243 results about "Cellular automation" patented technology

The invention provides an urban ecological risk prediction system based on a cellular automaton. The system integrates and obtains remote sensing images, meteorological data and human activity data through a data acquisition module, and extracts green land coverage, land utilization types and species distribution information of cities. Thirdly, a space grid is divided through a space grid division module by means of a dynamic quadtree algorithm, ecological units are generated, and therefore microcosmic space heterogeneity is captured; then, the system utilizes a cellular automaton model to optimize a state transition rule through deep reinforcement learning, and couples a soil erosion model and a hydrological model to simulate the dynamic change of urban ecology. And finally, quantizing parameter uncertainty through a risk assessment module by using a Bayesian belief network and Monte Carlo simulation. According to the system, urban ecological risks can be effectively simulated and predicted, the spatial precision and scientificity of prediction are improved, and the coupling influence of multiple ecological factors is considered at the same time.

The invention discloses a charging pile load prediction method based on deep learning. The method comprises the following steps: S1, constructing a regional grid and initializing a cellular basic attribute vector; s2, collecting historical load data and external influence factors, and unifying the historical load data and the external influence factors into an input tensor; s3, generating a time continuous evolution state by using an ordinary differential equation modeling module; s4, constructing a state vector at the current moment; s5, the differentiable cellular automaton module executes primary spatial state propagation; s6, performing multiple rounds of spatial propagation iteration, and outputting enhanced state representation; s7, the load prediction module outputs a future multi-moment prediction value; s8, errors are calculated, and end-to-end training optimization model parameters are executed; and S9, outputting a final optimization model as a load prediction method. According to the method, the response capability of the model to sudden disturbance and the stability of multi-region collaborative prediction are remarkably improved, and the method can be widely applied to multiple application scenes such as smart energy management, electric traffic scheduling and urban public infrastructure intelligent optimization.

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 relates to the technical field of coal mine safety, in particular to a mine rock burst early warning, prevention and control method, device and equipment and a medium, and the method comprises the steps: obtaining multi-source monitoring data based on a high-precision positioning method; performing feature extraction on the multi-source monitoring data to obtain deep feature data; constructing a network prediction model, and training the network prediction model by using the deep feature data to obtain a trained network prediction model; and constructing a three-dimensional cellular automaton model of the coal and rock mass fracture process, and simulating a prevention and control strategy by using the three-dimensional cellular automaton model of the coal and rock mass fracture process to obtain a final prevention and control strategy. By integrating multi-source data such as coal and rock mass microstructure parameters, geological conditions and mining processes and combining machine learning and dynamic weight distribution technologies, accurate prediction and active prevention and control of rock burst risks are realized, and the problems of low early warning precision, poor real-time performance and weak adaptability in the prior art are solved.

The invention relates to the technical field of computer aided design, in particular to a steel plate pitting corrosion simulation method and system based on a cellular automaton model.The method comprises the steps that all slope curve segments corresponding to all corrosion pit morphology curves are obtained, and the directional variation index of each depth position is determined according to the depth position of the slope curve segments; acquiring a plurality of slope areas in the corrosion pit under different corrosion time with different solution concentrations to analyze the abnormal corrosion condition, and determining the local corrosion progressive coefficient of each depth position by combining the expansion coefficient; obtaining the number of intersections between the depth positions of each type of slopes and each type of local erosion progressive coefficients, and generating a dynamic rule of evolution of the cells to the corresponding type of slopes in combination with a directional variation index; on the basis of a dynamic rule, the cellular automaton model with the optimal goodness of fit is obtained by training the LSTM network, the problem of directional evolution under a finite element rule is avoided, and powerful help is provided for researching the real pitting process of the steel plate.

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.

A method for predicting microscopic holes of an aluminum alloy product and impact on macroscopic service properties includes: casting simulation, i.e., obtaining a casting simulation finite element mesh by dividing, and using casting simulation software to simulate a solidification process of a casting under corresponding process conditions to obtain macroshrinkages of the casting and physical information of each node on the mesh; cellular automata simulation, i.e., simulating microstructure growth by using a cellular automata model to obtain a secondary dendrite arm spacing (SDAS) value at each node of the casting simulation finite element mesh, and the morphology and size of microscopic holes including microshrinkages and microscopic blowholes; mechanical property simulation, and mapping and inputting mesh information of the casting simulation finite element mesh into the mechanical and fatigue property simulation finite element mesh to obtain a mechanical property simulation result; and fatigue property simulation.

The invention discloses an image encryption method and system based on a chaotic system, and relates to the technical field of image processing, and the method comprises the steps that the chaotic system is constructed by a cellular automaton and tent mapping combination; performing coordinate scrambling on the coordinate values of the pixel points; all the pixel points form a pixel matrix, and the pixel matrix is traversed to obtain an initial encryption matrix; a pseudo-random sequence is obtained through a chaotic system, the pseudo-random sequence is arranged into a row matrix and a column matrix, the row matrix and the column matrix are subjected to XOR operation, and a plaintext image is converted into a two-dimensional matrix; converting the initial encryption matrix into a two-dimensional encryption matrix; on the basis of a CA model of a cellular automaton, calculating and assigning pixel point values of the initial encryption matrix and the two-dimensional encryption matrix to obtain a target encryption image, and the image encryption method based on the chaotic system can solve the problems that in the prior art, an image encryption method based on the chaotic system is not sensitive to plaintext images, cannot effectively defend differential attacks, is poor in robustness, and cannot effectively defend differential attacks. And the technical problems of periodicity and generally small key space are solved.

The invention relates to the technical field of risk assessment, in particular to a scene-oriented forest fire dynamic risk assessment method, and solves the problems that an existing model depends on existing data, is poor in adaptability to sudden fire or multi-scene conditions, and is difficult to meet real-time and precise assessment requirements. The method comprises the steps of building a forest fire behavior simulation model based on a cellular automaton, building a forest fire scene simulation knowledge graph based on semantic driving, and building a comprehensive forest fire dynamic risk assessment model. According to the method, the risk assessment framework capable of being dynamically updated is constructed through fire behavior simulation, semantic modeling, comprehensive risk assessment model construction and other technologies, real-time monitoring and prediction of fire risks can be achieved through the method, and the disaster prevention and reduction capacity is enhanced.

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 discloses an oil reservoir polymer flooding process oil-water distribution simulation method and device based on a cellular automaton, equipment, a medium and a product, and relates to the field of oil reservoir engineering and seepage mechanics. The method comprises the steps that rectangular grid division is conducted on an oil reservoir space, three-dimensional coordinates of cellular nodes are determined, and a cellular automaton model is constructed; according to the polymer oil displacement scheme parameters, oil reservoir state parameters at the end moment of water displacement are initialized, and boundary conditions of the mole-type cellular automaton model are established; making an evolution rule for each neighborhood cellular node in the Moire cellular automaton model; according to a numerical value stability rule, adjusting parameters of each neighborhood cellular node in the Mohr-type cellular automaton model; according to the method, a relative permeability nonlinear regulation and control rule is formulated for the Mohr-type cellular automaton model to simulate oil-water distribution in the oil reservoir polymer flooding process of the Mohr-type cellular automaton model, the starting pressure gradient is accurately simulated, and the low-permeability oil reservoir evolution process is accurately described.

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 discloses a dangerous source dynamic prediction method and system based on a cellular automaton and a medium. Relates to the technical field of building construction. The method is improved on the basis of a traditional hazard source monitoring technology, the dynamic evolution characteristics of a construction site hazard source are effectively captured by constructing a construction site hazard source CA model, the generation, diffusion and elimination process of the hazard source is simulated, and a theoretical basis is provided for identifying a high-risk area; dynamic evolution is carried out on different construction stages, corresponding management strategies are generated, and safety management is enhanced, so that spreading of dangerous sources can be effectively inhibited; according to the scheme, the cellular automaton theory is applied to the field of construction safety management, and a new hazard source dynamic analysis method is provided.

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 invention relates to a new energy power system power flow prediction method considering source-network-load uncertainty, and the method comprises the specific steps: carrying out the normalization processing of the historical parameters of a power grid, extracting multi-dimensional track features through variational mode decomposition, and constructing an operation trend set; a cellular automaton model is combined to simulate a spatio-temporal evolution law, and a fault feature recognition mechanism is optimized; constructing a trend state classification database based on the residual transformation rate; dynamically distributing state contribution weights by using a multi-constraint regression model, and establishing a prediction model; and iteratively optimizing the model through error feedback, and constructing a vulnerability evaluation system based on sensitivity and interference analysis. According to the method, multi-source data and dynamic weight distribution are fused, the problems that traditional prediction precision is low and fault response is lagged are solved through a bidirectional feedback optimization framework, the accuracy of power grid trend prediction and vulnerability assessment can be improved, and support is provided for security defense.

The invention provides a city extension simulation method and device, equipment and a storage medium. Relates to the technical field of city simulation. The method includes: selecting an interaction target based on a neighborhood configuration; performing feature extraction on the interaction target to obtain features of the interaction target; constructing a graph structure based on the characteristics of the interaction target; based on the graph structure, constructing a high-order graph attention network; a vector cellular automaton is integrated in the high-order map attention network to obtain an HGAT-VCA model, and city expansion simulation is realized based on the HGAT-VCA model; wherein the vector cellular automaton is used for calculating the total transition probability and determining the land utilization type. According to the method, the expansion of the urban construction land can be simulated and analyzed from a multi-scale and multi-level angle, the complex process of urban expansion and expansion paths under different scenes can be disclosed, and an existing urban expansion theoretical model is enriched.

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 a microstructure digital processing method and system for a metal material EBSD result, and the method comprises the steps: defining the physical orientation of a material, namely describing the orientation of a crystal through three continuous rotations; performing image processing on the acquired microstructure image to obtain a distribution rule of crystal grains; the microstructure of the material is re-identified and combined, and small crystal grains which cannot be identified in EBSD data processing software are eliminated; assigning the microstructure image obtained by re-identification to obtain a material microstructure segmentation result; and linking the crystal grains with Euler angle orientation exported from EBSD data processing software to obtain orientation distribution data of the microstructure of the material. On the basis of EBSD data processing software, grain distribution and Euler angle orientation information in the EBSD data processing software are extracted, the Euler angle orientation information can be used for subsequent cellular automaton model and theoretical analysis, the resolution can be adjusted according to needs, and the recognition precision is improved.

The invention relates to a traffic signal control system and method, and relates to the technical field of traffic control, and the method comprises the steps: collecting traffic flow data through multi-source sensing equipment and V2X communication, and carrying out the preprocessing to construct a traffic feature matrix; a hybrid modeling framework is adopted, a queue theory, a cellular automaton and deep reinforcement learning are integrated, a traffic state is evaluated, and a signal control strategy is optimized. Through a double-closed-loop feedback mechanism, the inner loop uses a PID controller to finely adjust the phase, and the outer loop learns online to update reinforcement learning proxy parameters. And finally, an optimization strategy is executed, the variable lane control equipment is linked, and a signal state and a vehicle speed guide instruction are pushed to the vehicle-mounted terminal, so that remarkable beneficial effects can be generated in the aspects of improving traffic efficiency, reducing congestion, improving safety and the like.

The invention discloses a cellular automaton-based land utilization and urban element collaborative simulation method. The method comprises the following steps of: calculating the development probability of each element; the method comprises the following steps of: constructing a cellular automata (CA) model of each element; space collaborative simulation calculation; and evaluating the result precision. According to the method, the urban land use change simulation precision can be effectively improved, the land use change in reality can be better simulated, the simulation precision of population and economic elements can be improved, and the interaction and synergistic effect between different urban elements are effectively revealed. By means of multi-element collaborative simulation, small changes of urban development elements can be caught more finely, and the method has good perception ability for residential land, industrial land, commercial land, public management land and the like and has important significance for simulating urban collaborative development and reasonably planning urban layout.

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 discloses an urban power distribution network fault defense method under flood disasters based on a cellular automaton, relates to the field of power systems, and aims to accurately predict the failure probability of power distribution network equipment by simulating the dynamic evolution process of accumulated water under the flood disasters and combine emergency resource configuration and deployment of pre-disaster defense. Accurate defense of power distribution network fault conditions under flood disasters is realized. According to the method, complex physical calculation and high-precision data requirements are avoided by utilizing the spatial evolution characteristics of CA, so that the calculation efficiency is improved, the adaptability to large-scale urban areas is enhanced, the toughness of the power distribution network under extreme disasters can be remarkably improved, resource allocation can be optimized, economic losses caused by the disasters can be reduced, and the economic benefits of the power distribution network are improved. And a powerful guarantee is provided for safe and reliable operation of an urban power system.

The invention relates to the field of artificial intelligence, and discloses a community epidemic risk assessment model construction method based on big data, and the method comprises the following steps: S1, collecting data to construct a heterogeneous space-time atlas; s2, performing node representation learning on the map by using a heterogeneous map attention mechanism in a map neural network, and extracting propagation association features between communities; s3, constructing a coupled cellular automaton model, and simulating a disease transmission process between communities; s4, performing structure mapping and adaptive optimization by adopting a structure transfer learning mechanism; and S5, in combination with a multi-agent reinforcement learning strategy, community-level prevention and control strategy recommendation and risk assessment output are carried out by taking the risk cost as an optimization target. The community propagation potential is modeled by combining population mobility, environmental meteorology and medical resource data, accurate description of a propagation chain in space and time dimensions is realized, the dynamic perception ability of a real propagation path is improved, and the problems of rough description of a propagation scene and large prediction deviation are solved.

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 a pest physicochemical control gradient decision-making method and system based on multi-mode perception driving, and belongs to the field of agricultural pest control. The method comprises the steps that multi-modal perception data are acquired, a multi-modal perception model is constructed, perception information is output, and the perception information comprises insect quantity density, a voiceprint migration index and an environmental stress coefficient; constructing a cellular automaton model based on the perception information, simulating pest diffusion and outputting a risk thermodynamic diagram; and outputting a pest physicochemical control gradient decision based on the risk thermodynamic diagram. The method solves the long-standing problems of response lag, too high cost, inaccurate prediction and the like in the field of pest control, and has an application prospect.

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.