155 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 discloses a millisecond regulation and control method for hydrogen fluoride production based on reinforcement learning and model prediction control, and relates to the field of hydrogen fluoride production regulation and control. In the multi-scale neural symbol dynamics modeling step, GNN, a cellular automaton and a state space model are fused, and parameters are updated in real time; in the causal reinforcement learning decision optimization step, an effect is calculated through a causal graph, and a reward function is optimized; in the time-space fractional order sliding mode control step, a fractional order sliding mode surface and a controller are designed, and rapid and stable control is achieved; in the memory enhancement element learning adaptation step, a DNC storage strategy is utilized, new working conditions are quickly adapted through element gradient, and millisecond-level precise regulation and control are achieved. According to the method, prediction errors are greatly reduced, the response speed is increased, and overshoot is reduced; the product yield is improved, the energy consumption is reduced, and new working conditions are quickly adapted; fault detection and risk early warning are more accurate, equipment operation is more stable, production efficiency is effectively improved, cost is reduced, and safety is enhanced.

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 provides a dynamic planning method for a fire evacuation path of a deep subway station based on a cellular automaton, and the method comprises the steps: building a subway space topology through employing the cellular automaton, simulating a fire environment through employing an FDS tool, and obtaining environment data; constructing a multi-field model based on subway space topology and environment data; based on the multi-field model, the pedestrian transition probability is calculated, then the pedestrian transition probability is corrected in combination with the deep subway facility characteristics and the pedestrian consensus behaviors, and finally the corrected pedestrian transition probability is output; and optimizing a cost function of an A star algorithm based on the corrected pedestrian transition probability to obtain a dynamic cost function, further obtaining an improved A star algorithm, planning an initial path based on the improved A star algorithm, formulating a path updating rule, and implementing a smooth transition strategy to realize dynamic path planning. According to the invention, the evacuation safety and the scheduling efficiency under the fire situation can be improved.

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 belongs to the technical field of electric power information physical system security, and discloses an urban power grid information physical system security situation early warning method based on cross-space risk propagation. A power grid physical layer and information layer coupling model and a cellular space false data injection attack model are constructed, and a fault cross-space propagation mechanism is simulated through an event-driven cellular automaton theory; an integrated kernel extreme learning machine model is adopted to predict the operation state of the power grid, multi-dimensional data fusion is realized through a radial basis kernel function, and an integrated structure is adopted to fuse a plurality of model prediction results so as to improve the model prediction precision; and establishing an early warning system including voltage out-of-limit, line overload and load loss indexes, and dynamically distributing weights and dividing early warning grades in combination with an entropy weight method. According to the method, the problem of low precision of urban power grid security situation early warning under multivariate disturbance is solved, and the accuracy and robustness of security situation early warning can be effectively improved.

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 provides a rural sewage treatment simulation optimization method based on a cellular automaton, which comprises the following steps of: establishing a cellular grid, and defining basic attributes of cells according to space coordinates, terrain types, population density and sewage treatment capacity; according to the sewage treatment scene, respectively establishing a cellular state evolution rule base; the sewage treatment scene comprises a pipe network collection type, an ecological wetland type and a decentralized recycling type; acquiring environmental parameters and pollutant parameters of a target area to be subjected to sewage treatment; selecting a cellular state evolution rule base according to input environmental parameters and pollutant parameters of a target area to be subjected to sewage treatment; and performing sewage treatment simulation, optimizing the sewage treatment simulation process by the cellular state evolution rule base, and outputting a sewage treatment facility layout scheme and a dynamic control strategy. The sewage treatment scheme can be quickly generated, the optimal sewage treatment scene model is automatically selected according to actual conditions, and accurate customization of the sewage treatment scheme is realized.

The invention provides a chaos enhanced credible authentication method based on a vehicle-mounted gateway, and relates to the technical field of credible authentication, and the method comprises the following steps: generating a chaos sequence by using improved Tent chaos mapping; dynamically disturbing a round constant CK of an SM4 encryption algorithm by using the chaos sequence to generate a confused round key; synchronously updating a session key between the vehicle-mounted gateway and the ECU based on a cellular automaton and a chaotic mapping mechanism; identity authentication is executed through a bidirectional challenge-response mechanism; the vehicle-mounted gateway sends an encrypted challenge frame containing a random number R1 to the ECU; after decryption, the ECU generates a response frame by using a SipHash function and returns the response frame, and meanwhile, sends a reverse challenge frame containing a random number R2; and the vehicle-mounted gateway verifies the response frame and then generates a reverse response frame, and the two parties update the session key after the ECU passes the verification. According to the method, the anti-attack capability can be remarkably improved, the method can efficiently adapt to an embedded environment, and meanwhile the system reliability can be effectively enhanced.

The embodiment of the invention provides a chip packaging defect diagnosis method and system based on computer vision, and belongs to the technical field of computer vision, and the method comprises the steps: firstly, collecting a dynamic multi-source image sequence of chip packaging in key processes of soldering tin melting, packaging glue curing and pin shaping; and performing defect feature cellular coding processing on the dynamic multi-source image sequence to form a defect feature cellular coding set. And then, constructing a cellular automaton evolution model based on the defect feature cellular coding set, then, executing multi-source association verification processing on the cellular automaton evolution model, and matching an evolution trajectory of a cellular with a defect evolution rule base to generate a verification result. And finally, analyzing the verification result to reconstruct a complete evolution path and a source association chain of the defect cell, thereby accurately determining the type, the specific occurrence process and the position of the defect, and outputting a comprehensive diagnosis result. According to the method, cross-process and multi-source fusion accurate traceability and diagnosis of the dynamic evolution process of the packaging defect are realized.

The invention discloses an electrolyte bubble distribution monitoring method, system, equipment and medium, and relates to the technical field of electrochemical equipment monitoring. The method comprises the following steps: acquiring multi-source physical data of a target electrolyte; key feature parameters in the multi-source physical data are extracted; inputting the key characteristic parameters into a pre-constructed multi-physical field coupling model to analyze a dynamic rule of bubble generation and distribution in the target electrolyte; the multi-physical field coupling model is coupled with an electric field, a flow field and a temperature field; based on the output of the multi-physics field coupling model, reconstructing the bubble three-dimensional distribution of the target electrolyte through a region growing algorithm and a cellular automaton model; and carrying out abnormal state analysis and early warning according to the three-dimensional distribution of the bubbles. According to the invention, real-time and high-precision monitoring and abnormity early warning of electrolyte bubble distribution can be realized through multi-source data fusion and multi-physical field coupling analysis.

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 quantum reinforcement learning, in particular to a reinforcement learning method and system for quantum gate regulation, and the method sequentially comprises the steps: carrying out the normalization of an offline sample, carrying out the principal component analysis, and then preparing a prior quantum state; performing wavelet scattering and persistent coherence on the quantum pulses to extract topological features, and mapping the topological features into a non-Abell geometric gate rotation angle to generate an action probability; constructing a sparse high-dimensional vector, selecting a target super vector through Grover amplification, writing the target super vector into the silicon carbide memristor array, and retrieving an action vector according to a conduction function determined by the topological feature vector; the motion vector drives the Heisenberg cellular automaton to predict the environment, the local error triggers the fine tuning of the rotation angle and the conduction value, and the global distance triggers the topology recompilation and the quantum natural gradient. According to the method, decision making within 250 microseconds, low-energy-consumption retrieval and steady-state self-healing are 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 forest fire spreading prediction method based on a joint prediction model, and the method comprises the steps: discretizing a forest region into cells, and associating forest fire data with environment data; according to the forest fire data, it is determined that the initial state of the cells is unburned, combusted, burnt out or incombustible; on the basis of a cellular automaton model, according to the environment data and the cellular neighborhood, predicting a cellular state sequence of cellular states changing along with time; extracting a cellular state sequence according to a time step length, organizing a fire spread space-time matrix, fusing the fire spread space-time matrix with external environment features to form a joint feature matrix, and inputting the joint feature matrix into a space-time convolutional neural network; and the space-time convolutional neural network generates a real-time fire spreading state prediction result. According to the joint prediction model, the physical law of fire spreading is simulated through the cellular automaton, and the space-time dependency relationship of multi-dimensional environment data is learned in combination with the space-time convolutional neural network, so that the adaptability and accuracy of the model are improved, and the dynamic prediction of the forest fire can be effectively and quickly completed.

The invention relates to the technical field of public health information, in particular to an infectious disease transmission path generation and early warning method and system based on big data, and the method comprises the steps: collecting and cleaning multi-modal data, and obtaining a pulse event flow through neuromorphic coding; and constructing a variable order hyperedge graph in a second-level window, extracting loop life to adjust diffusion of the cellular automaton, and iteratively generating an infection prediction field. And then splicing the hyperedge graph, the prediction field and the topological fingerprint into a conditional tensor, and inputting the conditional tensor into a conditional score diffusion network to generate a future propagation event sequence. And constructing a tensor network according to the sequence, mapping the tensor network into Isin Hamiltonian, quickly selecting an intervention node through an optical delay ring reservoir and gradient descent, and issuing a ventilation or purification instruction. And after a virus load difference value before and after intervention is normalized, a tensor network, a diffusion network and a cellular automaton are synchronously updated to form a self-adaptive closed loop, so that a peak time error can be shortened, and the recall rate of a high-risk region is improved.

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.

The application discloses an engine cylinder body lost foam mold modeling device and a casting method. A plurality of modeling units are arranged in the form of an annular array, and each adjacent modeling unit has a spacing a. The modeling unit comprises a lifting linear degree of freedom, a feeding linear degree of freedom and a universal linear degree of freedom, and a seismic source for modeling. For each cell i, the following steps are performed: calculating the temperature gradient of the cell i using the temperature value difference calculation in the Moore neighborhood Ni. The technology provided by the application uses a temperature sensor to monitor the temperature distribution inside the mold in real time, calculates the rate prediction value through a cellular automaton, and thus realizes accurate control of fluid flow. Based on the prediction ability of the temperature distribution, the fluid flow situation can be predicted in advance, and corresponding control can be performed to ensure the casting quality.

The present invention provides a digital simulation method for village and town settlements based on cellular automata. First, basic data on village and town settlement development is collected, cleaned, and refined. The decisions and behaviors of various stakeholders are dynamically captured through a multi-agent system and incorporated into a preliminary analysis. These basic data and analysis results are input into a cellular automaton based on machine learning, and ultimately the transformation rules of the cellular automaton are learned. Cell states are iteratively updated according to these rules to simulate and record land behavior changes over time. The land use conversion probability of each cell is calculated, and land use change trends are analyzed. Land development projects are randomly selected using a parallel Monte Carlo method, and the total land supply is determined. Finally, a determination is made as to whether the total supply meets a preset supply threshold. If not, iterative adjustments are made. If so, the results are summarized and visualized. This method provides a systematic, intelligent, and efficient solution for complex land management needs.

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 belongs to the technical field of microstructure simulation in the solidification process, and particularly relates to a nickel-based superalloy laser welding solidification structure evolution law prediction method and system.The method comprises the steps that simulation parameters are determined, a macroscopic temperature field is simulated to obtain molten pool temperature field data, a cellular automaton model is constructed, and grids are divided; initializing state variables such as component concentration and solid-phase fraction; coupling temperature field data, solving an optimized solute diffusion and redistribution equation to obtain a concentration field, updating a solid phase fraction according to local supercooling drive, and marking grain growth; counting a solid-phase fraction, and updating a solid-phase position; and judging whether the temperature of the cladding layer is lower than the solidus temperature and the solid fraction reaches a complete solidification threshold value or not, and if yes, outputting weld microstructure and target element concentration data. According to the method, the evolution law of the welding macroscopic temperature field and the structure and solute concentration field can be effectively predicted, experiments are replaced with numerical simulation, the cost is reduced, the technical blank is filled up, and reliable support is provided for precise regulation and control of the nickel-based high-temperature alloy laser welding process.

The application discloses an image encryption method and system based on a chaotic system, and relates to the technical field of image processing.The method comprises the following steps: a chaotic system is constructed by combining a cellular automaton with a tent map; the coordinate values of pixel points are subjected to coordinate scrambling; all the pixel points are formed into a pixel matrix, the pixel matrix is traversed, and an initial encryption matrix is obtained; a pseudo-random sequence is obtained through the chaotic system, arranged into a row matrix and a column matrix, and subjected to exclusive or operation to convert a plaintext image into a two-dimensional matrix; the initial encryption matrix is converted into a two-dimensional encryption matrix; based on a CA model of the cellular automaton, the initial encryption matrix and the two-dimensional encryption matrix are subjected to pixel point value calculation and assignment, and a target encryption image is obtained.The application can solve the technical problems of the prior art, such as that the image encryption method based on the chaotic system is not sensitive to the plaintext image, cannot effectively defend against differential attacks, has poor robustness, and has the problems of periodicity and a generally small key space.

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 green wave optimization method based on a cellular automaton under BIM-GIS-MLP, and relates to the field of road digitization, and the method comprises the steps: segmenting a road according to a BIM and GIS platform; the terminal point information of the multiple journeys of each vehicle is counted based on the IOT, and corresponding traffic light calibration information and access information of the subsequent vehicles through the IOT are fed back to the subsequent vehicles; the BIM and GIS platform analyzes the average speed range passing through the multiple sections of continuous travel and calculates the sum of the duration of the green wave section; and calculating the optimization problem of the sum of the duration of the green wave section through linear programming, and solving the maximum value of the target function. According to the method, data networks of the Internet of Vehicles and the Internet of Things are accessed to realize data fusion and intercommunication, an MLP model is accessed to analyze individuals, and then the maximum value of the target function about the green wave road section time is obtained through linear programming, so that the traffic smoothness is improved, the energy loss and exhaust emission are reduced, and the urban environment quality is improved.

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.