647 results about "Rule sets" patented technology

The invention relates to the technical field of building engineering, in particular to a building data processing method and system based on multiple building specifications, and the method comprises the steps: building model data standardization processing: extracting geometric attributes, material attributes and spatial topological relations of components through a BIM software interface, and generating model data in a standard format; constructing a multi-source specification rule tree, performing semantic analysis on national standard, local standard and industrial standard provisions, extracting triple constraint conditions, and fusing to generate a unified rule tree containing hierarchical relationships and conflict marks; on the basis of a spatial topology mapping relationship between the model and the rule tree, identifying a specification conflict and generating an optimization rule set according to a specification effectiveness level and a spatial attribute priority; and finally, real-time compliance verification is executed, and a three-dimensional compliance report including conflict positioning, article basis and correction suggestions is output. According to the method, multi-source specifications can be automatically adapted, model violation components are accurately recognized, a correction scheme is given, and the specification compliance and the verification efficiency in the design stage are improved.

The invention provides a computer document intelligent compliance detection system based on deep learning, and relates to the technical field of data processing, and the system comprises the steps: carrying out the topological structure analysis of an adjacent matrix through a graph neural network, so as to detect an abnormality, obtaining a structure deviation degree, and analyzing a decision path node sequence through a pre-training language model to generate semantic conflict features; fusing the structure deviation degree with the semantic conflict feature to generate a risk feature vector; generating an augmented rule set according to the risk feature vector, and updating the formalized rule base; updating parameters of the feature coding component through gradient back propagation; optimizing a differentiable logic layer judgment threshold value and the weight of an analysis module; and obtaining the updated feature coding component, the rule base version and the risk quantification parameter. According to the invention, the efficiency of document compliance detection is effectively improved.

The invention provides an AI-based multi-user data storage docking and secure transmission method, which comprises the following steps of: acquiring a propagation path of a hot topic according to a multi-modal content association graph, performing fragmentation storage on the propagation path by adopting a clustering algorithm, and generating a fragmentation storage index table; when the relevance of different modal contents is higher than a preset threshold value, determining that the sensitivity grading result is consistent with the multi-modal relevance, performing dynamic rule adjustment by adopting a deep learning model, generating a dynamic auditing rule set, and obtaining boundary judgment parameters of the rule set; and distributing the dynamic auditing rule set and the decision transparency index to each service line by adopting a cross-platform data synchronization protocol according to the auditing decision log, generating a cross-platform consistency auditing standard, and obtaining a synchronization log of standard execution.

The invention discloses an electric power analysis method and system based on artificial intelligence, and relates to the technical field of electric power system intelligent analysis. Establishing a hierarchical depth map neural network model, and setting a cross-layer information interaction channel to connect each sub-network; executing a bidirectional knowledge distillation algorithm, extracting a rule set from neural network output, and forming a parameter-rule bidirectional mapping matrix; deploying a federal reinforcement learning architecture, performing parameter aggregation on the local models on the distributed power nodes, and generating a power system state evaluation result and a risk coefficient matrix; and starting the self-repairing intelligent agent network, calculating a regulation and control parameter set of the multilevel power system, and outputting a regulation and control instruction. According to the invention, through the hierarchical depth map neural network and a cross-layer information interaction mechanism, collaborative optimization among all levels of the power system is realized, and the operation efficiency and stability of the system are improved.

The invention relates to a city planning checking method and system based on natural language processing, and the method comprises the steps: receiving a user request instruction, carrying out the analysis, and obtaining the checking intention information and target region information; retrieving and matching a corresponding region rule set based on the target region information, and generating a checking task list and a data demand list; obtaining multi-source heterogeneous data and preprocessing the multi-source heterogeneous data to obtain standardized check data and historical fusion features; executing a preset differentiation checking process according to the task type; generating a checking report based on the differentiated checking process result; in conclusion, according to the method, the user request is processed and analyzed through the natural language, the planning knowledge base is constructed, deterministic and fuzzy rule tasks are processed in combination with the differentiated checking process, cooperation of rule terms and case features is achieved, the problems that a traditional checking method is low in automation degree and insufficient in fuzzy term processing capacity are solved, and the efficiency of checking is improved. The method has the effects of improving the checking efficiency and enhancing the interpretability of decision suggestions.

A system and method are provided for dynamically modifying textual content by applying a text analysis tool and selectively using a large language model (LLM). The method includes applying a text analysis tool to text having been added to a document, identifying a portion of text in the document as violating a rule in a rule set associated with the document, and providing a first input to a large language model (LLM). The first input comprises at least one prompt requesting a revision to the portion of text in the document. The method also includes receiving, from the LLM, a response to the first input, the response comprising a suggested modification to the document related to the portion of text; and providing an option to apply the suggested modification to the document.

The invention provides an attack defense method and device for an e-commerce intelligent customer service large model, and relates to the technical field of artificial intelligence security, and the method comprises the steps: 1, monitoring an interaction data flow between a user and an intelligent customer service in real time, recognizing an abnormal interaction behavior, and activating a multi-layer defense strategy when the abnormal behavior is detected; 2, based on a multi-layer defense strategy, sensitive words, special character sequences and potential injection marks in an input text are recognized and processed through the dynamically updated multi-field sensitive word detection rule base and the special symbol matching rule set, and a preliminarily filtered text is generated; according to the method, various malicious attacks aiming at the e-commerce intelligent customer service large model are effectively resisted, the user information security is maintained, and the customer service quality and the e-commerce operation reliability are improved.

Systems, devices, and techniques are disclosed for network security policy generation and distribution. A security policy written using a Domain Specific Language (DSL) for network security may be received. The security policy may be associated with a service owner and a control plane. A representation of the security policy may be generated from the security policy. A configuration bundle of the service owner may be updated with the representation of the security policy. The security policy may be determined to be approved. A rule set may be generated from the representation of the security policy. A differential between the rule set and a current rule set may be determined. A security component associated with the control plane based on the differential may be configured.

The invention discloses a data quality monitoring method and system, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring a data flow fluctuation curve of a target data table, and constructing a data quality baseline library according to the data flow fluctuation curve; extracting abnormal data features based on the data quality baseline library, and generating a feature defect matrix corresponding to the abnormal data features; screening rule atoms from a preset rule pool according to the characteristic defect matrix, and constructing a rule atom link corresponding to the rule atoms; performing service scene adaptation on the rule atomic link to obtain a corresponding quality monitoring rule set; and performing data scanning on the target data table based on the quality monitoring rule set to obtain an abnormal data scanning result, and sending the abnormal data scanning result to a corresponding associated person. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of data quality monitoring is achieved.

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.

Computer-implemented systems and methods are disclosed, including systems and methods for performing compliance testing using language models or other machine learning models. A computer-implemented method may include, for example, accessing a content item; accessing a compliance ruleset; executing a compliance checker that utilizes a set of machine learning models; generating a prompt that includes the content item and the compliance ruleset; processing the prompt using the compliance checker; responsive to receiving a compliance determination dataset that indicates whether the content item satisfies one or more criteria within the compliance ruleset from the compliance checker; and generating an output based at least in part on the compliance determination dataset.

The invention discloses a multi-database compatible query processing method and device and a medium, and relates to the technical field of data query processing. The method comprises the steps that according to configuration information of all compatible databases, dialect types of all the compatible databases are dynamically recognized, and corresponding dialect configuration rule sets are loaded; when an original SQL statement of the database operation is received, intercepting the original SQL statement before the database operation is executed through a preset interceptor mechanism; according to a dialect configuration rule set of the target database to be operated corresponding to the original SQL statement, reflecting and modifying the original SQL statement in the BoundSql object to obtain a standard SQL statement; and submitting the standard SQL statement to the to-be-operated target database for execution, and returning an execution result.

The invention discloses a financial abnormal event analysis method, electronic equipment, a medium and a product. The method comprises the steps of obtaining at least one financial clue event, and performing structured element extraction and standardization processing on each financial clue event to obtain a standardized event behavior sequence; performing semantic feature and behavior pattern analysis based on the normalized event behavior sequence, performing event reasoning rule recall in combination with a pre-constructed event reasoning library, and determining a recalled rule set; generating an abnormal event analysis execution plan based on at least one inference rule in the rule set; querying original data in the data system according to an inference rule included in the abnormal event analysis execution plan, generating a data view, executing the abnormal event analysis execution plan based on the data view, and generating an inference execution result set and execution path information; and an event reasoning analysis result is generated according to the reasoning execution result set and the execution path information, and the problems of complex abnormal event analysis modeling, low interpretability and the like are solved.

The invention discloses a cross-department policy conflict detection method and system based on knowledge graph reasoning, and the method comprises the following steps: S1, obtaining a cross-department policy document set, forming a cross-department policy knowledge graph, and defining a structure constraint condition; s2, constructing a ChainsFormer chain type multi-hop reasoning model, and generating a multi-hop reasoning path between policy entities; s3, defining a standardized policy conflict rule set; s4, constructing a graph constraint large language model reasoning framework, and generating a graph structure path constraint rule; s5, fusing the models and constraining the reasoning process to obtain a chained multi-hop reasoning path set among policy entities; and S6, performing cross-department policy automatic conflict detection by adopting the standardized policy conflict rule set. According to the invention, policy conflict automatic detection is realized, and policy conflict identification efficiency and accuracy are improved.

The invention provides a freight service verification method and system fused with multi-dimensional data in the cross technical field of artificial intelligence and freight information verification. The method comprises the following steps: S1, creating a freight service risk identification model; s2, acquiring a large amount of historical freight service data to construct a data set to train a freight service risk identification model; s3, the server sets a service verification rule set comprising a plurality of verification rules; s4, the server obtains real-time freight service data through ETL; s5, calling a service verification rule set through a rule engine, and carrying out pre-verification on the real-time freight service data; s6, performing in-event verification on the real-time freight service data through the freight service risk identification model; and S7, constructing an incremental data set based on the real-time freight service data to carry out post optimization on the freight service risk identification model. The freight service verification method has the advantages that the accuracy and timeliness of freight service verification are greatly improved.

The invention relates to the technical field of intelligent decision rule base construction, and discloses an intelligent decision framework construction method and device based on a dynamic ontology. The method comprises the following steps: acquiring original data streams from a plurality of heterogeneous data sources in real time, and constructing an initial dynamic ontology structure through a dynamic ontology modeling unit; performing semantic annotation and relation extraction on the original data flow by using the structure to generate semantic enhanced data; generating a candidate decision rule set based on the semantic enhancement data, and obtaining a verified rule set through consistency verification and conflict detection; calculating the adaptability score of the verified rule set in combination with the real-time environment data, and screening out an optimal decision rule subset according to the score and a preset threshold value; the method is integrated into an intelligent decision rule base, and an optimization loop is triggered based on an update state of the base. According to the method, the data utilization efficiency and the rule quality are improved, the rule base can dynamically adapt to the environment, and decision effectiveness and reliability are enhanced.

The invention belongs to the technical field of intelligent document processing, and discloses an intelligent data processing method for collected files based on artificial intelligence, which comprises the following steps: acquiring a multi-source multi-version file to carry out layout rectification and OCR (Optical Character Recognition) to obtain a page fragment object set; using a controlled decoding language model to extract clause records according to preset Schema to form a clause record set; performing cross-version alignment on homonymous or synonymous terms, and constructing a time sequence knowledge graph; compiling the cross-document consistency rule into an executable rule set which can be evaluated on the time sequence knowledge graph by adopting an executable collection constraint language; evaluating the scope of action of each rule to generate conflict candidates; and calculating a minimum hit set to obtain a minimum conflict interpretation set, positioning a root cause term instance, and generating a minimum repair suggestion according to a normalization result of the operator and the node violating the rule. According to the method, the unification of clauses and calibers and the automatic judgment of the latest effective state, the accurate positioning of conflicts and the minimum cost repair are realized.

The invention relates to the technical field of semantic understanding, in particular to an environmental impact report auxiliary auditing system based on semantic understanding, which comprises a text cleaning module, a chapter segmentation module, a rule generation module, a content auditing module and a result output module. According to the method, the paragraph structure and the layout identification are analyzed in a unified mode, key paragraph types in environmental influence chapters are efficiently recognized, the text integration accuracy is improved by combining inter-paragraph similarity calculation and repetition rate screening, chapter boundary accurate positioning and affiliation adjustment are achieved on the basis of format feature comparison of serial numbers and title styles, and the text integration efficiency is improved. A multi-layer parameter alignment rule set is constructed to support comprehensive verification of standard numbers, monitoring frequencies and periodic elements, an exception labeling mechanism is combined to complete difference item logic judgment and consistency evaluation, an auditing basis with rule driving and data comparison capabilities is formed, chapter positioning precision, element extraction comprehensiveness and logic exception recognition efficiency are improved, and the method is suitable for popularization and application. And the pertinence and the automatic processing depth in the auditing process are enhanced.

The invention discloses an operation data processing method and system based on a compliance verification library and a risk control plan, and belongs to the technical field of data compliance and risk control, and the method comprises the steps: obtaining an execution plan of a target operation task and risk control strategy information of the execution plan, analyzing operation data items, distributing corresponding compliance verification time windows and levels, and forming an operation data verification scheduling table; according to the verification level and the business domain, a rule set is extracted from a compliance verification library, and rules comprise predefined rules, model output rules and historical violation rules; field-level matching, logic consistency verification and cross-source linkage verification are executed in the verification time window; when the verification result meets the triggering condition, generating an abnormal event, and executing response operations such as delay, recheck or interception; and finally, writing the execution state and the verification result into an abnormal log queue for risk control strategy parameter updating or re-evaluation. According to the method, the dynamic compliance verification and strategy closed-loop optimization of the operation data are realized, and the intelligent compliance control capability of the system is improved.

The invention discloses an emergency pre-examination grading system and method based on collaborative decision of a large language model and a tree model. The method comprises the following steps: constructing a data set according to patient information and screening data; training an initial random forest model based on the data set, generating a basic decision rule, mining and extracting high-frequency features based on association rules, and combining to obtain a candidate rule pool; constructing a cue word structure adaptive to the field, driving LLM to complete rule correction, and forming a correction rule set; performing multiple rounds of rule random division and rule combination generation based on the correction rule set, and then screening out an optimal rule; and on the basis of a specific scene, the expert rule and the corrected optimal rule are fused, matching is carried out on patients, and emergency pre-examination grading is realized. According to the method, the interpretability is improved while the grading accuracy is improved, and the problem of poor cross-courtyard generalization of a machine learning model is effectively solved. The generalization ability of the rule is remarkably improved, and the method is adaptive to a multi-center combined diagnosis and treatment scene.

Examples of the present disclosure describe systems and methods for automating the identification of events in a text file. In examples, a computing system identifies a subset of a text file that comprises an unknown event using a set of rules. Each rule of the set of rules specifying a first pattern of characters is compared to the subset of the first text file. When the set of rules does not identify the unknown event, the subset of the text file is provided to a language model to generate a new rule with a second pattern of characters and an identifier of the new rule. The system then generates an updated set of rules by adding the new rule to the set of rules.

The invention provides a heterogeneous data stream real-time automatic cleaning and correcting method based on a dynamic rule engine, and relates to the technical field of data processing, and the method comprises the steps: accessing a multi-source heterogeneous data stream; loading a predefined dynamic cleaning rule set to a rule engine; monitoring an input data stream in real time, analyzing data content in parallel through a rule engine, and triggering a cleaning action; dynamically generating a correction operation instruction according to a rule matching result; a data credibility weight factor and a correction utility attenuation coefficient are introduced, and a cleaning strategy is dynamically adjusted; and outputting the cleaned standardized data stream and recording a correction log. Through the dynamic rule engine and the intelligent algorithm, flexible adjustment and real-time processing of the heterogeneous data stream cleaning rule are realized, the efficiency and accuracy of data cleaning are improved, and the problems of insufficient rule flexibility, low processing efficiency and difficulty in ensuring data accuracy in the prior art are solved.

The systems and methods disclosed herein receive a dataset including an observed set of values for a set of variables. The system can use a first set of AI models to identify a set of anomalies in the observed set of values by comparing an observed set of patterns against multiple reference patterns. The system can use a second set of AI models to evaluate the identified anomalies by comparing an observed set of association rules with an expected set of association rules. The system can use a third set of AI models to generate reconfiguration commands to remove the identified anomalies. The reconfiguration commands can be automatically executed to modify the observed association rules to align with the expected association rules.

The invention discloses a data auditing method and device, electronic equipment and a nonvolatile storage medium. The method comprises the following steps: acquiring cross-modal data to be audited; a verification rule set corresponding to the multi-modal knowledge base is determined, first verification processing is carried out on the cross-modal data based on the verification rule set, a first verification result is obtained, and the first verification processing is used for verifying whether the cross-modal data has exceptions which do not conform to rules in the verification rule set or not; the large auditing model is adopted, second verification processing is conducted on the cross-modal data, a second verification result is obtained, and the second verification processing is used for detecting whether logic contradictions exist in the cross-modal data or not based on the semantic reasoning ability of the large model; and determining an exception report corresponding to the cross-modal data according to the first verification result and the second verification result. According to the method and the apparatus, the technical problem of poor content quality inspection effect of cross-modal data in related technologies is solved.

The invention provides an experience knowledge graph construction and question answering method based on heuristic self-question answering, and the method comprises the steps: randomly dividing a text corpus into small batches, inputting the small batches into a large language model, and obtaining a heuristic rule set through induction processing and quality evaluation; constructing question and answer pairs by using the heuristic rule set and the text content cues, converting the question and answer pairs, and then carrying out confidence calculation to obtain a structured experience triple with confidence; constructing a knowledge graph by using the structured experience triad with confidence; extracting the core fragment of the original narrative query by using the heuristic rule set and generating a refined query text; obtaining an empirical path by using the embedded vector of the refined query text and the knowledge graph; and inputting the original narrative query and the experience path into the large language model to generate a final answer. According to the method, logic confusion and content contradiction possibly caused by a traditional RAG method are effectively avoided, the generated answer is more reliable, and the reasoning process is more interpretable.

Embodiments of the present disclosure are directed to a cloud-based vehicle monitoring platform (CVMP) configured to manage events impacting the operation of a plurality of vehicles. An onboard vehicle monitoring system associated with a particular vehicle is communicably coupled to CVMP centralized services and can determine corrective actions to employ to mitigate the events. The corrective actions in some contexts are generated by a rules engine based on a rule set generated by a federated learning model associated with the CVMP centralized services. If the onboard vehicle monitoring system determines that a first set of corrective actions generated based on a first set of rules is unsuccessful at mitigating the events, the onboard vehicle monitoring system can request a secondary set of rules from the CVMP centralized services. Various data recovery operations in some contexts are employed to revert the vehicle from a problem state back into a steady state.

The invention relates to the technical field of model testing, in particular to a model testing method and device and electronic equipment, and is used for solving the problems of long test data combination time consumption and low coverage rate of a model testing mode in the related technology. The multiple features are classified, a core feature set, a common feature set and an edge feature set are obtained, and the model can be a deep learning model; generating a test set of the model according to a differentiated data generation strategy of feature importance on the basis of the rule set of the service type adapted to the model, the core feature set, the common feature set and the edge feature set, and performing model performance test on the model by using the test set to obtain a test report; therefore, the efficiency of model algorithm testing is improved.

The invention provides an electric power data product quality evaluation system. The system comprises a data acquisition module; the rule generation module is used for extracting data features from the power data and inputting the data features into a rule generation model obtained through training to output a candidate rule set, and rules in a candidate rule base are reordered based on service weights to obtain a recommendation rule set; the dynamic threshold value optimization module is used for dynamically updating an abnormal threshold value based on the data distribution condition of the power data in the historical time period and the business demand adjustment coefficient; the quality evaluation module is used for performing quality evaluation on the power data based on rules in the recommendation rule set and corresponding abnormal thresholds to obtain a quality evaluation result; and the feedback optimization module is used for feeding back and adjusting the rule generation module and the dynamic threshold optimization module based on the quality evaluation result, and realizing intelligent recommendation and dynamic threshold optimization of the power data quality evaluation rule and self-adaptive updating of the rule base.

The invention relates to the technical field of data processing, and discloses a hydrocephalus correlation analysis method and system based on multi-dimensional data. The method comprises the steps that a feature matrix is generated by obtaining and standardizing hydrocephalus multi-dimensional data, after the matrix is discretized, an association rule mining algorithm is adopted to extract an association mode, a rule set is obtained in combination with knowledge graph verification, data clusters are divided based on mahalanobis distance clustering, mixed effect time sequence model fitting parameters are established for all the clusters, and a mixed effect time sequence model is obtained. And calculating a target sample attribution cluster and predicting a symptom improvement trajectory and a confidence interval. According to the method, automatic integration, credible association rule extraction, precise subtype division and individualized symptom trajectory prediction of the multi-dimensional data of the hydrocephalus patient are realized.

The invention relates to a self-adaptive firewall rule generation method and device based on real-time threat intelligence, and the method comprises the steps: carrying out the semantic analysis of a threat intelligence text, extracting attack indexes and attack technique and tactics labels, and generating an intelligence triple set and an attack technique and tactics label set; calling a language model to generate candidate rules based on an intelligence analysis result, carrying out credibility scoring, intercepting a low-scoring rule as a high-risk illusion rule, and entering other rules into a to-be-verified set; constructing a digital twinborn shadow sub-domain to perform real-time traffic verification on the rule set to be verified, and dividing the rule set to be verified into an effective rule set and an invalid rule set according to a verification result; constructing a rule dependence graph based on the invalid rule set to perform pollution traceability, and adding nodes with excessive pollution into an isolation list; and cleaning the historical training samples based on the isolation list and the interception rule log, and performing incremental training on the language model to generate an updated model.