187 results about "Causal maps" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal causal reasoning and interpretation method, device, equipment and medium, and the method comprises the steps: obtaining original data streams of at least two different modals, and extracting modal features; a cross-modal attention mechanism is utilized to fuse modal features, and causal features are extracted through feature distillation; constructing a dynamic causal graph based on causal features, and updating an edge weight through a causal intensity function; identifying the causal relationship in the dynamic causal graph and performing anti-factual reasoning verification to evaluate the reliability of the causal relationship; and generating a causal interpretation result in combination with the dynamic causal graph and the causal relationship reliability. According to the method, the multi-modal data are fused, the causal features are extracted, and dynamic causal graph updating and anti-factual reasoning verification are combined, so that reliable modeling and explanation of the causal relationship in a complex scene are realized, the defects of single modal or simple fusion in the prior art are overcome, and the accuracy and interpretability of causal reasoning are improved.

The invention discloses a high-quality pseudo-annotation data set construction method based on a complex task, and relates to the technical field of multi-modal learning, and the method comprises the steps: constructing a cross-modal causal graph based on multi-modal original data, loading a domain knowledge graph, recognizing an inter-modal confusion variable, and generating an initial pseudo-tag; an anti-fact sample is generated by forcibly cutting off a non-causal path in the cross-modal causal graph, and a cross-modal depolarization pseudo-label is generated by comparing the pseudo-label difference between the original sample and the anti-fact sample; and in combination with the cross-modal depolarization pseudo-labels and the semantic consistency pseudo-labels, a standardized pseudo-annotation data set with multi-modal alignment, clear entity relationship and semantic consistency is generated through fusion. According to the method, an anti-fact intervention framework is adopted, non-causal path influence in cross-modal interaction is identified and eliminated by analyzing probability distribution difference, and false association is effectively inhibited.

This disclosure describes utilizing a causal query system to determine causal outcomes for domain-specific causal queries using a framework that includes causal graphs for targeted domains, a large generative model (LGM), and other models or systems. In various implementations, the causal query system provides a framework that includes generating domain-specific causal graphs, encoding or mapping the causal graphs with local data values, and using the encoded causal graphs to determine causal outcomes to causal queries. In some implementations, the causal query system uses the LGM and data resources (e.g., external sources) to populate missing values of an embedded causal graph before using the causal graph to determine causal outcomes.

The invention discloses a target behavior prediction method and related equipment, and relates to the technical field of target prediction, and the method comprises the steps: obtaining multi-modal historical behavior data and real-time environment semantic information of a target object; determining key causal event nodes based on the multi-modal historical behavior data and a space-time causal model; based on the key causal event nodes and the real-time environment semantic information, determining a dynamic causal graph; determining a future potential event type and a corresponding event occurrence probability based on the dynamic cause and effect graph; determining an event triggering constraint condition based on the event occurrence probability; determining node weight updating parameters of the dynamic causal graph based on an incremental learning framework and multi-modal historical behavior data input in real time; and determining a predicted behavior track of the target object based on the event triggering constraint condition and the updated dynamic causal graph. According to the invention, by fusing the multi-modal data and space-time causal reasoning, the accuracy and environmental adaptability of target behavior prediction are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a strategy generation method and device based on hierarchical reinforcement learning, equipment and a medium. And processing environment state information to generate a sub-target and a specific action, generating a strategy reward signal in combination with state change, carrying out joint training and updating on the dynamic causal graph and the hierarchical reinforcement learning model based on the strategy reward signal, and generating an optimized action strategy. The state evolution relation is modeled by constructing the dynamic causal graph, so that the reinforcement learning can obtain causal understanding of the state change trend, decomposition and optimization of sub-targets and actions are realized in combination with a layered reinforcement learning architecture, the response precision and generalization ability of the action strategy in a complex environment are improved, and the method is suitable for application and popularization. Therefore, the task completion stability and the convergence efficiency of strategy training are improved.

The invention discloses a clinical research data analysis method based on machine learning, and the method comprises the steps: constructing a multi-modal variable structured causal map, and building a direction adjustable mechanism of a causal path; constructing a bidirectional nested structure attention mechanism, and capturing a cross-modal dependency and dynamic evolution relationship between variables; recording each layer of information propagation path and variable participation degree, and realizing reverse reconstruction of a model decision path in a reasoning stage; target-oriented attribution path regularization is introduced to carry out regularization constraint on an attribution path set of the key target variables; and constructing a nested attribution graph visualization system, and realizing interactive presentation of interpretation sub-graphs corresponding to prediction results so as to improve cognitive trust of model output. According to the method, from structure expression, path tracing and causal constraint to visual presentation, the core problems that a deep model is poor in interpretability, clinicians are not trusted, and existing interpretation tools are insufficient in applicability are solved in a full-link mode.

The invention belongs to the technical field of artificial intelligence and data science, and discloses a causal discovery method and related device based on a large model and structural constraint, comprising the following steps: according to meta-information of each causal variable, adopting a large language model and combining multiple rounds of dialogues, multi-role play and auxiliary cue word modes, generating an initial potential causal graph of each causal variable; repeating the following steps until a preset condition is met, and outputting a candidate causal graph with the highest value score: generating the candidate causal graph by adopting a policy network based on an encoder-decoder architecture, and carrying out iterative optimization on the candidate causal graph by adopting a reinforcement learning algorithm based on an act-critic framework; and calculating a value score of the candidate causal graph after iterative optimization according to the value network, and updating parameters of the policy network based on the encoder-decoder architecture according to the value score. The efficiency and accuracy of causal relationship recognition are remarkably improved, and a causal relationship graph with high confidence can be generated in a complex data environment.

The invention discloses a multi-modal harmful model factor detection method based on a thinking chain, and belongs to the field of harmful model factor detection, and the method comprises the steps: firstly obtaining an image-text model factor, and automatically generating the thinking chain containing a problem summary, an image subtitle, and harmful reasoning and conclusion; constructing an annotation data set according to the annotation data set; then, the multi-modal model is finely adjusted in two stages, in the first stage, the visual encoder and the language model are fully finely adjusted to improve image-text understanding, and in the second stage, the visual encoder is frozen, and the language model is finely adjusted through LoRA to strengthen reasoning; introducing reinforcement learning rewards to optimize implicit harmful discrimination; then extracting an entity relationship to construct a knowledge graph and a causal graph, and embedding into the model; and finally, dynamically updating the atlas through an RAG mechanism, so that a detection result adapts to an emerging model cause in real time.

The invention relates to the technical field of financial risk prediction, in particular to a financial risk prediction method and device, a storage medium and equipment. The method comprises the following steps: acquiring financial data, and extracting multi-modal features; based on the multi-modal features, generating a causal graph by adopting a constraint-based causal discovery algorithm, and quantifying causal intensity by adopting a structural equation model; adopting the quantized causal graph as a constraint of an anti-fact generative adversarial network, generating an anti-fact scene and analyzing a risk conduction path; training a causal graph neural network model based on the multi-modal features, the causal graph and the risk conduction path; and interpretability analysis and risk monitoring are carried out. According to the technical scheme, financial risk causal association can be accurately constructed, an anti-fact scene clear conduction path can be generated, and through interpretable analysis and monitoring, the risk prediction accuracy and interpretability are improved, financial risks are helped to be prevented and controlled in time, and the financial system stability and the risk response capacity are enhanced.

The invention provides a method, a device and equipment for extracting polymerizable causal information of a medical image and a medium. Relates to the technical field of representation learning, causal inference and depth generation models. The method comprises the following steps: constructing a causal representation learning framework; the causal representation learning framework comprises an encoder, a structural causal model, a decoder and a discriminator, the encoder is used for encoding an input medical image into a low-dimensional exogenous variable, the structural causal model takes the low-dimensional exogenous variable as input and generates a causal representation based on the low-dimensional exogenous variable, the decoder is used for intervening and reconstructing the causal representation, and the discriminator is used for discriminating the causal representation. The discriminator is used for adversarial training; and establishing a model training loss function, training the causal representation learning framework based on the model training loss function, and extracting the polymerizable causal information in the medical image by using the trained causal representation learning framework. The causal graph identified by the method can clearly describe the causal relationship among different features, and more transparent decision support is provided for clinicians.

The invention relates to the technical field of financial management, and discloses a causal interpretability and illusion suppression method, system and device for text generation, and the key point of the technical scheme is that the method comprises the following steps: S1, obtaining related data according to a generation target, extracting the causal relationship between entities, and constructing a causal map; s2, extracting a causal chain related to the generated target from the causal atlas, and inputting the causal chain and the input variables into the large language model to obtain a target text; s3, performing anti-fact intervention processing on the input variable, inputting the input variable into the large language model, and recording a logic test result; s4, identifying an entity from the target text, comparing the entity with the fact data related to the generated target, and calculating an entity alignment score; and S5, according to the causal chain, the target text, the logic test result and the entity alignment score, generating an interpretation report and performing structured output, so that the output text has higher logicality and higher credibility.

The invention relates to the technical field of artificial intelligence, can be applied to the digital medical field and the financial field, and discloses a causal graph model-based data processing method, device and equipment and a storage medium, the method comprises the steps of obtaining target data and a corresponding to-be-explained reasoning result, and constructing a causal graph according to the target data, obtaining a corresponding structured causal graph; performing causal constraint on the structured causal graph by adopting a causal constraint graph network learning mechanism to obtain an optimized causal graph; and performing anti-fact reasoning on the reasoning result based on the optimized causal diagram, and generating an interpretation result of the target data according to an anti-fact reasoning result. By fusing the causal graph model and the graph neural network technology and introducing the anti-fact reasoning module, accurate causal modeling and explanation of the high-dimensional nonlinear data are realized, the limitation of a traditional causal graph model in the aspect of processing the high-dimensional nonlinear data is solved, and the defect of a graph neural network output result in causal explanation is made up.

The invention discloses a management decision-making method and system based on a knowledge base construction technology. The method comprises the following steps: performing sequential relationship extraction on multi-source financial data to obtain a sequential relationship set related to query content; constructing an event-entity incidence matrix corresponding to the time sequence relation set; according to the time sequence relation set and the event-entity incidence matrix, constructing a dynamic knowledge graph; determining causal effect parameters in the causal graph structure by adopting a dual machine learning model; constructing a structural causal model according to the causal graph structure and the causal effect parameters; and generating an anti-fact prediction result by using the structural causal model, and generating a decision scheme corresponding to the query content based on the anti-fact prediction result. The technical problem that decision information including accurate causal basis and prospective simulation information cannot be generated due to the fact that the causal relationship between financial data is difficult to determine and the intervention effect cannot be dynamically deduced in a related management decision method is solved.

The invention discloses a large model fine tuning method based on a causal graph and thinking chain enhancement and a related device, and relates to the technical field of large model fine tuning in the power industry, and the method comprises the steps: carrying out the causal mining of power equipment data, and constructing a power equipment causal graph containing causal weight information; disassembling the input of the large model into a thinking chain, correspondingly generating a chain type causal pair according to the thinking chain, and performing path retrieval matching and causal consistency check through the chain type causal pair and the constructed electrical equipment causal graph to realize alignment of the reasoning process; and exciting a reinforcement learning process through an alignment result of the reasoning process, optimizing a pre-established reinforcement learning reward model, constraining a thinking chain generation process, guiding the large model to generate a thinking chain under a causal constraint condition, and realizing fine tuning of the large model. According to the method, causal reasoning and causality are embedded into a reinforcement learning feedback process of large model fine tuning, so that the large model can learn a basic causal reasoning rule, and the logicality, the interpretability and the robustness of thinking chain reasoning can be improved.

The invention relates to the field of network security, in particular to a root cause analysis method, system and device based on large model causal reasoning, and aims to improve the positioning precision of an attack entry node. The root cause analysis method comprises the following steps: extracting a network entity and an attack event chain from a network traffic log; constructing a first causal graph based on a pre-trained graph neural network according to the network entity and the attack event chain; wherein nodes of the first causal graph represent network entities or security events, and edge weights of the first causal graph represent causal dependence intensity dynamically learned through an attention mechanism; performing logical reasoning on the first causal graph by using a pre-trained large language model, and optimizing the first causal graph into a second causal graph; and calculating the root cause probability of each node in the second causal graph based on Bayesian inference, and positioning the attack entry node with the highest abnormal value. According to the method, emerging threats can be detected without frequently updating the rule base, and the positioning precision of the attack entry node is effectively improved.

The invention discloses an anti-illusion training method and device for a multi-modal model, equipment and a medium, and the method comprises the steps: firstly introducing a time delay mutual information causal discovery algorithm in a data preprocessing stage, and building a cross-modal causal atlas between modal data; and dynamically adjusting the fusion weight of each modal data according to the dynamic gating mechanism of the second stage and the causal relationship between the modal data corresponding to the cross-modal causal atlas so as to solve the cross-modal conflict resolution capability and solve the problem of data fusion distortion caused by traditional static weight distribution. And on the basis of the causal relationship between the modal data corresponding to the cross-modal causal atlas, constructing an adversarial multi-modal sample on the basis of the original multi-modal data, and carrying out constrained adversarial training on the multi-modal model on the basis of causal regularization to obtain an anti-illusion multi-modal model, so that the reasoning precision of the multi-modal model is improved. The method can be applied to the financial risk prediction field and the medical diagnosis field so as to improve the risk prediction accuracy and the diagnosis accuracy.

The embodiment of the invention provides a causal knowledge constraint-based event extraction method and device. The method comprises the following steps of: constructing a causal graph of an event text to obtain a causal intensity matrix of feature data of the event text; constructing a problem template of an event argument according to the type of the event; the question template and the event text are combined and coded, and a coded sequence is input into a BERT model so that the hidden state of the last layer can serve as semantic representation of the input sequence; weighting the semantic representation of the input sequence by applying a self-attention weight matrix to obtain a weighted output; comparing the causal intensity matrix with the self-attention weight matrix to obtain a causal loss item; and defining a loss function in combination with the causal loss item to obtain an answer to the question according to the weighted output. Through the processing scheme disclosed by the invention, the interpretability of the model is improved.

The invention provides an adaptive causal discovery method and device based on a Monte Carlo tree, and belongs to the technical field of causal discovery data processing. Comprising the steps of obtaining an observation data set and a statistical information set, and dividing the observation data set into a training set and a verification set; constructing a causal discovery algorithm set A, and training the gradient boosting tree model by using the training set and the path combination to obtain an agent model Fb; establishing a Monte Carlo tree, selecting child nodes of root nodes according to performance expectation and exploration rewards, and expanding the child nodes to leaf nodes layer by layer; running the Monte Carlo tree, and obtaining a causal graph G * and a performance score by using a voting mode according to all causal relationships in the causal graph obtained by each node; calculating an exploration reward and a performance expectation of each node in a complete algorithm path, and returning the exploration reward and the performance expectation upwards to a root node from a leaf node along the path; updating Fb based on the searched path; and processing a causal discovery task by using a Monte Carlo tree and Fb, and finding a causal discovery algorithm path with the highest performance score.

The invention provides a financial account distribution and anomaly detection method and system based on machine learning, and relates to the technical field of machine learning, and the method comprises the steps: building a causal graph model, generating a time sequence attention weight matrix through a conditional random field, carrying out the weighted fusion, and carrying out the attribution of a difference reason; and processing the reconciliation difference feature vector by adopting a reinforcement learning method, generating and optimizing a processing strategy, and finally outputting a difference processing scheme. The account checking accuracy can be improved, abnormal transactions can be automatically found, the manual processing cost is reduced, and financial risks are effectively prevented.

The invention discloses an unsupervised time sequence missing data filling method based on a dynamic cause and effect graph structure, which comprises the following steps: S1, collecting a data sample, and processing to obtain an initial filling complete data table; s2, clustering all the initial filling complete data tables, and selecting representative data tables; s3, constructing a sub-causal graph of each representative data table, and further constructing a total causal graph; s4, on the basis of a causal relationship in the total causal graph, constructing and training a corresponding weighted expandable depth time sequence convolutional network for the to-be-filled target feature column to obtain a missing value filling model, and further performing coarse filling on missing values; and S5, performing refined secondary filling on the target feature column by using the obtained missing value filling model to obtain a complete data table. The invention provides a filling method without real missing labels, optimization is carried out through a self-consistency or structure maintenance principle, the dynamic dependence and real causal relationship between the time series data can still be captured in a dynamic complex scene, and a better data filling effect is achieved.

The invention provides a geological disaster prediction method and device fusing space-time sequence analysis and causal reasoning, and belongs to the technical field of geological disaster monitoring and early warning. Aiming at the problems of non-uniform data space-time reference, lack of causal logic, poor real-time performance and weak scene adaptability of a model in the prior art, the method comprises the following steps: performing standardization processing on acquired multi-source data, processing missing values by adopting an improved K nearest neighbor algorithm in combination with stratum characteristics, and processing abnormal values through a 3 sigma criterion and geological verification; based on an information theory and an improved SURD algorithm, three types of causal entropies among variables are calculated, a time attenuation coefficient is introduced, a core causal chain is constructed, and a dynamic causal graph is constructed; a core causal variable is used as input, a multi-feature attention-multi-relation space-time diagram recursive network model is constructed, a hour-level predicted value is output through space-time diagram convolution, residual training and a geological physical constraint layer, and'causal-space-time 'fusion is realized through a causal weight adjustment model; the method can be widely applied to early warning of geological disasters such as landslide and debris flow.

The invention provides a rock and soil construction quality monitoring and diagnosing method, which comprises the following steps of: modeling historical engineering events and expert rules, constructing an event causal atlas prototype containing weights, and realizing event sequence feature extraction and real-time causal atlas dynamic updating in combination with on-site multi-modal sensing data and construction logs; neural symbol reasoning and tensor completion technologies are adopted to predict a novel causal relationship, and a causal atlas structure is perfected through space, time and logic consistency verification; a multi-layer risk early warning mechanism is set, a causal map local risk assessment and event chain propagation are combined, spatial positioning early warning signals are generated in a grading manner, closed-loop backtracking optimization is supported, causal reasoning accuracy and early warning efficiency are improved, construction process risk identification and dynamic early warning can be realized, and the engineering safety management level is improved.

A method includes generating a causal graph based on a plurality of values, each value corresponding to a causal relationship between two or more sensors of a plurality of sensors in one or more manufacturing systems. The method further includes determining a causal strength index matrix. The method further includes responsive to identifying an anomalous behavior in at least one of the plurality of sensors, determining a root cause of the anomalous behavior using at least one of the causal strength index matrix or the causal graph. The method further includes causing a recommended corrective action to be issued based on the root cause of the anomalous behavior.

The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis model construction method based on distributed causal discovery and federated learning. According to the method, a covariance tensor containing statistical association information of an observation variable and an agent variable is obtained, a global causal graph is obtained by combining a federal causal discovery method introducing the agent variable, then a converted causal intensity matrix is embedded into a graph convolutional neural network, and finally a personalized and global diagnosis model is trained by using a FedAvg framework fused with a Dito algorithm. According to the method, distributed heterogeneous data can be effectively processed while data privacy is protected, model interpretability is improved by mining a causal relationship between variables, global generalization and client personality requirements are considered, the accuracy and generalization ability of fault diagnosis are remarkably improved, and the fault diagnosis efficiency is improved. The method is especially suitable for fault diagnosis of industrial bearings and other scenes needing dispersed sensitive data processing.

The invention relates to the technical field of atmospheric environment prediction, and discloses an atmospheric environment multi-parameter intelligent prediction method based on causal VAE and CNN-LSTM. The method comprises the steps of collecting multi-parameter historical data of an atmospheric environment to form an atmospheric parameter sequence; sequence causal features are extracted, a causal graph structure is constructed, a causal VAE model is trained according to the causal graph structure, and potential causal characterization is generated. And extracting spatial features represented by potential causality through a CNN module, capturing time-dependent features of the spatial features through an LSTM module, fusing the two types of features to obtain a fused feature sequence, and outputting a multi-parameter prediction result through a prediction network. And dynamically adjusting hidden variable distribution parameters of the causal VAE according to the prediction error, updating a causal graph structure according to the adjusted parameters, and re-predicting a subsequent atmospheric parameter sequence by using the updated causal graph. The method can improve prediction precision and generalization ability, and enhance model interpretability and dynamic adaptability.

The invention provides a hot continuous rolling process fault diagnosis method and device based on a multilayer dynamic causal diagram, and relates to the technical field of engineering process monitoring. The method comprises the following steps: according to a historical key variable data set, performing causal relationship analysis by using a dynamic causal entropy algorithm to obtain a multi-layer dynamic causal graph, a causal entropy data set and a quality-related causal entropy data set; training a causal entropy prediction model to be trained by using the causal entropy data set and the quality-related causal entropy data set to obtain a causal entropy prediction model, a fault detection threshold and a quality-related fault detection threshold; according to the actual data, performing fault prediction by using a causal entropy prediction model to obtain fault prediction data; and performing fault analysis based on the fault detection threshold and the quality-related fault detection threshold to obtain a fault detection result and a fault tracing path. The hot continuous rolling process fault diagnosis method is based on the multi-layer dynamic causal diagram and is high in judgment precision and high in interpretability.

The invention discloses a CKD special disease database construction method and system, and relates to the technical field of database construction, and the method comprises the steps: obtaining and predicating a multi-source heterogeneous atomic fact; constructing and fusing three sets of ontology models; verifying and deducing facts in a layered manner according to a multi-layer priority rule base; calling a constraint completion engine to generate inference facts for missing or conflicting fields; streaming follow-up visit, test and intervention logs into events and triggering graded alarms; an anti-fact intervention scene is simulated for the high-risk patient, and pilot groups are screened; dynamically evaluating and adjusting the rule priority; and based on a causal chain and an event mode, clustering patients and constructing a generality map, and recording a whole-process operation chain. Semantic management is realized through fusion of ontology, anti-fact intervention simulation is performed on high-risk patients by means of multi-layer rule verification, and database semantic consistency, reasoning depth, decision support and traceability are improved through causal atlas driven clustering.

The invention provides a causal enhanced soft measurement modeling method based on joint causal topological reasoning. The method comprises the following steps: firstly, obtaining a joint causal weight between an input variable and a target variable based on multivariable joint causal topological reasoning of a Copula theory; secondly, correcting an ordered prior structure based on dynamic feedback of independent confidence, and performing feedback correction on expert knowledge based on data contribution degree; then, based on correlation graph structure enhancement guided by causal weights, the causal weights are fused on the basis of correlation, a causal graph structure matrix is obtained, meanwhile, a fusion causal time sequence graph network is designed, and extraction of causal and time sequence information and final modeling of target variables are achieved with alignment loss as a constraint. According to the method disclosed by the invention, the correlativity in a data form and the causal relationship in an expert experience form can be organically combined to realize soft measurement modeling with high stability and interpretability, and the method can be widely applied to industrial sites with requirements on soft measurement modeling.

The invention relates to a self-interpretation multi-modal personality assessment method based on a multi-agent hierarchical reasoning chain of a large language model, which comprises the following steps of: firstly, unifying multi-modal features such as voice and vision to a text space through modal self-interpretation personality traits representation of a unified space, and generating text description codes related to personality traits; constructing a causal graph model by using a Bayesian causal reasoning theory, analyzing specific contribution of each modal data to personality traits evaluation, and generating a clear reasoning chain and explanation; a two-stage hierarchical reasoning framework is utilized, coarse granularity personality classification is completed in the first stage, psychological measurement norm data is introduced in the second stage, a'classification-scoring 'dynamic mapping mechanism is established, and fine granularity scoring calibration is achieved. Meanwhile, the interpretability of the model is deepened through a hierarchical confidence transfer mechanism, and it is ensured that each scoring result can be traced to the original feature, the classification credibility and the adjustment rule; according to the method, a multi-modal personality assessment special large language model is constructed, and a more transparent, explainable and credible technical basis is provided for personality assessment.

The invention relates to the technical field of case analysis, and discloses a multi-case collision method based on file-related personnel archives, which comprises the following steps of: integrating multi-source heterogeneous data such as file-related personnel, time tracks, geographic positions and the like, constructing a three-dimensional digital twinborn body of a case through a hierarchical feature extraction and cross-modal alignment technology, and establishing a three-dimensional digital twinborn body of the case; causal association between case elements is discovered based on data driving and domain knowledge double channels, after an initial causal atlas is constructed, illegal logic connection is automatically filtered through a reality constraint library, and a trusted causal network conforming to law enforcement specifications is generated. According to the method, a constraint enhanced deduction framework is constructed, physical logic compliance guarantee of an anti-fact scene is realized on the basis of keeping case total-factor digital reconstruction and dynamic association mining capability, and a reconnaissance strategy scheme conforming to real law enforcement constraints and having tactical innovation is generated; and the performability and decision credibility of a deduction result in a complex actual combat environment are remarkably improved.