50 results about "Problem transformation" patented technology

The invention belongs to the technical field of intelligent question-answering systems, and particularly relates to an intelligent question-answering inference system based on a knowledge graph, which is characterized in that firstly, a knowledge graph construction module fuses multi-source data to generate a structured graph, and after a user inputs a natural language question, a question-answering analysis module completes intention classification and entity disambiguation and converts the question into structured query; an inference engine module fuses symbol rules and graph neural network inference through a hybrid inference sub-module, and a dynamic weight adjustment sub-module optimizes weights according to errors and attenuation factors to generate an inference result; the knowledge updating module incrementally updates the atlas in real time and detects conflicts, the interactive interface module visually presents a result, and the evaluation optimization module iteratively optimizes parameters in combination with offline evaluation and online feedback. The whole process is from user question asking to result output, accurate reasoning and continuous performance improvement are achieved, and multi-field question and answer requirements are met.

The invention provides a zero-sum game-based distributed elastic consensus optimal control method under denial of service attack of a multi-agent system, and the method specifically comprises the steps: firstly, constructing a multi-agent formation model in which a leader and a plurality of followers cooperatively move through graph theory knowledge and a multi-agent second-order state equation; secondly, in order to reduce the influence of denial of service attack on communication topology, a time-varying weight distributed elastic observer is provided to estimate the state of a leader, and the attacked condition of the leader is considered; then, by constructing an augmentation system, a distributed consistency tracking problem with a leader is converted into a local tracking problem between each follower and a virtual leader thereof; and finally, in order to solve the zero-sum game problem, introducing a Hamiltonian-Jacobian-Ansaxophone equation to realize optimal control input under maximum external disturbance, and realizing algorithm design by using single-evaluation reinforcement learning with experience playback and combining a gradient descent method.

The invention relates to the field of earth-rock engineering scheduling, in particular to an earth-rock multi-target intelligent allocation method and device, a medium and a program product. The method specifically comprises the following steps: S1, collecting and sorting basic data; s2, constructing an earthwork allocation mathematical model; s3, using a sparrow search algorithm to optimize the earth-rock allocation scheme; and S4, scheme implementation and dynamic adjustment. The method comprises the following steps: firstly, constructing a mathematical model of earth-rock deployment, taking the shortest transportation time and the shortest transportation path as objective functions, comprehensively considering constraint conditions such as earth-rock supply and demand balance, transportation vehicle load limitation and construction progress requirements, and converting an actual deployment problem into a mathematical planning problem; and then, a sparrow search algorithm is introduced to carry out intelligent deployment, an optimal deployment scheme is obtained, efficient deployment of earthwork in time and path dimensions is realized, and a scientific and reasonable earthwork deployment decision scheme is provided for engineering construction.

According to the knowledge base-oriented interactive causal inference support method and system provided by the invention, a user causal analysis intention is precisely limited through interactive dialogue, a multi-dimensional intention is precisely captured, a fuzzy causal problem is converted into a computable analysis parameter, and ambiguity is remarkably reduced; multiple strategies such as an explicit causal declaration mode, time sequence correlation, statistics of significant correlation and comparative difference analysis are integrated to collect causal evidence, the four strategies are executed in parallel, multiple evidence sources of causal inference are covered, and limitation of a single method is avoided; and the query efficiency is optimized in combination with a special index and a graph algorithm, and finally evidence is presented in a structured and interactive mode and confidence is clearly distinguished, so that the response efficiency is greatly improved. The system solves the problems of fuzzy intention, single evidence and low efficiency in traditional causal inference, is suitable for the fields of industrial fault analysis, medical diagnosis, social phenomenon research and the like, and provides scientific and transparent causal hypothesis generation support for users.

The invention discloses a data analysis method and device, a storage medium and a processor. In the scheme, description information of an insurance service problem is obtained, and the description information is converted into a data query vector; retrieving data matched with the data query vector in a corresponding knowledge base based on a plurality of preset recall channels to generate a retrieval result; converting the insurance business problem into a standard insurance business semantic expression by the large language model according to the data caliber to obtain target problem description information; generating a corresponding target SQL query statement by the large language model according to the target problem description information, the SQL template and the database table structure, and querying matched insurance service data based on the target SQL query statement; and performing data analysis on the insurance service data by the large language model according to the target problem description information and the data analysis template to obtain an analysis result. According to the technical scheme, the accuracy of insurance service data analysis is remarkably improved through deep collaboration of a multi-path recall mechanism and a large language model.

The invention provides a DIKWP semantic modeling method. Complex problems of an enterprise are converted into a five-layer linkage semantic map. The method comprises the following steps: 1, cleaning multi-source heterogeneous data to form nodes by a data layer; (2) information layer construction domain ontology clarification concepts and constraints; (3) the knowledge layer aligns with the knowledge base to infer and complement the relationship to generate a knowledge graph; (4) the wisdom layer outputs scheme elements, benefits and risks based on decision rules; and (5) modeling each intention layer refining strategic target constraint layer. Full-link semantic mapping from data to intention is achieved, consistency and accuracy of problem definition are improved, cross-department implicit causality is mined, it is ensured that a scheme is accurately aligned with a strategic target, an interpretable semantic basis is provided for automatic scheme generation, scene simulation and optimization decision, and the method has remarkable commercial application value.

The invention discloses a micro-grid edge control method and system based on multi-objective collaborative optimization, and belongs to the technical field of micro-grid control. The method comprises the following steps: acquiring micro-grid operation parameters in real time through a multi-source sensor network; performing data preprocessing and feature extraction at the edge computing node; a parameter space is pre-divided based on an optimal segmentation theory to generate a critical region (CR) and a linear decision mapping table thereof, and a millisecond-level initial decision is realized in a table look-up mode; constructing a dynamic weighted optimization function aiming at economy, safety and environmental protection to perform collaborative optimization; a lightweight deep Q network (DQN) is adopted to carry out on-line fine tuning on the mapping relation; implementing a hierarchical response strategy according to a comprehensive performance index (DPI); and updating the strategy through a cloud edge cooperation mechanism and realizing network disconnection fault tolerance. According to the method, the complex optimization problem is converted into low-delay table lookup operation on the edge side, the problems of high centralized control delay, local strategy stiffness, uneven resource allocation and the like are effectively solved, and efficient, real-time and stable operation of the microgrid is achieved.

The invention relates to a production scheduling optimization method based on DeepSeek-R1. The method is used for improving modeling and coding efficiency when a production scheduling optimization problem is solved. According to the method, natural language processing and code generation capabilities of DeepSeek-R1 are introduced into a production scheduling task, and a scheduling optimization process including three stages of cue word design, modeling solution and iterative optimization is constructed. In the cue word design stage, a production scheduling problem is converted into structured cue word input including problem hypothesis, constraint conditions, optimization targets and the like, and a model is guided to automatically extract problem elements; in the modeling solving stage, DeepSeek-R1 automatically generates solving codes according to cue words to obtain an initial scheduling scheme; and in the iterative optimization stage, the user obtains a final scheme conforming to the problem hypothesis and constraint through check-feedback circulation based on the initial scheduling scheme. The method provided by the invention is finally applied to an island type assembly line scheduling optimization case, and the effectiveness of the method is verified.

The invention discloses an SAR ship target detection system and method for efficient neural architecture search, and belongs to the technical field of SAR ship target detection, and the system comprises a super-network architecture module, a multi-path search module, a PANet module and a model construction module. The super-network architecture module is used for constructing an efficient super-network framework based on weight sharing and convolution kernel compounding by reconstructing a convolution kernel; the multi-path search module is used for constructing a multi-path search structure and converting a discrete multi-path selection problem into a continuous differentiable optimization problem through a Gumbel-Softmax function; the PANet module constructs a searchable PANet structure by unifying a backbone network and a PANet architecture search space to a joint optimization framework; the model construction module constructs a target detection model based on an efficient super-network architecture, a multi-path search structure and a searchable PANet structure, and performs SAR ship target detection by using the target detection model.

The invention provides a unit commitment optimization method based on solution space expansion and space-time diagram modeling. A space enhancement strategy and a diagram neural network modeling technology are innovatively combined. According to the method, disturbance optimization and diversity constraints are introduced, a training data set containing rich approximate optimal solutions is constructed, and the generalization ability of the model is remarkably improved. Meanwhile, the unit commitment problem is converted into a space-time dependency graph structure, the sequential relation and structural constraint between variables are effectively extracted through a graph neural network, and deep modeling and prediction of the optimization problem are achieved. Furthermore, by designing a double-order constraint repair mechanism, feasibility correction and fine adjustment are carried out on a prediction result, and the feasibility and scheduling precision of a solution are guaranteed. The finally constructed scheduling framework has good real-time performance and deployability, and can provide efficient and accurate combined scheduling decision support for a large-scale power system.

The invention discloses a multi-technology fusion prediction method based on hypergraph modeling, and relates to the technical field of computers. According to the method, hyperedge weights are corrected by introducing a probability-based zero model so as to eliminate deviations caused by patent number differences among different technical fields, and then a multi-technology fusion mode with statistical significance is identified. Further converting a multi-technology fusion prediction problem into a hyperedge prediction problem in a hypergraph, calculating an average similarity among technologies in a technology combination by adopting a random walk algorithm with restart, and establishing a mapping relation between a feature and a hyperedge formation probability through a logistic regression model by taking the average similarity as the feature; therefore, prediction of the technology fusion probability is realized. According to the method, the fusion trend of three or more technical fields can be effectively identified and predicted, the method is more suitable for the analysis requirement of current multi-technology cross-field cross fusion, and data support and decision basis are provided for technical innovation layout and industrial policy planning.

This invention relates to the field of navigation assistance technology, and in particular to a dynamic obstacle prediction and obstacle avoidance path planning method based on multi-sensor fusion. This invention achieves comprehensive perception of dynamic obstacles through multiple sensors, providing a rich and reliable data foundation for subsequent processing. At the same time, it predicts the motion of dynamic obstacles through data processing fusion and model prediction, fully considering the motion inertia of obstacles to make their trajectory prediction closer to real physical laws. Furthermore, by "delineating the final obstacle avoidance target area", the global path search problem is transformed into a local but sufficiently large spatial search problem, significantly reducing the number of grids to be processed and the amount of search computation, meeting the stringent real-time requirements of mobile devices. Moreover, by constructing a cost function, it avoids areas that obstacles may occupy in the future in real time, thereby generating an obstacle avoidance planning path, which helps to improve path safety.

This invention discloses a multi-feature fusion time series prediction method, system, device, and medium for power load peak prediction, belonging to the field of power system load prediction technology. It includes: acquiring and preprocessing raw power load time series data; extracting and constructing a load peak time series for feature construction to obtain a prediction feature set; constructing supervised learning samples to transform the time series prediction problem into a regression problem; and training the supervised learning samples using a machine learning model to obtain a load peak prediction model for predicting and evaluating future load peaks. The beneficial effects of this invention are as follows: Through a feature engineering method of multi-feature fusion, this invention can effectively capture the historical dependence, short-term volatility, and long-term periodicity in power load time series, thereby significantly improving the accuracy of load peak prediction. It has a predictive advantage, especially for power systems with large load fluctuations, in environments with large-scale integration of new energy sources.

The application belongs to the field of bridge health monitoring and rapid detection, and discloses a bridge bending stiffness identification method based on multi-point deflection influence line input, and steps are as follows: firstly, a linear model for solving bending stiffness is established by using measured deflection influence lines at multiple positions of a bridge and linear elasticity theory; then, a redundant dictionary composed of multiple types of global basis functions is used for sparse representation of the bending stiffness distribution curve, and the bending stiffness reconstruction problem is converted into a sparse vector solving problem; finally, a sparse vector solution model with 1-norm regularization constraint is established, and the optimal bending stiffness distribution is obtained by solving the model. l The application can realize continuous bending stiffness curve solving by using multi-point measured deflection influence lines only, is suitable for stiffness evaluation of statically indeterminate in-service bridges such as continuous girder bridges, and has the advantages of strong applicability, high solving efficiency and strong robustness.

The invention discloses a sliding bearing fault diagnosis model construction method and device, equipment and a medium. The method comprises the steps of generating a training set based on historical operation data of a sliding bearing and corresponding multi-dimensional target features; constructing a convex quadratic programming problem of a support vector machine by using the training set according to an interval maximization strategy, and converting the convex quadratic programming problem into a second-order cone programming problem; and solving the second-order cone programming problem by using a solver to obtain an optimal sparse weight vector and offset, and obtaining a sliding bearing fault decision function based on the optimal sparse weight vector and offset to obtain a sliding bearing fault diagnosis model so as to carry out sliding bearing fault diagnosis. By fusing the multi-source operation data and the multi-dimensional target characteristics of the sliding bearing, the defect of single-dimensional data is overcome, and the accuracy of fault state diagnosis is improved; and moreover, the convex quadratic programming problem of the support vector machine is converted into the second-order cone programming problem for efficient solving, so that the solving efficiency is improved.

This invention discloses a multi-technology fusion prediction method based on hypergraph modeling, relating to the field of computer technology. This method introduces a probability-based null model to correct hyperedge weights, eliminating bias caused by differences in the number of patents across different technology fields, thereby identifying statistically significant multi-technology fusion patterns. Furthermore, the multi-technology fusion prediction problem is transformed into a hyperedge prediction problem within a hypergraph. A random walk algorithm with restart is used to calculate the average similarity among technologies within a technology combination, and this similarity is used as a feature. A logistic regression model is then used to establish a mapping relationship between this feature and the probability of hyperedge formation, thus predicting the probability of technology fusion. This method can effectively identify and predict fusion trends in three or more technology fields, better adapting to the current analytical needs of multi-technology cross-domain integration, and providing data support and decision-making basis for technology innovation layout and industrial policy planning.

The invention discloses an equipment connection diagram scoring optimization method based on data analysis, and relates to the technical field of scoring optimization, and the method comprises the steps: carrying out the modeling of a communication equipment connection diagram through employing an attribute diagram, and converting a scoring problem of an equipment connection networking examination system into a similarity calculation problem between calculation attribute diagrams; the score optimization method comprises the following steps: defining a cost function in a graph editing distance; calculating a graph editing distance by using a DP-GED algorithm; converting the graph editing distance into a score by using score mapping; performing re-layout on the graph B by utilizing a Fuchterman-Reingold graph layout algorithm and the vertex corresponding relation L so as to realize clear comparison between the graph A and the graph B; according to the GEDS scoring algorithm based on the graph editing distance, compared with a system original VECS scoring algorithm, the GEDS scoring algorithm based on the graph editing distance is closer to expert scoring in the aspect of test data, and meanwhile it is avoided that wrong answers heterogeneous with standard answers are marked as full scores.

The present application relates to a kind of multi-star task allocation method based on conflict graph minimum weight vertex cover, for the multi-star task allocation problem with filtering constraint, pair constraint and cumulative constraint, utilize the minimum weight vertex cover in graph theory and neighborhood search design a kind of centralized optimization algorithm.Satellite's feasible observation window is regarded as vertex, pair constraint conflict is regarded as edge, observation benefit is regarded as vertex weight, constructs pair constraint conflict graph, the original problem is converted into the iterative optimization solution containing conflict graph vertex cover and cumulative constraint satisfaction;Based on neighborhood search technology, minimum weight vertex cover solving algorithm and cumulative constraint elimination operator are designed, can effectively guarantee the fast calculation of task allocation scheme.

The application discloses a sliding bearing fault diagnosis model construction method and device, equipment and medium. The method comprises the following steps: generating a training set based on historical operation data of a sliding bearing and corresponding multi-dimensional target features thereof; constructing a convex quadratic programming problem of a support vector machine by using the training set according to an interval maximization strategy, and converting the convex quadratic programming problem into a second-order cone programming problem; obtaining an optimal sparse weight vector and a bias by using a solver to solve the second-order cone programming problem; obtaining a sliding bearing fault decision function based on the optimal sparse weight vector and the bias to obtain a sliding bearing fault diagnosis model and to perform sliding bearing fault diagnosis. By fusing multi-source operation data of the sliding bearing and multi-dimensional target features thereof, the shortcomings of single-dimensional data are made up, and the accuracy of fault state diagnosis is improved. Furthermore, the convex quadratic programming problem of the support vector machine is converted into the second-order cone programming problem for efficient solving, and the solving efficiency is improved.

The application discloses an interactive question and answer method and system based on artificial intelligence, relates to the technical field of interactive question and answer, and comprises the following steps: converting a user input text question into a vector representation, combining a dependency relation tree of a text entity to construct a relation graph, capturing the dependency relation in the question as an edge weight value, composing an adjacency matrix to construct a node degree matrix, determining a normalized graph Laplacian matrix, performing feature decomposition, determining an optimal clustering number, clustering language phrases of the text question, and determining a task fragment set of different clusters. The method disclosed by the application not only has semantic association between language phrases by constructing a dependency relation graph, but also integrates syntax relation, ensures the integrity of text structure information, takes the degree of a node as a quantitative index of local strength in the graph, the information directly supports subsequent normalization processing of the Laplacian matrix, provides a uniform scale, and avoids the problem of uneven data range.

The invention discloses a water supply network intelligent diagnosis method and system based on a large visual language model, and the method comprises the following steps: collecting and standardizing multi-source time sequence data, converting the data into a visual chart, and constructing a diagnosis map containing context semantics in combination with domain knowledge; and after interference is filtered by a fixed fluctuation pattern library, inputting a universal visual language model, completing anomaly perception, type identification and root cause inference by zero / few sample visual semantic analysis and causal reasoning, and generating a structured natural language report. The core of the method is to convert an abnormal diagnosis problem of a numerical sequence into a visual semantic analysis and reasoning task which can be understood by a visual language model, so that high-precision and explainable diagnosis can be realized under the condition of extremely few annotations, false alarms are reduced, the credibility is enhanced, and the reliability is improved. The problems that an existing pure numerical method depends on a large amount of labeled data, the interpretability is poor, and long-term hidden leakage is difficult to detect are effectively solved, and the method is suitable for long-term hidden leakage monitoring scenes.

The invention discloses a trajectory collision risk judgment method, system and device based on multi-dimensional state features and linear separability, and relates to the technical field of intelligent traffic and machine learning. The method provided by the invention comprises the following steps: acquiring a multi-dimensional state vector set corresponding to each of two moving entity tracks, wherein the multi-dimensional state vectors are used for representing the space occupation state of a moving entity at each timestamp; respectively marking the state vector sets of the two tracks as a first category and a second category, and constructing a feature data set; a linear classifier is adopted to process the data set, and a judgment index representing the linear divisible degree of the two categories is generated; and finally, judging whether a collision risk exists or not based on the judgment index, and if the judgment index shows that the collision risk is completely linearly separable, judging that the collision risk does not exist. According to the method, a complex time sequence geometry problem is converted into an efficient high-dimensional data classification problem, the calculation complexity is remarkably reduced, and the efficiency and the real-time performance of large-scale trajectory data analysis are greatly improved.

The application relates to a steel hot rolling process task allocation method based on an Ising model. The method is realized through the following steps: firstly, setting a hot rolling scheduling problem parameter, establishing a multi-traveling salesman problem model of the problem and further converting the model into a conventional traveling salesman problem model; secondly, combining the Ising model to transform the original steel hot rolling scheduling problem model, converting the problem into a form capable of being solved by using the Ising model; then, based on the transformed Ising model, an Ising machine based on a Hamilton Monte Carlo sampling simulated annealing algorithm is used for solving; finally, the solving result is converted into an understandable hot rolling scheduling scheme. Compared with the prior art, the application introduces a novel simulated annealing algorithm based on a Hamilton Monte Carlo sampling, which is used for solving a large-scale scheduling problem. The algorithm can improve the search solving efficiency, and enhance the accuracy and applicability of multi-task allocation planning. Therefore, the application has important practical engineering application value.

This application discloses a method and system for identifying connection relationships in pipe and instrument diagrams based on graph neural networks. The method includes: acquiring a pipe and instrument flow diagram to be identified; detecting components and connecting lines in the pipe and instrument flow diagram to construct graph structure data; encoding the features of nodes and edges in the graph structure data respectively, and mapping the features of nodes and edges to a high-dimensional latent space to obtain initial feature vectors; inputting the initial feature vectors into a preset graph neural network, using multiple graph convolutional layers for information propagation and aggregation, learning and updating the latent space features of topological relationships between components; based on the updated topological representation features, identifying whether the connection relationships corresponding to each edge in the graph are valid through an edge classifier, and outputting the connection relationship identification result. This invention transforms the connection relationship identification problem into a graph connectivity judgment task, significantly improving the accuracy and robustness of pipe and instrument diagram connection relationship identification in complex scenarios.

The invention discloses a knowledge graph question and answer simplification and reasoning method and device based on a large language model. The method comprises the steps that a multi-hop question is obtained, and a to-be-queried entity is extracted; b, determining a relationship set with the to-be-queried entity from the knowledge graph; c, identifying an Ith hop sub-problem of the multi-hop problem, and determining an optimal relationship corresponding to the Ith hop sub-problem; d, according to the entity to be queried and the optimal relationship, determining a triple corresponding to the I-th jump sub-problem; e, in response to the large language model, judging that the answer cannot be made due to insufficient current information according to the multi-hop question and the triple corresponding to the I-hop sub-question, and converting the multi-hop question into a K-I-hop sub-question; and F, updating a tail entity in the triad corresponding to the I-hop sub-question into a to-be-queried entity, and executing the steps B to E until the large language model judges that current information is sufficient according to the multi-hop question and the triad corresponding to the I-hop sub-question and gives an answer. Path prejudice can be avoided, and comprehensiveness and accuracy of answers are improved.

The application provides a lightweight multi-scale remote sensing image rotating target detection method and system, aiming at the problem that the target and background in the remote sensing image are more complex, based on a YOLOv4 model, shallow feature information is added in a feature fusion network; in order to alleviate the problem of feature loss caused by too many network layers, a weighted bidirectional feature pyramid is used as the feature fusion network; in order to improve the receptive field of the feature, a multi-scale hollow convolution module is added after the feature fusion, so as to improve the detection precision; the remote sensing image rotating target detection, the long side representation method represents the angle parameter, the annular smoothing label of the Gaussian function is used to convert the angle parameter from the regression problem to the classification problem, and the loss problem caused by the angle periodicity is reduced; a depth separable convolution and a global attention module are used to design a lightweight module, and the traditional convolution is replaced in the detection model, so that the model is lightweight.

The invention provides a method and device for reliability analysis of a reconfigurable multi-stage system.The method comprises the steps that an initial set of a system diagram model is determined, and the initial set comprises a first node set C, a second node set U and a third node set I, and selecting a first node from the second node set U to perform communication verification with the nodes in the first node set C, updating the second node set U according to a communication verification result, and repeatedly executing until the second node set U is an empty set, thereby generating an effective working path, and analyzing the reliability of the reconfigurable multi-stage system based on the effective working path. According to the embodiment of the invention, the probability enumeration problem in the traditional reliability analysis is converted into the path search problem of the graph model, so that all system states are prevented from being directly traversed. And the path feasibility during reliability calculation is ensured.

The present application relates to a kind of federated learning method and system based on deep reinforcement learning.The method includes the following steps: modeling analysis is carried out on the time delay of federated learning system;The energy consumption of federated learning system is modeled and analyzed;Determine optimization target;Build image classification network and reinforcement learning intelligent agent network;The node selection problem is converted into Markov decision process;The reinforcement learning model is trained;Using Q network carries out federated learning.The present application selects suitable equipment to participate in training by reinforcement learning intelligent agent auxiliary federated learning system, and balances the relationship between communication time delay and energy consumption by adjusting weight coefficient, reduces the cost of communication and energy consumption, to solve the device heterogeneity and channel instability problem existing in industrial internet of things, improves the efficiency and performance of federated learning.

The present application relates to the technical field of artificial intelligence and algorithm development, solves the technical problem that the traditional clustering method cannot identify the obstacle area in the space, relates to a clustering method with specified category capacity, comprising the following processes: a connected graph is established according to the spatial coordinate information of a discrete point set to be clustered; the connected relationship between any two discrete point sets in the connected graph is determined and the cost is calculated; the initial points of two nodes connected with a virtual source point in 2-category specified capacity clustering are selected according to a two-point selection algorithm; and further relates to a system applied to the clustering method with specified category capacity, comprising: a connected graph establishment module used for establishing a connected graph according to the spatial coordinate information of a discrete point set to be clustered. The present application converts the clustering problem with specified capacity into a network flow model and obtains an optimal solution, and the solution of the model can perfectly process the clustering problem with specified category capacity and effectively avoid the problems of spatial interlacing and containing between categories.

The application discloses an AI five-step deterministic reasoning method and system for open problems, and belongs to the technical field of artificial intelligence, rational AGI and high-trust decision-making. The application changes the traditional AI route of "probability generation, text continuation, semantic guessing" for open problems into a deterministic route of five-dimensional problem decomposition, rule library calling, boundary feature combination, axiomatic deterministic reasoning and traceable non-fabricated output. There is no probability, generation, fitting or dynamic learning throughout the process. The reasoning is strictly based on the fact library and the objective law library, which completely eliminates illusions and subjective speculation from the root. The application converts fuzzy open problems into a deterministic process executable by machines, realizes reproducible and verifiable rational decision-making, and has a substantial difference in route from the prior art, is highly creative, and can be widely applied to strong supervision and high-trust scenes such as finance, law and medical treatment.