probability Pd; s4, a chloride ion concentration matrix, a probability enhancement coefficient and an attenuation coefficient are introduced, the cellular transformation probability in the iteration process is corrected through the real-time concentration value of chloride ions, and aggravation of the local corrosion process is achieved; and S5, comparing test and simulated corrosion morphology results under the same mass loss rate. According to the method, local corrosion difference can be effectively reduced on the basis of comprehensive corrosion, and support is provided for durability evaluation of the steel structure.

The invention discloses a wheel forging and rolling composite forming process based on deformation distribution and a structure regulation and control method, and belongs to the technical field of wheel manufacturing. The method comprises the steps that a composite forming path of pre-forging, finish forging and finish rolling is adopted, the deformation amount of each procedure is scientifically distributed, pre-forging accounts for 70%-80%, finish forging accounts for 15%-20%, and finish rolling accounts for 5%-10%, so that forming precision and microstructure control is achieved. Meanwhile, quantitative prediction is carried out on microstructure evolution in the whole forming process by constructing a multi-scale digital simulation platform integrating a material constitutive model, a microstructure evolution model and a cellular automaton. And based on a prediction result, active and accurate regulation and control of microstructures of different parts of the wheel are realized through virtual iteration optimization of key process parameters such as temperature, deformation and strain rate, and finally, the complex-structure wheel with high dimensional precision, uniform and fine structure and excellent comprehensive performance is obtained. According to the wheel shape-character collaborative manufacturing method, shape-character collaborative manufacturing of the wheel is achieved, and the scientificity and efficiency of process design are improved.

The invention discloses a method for evaluating the ablation resistance of a graphene-enhanced copper-tungsten contact based on a cellular automaton, and the method comprises the steps: obtaining a graphene-enhanced copper-tungsten alloy cellular automaton model with different mass fractions and different cellular sizes through a Poisson disk sampling method, introducing the dependence characteristics of each material on the temperature, and carrying out the evaluation of the ablation resistance of the graphene-enhanced copper-tungsten contact. Setting boundary conditions and evolution rules, inputting arc heat and obtaining a model ablation result, and evaluating the modified material through loss, heat distribution, ablation rate and maximum erosion heat. Based on design schemes of different proportions of simulation selection, ablation characteristic experimental research is performed on copper-tungsten contact and graphene enhanced copper-tungsten contact samples. According to the method, anti-ablation characteristic prediction and aided design of the graphene reinforced copper-tungsten alloy electrical contact material can be realized, and a reference can be provided for promoting design and manufacturing of a high-performance electrical contact of a high-voltage circuit breaker.

The present application relates to the technical field of flood forecasting, and more particularly to a city flood rapid forecasting method based on multi-process generalization. The method comprises the following steps: dividing sub-catchment by obtaining basic geographic information such as river system, terrain and rainwater pipe network of the target area, analyzing city underlying surface characteristics to calculate runoff, and calculating surface and pipe network confluence process based on improved equiflux line method and generalized trunk pipe model; through the interaction of trunk pipe and series-parallel virtual reservoir model, the surface detention, overland flow and return pipe are realized; finally, the one-dimensional river confluence and the cellular automata two-dimensional surface inundation model are coupled to realize the rapid simulation and visualization forecasting of city flood process. The present application efficiently couples runoff, surface confluence, pipe network flow limitation, surface and pipe network interaction, one-dimensional river confluence and two-dimensional inundation process through multi-process generalization, greatly reduces the calculation complexity and improves the timeliness of city flood forecasting.

The invention discloses a production process quality system risk assessment method for a digital workshop, and relates to the technical field of intelligent quality management and control, and the method comprises the steps: endowing each work-in-process entering the digital workshop with a unique initial risk identifier; after the processing of the current procedure is completed, collecting industrial data of the in-process product, generating a risk characteristic value based on the industrial data, and updating the initial risk identifier to form a dynamic risk chromosome; the industrial data comprises quality detection data, process parameter flow and equipment state data; constructing a cellular automaton network based on a process logic relationship in the production scheduling plan; when the work-in-process flows to a new process, inputting the risk characteristic value into a cellular automaton network, and calculating a risk conduction direction and a risk conduction probability through a cellular state evolution equation; according to the method, the process logic relation in the production scheduling plan is mapped into the cellular automaton network, so that dynamic modeling and quantitative analysis of the risk conduction path in the production process are realized.

The application discloses a kind of chloride ion concentration driven corrosion evolution probability type cellular automaton simulation method, it is related to metal corrosion evolution simulation field, including the following steps: S1: setting cell space size and cell size, construct three-dimensional cell space;S2: definition cell type and in three-dimensional cell space complete the initial distribution of each type cell;S3: set the reaction mechanism of model iteration process, to corrosion probability P a , passivation probability P b , passivation film rupture probability P d Determine the conversion rule between each type of cell;S4: introduce chloride ion concentration matrix and probability enhancement coefficient, attenuation coefficient, by the real-time concentration value of chloride ion to the cell conversion probability in iteration process is modified, realize the aggravation of local corrosion process;S5: compare the corrosion morphology results of test and simulation under the same mass loss rate.This application can effectively reduce the difference of local corrosion on the basis of general corrosion, provide support for steel structure durability evaluation.

Embodiments of the present disclosure provide a cellular automaton method for simulating static recrystallization of a nickel-based superalloy, comprising: establishing an initial microstructure geometric model on the basis of an initial microstructure metallographic picture; inputting static recrystallization conditions into the initial microstructure geometric model, wherein the static recrystallization conditions comprise a simulated temperature, a strain velocity, and a strain; determining a nucleation number in static recrystallization, wherein the nucleation number is determined by a nucleation rate; selecting a position for nucleation; calculating a driving force for growth of static recrystallization grains; and outputting a simulation result. In the embodiments of the present disclosure, by means of the above cellular automaton simulation method, preferential nucleation can be effectively performed at a high-energy position at a grain boundary triple junction, and recrystallization evolution is more in line with reality, thereby accurately reflecting the morphology characteristics and transformation dynamics of microstructure evolution inside the material under the combined action of thermal activation, grain boundary energy, etc., which cannot be realized by conventional cellular automaton simulation methods.

The embodiment of the application discloses a fish breeding environment optimization method and system based on a cellular automaton, and the method comprises the following steps: a three-dimensional cellular automaton model is established, wherein a first layer of grids is used to simulate the physical environment state of water, and a second layer of grids is used to simulate the pheromone concentration field of fish breeding; pheromone generation rules, diffusion rules and attenuation rules are set in the second layer of grids, so that pheromone is only generated in the cells where mature female fish or male fish are located, and diffuses in the adjacent cells according to the concentration gradient; according to the breeding trigger rules, the cellular automaton is iteratively run to obtain the breeding success rate distribution of each region under different environment regulation schemes; the breeding trigger rules comprise that only when the pheromone concentration of the target cell continuously exceeds the preset threshold and the physical environment suitable conditions are met at the same time, the cell is allowed to have the fish spawning or fertilization behavior; and the optimal breeding environment parameter combination is determined according to the breeding success rate distribution. The fish breeding success rate is improved.

The invention relates to the field of abrasion prevention and control, and discloses a low-temperature economizer abrasion prevention and control system and method which are used for achieving accurate prediction and active prevention and control of low-temperature economizer abrasion through a multi-model fusion innovative method. The abrasion prevention and control method for the low-temperature economizer comprises the steps that firstly, a time-varying memory kernel function is established to quantify historical working condition influences; a space-time risk map is generated through a cellular automaton in combination with the flow field parameters; phase-space reconstruction and nonlinear kinetic analysis are carried out on the atlas to establish early warning indexes; wear mode characteristics are extracted through topological data analysis; and generating accurate prevention and control parameters based on the atlas and topological characteristics. Fractional calculus, cellular automaton, nonlinear dynamics, topological data analysis and variational optimization are organically fused to form a technical chain, conversion from passive early warning to active guide control is realized, prediction precision, early warning perspectiveness and prevention and control pertinence are improved, the service life of equipment is effectively prolonged, and operation and maintenance cost is reduced.

The invention provides an emergency command method and system based on the Internet of Things, and relates to the technical field of emergency command systems, and the method comprises the steps: obtaining Internet of Things monitoring data including underground pipeline information, accumulated water depth data and historical disaster information, and carrying out the alignment fusion to generate comprehensive situation data; based on this, determining a waterlogged road section and analyzing a sound signal thereof to identify an acoustic abnormal region; fusing the acoustic features and pipeline attributes by using a manifold learning algorithm to generate risk feature vectors; discretizing a geographic area of a ponding road section into grid cells, and distributing and inputting risk vectors into the cellular automaton model; the model carries out iterative calculation among grids based on a risk propagation rule to generate disaster prediction data; and finally, according to the data and historical information, a command instruction for emergency resource scheduling is generated through a resource scheduling optimization model. According to the invention, the accuracy of early warning and resource scheduling of urban inland inundation secondary disasters is improved.

The invention discloses a power distribution network electric vehicle bearing capacity evaluation method and system. The method comprises the following steps: for a target power distribution network, analyzing points of interest and electric vehicle parameters, and simulating charging behaviors by using an agent-cellular automaton model to generate diversified charging scenes. Actual operation data are collected through an intelligent electric meter, a DistFlow model is adopted to calculate node power of each charging point, the model is solved in different time intervals, an unscented Kalman filtering algorithm is applied to process a power flow relation, and whether voltage and line load meet constraint conditions or not is checked so as to judge operation feasibility. Then, an optimized random forest model is established, key features are extracted and trained, mapping between the features and scene feasibility is established, and the electric vehicle charging load bearing capacity of the power distribution network is assessed. Through implementation of the method, dependence on a large number of simulation scenes can be reduced, calculation complexity can be reduced, uncertain factors can be better processed, and the reliability of the power distribution network is improved; and the real-time scheduling requirement of the power grid is met.

Method for determining the thermal behavior of a circuit board using a thermal cellular automaton model, comprising the following steps: a) Modeling a circuit board in a thermal cellular automaton model such that the circuit board is divided into individual cells (1, 2, 3), each cell (1, 2, 3) being implemented as a cellular automaton, b) Defining a plurality of thermal states for each cell (1, 2, 3) such that each cell (1, 2, 3) is in one of the states at each time step, c) Define a rule based on Fourier's law of heat conduction according to which the state of each cell (1, 2, 3) is determined at each time step, and d) Determining the state of all cells (1, 2, 3) in parallel time step by time step, so that the thermal behavior of the board is determined in real time.

The application discloses a land use type prediction method and system based on multi-source data fusion, and the method comprises the following steps: acquiring historical land use and multi-source driving factor grid data of a target region; generating a shape self-adaptive conversion probability through a neural network coupled with deformable convolution; determining future land demand based on scenario and policy coupling; iteratively simulating by using a dynamic double feedback cellular automaton model, the model synchronously adjusts the competition inertia coefficient and the spatial neighborhood weight, and dynamically integrates spatial constraints such as ecological protection red lines in a three-dimensional lookup table; finally, the prediction result is converted into standardized land cover data through a probabilistic multi-level classification mapping engine, and a spatial uncertainty distribution map and an area confidence interval are synchronously generated. The method realizes the deep internalization of regulatory rules, provides complete decision support information containing uncertainty quantification, and provides a scientific and reliable technical tool for land space planning and management.

This invention discloses a method and system for assessing tunnel smoke conditions, belonging to the field of traffic management technology. The method includes: simulating predicted diffusion data of air pollutants in the current tunnel using the lattice Boltzmann method, which can accurately simulate the diffusion of air pollutants and facilitate subsequent analysis; acquiring environmental parameters of the current tunnel to correct the predicted diffusion data, which can improve the accuracy of the predicted diffusion; simulating the discrete migration behavior of moving units in the tunnel based on cellular automata and two-dimensional grids, which can simulate the behavior of moving units in the tunnel and facilitate the determination of the development of the situation; and establishing a tunnel smoke accident situation assessment model based on fuzzy hierarchical analysis, which combines the predicted diffusion data, the environmental parameters, and the discrete migration behavior to output an assessment value, which can numerically describe the changes in the accident situation.

The application discloses a kind of intelligent manufacturing workshop dynamic scheduling optimization methods, belong to intelligent manufacturing production scheduling technical field.The application first constructs the intelligent manufacturing workshop full-element coupling constraint model including processing time sequence parameter module, space resource cell parameter module and coupling constraint hierarchical rule module, through cell automaton executes workshop space dynamic state pre-evolution and completes the feasible region screening of scheduling solution space, then through improved sparrow search algorithm executes scheduling scheme optimization and obtains initial scheduling execution scheme, finally combined with workshop real-time production data triggers hierarchical scheduling response mechanism, adjusts and obtains final dynamic scheduling scheme.The application can realize the integrated coupling modeling and scheduling optimization of workshop multi-production elements, improve the adaptability of scheduling scheme and production scene, reduce the invalid calculation of optimization process, and guarantee the dynamic response capability of scheduling scheme and the continuity of production process.

The invention discloses a digital design and performance prediction method for the whole forging and rolling process of a wheel with a complex structure, and belongs to the technical field of metal plastic forming and digital simulation. In order to overcome the defects of an existing method, an integrated forming module is designed; the invention provides a parametric modeling method, based on Visual Basic and SolidWorks secondary development, wheel parameters in an Access database are accessed, and three-dimensional modeling and assembling of all molds and blanks are automatically completed; a structure performance prediction method is provided, a parameterized model is automatically imported into finite element software, a microstructure model and a cellular automaton are integrated through secondary development, macro-micro coupling calculation is carried out, and microstructure distribution after the wheel is formed is predicted. According to the method, full-process digitization and automation from wheel design parameter input to final structure performance prediction are achieved, the research and development efficiency and the forming quality prediction precision of the complex-structure wheel are remarkably improved, and a core technical support is provided for optimal manufacturing of rail transit key components.

The invention discloses an atmospheric corrosion dynamics joint simulation method, device and system and a storage medium, and the method comprises the steps: constructing a finite element model which is used for calculating metal atmospheric corrosion parameters in a certain specific state; constructing a cellular automaton model for simulating an atmospheric corrosion dynamics evolution rule; finite element and cellular automaton joint simulation is carried out, and a corrosion dynamics rule in an actual environment is simulated through effective transmission of simulation parameters between the finite element and the cellular automaton; the corrosion morphology evaluation and comparison verification is used for evaluating the similarity between a calculation result of the simulation method and an actual test result; and corrosion condition prediction, which is used for corrosion prediction in an actual environment. Through effective combination of the finite element method and the cellular automaton, dynamic updating of the corrosion environment can be achieved, and the accuracy of the simulation method is ensured through surface topography verification.

The invention belongs to the technical field of computer aided design and simulation, and relates to a BIM-based land space intelligent planning method and system, and the method comprises the following steps: monitoring a database atomic-scale change event, and matching a knowledge graph to generate a dynamic causal event package; positioning a trigger position in the three-dimensional field, injecting causal energy, and instantiating a dissipation gradient field; performing iterative deduction based on the cellular automaton, simulating a propagation process of causal energy under the action of resistance and inertia, and generating a verified causal pathway record; analyzing the path to extract core component data to construct a localized BIM sub-model, and mapping a deduction result into a self-adaptive physical boundary condition; loading the sub-model and boundary constraint to start a physical simulator for quantitative evaluation; the technical problems that when the macroscopic influence caused by local changes is evaluated, the simulation range and boundary condition determination lacks objective basis, the automation degree is low, and the evaluation accuracy and the response efficiency need to be improved are solved.