875 results about "Automaton" patented technology

The invention discloses an intelligent monitoring and early warning system and method for agricultural non-point source pollution, and relates to the technical field of environmental monitoring, accurate prediction of water pollutant concentration is realized through multi-source heterogeneous data acquisition and fusion, a dynamic attention mechanism and a PINN-Transformer coupling model, the system extracts data spatial and temporal characteristics by using an optimized Transformer model, and the method is applied to the intelligent monitoring and early warning of agricultural non-point source pollution. A water pollution diffusion physical constraint is embedded, it is ensured that a prediction result conforms to an actual hydrodynamic law, and based on high-precision spatial-temporal distribution data, an intelligent algorithm is adopted to track a pollution diffusion path and rapidly lock a pollution source; meanwhile, the cellular automaton model simulates pollution risk dynamic diffusion and assists regional risk assessment, the system also combines a block chain technology to carry out credible evidence storage on key monitoring data, and real-time data processing and early warning pushing are realized through a cloud edge collaborative architecture. And an efficient and reliable technical solution is provided for agricultural water environment management and pollution prevention and control.

The invention discloses a nuclear radiation index early warning system based on multi-algorithm fusion, and the system comprises an environment data collection module which is used for collecting gamma-ray dose rate data, wind speed data, wind direction data and geographic position information, and constructing a nuclear radiation environment data set; the dose rate prediction module is used for predicting a future change trend of gamma ray dose rate data based on a sparse recurrent neural network model; the anomaly detection module is used for identifying an abnormal mutation point and positioning the position of a high-risk sensor; the diffusion region modeling module is used for calculating a nuclear radiation diffusion influence region; the grid risk evolution module is used for simulating time sequence evolution of a regular grid risk state based on the multi-state cellular automaton; and the fusion judgment module is used for fusing output results and generating a final early warning grade and a spatial risk distribution result. According to the invention, accurate prediction of the nuclear radiation risk and dynamic space early warning are realized, and the early warning accuracy and response efficiency are significantly improved.

An exemplary system and method that generate a finite state automaton of a controller for sequential decision-making of a computing device for a user-requested task, the generation using automaton-based representations derived from outputs of a large-scale generative language model. The exemplary system and method receive a user command and (i) construct the finite state automaton by parsing GLM responses to prompts / queries for candidate steps and sub-steps for the task, and (ii) verify the finite state automaton, e.g., for logical errors, safety specification, performance specification, and intent of the user, and / or update the finite state automaton to meet such specifications. The state machine can be synthesized on the fly for real-time operation via the GLM outputs and evaluated in the same pipeline operation against physical models, safety models, etc., that can be combined to iteratively modify the state machine until it meets the verification requirements.

The invention relates to an equipment fault intelligent question-answering method based on large model enhancement, and the method comprises the following steps: obtaining steel production line data, and constructing a fault knowledge graph; user questions are obtained and classified through semantic analysis, and classification results comprise single-hop questions, short-hop questions and multi-hop questions; calling a corresponding reasoning mechanism according to the category of the user question, generating an answer according to the fault knowledge graph, and outputting equipment fault analysis in a natural language form through a retrieval enhancement generation technology; for a single-hop problem, core entities and relationships are matched based on an Aho-Corasick automaton, and answers are directly retrieved from the fault knowledge graph; for a short jump problem, deriving an answer from the fault knowledge graph by adopting a single-agent inference model; for a multi-hop problem, a multi-agent collaborative reasoning model is adopted to generate a multi-step reasoning path based on the fault knowledge graph. Compared with the prior art, intelligent question answering with high reliability and high readability can be efficiently realized in a complex industrial scene.

The invention discloses a sewage plant accurate dosing method based on multi-mode automatic machine learning, and belongs to the technical field of sewage treatment. The method comprises the following steps: acquiring historical sensing data and image data of a sewage plant; extracting key features in the image by using a YOLO visual model, fusing the key features with sensor data, and constructing a multi-modal sample data set; carrying out modeling training on the fused data by adopting an automatic machine learning framework, and constructing a'water inlet-dosing-water outlet 'prediction model; generating a reference sample data set according to an existing dosing rule; inputting the sample data into the trained prediction model to obtain predicted effluent quality data, and adjusting the dosage until the predicted effluent quality of all samples meets a water quality standard condition; and finally outputting an optimized dosing sample data set. According to the invention, precision and real-time dosing of the sewage plant can be realized, chemical waste can be effectively reduced, the treatment efficiency is improved, the cost is saved, and the water quality is ensured to stably reach the standard.

The invention provides a multi-round automatic machine learning agent system based on reinforcement learning optimization. Comprising a task analysis module used for generating an initial prompt for an MLE agent to call; the MLE agent module is used for generating an executable code; the code executor is used for generating an execution result; the evaluator is used for outputting a normalized value of each index and a code correctness identifier; the reward construction module is used for generating a reward value; the reinforcement learning optimizer is used for calculating group average return and candidate advantages and updating strategy parameters of the MLE intelligent agent module based on the candidate advantages; and the multi-round interaction control module is used for feeding back the execution result of the previous round and the reward value to the MLE agent module in the multi-round interaction process, and controlling code generation of the next round until a preset termination condition is met. According to the invention, strategy adaptive evolution, reinforcement learning optimization of fine-grained credit distribution and multi-round closed-loop automatic process improvement can be realized.

Technology is described for determining a task plan that is usable by a robotic device in a workspace. The method can include converting instructions received for the robotic device into temporal logic (TL) statements and to a non-deterministic Buchi Automaton. A task probabilistic machine learning model can be generated with feasible task plans using the non-deterministic Buchi Automaton. A plurality of task plans can also be created or generated using the task probabilistic machine learning model. A sensor probabilistic machine learning model of the workspace can be constructed using information from sensors of the robotic device. The task plans from the task probabilistic machine learning model can be compared with the sensor probabilistic machine learning model to select the task plan with a high probability of correlation to the workspace.

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 discloses an automatic machine learning collaborative optimization method based on hardware perception. The method is used for design of an integrated learning field programmable gate array accelerator in edge artificial intelligence. The method aims at solving the balance challenge between algorithm performance and hardware resource consumption. The method comprises the following steps: defining a joint optimization space containing algorithm hyper-parameters and FPGA hardware configuration parameters; a collaborative optimization objective function is established, the function is determined by algorithm performance indexes and predicted FPGA hardware resource occupancy, and resource prediction quantifies hardware influence according to algorithm hyper-parameters; and searching according to the objective function in the joint optimization space by utilizing an AutoML optimization algorithm so as to identify the optimal configuration of the balance performance and resources. According to the method, the problems of low resource efficiency and high development cost caused by separate optimization of an algorithm and hardware in the prior art are effectively solved, balanced design is realized, automation is enhanced, and complexity is reduced.

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 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.

According to the automatic software development method and device and the computer program, intelligent demand analysis is achieved through a natural language processing technology, dynamic task arrangement is conducted through a finite state automaton, collaborative development is completed by means of a multi-specialized role agent, intelligent verification is implemented based on a template matching mechanism, and the development efficiency is improved. And a complete automatic closed loop from demand to delivery is constructed. According to the scheme, full-process automation is realized, manual intervention is greatly reduced, and the development efficiency is remarkably improved; through division and cooperation of multiple agents, the professionality and decision-making quality of each link are ensured, and the capability limitation of a single agent is overcome; flexible and reliable process control is provided based on state machine management, and development state changes are dynamically adapted; and the standard consistency of the delivery result is ensured by combining template verification. According to the method, the problems of long development period, large quality fluctuation and the like caused by chain splitting, low intelligent level and excessive dependence on manpower of an existing development tool are effectively solved, and a feasible path is provided for comprehensive intelligence of software development.

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 disclosed methods and systems automate the process of building machine learning models. A user interface receives a selection of a dataset for a machine learning experiment. An execution plan for the experiment is determined based on the selected dataset. The experiment is executed according to the execution plan to generate a plurality of machine learning models. The performance of the generated models is evaluated based on one or more performance metrics. A model is selected from the generated models based on the evaluation of the performance metrics. The selected model may be stored for future use.

One or more information maps are obtained by an agricultural work machine. The one or more information maps map one or more agricultural characteristic values at different geographic locations of a field. An in-situ sensor on the agricultural work machine senses an agricultural characteristic as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural characteristic at different locations in the field based on a relationship between the values in the one or more information maps and the agricultural characteristic sensed by the in-situ sensor. The predictive map can be output and used in automated machine control.

The invention discloses a bridge safety early warning method and system, and the method comprises the steps: collecting bridge structure response and environment parameter data, and carrying out the preprocessing of the data, and obtaining a distributed multi-source sensing information matrix; constructing a distributed neuron cell automaton network, interacting state information through unit local communication, operating a lightweight feature extraction algorithm, and generating a multi-dimensional feature vector; carrying out distributed modeling by utilizing a pre-trained distributed LSTM-Transform hybrid model, and carrying out parallel calculation on a local prediction result by each unit to obtain a global health state evaluation result; a local gradient change anomaly detection mechanism is established, data anomaly is identified by comparing prediction results of adjacent units, and a potential risk area is positioned by combining a related algorithm; and constructing a hierarchical early warning threshold system, activating a corresponding early warning response by means of local decision logic, executing an alarm operation, generating recovery guidance information and monitoring uploaded data. According to the invention, two types of network models are combined, so that the system reliability and early warning accuracy can be remarkably improved.

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.

Automated machine learning (Auto ML) for creating and optimizing ML models using a model store for storing: trained ML models and hardware models; test metrics data corresponding to the stored models; ML advised-models. Using a model meta-services (MMS) for: accessing the stored models and the test metrics data; creating the ML meta-models based on the runtime test metrics data; and answering MPC queries. Using a models producer and consumer (MPC) for: selecting a ML advised-model; testing the selected ML advised-model using selected ML test inputs and outputs to provide runtime test metrics data; optimizing the selected ML advised-model using the runtime test metrics data; sending the optimized ML advised-model to the model store unit for storing as one of the stored ML advised-models; and sending the runtime test metrics data to the model store unit for storing as part of the runtime test metrics data; and sending the MPC queries.

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 discloses a ship knowledge graph construction method based on a large model and a graph neural network, and the method comprises the steps: carrying out the format conversion and protection type partitioning processing of a professional document, and completing the recursive segmentation through a placeholder protection formula, a table and other structures according to the semantic hierarchy, and obtaining a text block suitable for the extraction of an entity and a relation; extracting domain entities by utilizing a large language model containing thinking chain cues, and positioning candidate entities in combination with an AC automaton so as to improve the coverage rate and accuracy of relation triple extraction; performing new entity backfilling and relation standardization on an extraction result, and constructing an initial knowledge graph with a consistent structure; and inputting the initial knowledge graph into a graph neural network model with directional expansion and a multi-scale decoder, and complementing a missing relationship through link prediction, so that node distribution and a relationship structure of the graph are more complete. According to the method, a continuous processing chain from text preprocessing, knowledge extraction to graph completion is formed.

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 belongs to a specification mining technology of software engineering, and relates to a construction method of an approximate specification mining model and a software behavior verification system. The construction method comprises the following steps: generating a group of linear temporal logic LTL formulas for describing positive example time sequence attributes according to a positive example set; using an LTL formula to generate a potential negative example for evaluation and training; designing and constructing a neural network for analyzing the finite state automaton from the parameter assignment so as to simulate an acceptance behavior of the finite state automaton; and iteratively searching the neural network through a gradient descent algorithm until the maximum number of iterations is reached, and mining to obtain the finite state automaton. The neural network can simulate acceptance of the finite state automaton in the reasoning process, the finite state automaton can be explained at low cost, and the problem of search space explosion is solved.