99 results about "Symbolic reasoning" patented technology

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

A federated distributed AI reasoning and action platform utilizing decentralized, partially observable hierarchical computing for neuro-symbolic reasoning. It features a federated Distributed Computational Graph (DCG) system integrating core components like pipeline orchestration, transformers, and marketplaces. The platform enables privacy-preserving dynamic resource allocation, intelligent task scheduling, and variable information sharing across diverse computing environments. By coordinating with an AI-based operating system and analyzing performance metrics, environmental conditions, and resource availability, the system optimizes efficiency across AI workloads and decision-making processes. This results in an adaptive, power-efficient, and scalable AI-enabled data processing system capable of handling complex tasks while maintaining peak performance under various operating conditions.

The invention relates to the technical field of multi-mode interpretable decision scheme design, in particular to a multi-mode interpretable decision method and system and electronic equipment. According to the method, the intelligent decision-making level in the industrial manufacturing environment can be effectively improved through dynamic knowledge path optimization, multi-modal data fusion, symbol reasoning and self-adaptive feedback. A reinforcement learning driven dynamic knowledge retrieval technology is introduced, so that efficient organization and retrieval of multi-source heterogeneous data are realized; reasoning is enhanced based on the knowledge graph, the relevance between cross-modal data is improved, and the reasoning logic between the data is clearer and more reliable; and in combination with an ontological reasoning mechanism, the interpretability and transparency of the system are enhanced, so that the system conforms to causal derivation rules in industrial production. According to the technical scheme, dynamic knowledge path optimization, ontology reasoning and multi-modal data fusion are combined, and an efficient, accurate and explainable industrial manufacturing decision-making scheme is provided.

A distributed generative artificial intelligence (AI) reasoning and action platform that utilizes a cloud-based computing architecture for neuro-symbolic reasoning. The platform comprises systems for distributed computation, curation, marketplace integration, and context management. A distributed computational graph (DCG) orchestrates complex workflows for building and deploying generative AI models, incorporating expert judgment and external data sources. A context computing system aggregates contextual data, while a curation system provides curated responses from trained models. Marketplaces offer data, algorithms, and expert judgment for purchase or integration. The platform enables enterprises to construct user-defined workflows and incorporate trained models into their business processes, leveraging enterprise-specific knowledge. The platform facilitates flexible and scalable integration of machine learning models into software applications, supported by a dynamic and adaptive DCG architecture.

The invention discloses a government affair text auditing method and system based on knowledge graph reasoning. The method and system are used for improving the accuracy and the intelligent level of government affair text auditing. The method comprises the following steps: acquiring an original policy text, and performing semantic analysis processing on the original policy text by utilizing a two-channel semantic disambiguation engine; fusing semantic analysis results of the rule channel and the neural channel to obtain a standardized entity set subjected to semantic disambiguation and structured attribute calibration; mapping the standardized entity set, the case original text data and the declaration material data into a knowledge graph; a graph neural network model is utilized to learn node embedding representation in the knowledge graph so as to realize semantic alignment among the normalized entity set, the case original text data and the declaration material data; on the knowledge graph, performing compliance evaluation by using symbol reasoning based on a predefined logic rule and neural reasoning based on node embedding representation or a graph path; and generating a government affair text auditing result according to a compliance evaluation result.

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 discloses a multi-agent collaborative decision-making system and method for neural symbol fusion, and relates to the technical field of artificial intelligence, and the system comprises a neural symbol fusion engine which constructs a knowledge double-layer representation architecture, and achieves the organic fusion of symbol reasoning accuracy and neural learning adaptability; the intelligent agent coordination optimizer quantifies the intelligent agent difference through cognitive state mapping, constructs a consensus feasible region, carries out hybrid verification and constraint optimization, and selects an optimal decision scheme; and the adaptive interpretation system constructs a decision evidence chain and realizes continuous optimization of system parameters through feedback learning. The technical challenges of symbol reasoning and neural learning fusion, multi-agent cognitive difference coordination, decision reliability and interpretability and the like are effectively solved, and the method is suitable for complex decision scenes of medical treatment, finance, intelligent manufacturing and the like.

The invention relates to the technical field of knowledge maps, and provides a neural symbol reasoning method of a multi-modal knowledge map and a logic rule injection algorithm, and the method comprises the following steps: 1, constructing the multi-modal knowledge map which comprises entities, attributes and relationships corresponding to multi-modal data such as texts, images and audios, generating an initial vector representation for each entity and relationship; 2, performing feature extraction and fusion on the multi-modal data by using a cross-modal feature fusion module; step 3, combining the learning ability of a neural network and the inference rule of symbol logic through a neural symbol inference module; and 4, injecting a preset logic rule into the neural symbol reasoning module by using a logic rule injection module. And through a cross-modal feature fusion module, each modal feature is extracted by using models such as BERT and ResNet-50, and feature weighted fusion is realized in combination with an attention mechanism, so that the semantic association capture capability of multi-modal data is remarkably improved, and cross-modal entity alignment is effectively realized.

The invention discloses a version knowledge graph reasoning method and system based on large language model enhancement, and relates to the technical field of dynamic knowledge graphs, and the method comprises the steps: employing a semantic drift detection and compensation mechanism, comparing context coding differences of same entities of different versions in an initial knowledge graph, recognizing drift, and dynamically adjusting entity embedding vectors, outputting a compensation update atlas; an LLM enhanced multi-hop reasoning algorithm is adopted, multi-hop reasoning is carried out on the compensation updating map, symbol reasoning, vector reasoning and context reasoning are carried out in parallel in each hop, and an entity relation reasoning result is obtained through dynamic weight fusion; and superposing an entity relationship reasoning result to the compensation updating graph through a cloud collaborative node, adding an entity edge and automatically maintaining a version history log, and obtaining a reasoning fusion version knowledge graph. Through a multi-hop reasoning algorithm enhanced by a large language model, the deep semantic mining capability and reasoning accuracy of a cross-version entity relationship are effectively improved.

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 provides a lithium battery K value real-time prediction method based on neural symbol reasoning and multi-modal learning, and the method specifically comprises the steps: S1, carrying out the coding of an industrial signal through a binary coding mode, converting an input voltage sequence into a binary sequence, and extracting a frequency domain feature through a wavelet transformation technology; s2, using a neural symbol inference engine to realize verifiable feature selection, and executing feature selection under logic constraints; s3, by means of a multi-head potential attention mechanism fusion process knowledge graph, a potential space projection matrix is constructed, and the number of attention heads is adjusted through a dynamic head number adjusting mechanism; and S4, completing online knowledge migration by utilizing a dynamic distillation expert system, and realizing knowledge transfer and model optimization by combining a hybrid expert architecture and an online distillation technology and applying an expert dynamic activation function and knowledge distillation loss. According to the method, advanced technologies such as neural symbol reasoning and multi-modal learning are fused, the prediction precision is high, the response delay is small, the energy consumption ratio is low, and the interpretability score is high.

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 embodiment of the invention discloses an intelligent reasoning method and device based on neural symbols and a medium, and relates to the technical field of artificial intelligence, the method comprises the steps that user input information corresponding to a user and a pre-constructed mixed domain knowledge base are acquired, and the mixed domain knowledge base comprises a first-order predicate logic rule base and a closed Markov logic network; analyzing the user input information, determining an input feature vector, performing neural network reasoning on the input feature vector through a pre-constructed neural reasoning module, and determining corresponding pseudo tag information; and determining a candidate rule through a pre-constructed symbol reasoning module according to the pseudo-tag information and a first-order predicate logic rule base, so as to carry out collaborative reasoning on the pseudo-tag information and the candidate rule by utilizing a closed Markov logic network, and determine corresponding reasoning knowledge information.

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 discloses an AI accompanying system and method based on general artificial intelligence, and the method comprises the following steps: S1, collecting and preprocessing user data, and generating a standardized data set; s2, performing dimension reduction by adopting diffusion mapping, extracting voice and behavior characteristics, and retaining a state change trend; s3, constructing a behavior knowledge graph based on symbol logical reasoning, and recording symbol state changes; s4, similar states and coping strategies are retrieved by using sparse distributed memory storage symbol representation; s5, combining manifold embedding, historical matching and symbolic reasoning, updating a user state and generating a personalized accompanying strategy; s6, analyzing the voice rhythm and the word frequency, constructing an emotion evaluation model, and generating a personalized emotion intervention scheme; s7, optimizing the knowledge graph by adopting incremental memory, and improving the companion strategy adaptability; and S8, recording feedback, continuously optimizing emotion intervention, and realizing intelligent accompanying. The AI accompanying system improves intelligence, personalization and long-term adaptability of AI accompanying, and is suitable for the fields of old-age care, mental health management and the like.

The invention requests to protect a drug comprehensive evaluation system based on machine learning and an expert database, and the system comprises a multi-source data processing module which is used for processing and standardizing the molecular structure, genome characteristics and clinical data of a drug; the expert knowledge base comprises drug attributes, disease characteristics and clinical guidelines; the prediction model cluster is composed of a deep neural network, an integrated learning model and a symbol inference engine and used for predicting the IC50 value, the clinical response rate and the adverse reaction probability of the medicine; the dynamic optimization module is used for uncertainty quantification of the model, incremental learning of a training set and dynamic updating of a knowledge base; and the interpretability output module is used for interpreting the prediction result and visually outputting the prediction result.

A system and method AI-enabled telematics and actuation for electronic entertainment, simulation, training, and remote operations systems. The system and method disclosed support neuro symbolic reasoning and generative AI enabled experience generation to allow a user or collection of users to experience a wide range of realistic scenarios where the user can pick and choose an experience that best fits their individual or collective preferences. Additionally, the system and method have wide applications to a variety of environments, including but not limited to, racing, sports, military training, vehicle and aircraft operation, and training simulations. The proposed system and method enable realistic, immersive video game, simulation, training, and remote operations environments which are applicable to a wide range of devices, platforms, and mediums for recreational, commercial, industrial, and security uses.

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 one-object-one-code-based bC integrated marketing method, and particularly relates to the field of bC integrated marketing, and the method comprises the steps: introducing a Neure-Symbolic reasoning method into a one-object-one-code bC integrated marketing system, carrying out the dynamic reasoning, and binding a b-end control strategy and a C-end excitation path which are adaptive to the current attribute of a commodity, a sales scene and a user tag, thereby achieving the one-object-one-code-based bC integrated marketing. A one-to-one mapping relation between an entity code and a digital twin code is constructed, and adaptive evolution and remote updating of a strategy are realized based on a code scanning behavior in a commodity circulation process, so that the problems that the strategy is fixed, the response is rigid, the behavior data utilization rate is low and a B end is difficult to perceive feedback of a C end in real time in an existing one-object-one-code system are solved; the strategy personalization of the commodity granularity level, the logic dynamic updating driven by the code scanning behavior and the visual feedback of the code scanning heat are realized, and the bC linkage efficiency and the accuracy of the marketing strategy are greatly improved.

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 relates to the technical field of machine tool machining, and discloses an interrupt recovery method and system.The interrupt recovery method for the machine tool machining file comprises the steps that G code semantic analysis and graph structure conversion are conducted, and a G code machining program is converted into a structured knowledge graph with semantic association; processing intention reasoning is carried out based on neural symbol reasoning, and a deep processing intention behind a program is understood; self-evolution learning of processing experience is realized, and continuous learning and optimization are carried out from historical experience; carrying out interruption recovery point positioning based on the intention map, and accurately finding out an optimal recovery position; generating and executing an interrupt recovery strategy to realize stable recovery of the machining process; by deeply understanding G code semantics and machining intention, the optimal recovery point is accurately recognized, the success rate and precision of interruption recovery are improved, and the method is suitable for high-value part machining scenes.

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.