73 results about "Symbolic reasoning" patented technology

The present invention discloses a method and system for artificial intelligence-based cryptocurrency regulatory analysis capable of performing automated, adaptive, and verifiable compliance evaluation across multiple blockchain ecosystems. The invention integrates blockchain data acquisition, data normalization, graph-based behavioral modeling, artificial intelligence inference, and cryptographically anchored reporting within a unified architecture. The system comprises a blockchain data acquisition unit for retrieving multi-chain transaction data, a data normalization unit for harmonizing heterogeneous blockchain formats, a graph construction unit for generating dynamic transaction graphs, a regulatory knowledge base unit storing jurisdiction-specific regulatory rule graphs, an artificial intelligence processor configured for hybrid neural and symbolic reasoning, and a regulatory reporting unit for generating explainable compliance reports cryptographically anchored to a blockchain ledger.

The invention provides a method and device for generating an SQL instruction through a dialogue type natural language, and relates to the technical field of language processing.The method comprises the steps that a natural language query request input by a user is received; calling a neural symbol reasoning module to extract a query intention and a key entity list, generating a plurality of candidate SQL instructions through symbol reasoning, and evaluating confidence; if the highest confidence coefficient is lower than a set threshold value, multiple rounds of dialogue clarification are initiated and fed back to the neural symbol reasoning module; calling an adaptive SQL reconstruction engine to perform screening, reconstruction or optimization based on a dynamic data environment on the target candidate SQL instruction; and executing the final SQL instruction, obtaining a query result and returning the query result to the user. By means of the method and device, the technical problem that in the prior art, due to fuzziness of natural language query and dynamic complexity of a data environment, SQL instruction generation accuracy is low, and then query execution efficiency is affected is solved, and the query execution efficiency is improved while SQL semantic accuracy is guaranteed.

The invention belongs to the technical field of contract review, and particularly relates to an intelligent contract review analysis method and system based on a large language model. According to the method, dynamic generation of rules and a neural symbol reasoning mechanism are mainly fused, and the core is to realize automatic and precise risk identification and evaluation of contracts by utilizing the powerful language understanding and generation capability of a large language model (LLM); the method comprises the specific steps of generating a contract review rule, extracting facts from a contract to be reviewed, performing review reasoning and generating a review report. According to the method, the inherent logic preciseness of symbol deduction and the powerful semantic understanding capability of a large language model are organically combined, so that core elements and potential risks in a contract can be more accurately identified, and contract terms which are complex in expression or have hidden agreement can be effectively processed and expressed.

The invention relates to the technical field of data processing, in particular to a relational database intelligent query method based on machine learning, which comprises the following steps of: processing multi-modal flow data through time sequence alignment, generating a unified semantic representation vector, constructing a dynamic psychological state map, and modeling a psychological state evolution track by utilizing a neural common differential equation mechanism. After user query is received, historical dialogue nodes are retrieved from the graph, enhanced query intention representation is generated, the enhanced query intention representation is converted into an execution plan through a neural symbol inference engine, and a graph neural network is adopted to predict execution cost. And finally, a personalized analysis report is generated by combining a causal discovery algorithm, and system adaptive optimization is realized through feedback signals. According to the method, the problems of inconsistent time sequence semantics and strong context dependency of the multi-modal psychological data are effectively solved, and the query accuracy and the personalized level in a psychological dialogue scene are improved.

The invention particularly relates to a multi-level self-cognition system based on a large model, and relates to the technical field of large models. A neural symbol world model module; a large language model cognition core module; and a hierarchical decision planning system module. According to the method, deep integration of perception, cognition and decision making is achieved through the hierarchical fusion architecture, and compared with the prior art, the method has remarkable advantages; the multi-modal perception encoder adopts layered encoding and a cross-modal attention mechanism, so that the semantic alignment problem of multi-source perception data is effectively solved, and the understanding ability of the system to a complex scene is greatly improved; according to the neural symbol world model, the neural network and symbol reasoning are combined, the limitation of a pure neural network method in physical modeling is overcome, meanwhile, the calculation complexity of a pure symbol system is avoided, and efficient and accurate environment characterization and prediction are achieved.

The invention relates to a power grid multi-modal data compliance monitoring method based on a knowledge graph, and the method comprises the following steps: S1, obtaining multi-source heterogeneous data of a power grid, carrying out the preprocessing, and generating structured and semantic data representation; s2, constructing an initial knowledge graph according to the produced structured and semantic data representation; s3, according to the initial knowledge graph, combining a time perception mechanism TGN to identify compliance standards and laws of a power grid operation environment changing with time, combining rule-based logical reasoning with a deep neural model through neural symbol fusion, realizing compliance judgment under a fuzzy condition, and constructing a knowledge agent; s4, based on the knowledge agent, performing multi-modal compliance analysis in combination with deep learning and symbolic reasoning to obtain an analysis result; and S5, based on an analysis result, in combination with a natural language generation technology, automatically generating an interpretation report for the violation event. According to the invention, dynamic intelligent compliance analysis is realized, and the intelligent and automatic level of power grid management is significantly improved.

The invention relates to the technical field of aquatic product freezing processing, and discloses a prawn freezing process dynamic regulation and control method and system, and the method comprises the steps: extracting a damage feature vector through a lightweight instance segmentation network; generating a lightweight knowledge graph based on knowledge distillation; the damage features are converted into symbolic representation, and neural symbolic reasoning is carried out; calculating a quality degradation risk index and generating a differential control parameter; and collecting feedback data and updating the weight of the knowledge graph. According to the method, the technical problems of low response speed and poor interpretability in the freezing process of the damaged prawns are solved, and millisecond-level real-time decision and accurate control are realized.

The invention provides a method and a program product for simulating human thinking to intelligently solve plane geometry questions, and relates to the technical field of intelligent symbol reasoning. The method for intelligently solving the plane geometry questions by simulating human thinking comprises the following steps of: expressing a geometrical relationship in a question by using a statement in a form of predicate and parameter so as to perform literal translation on the question, establishing a graph structure, and maintaining reasoning steps and process results; geometric derivation and algebraic calculation are alternately carried out until a target conclusion is obtained; wherein the geometric derivation provides a new equation for the algebraic relationship; the side length relation is simplified through algebraic calculation, and the precondition of the geometric theorem is further provided; and finally, backtracking the reasoning path, and generating a proof process. According to the algebraic derivation and geometric derivation interaction method provided by the invention, the reasoning process strictly complies with the geometric theorem and the algebraic operation rule, and the correctness of the obtained result is ensured.

The invention is suitable for the technical field of artificial intelligence and multi-modal reasoning, and provides a neural symbol reasoning method and system for multi-modal information processing, and the method comprises the following steps: obtaining multi-modal input data; respectively extracting semantic attribute representations of the image data and the text data on a predefined attribute set to obtain a multi-modal semantic attribute representation; mapping the multi-modal semantic attribute representation into a predicate in a first-order logic form, and constructing a symbolized multi-modal attribute fact set; inputting the multi-modal attribute fact set and a logic rule in a pre-constructed first-order logic knowledge base into a Markov logic network for reasoning to obtain a reasoning result; and integrating the reasoning results, and generating an interpretable decision result and a reasoning logic link. According to the method, multi-modal shared attribute symbols are taken as bridges, information fusion, conflict resolution and high-level consistency constraint among multiple modals are realized, and a reasoning result with interpretability, high robustness and strong generalization ability can be obtained.

The invention provides a multi-modal long document generation method and system based on symbol reasoning and anchor propagation, and belongs to the field of document generation. Compiling a hierarchical BNF grammar rule through a domain knowledge base, generating a cue word constraint template, and initializing a state machine; acquiring a multi-modal sample, identifying an entity and constructing a cross-modal dependency graph, and further training an anchor point dependency propagation network; obtaining a user document instruction and modal query, inputting the document instruction into a state machine and an anchor point dependent propagation network, and generating an anchor point enhancement framework; retrieving a multi-modal fragment from the domain knowledge base based on modal query, and sorting according to a dependency graph path to obtain a multi-modal context list; and fusing the multi-modal context list and the anchor point enhancement framework to generate an ultra-long document. A document structure is compiled into a formalized grammar rule through a symbolic reasoning state machine, a generation process is constrained, a cross-modal dependency graph and a joint embedding space are established in combination with an anchor point dependency propagation network, and the problems of out-of-control of a long text and multi-modal splitting are effectively solved.

The invention discloses a multi-modal clinical scientific research data processing method and system based on neural symbol hybrid reasoning, and belongs to the field of medical informatization and artificial intelligence. The method comprises the steps of receiving a natural language analysis instruction; analyzing the instruction into a structured formal problem description data object through a language understanding unit by utilizing a nerve-symbol hybrid inference engine, and selecting a data analysis algorithm according to an expert rule base through a symbol inference unit; then, generating a directed acyclic graph analysis flow based on the selected algorithm; and finally, according to the directed acyclic graph analysis flow, processing the multi-modal clinical scientific research data stored in the database, and generating an analysis report containing quantitative indexes. The technical problems that in the prior art, a clinical scientific research data analysis process is split, efficiency is low, and a semantic gap exists in man-machine interaction are solved, automation and intelligence of scientific research analysis are achieved, and the efficiency, depth and scientificity of data processing are remarkably improved.

The application belongs to the technical field of artificial intelligence medical treatment, and discloses a medical health service system and method based on a multi-granularity semantic analysis and knowledge reasoning engine, which comprises the following steps: through a multi-granularity semantic analysis module, coarse-grained intention recognition and fine-grained medical entity state extraction are performed on the unstructured input of a user; a dynamic probability reasoning engine starts two reasoning paths in parallel: a generative reasoning path generates a group of candidate diagnosis hypotheses by using an LLM, and a symbolic reasoning path performs multi-hop probability reasoning on a unique probability knowledge graph to calculate another group of candidate diagnosis paths and their cumulative probabilities; a reasoning fusion and verification module cross-verify the results of the two paths, and only when the hypothesis of the LLM is verified by the high-probability path of the PKG, a final health service response containing a conclusion and an interpretable path is generated. The application significantly improves the accuracy, reliability and transparency of automated medical consultation services.

The invention discloses a cardiac magnetic resonance report generation method and system based on a multi-dimensional tissue feature knowledge graph. The method comprises the following steps: performing motion correction and multi-view feature extraction on a cardiac magnetic resonance multi-sequence image to obtain an original numerical attribute of an instance node; constructing a dynamic knowledge graph containing tissue characteristics and medical priori, and generating a target reasoning chain with a clinical basis in combination with neural symbol reasoning; then, in a controlled generation stage, an inference chain is injected into a prefix tree, in a decoding step, mask physical shielding logic conflict description is utilized, and a pointer network is synchronously started to directly copy original numerical value attributes into a text sequence; and finally, executing consistency logic verification through a text restoration technology, and identifying and intercepting factual errors. Through the atlas constraint and numerical unvarnished transmission mechanism, the factual illusion problem of a generative model is effectively solved, and the accuracy and reliability of a heart diagnosis report are remarkably improved.

The application discloses a lightweight image semantic segmentation network optimization method for complex scenes, and relates to the technical field of computer vision and artificial intelligence. The specific steps of the method are as follows: a lightweight network is built to extract multi-scale features and output an initial segmentation map; scene physical common sense and semantic rules are converted into first-order logic predicates to build a knowledge base, and a differentiable logical constraint loss is designed; a lightweight symbolic reasoning module is built to logically check and correct the initial result; segmentation loss and constraint loss are jointly optimized to make the model output a logically consistent accurate segmentation result; the application integrates scene physical common sense into network learning through a lightweight encoder-decoder architecture and a differentiable logical constraint loss, reduces the computing load while ensuring segmentation accuracy; in combination with a lightweight symbolic reasoning module and a dynamic weight joint training mechanism, logical checking and correction and loss collaborative optimization are realized, so that the lightweight model outputs a logically consistent segmentation result in a complex scene.

The application discloses a rolling bearing diagnosis system and method based on dynamic correction and neural symbol reasoning. The system comprises a hardware shell for integrating a working condition sensing module, a neural symbol hybrid diagnosis engine, a storage-computation integrated edge execution unit and a general control module. The working condition sensing module is used for collecting bearing running state signals and mapping working condition characteristics. The neural symbol hybrid diagnosis engine is used for fusing physical rules and data characteristics for fault reasoning. The storage-computation integrated edge execution unit is used for accelerating the diagnosis process and guaranteeing offline running capability. The general control module is used for overall planning of timing scheduling, abnormality processing and result output. The above modules are electrically connected through the general control module, and their collaborative operation is controlled through a preset program. The application combines dynamic working condition online correction and neural symbol hybrid reasoning, realizes faster diagnosis and supports offline running, effectively balances interpretability and precision, strengthens few-sample generalization capability, and improves integration and facilitates deployment.

The application discloses a version knowledge graph reasoning method and system based on large language model enhancement, relates to the technical field of dynamic knowledge graph, and comprises the following steps: adopting a semantic drift detection and compensation mechanism, comparing the context coding differences of the same entities in different versions in an initial knowledge graph, identifying drift, dynamically adjusting entity embedding vectors, and outputting a compensation update graph; adopting a multi-hop reasoning algorithm enhanced by an LLM, performing multi-hop reasoning on the compensation update graph, performing symbolic reasoning, vector reasoning and context reasoning in parallel in each hop, and obtaining entity relationship reasoning results through dynamic weight fusion; and superimposing the entity relationship reasoning results on the compensation update graph through a cloud collaborative node, adding entity edges and automatically maintaining version history logs, and obtaining a reasoning fusion version knowledge graph. Through the multi-hop reasoning algorithm enhanced by the large language model, the deep semantic mining capability and reasoning accuracy of the cross-version entity relationship are effectively improved.

A modular symbolic operating system enabling robotic agents—humanoid, aerial, aquatic, or morphing—to operate with lawful cognition, ethical routing, and energy-agnostic behavior through real-time symbolic reasoning. The system integrates a symbolic instruction layer, behavioral arbitration graphs, and energy-type decoupling logic that allows mission-adaptive reconfiguration of robotic form, function, and purpose. This invention supports AGI-level interaction across domains including mobility, dexterity, manipulation, environmental engagement, and cooperative swarm missions—while maintaining symbolic integrity across dynamic configurations.

This application discloses a method, apparatus, storage medium, and device for solving plane geometry problems. The method includes: first, acquiring the target plane geometry problem to be solved and its corresponding target image; then, using a preset first deep learning model to predict entity relationships in the problem text to obtain a first neural symbol representation corresponding to the problem text; and using a preset second deep learning model to transform the target image to obtain its corresponding second neural symbol representation; finally, inputting the first and second neural symbol representations into a symbolic reasoning system, and using beam search to perform reasoning to obtain the solution. It is evident that because this application first uses a deep learning model to convert the problem text and the target image of the target plane geometry problem into neural symbol representations, and then uses beam search to perform reasoning through a symbolic reasoning system, the accuracy of the solution to the plane geometry problem can be improved.

Methods and systems for integrating symbolic reasoning with neural network capabilities in artificial intelligence systems, with a particular focus on enabling automated workflow orchestration and agent-based decision making through a neuro-symbolic computation framework.

This invention belongs to the field of artificial intelligence and agricultural technology, proposing an interpretable intelligent agricultural prediction method and system based on neural symbol fusion. The method includes: preprocessing user-uploaded agricultural image data and environmental data acquired by IoT sensors to obtain standardized image data and structured environmental data; constructing a bi-branch deep learning model and training it with symbolic regularization; inputting the standardized image data into the trained bi-branch deep learning model for decision-making, outputting preliminary diagnostic results and confidence levels; using interpretive tools to generate a saliency heatmap highlighting the most important image regions for the decision; performing knowledge graph verification, rule extraction verification, and symbolic reasoning verification on the preliminary diagnostic results, generating verification results for each; and checking the consistency between the preliminary diagnostic results and the verification results to determine whether to output a decision. This invention solves the problem of opaque model decision-making processes in existing technologies.

The invention relates to a cognitive flashing system and method based on neural symbols, and the system comprises a multi-modal sensing module which is used for obtaining multi-modal data of a vehicle, and the multi-modal data comprises state data, software feature data and context data; the neural symbol fusion module is used for outputting a first flashing task schedule and a second flashing task schedule through a symbol inference engine and a neural network based on the multi-modal data; the flash task scheduling comprises flash target and flash resource allocation; the fusion module is used for fusing the first flashing task scheduling and the second flashing task scheduling through an attention mechanism and outputting a third flashing task scheduling; and the decision generation module is used for executing the flashing operation based on the third flashing task scheduling. By fusing symbolic reasoning and deep learning, the problems of stiffness and black box of a traditional flashing decision system are solved, and the adaptivity, safety and decision efficiency of vehicle software OTA flashing are remarkably improved.

The invention relates to a neural symbol multi-hop reasoning method and system for a domain knowledge graph. The method comprises the following steps: dividing a domain knowledge graph into multi-order sub-graphs, and performing symbol reasoning on each multi-order sub-graph according to a chained domain ontology to obtain an extended sub-graph; taking each multi-order sub-graph as a training sample, and coding instances in the multi-order sub-graphs to obtain class coding vectors and assertion coding vectors; constructing an assertion set reasoning network; taking assertions in the extended subgraph and the negative example set as a positive example and a negative example to construct a supervision label, and training an assertion set reasoning network by using a training sample and the supervision label; dividing a to-be-inferred domain knowledge graph into to-be-inferred multi-order sub-graphs, performing instance coding, inputting the to-be-inferred domain knowledge graph into the trained assertion set inference network, and obtaining corresponding extension sub-graphs according to the output extension type assertion and the established relation assertion; and combining the extended sub-graphs to output the extended domain knowledge graph. By adopting the method, a high-quality, strong-generalization and high-efficiency chain domain knowledge graph reasoning task can be realized.

The invention discloses a decision-making method and system for an organic semiconductor luminescent material, a terminal and a medium, and the method comprises the steps: integrating multi-source heterogeneous data in the field of organic semiconductor luminescent materials, and constructing a high-quality heterogeneous knowledge base; the method comprises the following steps of: converting natural language query of a user into structured query by adopting a semantic analysis model adaptive to BERT based on a material field, and dynamically constructing a knowledge sub-graph related to a task; constructing a symbol reasoning engine based on domain rules, executing interpretable logical reasoning to obtain a symbol reasoning conclusion, adopting a material domain heterogeneous graph representation learning model to perform distributed representation learning and multi-task optimization to obtain a learning reasoning conclusion, integrating the symbol reasoning conclusion and the learning reasoning conclusion, and generating a final decision suggestion. According to the method, the efficiency and the accuracy of material research and development are improved by constructing the multi-source heterogeneous knowledge base, designing the self-adaptive scheduling engine and realizing multi-level reasoning fusion.

The application provides a multimodal long document generation method and system based on symbol reasoning and anchor point propagation, and belongs to the field of document generation. A hierarchical BNF syntax rule is compiled through a domain knowledge base, a prompt word constraint template is generated, and a state machine is initialized; a multimodal sample is obtained, entities are recognized, and a cross-modal dependency graph is constructed, and an anchor point dependency propagation network is further trained; a user document instruction and a modal query are obtained, the document instruction is input into the state machine and the anchor point dependency propagation network, and an anchor point enhanced framework is generated; based on the modal query, multimodal segments are retrieved from the domain knowledge base, sorted according to the dependency graph path, and a multimodal context list is obtained; the multimodal context list and the anchor point enhanced framework are fused to generate a super-long document. Through the symbol reasoning state machine, the document structure is compiled into formal syntax rules and the generation process is constrained, and the cross-modal dependency graph and the joint embedding space are established in combination with the anchor point dependency propagation network, so that the problems of long text out of control and multimodal fragmentation are effectively solved.

The application discloses a government affair text auditing method and system based on knowledge graph reasoning, which is used to improve the accuracy and intelligent level of government affair text auditing. The method comprises the following steps: obtaining an original policy text, and performing semantic analysis processing on the original policy text by using a double-channel semantic disambiguation engine; fusing semantic analysis results of a rule channel and a neural channel to obtain a standardized entity set subjected to semantic disambiguation and structured attribute calibration; mapping the standardized entity set, case original text data and declaration material data to a knowledge graph; learning node embedding representation in the knowledge graph by using a graph neural network model, so as to realize semantic alignment between the standardized entity set, the case original text data and the declaration material data; on the knowledge graph, performing compliance evaluation by using symbolic reasoning based on pre-defined logical rules and neural reasoning based on node embedding representation or graph path; and generating a government affair text auditing result according to a result of the compliance evaluation.

The invention relates to the technical field of film and television investment intelligent decision, and discloses a film and television production actuarial and risk intelligent control system, which comprises seven modules, namely a data annotation module, a feature extraction and conversion module, a symbol reasoning and evaluation module, a scheme generation module, a monitoring and early warning module, a decision optimization verification module and a knowledge updating module. According to the method, semantic annotation is performed through a multi-channel adaptive method, multi-modal feature extraction is performed, a feature-to-symbol conversion mechanism is constructed, multi-level cognitive reasoning is performed by adopting an OpenNARS non-axiom reasoning engine, a personalized investment scheme is generated by adjusting cognitive parameter configuration, and a risk early warning report is obtained through real-time data monitoring and risk assessment. Decision optimization and cross validation are carried out through an imitation learning method and an inverse reinforcement learning method. According to the method, the precision, individuation and interpretability of film and television project investment decision can be realized, the return on investment is improved, and the project risk is reduced.

The invention relates to the technical field of knowledge management and artificial intelligence, and discloses a knowledge management system based on artificial intelligence big data. The system comprises knowledge flow capture, symbol reasoning, neural embedding, strategy synthesis and strategy deployment components. The system generates structured knowledge through multi-modal data acquisition and cleaning; performing symbolic reasoning by utilizing predicate logic and a rule base, and constructing a symbolic knowledge graph; a deep learning model is adopted to generate low-dimensional vector representation; fusing the symbolized knowledge graph and the vector representation, and generating a knowledge management strategy through a strategy gradient algorithm; and asynchronously executing the strategy in the cloud environment. According to the scheme, through combination of symbol reasoning and a neural network technology, interpretability and logic preciseness of a knowledge processing process are ensured, and meanwhile, the dynamic adaptive capacity and decision accuracy of system strategy generation are improved.

The application discloses a decision-making method, system, terminal and medium of an organic semiconductor light-emitting material, and the method comprises the following steps: integrating multi-source heterogeneous data in the field of organic semiconductor light-emitting materials, and constructing a high-quality heterogeneous knowledge base; adopting a semantic analysis model based on a material field adaptive BERT to convert a user natural language query into a structured query, and dynamically constructing a knowledge subgraph related to a task; constructing a symbolic reasoning engine based on a field rule, performing an interpretable logical reasoning, obtaining a symbolic reasoning conclusion, adopting a material field heterogeneous graph representation learning model to perform distributed representation learning and multi-task optimization, obtaining a learning reasoning conclusion, integrating the symbolic reasoning conclusion and the learning reasoning conclusion, and generating a final decision-making suggestion. Through the construction of a multi-source heterogeneous knowledge base, the design of an adaptive scheduling engine and the realization of multi-level reasoning fusion, the efficiency and accuracy of material research and development are improved.

The invention relates to the technical field of image classification, and particularly discloses an interpretable remote sensing target classification method and system based on neural symbol reasoning, and the method comprises the steps: generating attribute semantic embedding used for cross-class migration and discrimination semantic embedding used for distinguishing classes based on basic visual features; establishing a text semantic graph on the basis of semantic similarity among categories, establishing a visual feature graph on the basis of discriminant semantic embedding and visual similarity among the categories, fusing the text semantic graph and the visual feature graph, and outputting an enhanced category prototype; according to the attribute semantic embedding and the enhanced category prototype, outputting a final classification result and an interpretable reasoning path containing a rule-attribute-category link; a neural symbol reasoning module is integrated on the basis of a knowledge graph, and a graph neural network and symbol logical reasoning are combined, so that the system provides a logical basis while outputting a classification result, and the jump from visual identification to semantic understanding is realized.

The invention discloses a CTI attack chain reconstruction method based on multi-modal perception and neural symbol reasoning, and belongs to the technical field of network security and artificial intelligence. The method comprises the following steps: (1) preprocessing and standardizing multi-source heterogeneous CTI intelligence based on semantic perception; (2) high-precision element extraction based on nerve and symbol dual-channel collaboration; (3) self-adaptive entity alignment and isolation based on topological structure embedding; and (4) causal chain completion based on semantic topology hybrid anchor points and anti-fact perturbation. According to the method, the problems of visual information loss and semantic segmentation are solved through multi-modal information fusion and self-adaptive partitioning; high-precision element extraction is realized through nerve symbol collaboration and logic verification; the accuracy and relevance of knowledge fusion are ensured through a differential entity alignment strategy; the method is based on ATTamp; the CK path search and anti-factual reasoning mechanism realizes intelligent complementation and interpretable reconstruction of the fragmented attack chain.