166 results about "Logical rules" patented technology

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 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 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.

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 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 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.

The invention discloses a script generation method and device, equipment, a storage medium and a product, which are applied to the technical field of computer data processing, and the method comprises the following steps: obtaining business demand information corresponding to a target business function; semantic analysis is conducted on the service demand information, a template configuration file is generated, and the template configuration file at least comprises service parameters needed for achieving the target service function, attribute information of the service parameters and logic rules corresponding to the service parameters; analyzing the logic rule to generate an abstract syntax tree corresponding to the target service function; and performing data integration on the service parameters, the attribute information of the service parameters and the abstract syntax tree to generate an executable script for realizing a target service function. According to the scheme, manual code writing and manual template configuration are not needed, and the script generation efficiency is improved.

The embodiment of the invention provides an operator network selection method, terminal equipment, a readable storage medium and a program product, and relates to the technical field of mobile communication, the method comprises the following steps: receiving update information of a policy rule file issued by a server; the strategy rule file comprises an acquisition triggering rule and an operator switching rule, the acquisition triggering rule defines a condition for triggering acquisition of a network environment state, and the operator switching rule defines a logic rule for deciding whether to switch an operator network according to the network environment state; according to the updating information of the strategy rule file, updating the strategy rule file locally stored in the terminal equipment; when a condition meeting the definition of the acquisition triggering rule is monitored, acquiring the current network environment state of the terminal equipment; and based on an operator switching rule, according to the current network environment state, deciding whether to switch the currently used operator configuration file. According to the embodiment of the invention, the flexibility of switching the operator network is improved.

Various embodiments are directed to verifying outputs of large language models ("LLMs") using logical reasoning. Various embodiments can obtain raw data from a datastore which can be presumed as true. Various embodiments can then factorize the raw data into factorized data points using a large language model ("LLM"). The large language model can then be used to analyze, categorize, and summarize the factorized data points to generate an insight summary. Various embodiments can then validate the insight summary by identifying a chain of logic between the raw data and the insight summary, such that each statement can be proven using logical reasoning (e.g., deductive reasoning, temporal logic, syllogisms, etc.).

The invention provides a well drilling working condition identification method and device, and the method comprises the steps: obtaining the real-time well drilling parameter data of a target well; the real-time drilling parameter data is preprocessed; performing drilling working condition identification on the preprocessed parameter data through a pre-established identification model based on a logic rule to obtain first working condition identification result data; performing drilling working condition identification on the preprocessed parameter data through a pre-established intelligent identification model to obtain second working condition identification result data; and performing data fusion on the first working condition identification result data and the second working condition identification result data to obtain a real-time working condition of the target drilling well. By means of the mode, the technical problems that existing working condition recognition is low in recognition accuracy and few in adaptive working condition are solved, and the technical effect of accurately recognizing multiple drilling working conditions is achieved.

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 relates to the field of big language model reasoning, in particular to an intelligent reasoning method based on big language model chain reasoning and a self-verification mechanism. The scheme comprises the steps of performing semantic analysis on a text input by a user, and extracting a problem structure and a reasoning intention; the problem is disassembled into a plurality of sub-problems, and a reasoning chain is constructed for reasoning; verification is performed after reasoning is completed; the verification result is judged, if verification passes, the reasoning result is reserved, and if verification of the reasoning result fails, the reasoning path is automatically optimized. Through a chain structure and a self-verification mechanism, the inference error rate is remarkably reduced, an inference path, a verification score and a logic basis are provided, and the user trust is improved. And multi-mode and multi-field tasks, such as knowledge questions and answers, logical reasoning and task decomposition, are supported. When verification fails, a reasoning path is automatically optimized, reasoning efficiency is improved, various large language models, knowledge bases and logic rule bases can be accessed, collaborative reasoning is achieved, and the method is suitable for multi-modal and multi-field intelligent reasoning tasks.

The invention discloses a data quality evaluation and restoration method and system based on machine learning. The method comprises the following steps: firstly, preprocessing data, and constructing a structured generator network containing a parameterized adjacent matrix; in the training stage, the network outputs preliminary reconstruction data, and manifold constraint projection is utilized to find a logic correction target matrix meeting domain constraints; and constructing a potential consistency loop through gradient blocking, and calculating a consistency loss to drive the network to approach a compliance manifold. Meanwhile, an overall target integrating reconstruction errors, logic consistency and acyclic constraints is constructed, and an augmented Lagrangian method and a double-layer circulation strategy are adopted to jointly optimize parameters. And after the double convergence judgment is met, final inference is executed based on mask synthesis. According to the method, the logic rule is internalized into the generation capability, and the sparse causal structure is mined synchronously, so that high-quality data recovery with logic compliance, distribution consistency and interpretability is realized.

The application discloses a rule arrangement execution, a rule arrangement method, a device and a system, relates to the technical field of electronic information, and a service end can execute the following: pulling a logical rule configuration and saving to a service end local cache at startup; if a first rule change notification sent by a configuration service is listened to, a first changed rule configuration is acquired; the first changed rule configuration is used to update the logical rule configuration in the cache; if a notification message sent by a client is listened to, a target logical rule triggered by the notification message is queried from the service end local cache and executed, and the client is an exception detection and classification system application of a client tool package introducing a rule arrangement service. In the implementation process of the exception detection and classification system (FDC system), the application can realize quick adjustment of business rules, efficiently meets the individualized needs of users, the business rules are separated from application program codes, are easy to maintain and update, and the high cost problem of multi-version system maintenance is effectively avoided.

The invention belongs to the field of three-dimensional digitization, and particularly relates to a three-dimensional design system for a large transformer oil tank, which comprises an oil tank service rule table, a three-dimensional design table, a three-dimensional design table and a three-dimensional design table, wherein the oil tank service rule table is used for expressing execution logic rules and assembly information logic rules of oil tank services in a file form; the parameterization design module is used for inputting calculation order data to the parameterization design module by a user, calculating the data input by the user by inheriting the calculation order data in combination with an execution logic rule, and matching a rule of an oil tank business rule table with an assembly information logic rule; and calling the parameterized template library according to the assembly information logic rule of the fuel tank service rule table, matching the parameters in the fuel tank service rule table with the parameters defined by the matched model, and generating a fuel tank three-dimensional model through three-dimensional modeling to realize regular assembly or interactive assembly of the transformer fuel tank. According to the method, standardization, normalization and unification of design input are achieved, and human errors caused in the manual calculation and model creation process are reduced.

A search and retrieval data processing system for retrieving classified data for execution against a cyber-security logic rules includes one or more hardware processors and memory, networked to communicably couple through a secure connection to a classified data database, the classified data database including an aggregated plurality of classified data records captured from a plurality of classified data sources; and the memory in communication with the processor, the memory storing an execution environment and the execution environment including an input port for receiving, from an entity, data associated with requested operations; a search engine for searching the classified data records of the classified database; and a logic execution module. The logic execution module is operable for executing a set of cyber-security logic rules against the received data and classified data records; and based on a positive comparison between the received data to the classified data records, writing to memory one or more instructions for initiation of one or more termination actions of the one or more requested operations.