43 results about "Cellular automaton" patented technology

The invention relates to the technical field of power system scheduling, and discloses an intra-day look-ahead scheduling rapid solving method considering large-scale new energy cluster power generation volatility. Comprising the following steps of S1, new energy cluster space-time fluctuation scene generation based on a neuron cellular automaton, S2, power grid dynamic security domain definition and simplification based on a physical information neural network, S3, scheduling rapid optimization solution based on model prediction path integration, and S4, scheduling scheme dynamic elasticity and stability evaluation based on a Kupman operator theory. The new energy cluster space-time fluctuation scene generation method based on the neuron cell automaton can effectively generate a space-time scene reflecting large-scale new energy cluster power generation volatility, supports uncertainty analysis, has the advantages of being high in calculation efficiency and scene authenticity, and is suitable for large-scale new energy cluster power generation. The problem that scene generation is inaccurate due to the fact that a traditional statistical model ignores space-time coupling is solved.

The invention provides a data model dual-drive-based water pollution prediction method and device, and relates to the technical field of water pollution monitoring, and the method comprises the steps: obtaining multi-source information reflecting an incidence relation between a pollution source position and water quality abnormity; training a preset random forest regression model by using the multi-source information and a particle swarm optimization algorithm to obtain a target random forest regression model, inputting real-time data of which the type is the same as that of the multi-source information into the target random forest regression model, and determining pollution source position information; constructing a one-dimensional pollutant diffusion model based on the spatial-temporal characteristic information, and expanding the one-dimensional pollutant diffusion model to a two-dimensional cellular model in combination with a cellular automaton simulation method; and inputting pollution source position information and environmental influence information into the two-dimensional cellular model to generate a water pollution diffusion prediction result. Through comprehensive application of the algorithm and the model, the accuracy and the real-time performance of water pollution prediction are improved.

The invention discloses a wind power plant voltage and frequency cooperative tuning control method, and relates to the technical field of tuning control, and the method comprises the steps: obtaining the real-time operation data of a wind power plant, carrying out the preprocessing of the real-time operation data of the wind power plant, carrying out the voltage fluctuation and frequency disturbance prediction through a cellular automaton model in combination with the pre-obtained power grid data, and carrying out the voltage fluctuation and frequency disturbance prediction. Obtaining a prediction state matrix; carrying out multi-objective optimization solution in combination with a pre-constructed multi-objective optimization problem and constraint conditions, and obtaining an optimal droop coefficient vector in combination with a preset rule; and the optimal droop coefficient vector is used as a control reference, and voltage and frequency cooperative tuning is carried out in combination with grid-connected point voltage and frequency obtained in real time and communication network topology data. According to the method, prediction is carried out through the cellular automaton model, the rapid and cooperative regulation capability of the voltage and the frequency of the wind power plant is improved, and therefore the urgent demand of a power system for rapid, accurate and cooperative control under the access of high-proportion new energy is better met.

The invention discloses a user credit risk assessment method, system, equipment and medium based on multi-industry federated learning in order to improve data security of federated learning, and belongs to the technical field of information. The method comprises the following steps: extracting operator data and bank financial data, and performing encryption preprocessing through a Hash mechanism; performing federated learning on the operator data and the bank financial data after encryption preprocessing, accessing a PSI module as a plug-in to an FATE core, converting RSA of an FATE algorithm into an elliptic curve, and performing secret sharing through a hexagonal grid cellular automaton to obtain a trained federated model; and evaluating the credit risk of the user by using the trained federal model. According to the method, the requirement of hiding a keyword of a queried object or a client ID is met, the hidden query service provides a matched query result, calculation can be carried out without going out data of both parties, and the method follows the data authentication and access process of multi-party security calculation and the overall business process. Data barriers among different industries are broken through, and deep data cooperation of multiple industries is realized.

The invention discloses a hyper-converged Internet of Things gateway, and relates to the technical field of Internet of Things. Comprising a network state preloading unit, a cellular automaton control unit and a protocol stack mapping unit, the network state preloading unit is used for carrying out multi-dimensional state acquisition on a heterogeneous access network connected with the hyper-converged Internet of Things gateway and generating a network state preloading set; the cellular automaton control unit is used for creating a cellular automaton node for each piece of network state baseline data based on the network state preloading set, and converging to obtain an evolution result; and the protocol stack mapping unit is used for mapping the to-be-transmitted data packet into a target protocol stack format according to the evolution result, adding a security label, and outputting the to-be-transmitted data packet to a corresponding external network interface. The method has the beneficial effects of improving the network adaptability, the abnormity identification precision, the protocol selection flexibility and the data transmission security, and is suitable for complex, dynamic and high-concurrency application scenes of the Internet of Things.

The application relates to the technical field of power system dispatching, and discloses an intra-day forward-looking dispatching fast solving method considering large-scale new energy cluster power generation fluctuation, which comprises the following steps: S1: new energy cluster space-time fluctuation scene generation based on neuron cellular automata, S2: power grid dynamic security domain definition and simplification based on physical information neural network, S3: dispatching fast optimization solving based on model prediction path integral, and S4: dispatching scheme dynamic elasticity and stability evaluation based on the theory of Koopman operator. The new energy cluster space-time fluctuation scene generation method based on neuron cellular automata can effectively generate a space-time scene reflecting the large-scale new energy cluster power generation fluctuation, support uncertainty analysis, has the advantages of high calculation efficiency and scene authenticity, and solves the problem that the traditional statistical model ignores space-time coupling, thereby causing inaccurate scene generation.

A particle track discovery method based on graph neural network cellular automaton, which utilizes a high-energy physics detector to record a plurality of particle hit data generated in a particle collision event, including the position, direction and momentum of the particle at its hit point; using a plurality of particle hit data generated in multiple particle collision events to supervise the training of a graph neural network, and equivalently calculating a generated graph neural cellular automaton; adding the graph neural cellular automaton conversion rule into a conversion rule library; selecting a plurality of graph neural cellular automaton conversion rules from the conversion rule library, equivalently calculating a corresponding plurality of graph neural networks; applying the plurality of graph neural networks in step three to a new particle collision experiment, inputting a plurality of groups of measurement values of the same particle obtained in the new particle collision experiment into the plurality of graph neural networks, obtaining a plurality of different predicted tracks, and discovering the real track of the particle from the different predicted tracks according to the known physical properties of the particle.

The invention discloses a frame structure design method for material and thickness collaborative optimization under multiple working conditions, and the method comprises the steps: constructing a frame structure subregion cellular automaton model, and employing an internal and external loop iteration mechanism to achieve the optimal distribution solution of the structure material and quality under multiple working conditions. The outer cycle sets target cost and target quality for the inner cycle to provide optimization guidance and construct a combined target field variable function, and the inner cycle drives dynamic updating of cellular thickness and materials under multiple working conditions based on a material and thickness collaborative updating strategy and guides cellular field variables to approach target field variables, so that the structural performance is remarkably improved. In order to break through the algorithm efficiency bottleneck, the global search capability and convergence speed of the algorithm are effectively enhanced through the strategy of parallel point adding and search area expanding, and the method is particularly suitable for optimization design of a frame structure with a large number of materials and thickness design variables under the constraint of multiple working conditions. And an efficient and reliable technical path is provided for lightweight design and performance optimization of the frame structure.

The invention discloses a wetland nitrogen cycle optimization method and system based on machine learning, and belongs to the technical field of ecological engineering.The method comprises the steps that according to a comprehensive environment data set, a transverse lateral infiltration path and a vertical priority flow path are recognized, spatio-temporal evolution simulation is conducted through a cellular automaton model, and a nitrogen migration path is constructed; analyzing the interaction between the water flow driving flux and the underground water level on the path resistance, and determining the broadening degree of the transverse lateral seepage path; if the widening degree exceeds a preset threshold value, integrating the water body turbulence intensity data and the flow velocity shear effect value, and obtaining a bubble escape rate and a surface sediment disturbance frequency; calculating the nitrogen saturation and the spatial distribution of the sediment particle suspension amount, and obtaining the total re-release flux through flux overlay analysis; and constructing a nitrogen loss flux model in combination with the fluctuation rule, simulating the change of the nitrogen loss proportion, and determining the net nitrogen loss flux. According to the invention, fine monitoring of the dynamic change of wetland nitrogen can be realized.

The method may comprise determining a geometry of a stockpile based on the haulage data, engineering drawings and material characteristics; predicting, by the one or more processors using an algorithm implementing a cellular automaton, material movements within the stockpile, based on the geometry of the stockpile, physical parameters of the material movements and emergent rules for the material movements; predicting material movements exiting the stockpile, based on the material movements within the stockpile; predicting, using a concentrator transit model, the material movements across a comminution process in a concentrator, based on material movements exiting the stockpile; and optimizing mining operations based on the knowledge of the material movements through the comminution process. A determination of the material movements may include a determination of the material characteristics of the material.

The invention provides a fire hazard wind field and spread dynamic prediction method and system based on radar echo evolution characteristics. The method comprises the following steps: step 1, carrying out ground clutter suppression, radial high-frequency oscillation filtering, speed deblurring and time-space consistency check on reflectivity Z, radial speed V, spectral width W and dual polarization quantity of a plurality of weather radars; 2, under a three-dimensional variational 3DVAR framework, taking a numerical mode background field as prior, and performing inversion to obtain a boundary layer three-dimensional wind field; 3, establishing an anisotropic cellular automaton (CA) and live wire particle swarm coupling model under the constraints of a digital elevation model (DEM), combustible material types, water content and meteorological elements; 4, performing real-time correction and short-term and temporary extrapolation on the fire point and live wire states; and step 5, result output and early warning. By applying the technical scheme, the minute-level short-term and temporary prediction capability can be improved, and the safety of key assets such as power transmission channels is ensured.

The invention relates to a method for determining thermal properties of a printed circuit board by means of a thermal cellular automaton model, comprising the following steps: modeling the printed circuit board in the thermal cellular automaton model in such a way that the printed circuit board is divided into individual cells (1, 2, 3), each cell (1, 2, 3) being implemented as a cellular automaton; defining a plurality of thermal states for each cell (1, 2, 3) such that each cell (1, 2, 3) is in one of said states in each time step; defining a rule based on the Fourier thermal conduction law, and according to the rule, determining the state of each cell (1, 2, 3) in each time step; and ascertaining the state of all cells (1, 2, 3) in parallel step by step in order to ascertain the thermal properties of the circuit board in real time. The invention also relates to a control device, a computer program product and a computer readable storage medium.

The application relates to the technical field of robot autonomous navigation, in particular to a cellular genetic path planning method based on multi-population cooperation adaptability, which comprises that one cell corresponds to one grid in a grid map; the number of sub-populations is set according to a path planning task; a plurality of initial path individuals of the sub-populations are generated through cell evolution; in each sub-population, a path individual with the maximum gene coding fitness is extracted, and the rest path individuals are randomly paired, the cell state matrix is adjusted based on a crossover and mutation rule, and iterative evolution is carried out; the individual with the maximum fitness and the individual obtained through evolution are used as sub-population representative individuals; the cooperation fitness is calculated according to the sub-population representative individuals of each sub-population; the population is updated according to the cooperation fitness ranking; an optimal solution is output; the application considers the population cooperation relationship as an optimization condition, and evolution of a cellular automaton is utilized, so that the best path in a multi-task complex scene can be quickly generated.

This invention discloses a nuclear radiation indicator early warning system based on multi-algorithm fusion. The system includes an environmental data acquisition module for collecting gamma-ray dose rate data, wind speed data, wind direction data, and geographic location information to construct a nuclear radiation environmental dataset; a dose rate prediction module for predicting future trends in gamma-ray dose rate data based on a sparse recurrent neural network model; an anomaly detection module for identifying abnormal mutation points and locating high-risk sensors; a diffusion region modeling module for calculating the impact area of ​​nuclear radiation diffusion; a grid risk evolution module for simulating the temporal evolution of regular grid risk states based on a multi-state cellular automaton; and a fusion judgment module for fusing output results to generate a final warning level and spatial risk distribution. This invention enables precise prediction of nuclear radiation risks and dynamic spatial early warning, significantly improving warning accuracy and response efficiency.

The application relates to the technical field of computer simulation, in particular to a two-dimensional surface calculation method of a cellular automaton, which comprises the following steps: based on terrain data acquisition and recording, terrain data of multiple regions are analyzed, elevation and slope are recorded, terrain data is processed by using a cellular automaton, a terrain model is constructed, and regional terrain information is obtained. In the application, by means of terrain data acquisition and terrain model construction, the quality of basic data of the model is improved, accurate input data is provided for simulation, key positions are identified, the size and distribution of cells are adjusted, the simulation resolution of key regions is optimized, the local precision of simulation is improved, the consumption of computing resources is reduced, parameters of rainfall simulation are adjusted according to actual meteorological data, the reliability of prediction is enhanced, soil erosion and surface runoff are simulated, the erosion effect and anti-erosion capacity of multiple regions are evaluated, and the prediction accuracy and practicability of the two-dimensional surface calculation are improved.

The invention discloses a coal mine fire extinguishing intelligent command method, a coal mine fire extinguishing intelligent command system, coal mine fire extinguishing intelligent command equipment and a computer readable storage medium. The method comprises the following steps: acquiring underground multi-source heterogeneous data, performing space-time alignment and fusion processing, and constructing a data basis for reflecting a fire scene state in real time; mapping the fusion data to a pre-constructed digital twinborn model, and generating a three-dimensional visual expression of the fire scene; a cellular automaton model is utilized to dynamically predict a fire spreading trend, and an optimal fire extinguishing resource allocation scheme is generated through a scheduling optimization algorithm by taking response time and a resource utilization rate as optimization targets in combination with a current fire and a resource distribution state; and finally, the scheduling scheme is visually presented in the digital twinning scene, and a scheduling instruction is issued to a related execution unit. According to the invention, fire behavior prediction and resource scheduling are realized, and systematic technical support is provided for coal mine underground fire emergency disposal.

The invention discloses a DNA sequence direct encryption method and system based on automaton cryptography, and relates to the technical field of DNA storage security. By designing a DS-mealy machine and a DS-cellular automaton, direct encryption of a DNA sequence is achieved, and the problems that in an existing DNA storage encryption method, compatibility with a coding model is poor, safety is insufficient, performance is low, and expandability is weak are solved. According to the method, sequence-level encryption is realized through base diffusion and rotation operation, so that base distribution is more uniform and random, and the security of DNA storage data is improved; meanwhile, the space resource consumption of encryption and decryption is reduced, the processing speed is increased, and the method is suitable for various DNA storage scenes and particularly suitable for storage of sensitive information such as medical data with high requirements for safety and efficiency.

The invention relates to an image encryption method. The method comprises the steps of obtaining three groups of encryption keys based on a Hash method and superposition of preset real number key parameters; dividing the plaintext image into P11, P12 and P13; enabling the five-dimensional dual-memristor chaotic system to generate a sequence through the first group of encryption keys, and carrying out the global scrambling of P11, P12 and P13, thereby obtaining P21, P22 and P23; performing cat mapping scrambling on the P21, the P22 and the P23 to obtain P31, P32 and P33; performing block DNA operation on the P31, the P32 and the P33 through the second group of encryption key generation sequences; performing block cell automaton operation on the block DNA operation result to obtain P41, P42 and P43; performing block affine transformation scrambling on the P41, the P42 and the P43 to obtain P51, P52 and P53; and carrying out finite field diffusion on P51, P52 and P53 through the third group of encryption key generation sequences, and then merging layers to obtain a ciphertext image. According to the method, the differential attack resistance of the system can be improved, the key space of the system is improved, and the security of image data is improved.

The application discloses a corpus selection method and device, electronic equipment and a readable storage medium. The method comprises the following steps: generating a to-be-processed grid according to a plurality of segmented words obtained by segmenting a to-be-processed corpus and a preset rule; determining a first space state grid matched with the to-be-processed grid from a changing space state grid of a meta-cellular automaton in a change process of the space state grid according to the preset rule; assigning a corresponding related segmented word to each cell grid with a target value in the first space state grid; determining a to-be-corrected corpus corresponding to each target grid according to the related segmented word assigned to the cell grid with the target value in each target grid intercepted from the first space state grid; and selecting a target corpus corresponding to the to-be-processed corpus from the to-be-corrected corpora. According to the method, the purpose of quickly constructing a corpus is achieved.

The application discloses a development zone overall space structure evolution prediction and land scale calculation method considering industrial agglomeration effect, and belongs to the technical field of city planning and land management. The application firstly constructs a space economic model containing enterprise microsite selection behavior, industrial chain correlation degree and factor flow; secondly, the attraction relationship between the present industrial agglomeration zone and the city function node is identified by using multi-source space-time big data (POI, mobile signaling, night light); then, the improved cellular automaton model is introduced, the industrial agglomeration externality is quantified as a driving factor in the conversion rule, and the dynamic evolution path of the development zone space structure under different policy scenarios is simulated; finally, based on the evolution result, the reasonable land scale of each functional plate is calibrated and calculated in reverse by combining the input-output table and the employment density threshold method. The application solves the problem of land supply and industrial demand dislocation in the traditional static planning, and improves the scientificity and forwardness of land scale calculation.

The invention provides an ultrasonic magnetic field assisted low-speed wire cut electrical discharge machining microstructure simulation method, belongs to the technical field of wire cut electrical discharge machining, and aims to introduce ultrasonic waves and a magnetic field into existing low-speed wire cut electrical discharge machining, establish a thermomechanical coupling model, and combine finite element analysis and a cellular automaton model to simulate the microstructure of the low-speed wire cut electrical discharge machining. Accurate simulation is performed on distribution of a temperature field, a stress field and a strain field in the machining process, and meanwhile, dynamic evolution of crystal grains in a material cutting surface metamorphic layer is simulated through a cellular automaton, so that a visual microstructure evolution simulation result is obtained. According to the method, surface microstructures of materials such as shape memory alloy are remarkably improved by introducing ultrasound and a magnetic field, the grain size is reduced, and the uniformity of grains is improved; ultrasonic, magnetic field and thermomechanical coupling are combined, the influence of different factors on the microstructure of the material can be comprehensively considered, the limitation of a single factor in the prior art is broken through, and therefore more efficient machining optimization is achieved under the complex technological condition.

The application discloses a medical image morphing registration method and system, and belongs to the technical field of medical image processing. The application aims to solve the problems of insufficient precision, topological structure damage and limited cross-scale modeling capability of existing registration methods when processing large-scale non-rigid deformation. The proposed method combines the semantic perception ability of the diffusion model and the local evolution characteristics of the neural cellular automaton. By constructing a multi-scale image pyramid, a conditional diffusion model is introduced at each scale to extract potential semantic scores, guiding the NCA module to iteratively model local regions and generate a deformation field that maintains topological consistency. Finally, through the layer-by-layer progressive method between scales, a high-precision non-rigid registration result is obtained.

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 invention provides a scene demand peak-valley prediction method and system based on a space-time large model, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining multi-source heterogeneous space-time data of a target region, carrying out the non-uniform space grid division, calculating an instantaneous supply-demand ratio and a settlement price rolling fluctuation ratio, and constructing a settlement risk enhanced space-time feature tensor; inputting the settlement risk enhanced spatio-temporal feature tensor into a peak-valley prediction spatio-temporal large model, extracting features through a spatio-temporal diagram attention network fusing settlement price fluctuation features, and respectively outputting a transportation order demand prediction value and an uncertainty quantitative index by a double-branch decoder; inputting the transportation order demand predicted value and the uncertainty quantitative index into a strategy to generate a cellular automaton, simulating supply and demand propagation by dynamically adjusting state transfer resistance between cells, and generating a non-uniform distributed settlement parameter adjustment instruction; and directly outputting a settlement parameter configuration result of each sub-region in the target region based on the distributed settlement parameter adjustment instruction.

The application discloses a kind of based on the hydrophilic biological protection watershed ecological flow scheduling method and system, by comprehensively considering the final water flow velocity in target watershed and final flow direction as the first influencing factor, water conservancy engineering facilities of tributary where target watershed is as the second influencing factor, the activity range and migration route of target species are set as seed cell and organization cell, seed cell and organization cell are evolved using cellular automaton model control, and the evolution result of the target watershed is obtained;Subsequently, the ecological situation of the target species in the target watershed is determined according to the evolution result, and the corresponding scheduling strategy is selected in the resource library, and the intelligent scheduling of the target watershed is completed;To solve the technical problem that the existing technology cannot realize comprehensive scheduling in the face of whole watershed ecology, the present scheme uses cellular automaton model to comprehensively deduce the multidimensional data of whole watershed ecology, realizes the intelligent scheduling and ecological restoration of whole watershed.

The invention discloses a model construction method for representing a deep hard rock excavation true triaxial stress path, and the method comprises the steps: S1, constructing an excavation stress path coefficient, and quantifying different true triaxial excavation stress paths; s2, introducing an excavation stress path coefficient, correcting the anisotropic elastic stiffness matrix, and constructing an incremental expression of a stress-strain relationship; s3, establishing a three-dimensional yield function; s4-S7, constructing a brittleness index, an internal variable, a strength parameter evolution equation and a deformation parameter evolution equation representing the excavation stress path and the initial stress difference; s8, the formula is embedded into cellular automaton numerical calculation software for calculation; and S9, carrying out true triaxial rock tests of different excavation stress paths and stress differences, and verifying the accuracy of a stress-strain curve. According to the method, the stress-strain characteristics of the rock under the true triaxial excavation stress path are predicted, and a theoretical basis is provided for high-stress deep engineering hard rock excavation damage disaster prediction and stability analysis.

The application relates to an image encryption method, which comprises the following steps: obtaining three groups of encryption keys based on a hash method and superimposing preset real number key parameters; dividing a plaintext image into P 11 , P 12 , P 13 ; making a five-dimensional double-memory-block chaotic system generate sequences to globally scramble P 11 , P 12 , P 13 ; obtaining P 21 , P 22 , P 23 ; performing cat map scrambling on P 21 , P 22 , P 23 to obtain P 31 , P 32 , P 33 ; generating sequences by the second group of encryption keys to perform block DNA operation on P 31 , P 32 , P 33 ; performing block cellular automaton operation on the block DNA operation result to obtain P 41 , P 42 , P 43 ; performing block affine transformation scrambling on P 41 , P 42 , P 43 to obtain P 51 , P 52 , P 53 ; generating sequences by the third group of encryption keys to perform finite field diffusion on P 51 , P 52 , P 53 , and then merging the layers to obtain a ciphertext image. The application can improve the system differential attack resistance, the system key space and the image data security.

The application discloses a kind of 3D printing microstructure and performance accurate regulation and control method by thermal field behavior monitoring feedback, belong to the technical field of additive manufacturing, including: step one, obtain the process parameters in the process of additive manufacturing and real-time monitoring thermal field behavior;Step two, optimize 3D thermal field model and generate a group of selected process parameters under temperature field;Step three, utilize cellular automaton coupling through the temperature field under selected process parameters generated by 3D thermal field model optimized in step two and its corresponding multi-physical field, to simulate the microstructure topography under corresponding process parameters;Step four, train microstructure accurate regulation and control model;Step five, target part printing and its microstructure distribution real-time accurate regulation and control.It can be seen from this that the application can accurately regulate the generation of microstructure, improve the overall mechanical properties of target printed parts.

The present invention discloses a regional planning search and rescue method and system based on multi-agent collaboration, relating to the field of unmanned aerial vehicle (UAV) technology. The method comprises the following steps: obtaining the deployment position of a UAV and the initial position of a target to be searched and rescued; obtaining height field information between the deployment position and the initial position; using a cellular automaton algorithm to perform point-to-point path planning from the deployment position to a preset search and rescue starting position, and obtaining the UAV movement path from the deployment position to the preset search and rescue starting position; dividing multiple UAVs into leaders and followers, and controlling the leaders and followers to reach the preset search and rescue starting position according to the UAV movement paths; using an improved genetic algorithm to allocate search paths to multiple UAVs based on a pre-determined and divided search and rescue area; and controlling each UAV to search along its corresponding search path. The present invention can fully consider the search time of each UAV, quickly locate the target to be searched and rescued, significantly improve search and rescue efficiency, and reduce labor costs.

The present application relates to the technical field of power internet of things security, especially to a kind of power internet of things penetration testing method based on dynamic attack surface analysis, steps are as follows: the close degree between analysis equipment and attack accessibility are determined, the state transition relationship of information domain equipment under attack is determined;The probability of each device in the information domain being affected is calculated using the cellular automaton model;The influence degree of information domain under specific attack scenario is calculated;The physical domain state of power internet of things system is monitored, and the state transition relationship of physical domain node under attack is determined;The probability of each physical domain node being affected is calculated using the cellular automaton model, and the influence degree of physical domain under specific attack scenario is calculated;Traditional attack surface calculation method only considers attack cost and vulnerability availability, the present application considers attack cost, vulnerability availability, attack on system overall influence degree three parameters to calculate attack surface, more in line with the goal of power internet of things information physical system penetration testing to find security risks.