199 results about "Logical rules" patented technology

The invention relates to the technical field of knowledge maps, and discloses a semantic association and logic rule inference method based on a knowledge map, and the method comprises the steps: obtaining a cross-functional department first data set, and constructing a cross-department knowledge map; extracting index nodes from the cross-department knowledge graph, identifying semantic conflict indexes, generating an index semantic conflict list, and constructing the cross-department knowledge graph after semantic mapping based on the index semantic conflict list; obtaining a second data set of a cross-functional department, and obtaining a fused cross-department knowledge graph and a semantic association degree matrix based on the second data set and the cross-department knowledge graph after semantic mapping; dynamically adjusting the weight of each node and edge of the fused cross-department knowledge graph according to the semantic association degree matrix, and generating a cross-department collaborative knowledge graph; generating a cross-department collaborative decision suggestion based on the cross-department collaborative knowledge graph; according to the invention, the efficiency and quality of cross-department collaborative decision-making are significantly improved.

The invention provides a digital human interaction control method and device fusing emotion semantics and logical reasoning and a storage medium. The method comprises the following steps: analyzing multi-modal input data of a user, constructing emotion-semantics joint representation, and generating a logic decision path; and through a cognitive fusion module, emotion-semantic representation and a logic decision path are fused, and an interaction response adapting to emotion and logic consistency is generated. The system optimizes an emotion semantic model and a logical reasoning rule on line according to user feedback and interaction history, and real-time interaction of emotion dynamic and logical rules is achieved. The system can dynamically adjust the logic decision path based on the multi-mode emotional state of the user, and improves the naturalness and situation adaptability of interaction. A dynamic time warping algorithm and a factorization machine are introduced to process a multi-modal feature fusion problem, and the accuracy and robustness of emotional state recognition are improved. The online optimization mechanism enables the model and the rule to be evolved continuously, and reasoning errors are corrected automatically through user feedback, so that error circulation is avoided.

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 invention discloses a government affair intelligent interaction and information extraction method and device, equipment and a medium, and relates to the technical field of digital government affair, digital cities, intelligent interaction and the like. The traditional manual item-by-item filling is converted into end-to-end automatic processing, so that the operation steps of a user are remarkably reduced, the filling burden of a hundred-item-level form is solved, and the filling efficiency is improved; and an ID voice corrector is adopted to realize three-layer progressive verification, so that the output ID offset of the multi-modal large model is effectively inhibited, the accurate mapping of field identifiers is ensured, and meanwhile, a lightweight knowledge graph is adopted to automatically capture logic rules among fields, interspecific compliance conflicts of government affair scenes are intercepted in real time, the logic error risk is reduced, and the filling accuracy is improved.

The invention discloses a multi-source heterogeneous agricultural data element fusion system and method, and relates to the technical field of data fusion processing, and the method comprises the steps: carrying out the data screening of cleaned data; the screened data is preprocessed by the constructed agricultural field knowledge base, the logic consistency of the data is checked through a rule engine, and a corresponding coping strategy is adopted according to a check result; identifying abnormal points in the multi-source data by adopting a Gaussian mixture model and a constructed logic rule combination, and correcting or complementing the detected abnormal points in combination with a specific scene; key features are selected through information gain, multi-source fusion is carried out on the key features and original multi-source data, an abnormal mode library is established after abnormal data modes are classified, and source categories of the abnormal data are matched from a fault matching rule library according to abnormal features of the abnormal data; and the fault matching rule base is established, so that the source category of the abnormal data can be accurately identified, and a targeted solution is provided for processing the abnormal data.

The invention relates to the technical field of artificial intelligence and material engineering, and discloses a formula optimization method and device, equipment and a storage medium, and the method comprises the steps: carrying out the knowledge extraction of an obtained structured formula data set and unstructured technical literature data in response to a formula optimization target input by a user, and generating a table literature knowledge set; inputting the table literature knowledge set and the formula optimization target into a large language model to obtain a generated text corresponding to the formula optimization target; performing logic rule screening and risk assessment on the generated text through a knowledge fusion layer to obtain text output conforming to a confidence threshold, and generating a new formula scheme according to the text output; and performing multi-objective optimization on the new formula scheme based on a preset experimental cost constraint condition, and outputting an optimized recommended formula and a support evidence chain. By automatically fusing innovative components and process information in literatures, the output recommended formula has scientific basis and interpretability, and the practicability and innovativeness of an automatic formula are improved.

The invention relates to the technical field of security auditing, in particular to a security auditing method and system for an industrial control system, and the method comprises the following steps: aiming at a key control task, a communication task and a security task of a real-time operating system, collecting a period, a starting timestamp, a finishing timestamp, a central processing unit occupied time slice and peak memory usage amount data. According to the method, the period, timestamp, central processing unit occupation and memory usage data of a key task of a real-time operating system are collected, a task execution time boundary and a resource consumption envelope are set, and then an expected relation rule set of a task time sequence and resource consumption is constructed by applying historical data statistics and logic rule deduction; and meanwhile, the key length of symmetric and asymmetric encryption, initialization vector generation, hash algorithm selection, key derivation parameters and encryption operation context are stipulated, so that a comprehensive and specific ICS behavior specification baseline is established.

The invention discloses a high-precision multi-modal twin model assembly method based on a large language model and a multiple docking optimization mechanism. The whole system framework is composed of three modules: a database, an interaction system and human factors. Wherein the database module integrates a model library module, a user interaction data recording module and a temporary data recording module, and is used for storing and managing various data information required for constructing a virtual scene; the interactive system module is used as a core part, integrates a model dynamic loading module, a model accurate positioning module and a model adaptive assembly module, realizes instruction analysis and model generation by relying on a large language model, and combines a multi-docking optimization mechanism of eye movement tracking, gesture recognition, automatic docking and logic rule binding to realize multi-docking. Efficient and accurate interaction between the user and the database is effectively guaranteed; the method not only provides a solid foundation for virtual experiments and virtual-real symbiosis, but also significantly improves the construction efficiency and precision of digital twin model assembly.

The invention relates to the technical field of intelligent manufacturing, and discloses an intelligent control method and system for a stamping robot, and the method comprises the steps: constructing a knowledge graph containing the multi-dimensional relation of the quality of material technology equipment; the process knowledge is converted into a formalized logic rule, and the formalized logic rule is fused with a deep neural network to construct a neural symbol inference model; modeling the stamping process into a discrete event system, and verifying the correctness of a process control rule by using a linear temporal logic protocol; detecting and solving parameter constraint conflicts in the process control rule by using a satisfiability model theory solver; generating and executing a self-adaptive stamping control strategy based on the verified rule base and production process feedback; according to the method, symbol reasoning and deep learning are fused, the technical characteristics of high interpretability, high safety, high adaptive capacity and the like are achieved, and the quality consistency and production efficiency of stamping production can be effectively improved.

The invention relates to the technical field of bidding and tendering information processing, in particular to a bidding and tendering template generation method based on a large language model, which comprises the following steps: receiving and analyzing bidding and tendering project demand information input by a user, extracting key elements through a named entity recognition model, and generating standardized demand data; inputting the standardized demand data into a pre-trained large language model to generate an initial bidding and tendering template text; based on a bidding and tendering clause logic rule base, internal logic consistency scanning is carried out on the initial template text, and potential conflict clauses are identified and marked; and generating an optimization instruction according to a conflict detection result, driving the large language model to complete directional revision of conflict terms, and finally outputting a target bidding and tendering template with a standard structure and consistent logic. The method has the capabilities of efficient generation, logic self-inspection and automatic repair, and the intelligent level and compliance control capability of bidding document compilation are remarkably improved.

The invention provides a network operation and maintenance instruction security decision-making method, system and device based on topology constraint and multi-agent arbitration, and belongs to the technical field of artificial intelligence and network operation and maintenance crossing. The method comprises the following steps: constructing a network topology attribute graph, and collecting and updating network equipment states and connection relationships in real time; generating a physical rule constraint and a logic rule constraint based on the topological attribute graph; creating a corresponding device agent for each network device, and distributing the constraint rule to the corresponding device agent; receiving an operation and maintenance instruction, and carrying out cooperative verification on the instruction through the multi-intelligent experience certificate chain; performing weighted decision on the verification result based on a dynamic weight decision mechanism to generate a final execution decision; and analyzing the high-risk instruction scene through a rule evolution engine, and updating the topology constraint library. According to the invention, the problem that the traditional operation and maintenance system is insufficient in intelligence is effectively solved, and high-reliability intelligent operation and maintenance support is provided for key scenes such as industrial internet.

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 power transmission line fault positioning method and system, and relates to the technical field of power transmission lines. The method comprises the following steps: dividing a power transmission line into a plurality of logic sections, and setting a basic logic rule; performing coarse-grained threshold judgment and fine-grained logic check on the real-time monitoring parameters of each logic section, and screening out candidate fault logic sections; establishing a node logic link, detecting a breakpoint node of the node logic link, comparing the breakpoint node in a previous time period and a later time period, judging whether the breakpoint node is a persistent fault or not, and obtaining a breakpoint judgment result; performing logic consistency check on breakpoint judgment results of different monitoring points and different parameter types, and when conflicts occur, making decisions according to priorities to generate a unified judgment result; and synthesizing the candidate fault logic sections and the unified judgment result to generate a final fault positioning result of the power transmission line. According to the invention, high-precision, reliable and intelligent positioning of power transmission line faults is realized.

The invention discloses a personalized recommendation method and device based on a large language model and logical relationship mining. According to the method, user-driven logic relation extraction is constructed, the semantic reasoning ability and graph structure analysis of the large language model are fused, the high-order logic relation in the heterogeneous graph is mined, and the problems that an existing method is incomplete in logic relation and insufficient in semantic understanding are solved. The method comprises the following steps: analyzing user-article interaction data through a user-driven logic relationship extraction module, and identifying implicit association; performing rule expansion by utilizing a large language model, and generating a high-order logic rule in combination with semantic and structural information; screening high-quality rules through confidence score to construct an enhanced heterogeneous graph; and finally, demand perception recommendation is realized in combination with user preference embedding and logic relation characteristics.

The invention discloses a collaborative filtering recommendation method and system based on logic rule reasoning, and the method comprises the steps: obtaining the original attribute characteristics of a user and a project or the negative characteristics of the original attribute characteristics based on the binary values of the continuous attributes and discrete attributes of the user and the project; performing logic conjunct or logic disjunction on the original attribute features or the negative features to obtain a logic rule matrix of a user-project pair; fusing the attribute feature information of the user and the item to obtain a unique rule weight matrix associated with the user-item pair; obtaining an interaction prediction rule factor based on the logic rule matrix and the rule weight matrix; and performing model training and prediction by using the interactive prediction rule factor. Through fusion of a neural symbol reasoning technology and a complete logic operator, dynamic mining of a logic rule between a user attribute and a project attribute is realized.

One method includes extracting word-elements, each corresponding to a respective element of a ground truth cell-item array from an annotated document, applying logic rules to the extracted word-elements so that the applicability, or not, of each logic rule to each element of the ground truth cell-item array is determined. Based on the applying of the logic rules, metrics are obtained that indicate, for each word-element of the annotated document, the applicability of the logic rules, and the frequency with which applicable logic rules is satisfied. A first aggregation process is performed that aggregates the metrics across a group of unstructured, and annotated, documents, and a second aggregation process is performed that aggregates the metrics regarding a model-generated cell item array that was created based on the group of annotated documents. Finally, respective outcomes of the first and second aggregation processes are compared so as to identify logic rules of interest.

The invention discloses a weak supervision and thinking chain-based medical field dialogue method and apparatus, and a readable medium. The method comprises the steps of constructing a mixed training data set by using a trained first large language model and combining a first-order logic rule; performing fine tuning on the trained second large language model by using the mixed training data set and adopting a DoRA training mode to obtain a fine-tuned second large language model; inputting a dialogue history to be replied into the fine-tuned second large language model to obtain a corresponding predicted customer service intention; generating a dialogue information summary corresponding to the dialogue history to be replied by using the trained first large language model, and obtaining a second cue word for guiding reply generation according to the dialogue information summary corresponding to the dialogue history to be replied and the predicted customer service intention through a first-order logic rule; and inputting the dialogue history to be replied and the second cue word into the dialogue model to generate a corresponding customer service reply statement. According to the invention, efficient application can be realized at low cost.

The invention discloses a reciprocal calculation instruction sequence generation method oriented to a heterogeneous processor, and belongs to the field of compilation optimization and code generation. Aiming at the problems of instruction redundancy, weak precision control, poor hardware adaptation and high manual dependence of an existing method in a heterogeneous environment, characteristics of a reciprocal instruction and an operand are accurately identified by linearly scanning heterogeneous object codes (including vectorization, scalar and complex instruction sequences); in combination with hardware characteristics of RISC / SIMD / VLIW / DSP and the like, a multi-round iteration precision improvement and temporary register optimization allocation strategy is adopted, differential generation logic is formulated, and a high-precision low-redundancy instruction sequence is generated. The method comprises linear code scanning classification, reciprocal instruction and operand identification, cross-architecture generation logic rule formulation, instruction sequence generation and legality verification. Full-process automation is achieved, manual intervention is reduced, the execution efficiency and precision of reciprocal calculation of the heterogeneous processor are improved, and the method is suitable for embedded systems, high-performance calculation and other scenes.

The invention discloses a transformer substation anti-error logic verification method, system and device and a storage medium. The method comprises the following steps: configuring secondary locking primary logic for primary equipment; only secondary locking secondary logic needs to be configured for the protection pressing plate, and primary locking secondary logic does not need to be configured; when the primary equipment is operated, secondary locking primary logic is verified; when the protection pressing plate is operated, the secondary locking secondary logic is verified, and meanwhile, the secondary locking primary logic is verified based on the expected state of the simulation pressing plate, so that primary locking secondary verification is achieved. According to the method, the problem that secondary anti-error configuration of an existing transformer substation is complex can be solved, the engineering configuration efficiency is greatly improved, the logic rule acceptance time is shortened, the configuration error risk caused by human errors is reduced, the system reliability is remarkably improved, and the practical level of secondary anti-error configuration of the transformer substation is improved.

The invention provides an industrial cognitive base system based on multi-modal comparative learning and an execution method, and the industrial cognitive base system based on multi-modal comparative learning is characterized in that a system architecture comprising a knowledge graph management module and a cross-modal coding fusion module is constructed; industrial field knowledge is dynamically injected into a multi-modal feature learning process in the form of a structured sub-graph, and a comparative learning mechanism introducing industrial semantic constraints is combined, so that a model obtained by final training can realize semantic alignment of multi-modal data; it can be ensured that generated unified semantic representation and reasoning results strictly conform to predefined industrial logic rules, and therefore the defect that in the prior art, a pure data driving method may generate results violating industrial common knowledge is effectively overcome; and the output reliability, the decision credibility and the practical application value of the industrial cognitive system in key tasks such as fault diagnosis and state monitoring are remarkably improved.

The invention discloses an automatic game testing method and system, and the method achieves the automatic generation and execution of testing logic through the building of a knowledge base and an event base, and the cooperative management of the knowledge base and the event base, and reduces the manual intervention. Converting a natural language demand input by a user side into a semantic feature, and matching the semantic feature with an operation instruction in an event library to obtain an executable test logic so as to reduce a test threshold; triggering the event in the event library according to the executable test logic, pushing the execution result of the event to the user side, and updating the knowledge base and the event library by using the execution feedback data of the event, thereby forming an execution-feedback-update closed loop, and improving the self-learning ability of the test system. In this way, the logic rules in the game development kit are converted into the knowledge base, intelligent analysis and dynamic feedback optimization of natural language requirements are achieved in combination with the preset operation instruction of the event base, and the test efficiency and the adaptive capacity are remarkably improved.

The invention discloses a mapping knowledge domain embedding method fusing meta-information and logic rules, and relates to the technical field of computers. The method comprises the following steps: inputting a to-be-predicted knowledge graph into a knowledge graph embedding module; in the multi-granularity meta-information embedding module, for each entity to be predicted in the knowledge graph to be predicted, obtaining ontology information embedding and multi-hop neighbor relation embedding of the entity to be predicted; carrying out average pooling processing on each of the multi-hop neighbor relation embedding, and processing the neighbor relation embedding after the average pooling processing through a multi-layer perceptron and learnable parameters to obtain a hierarchical attention weight of each neighbor relation embedding; each multi-hop neighbor relation embedding comprises a plurality of neighbors; and performing weighted summation on the ontology information embedding and the multi-hop neighbor relation embedding based on the hierarchical attention weight to obtain meta-information embedding of a to-be-predicted entity in the to-be-predicted knowledge graph. According to the method, the determination precision of the meta-information embedding of the entity can be improved.

The invention discloses an uncertainty knowledge graph reasoning method based on rule mining. The uncertainty knowledge graph reasoning method is used for solving the problem of uncertainty knowledge graph completion. Uncertain knowledge graph reasoning not only needs to predict a missing tail entity, but also needs to predict the confidence coefficient of a triple. According to the method, for the characteristics of the uncertainty knowledge graph, the reliability of the relation path is measured by evaluating the degree of confidence, and a rule analyzer is designed to generate a complete logic rule, so that the prediction of the missing tail entity is realized. Meanwhile, a confidence coefficient calculation method combining structural information of the knowledge graph and semantic information of a pre-training language model is designed for the source of uncertainty information, accurate confidence coefficient prediction conforming to common sense is provided for a reasoning result, and finally the problem of uncertainty knowledge graph completion is solved.

The invention discloses an AI model manual annotation method with associated logic, relates to the technical field of artificial intelligence and optical character recognition (OCR), and aims to solve the problems that the existing OCR recognition technology is low in accuracy and lacks an effective content verification and correction mechanism when processing complex certificates. The method comprises the following steps of: firstly, extracting a driving license, a driving license, an electronic license and text contents of various certificates through OCR (Optical Character Recognition) scanning equipment; constructing an association logic AI model for storing certificate information association logic rules; performing structured labeling processing on an OCR recognition result to realize document information classification and regularization; performing logic verification on the annotated content by utilizing an association logic AI model, marking problems and proposing a modification scheme; and finally, corrected data is fed back to the model to complete optimization iteration. According to the method, the logic contradiction can be accurately found and corrected, the identification accuracy of the AI model on multiple types of certificates is remarkably improved, the manual labeling error is reduced, and the certificate information extraction efficiency and reliability are improved.

The embodiment of the invention provides a knowledge graph logic rule mining method based on a large language model, and the method comprises the steps: selecting a triple from a knowledge graph, and taking the triple as a sample triple; determining a sample logic rule through the sample triad; reasoning and expanding the sample logic rule through a large language model to obtain a final logic rule; and applying the final logic rule to the knowledge graph to complete the knowledge graph. According to the technical scheme, the large language model is combined with the knowledge graph, and a small number of rule examples in the knowledge graph are utilized to guide the large language model to generate a large number of high-quality logic rules, so that the efficiency and the accuracy of carrying out logic rule mining on the large-scale knowledge graph are improved.

The invention discloses a long sequence recommendation method based on de-noising multi-interest logical reasoning, and relates to the technical field of data mining. According to the method, two preference operators are defined, a mapping function based on a logic rule is defined for the two preference operators so as to obtain preference probability embedding of a corresponding article, and user interest embedding is extracted by utilizing a self-attention mechanism. And finally, logical reasoning is carried out based on the obtained user interest embedding to recommend the next article, so that the negative influence of noise articles on logical reasoning is avoided, and the recommendation accuracy is improved. A multi-interest learning strategy is designed, including IPD contrast loss and ILR contrast loss. The IPD loss comparison function is to require an article to be as close as possible to a multi-interest probability distribution space instead of only the probability distribution of a single interest. The ILR contrast loss function is to ensure that multiple interests of the user can be considered during logical reasoning instead of only paying attention to the main interests of the user.

The embodiment of the invention provides a verification code file generation method and device, equipment, a medium and a program product, and the verification code file generation method comprises the steps that an editing interface is displayed, in response to a configuration operation for the editing interface, domain knowledge of a target domain is obtained, the editing interface comprises a configuration area and guide information, and the domain knowledge is stored in the target domain; the guide information is used for guiding the front end to input domain knowledge in the configuration area, and the domain knowledge is used for representing a logic rule of a verification task in the target domain; based on the domain knowledge, verification guiding prompt information is constructed, and the verification guiding prompt information is used for guiding generation of a verification code file according to a logic rule; and calling a code generation model, generating a verification code file containing verification logic based on the prompt information, and deploying the verification code file to the target verification platform. Structured domain knowledge is collected through a guide type interface, accurate prompt information is automatically constructed, a verification code file conforming to a domain rule is generated, and the generation accuracy and efficiency are improved.

The invention relates to the technical field of pump system monitoring and diagnosis, and discloses a pump system performance monitoring and fault diagnosis method and system based on digital twinning, and the method comprises the steps: carrying out the topological correlation analysis of target pump equipment, and obtaining a working logic rule and component relation; performing digital three-dimensional mapping on the target pump equipment to construct a digital twin frame; performing operation simulation deduction on the digital twin frame to obtain simulation operation data; performing synchronous deviation analysis on the simulation operation data and the actual operation data, and performing evolution trend fitting on the analyzed deviation data in combination with historical operation data to obtain a degradation track; performing fault risk study and judgment on the degradation track to obtain a fault symptom category and a fault severity level; performing health state evaluation on the target pump equipment to generate a diagnosis report; according to the invention, the efficiency of performance monitoring and fault diagnosis of the pump system based on digital twinning can be improved.

A machine learning method for learning and applying a rule set from relational data includes receiving a graph representing relational data, wherein nodes represent elements of the graph, and edges represent relationships between nodes, and generating an intermediate representation of the graph by mapping features of the nodes and edges of the graph to an intermediate vector representation. Optimized logical rules that define the nodes and edges of the graph based on the intermediate vector representation are learned by: defining a maximum satisfiability (MAX-SAT) problem for the graph; and estimating a gradient around a solution of the MAX-SAT problem to produce the optimized logical rules, which are applied to a new graph. The data can be medical data and the graph can be used in a machine-learning task, such as using the medical data for disease prediction, for optimization of the machine-learning task and / or to support decision-making.

The invention discloses a sub-graph reasoning method fusing logic rule learning and attack semantic enhancement. The method comprises the steps that an input layer dynamically integrates knowledge graph topology and an AMIE rule base, an initial k-hop sub-graph is generated, and structured input is provided for attack chain mining; the sub-graph extraction module is used for executing double confidence filtering, screening high-value attack chains and applying dictionary filtering to enhance semantic reliability; the sub-graph coding module adopts an entity perception update layer and a relationship aggregation evolution layer of a dual-channel mechanism to collaboratively model the spatial-temporal characteristics of an attack chain; the relation reasoning optimization module is used for dynamically injecting high confidence rules and optimizing triple scores; and the training optimization module is used for implementing task perception negative sampling. According to the subgraph reasoning method fusing logic rule learning and attack semantic enhancement, based on inductive reasoning and semantic perception modeling, by taking subgraph modeling guided by a logic path as a core, attack chain rules with high confidence in a training graph are mined, and the understanding ability of a model structure is improved.