479 results about "Rule sets" patented technology

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.

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 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 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 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.

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 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.

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 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.

The invention relates to an electronic file compliance verification and closed-loop management method based on a rule engine, and the method comprises the following steps: automatically analyzing a policy file through a natural language processing technology, extracting compliance requirements related to formats, elements or catalogues of electronic files, and generating a first candidate rule set; performing deep feature analysis on historical compliance files, identifying abnormal cases and generating a second candidate rule set; submitting the two types of rule sets to an expert verification interface for fusion confirmation, converting the rule sets into executable logic through a rule translator, and injecting the executable logic into a detection engine; and performing compliance detection on the newly added electronic file by using the updated rule library, recording application efficiency data of each rule, and feeding back the application efficiency data to a preset rule life cycle management system. According to the invention, the automatic and precise updating and closed-loop management of the compliance rule base are realized, and the examination efficiency, the risk identification capability and the self-adaption level of the system are remarkably improved.

The invention provides a large model logical reasoning optimization method and system combined with a knowledge graph, and relates to the technical field of artificial intelligence, first, a logical reasoning task to be processed of a large model and a corresponding structured knowledge graph are obtained, and the logical reasoning task comprises knowledge units, association relationships and association strength; the logical reasoning task comprises a reasoning target, a premise and a constraint condition, then obtaining a large model reasoning guide rule set based on suitability analysis, injecting the large model reasoning guide rule set into a reasoning process, capturing and adjusting intermediate reasoning nodes in stages, correcting reasoning branches deviating from an incidence relation, and obtaining a large model reasoning guide rule set; then conflict resolution and propagation prediction are performed on the reasoning process after stage guidance, an inconsistent conclusion is identified, conflict rationality is verified, a propagation link is predicted, a multi-round correction scheme is generated, and finally reasoning path iterative optimization, integrating degree and efficiency evaluation and jump sequence and reference priority adjustment are performed based on the reasoning process after conflict resolution. Information is integrated to obtain an optimization result, and the logical reasoning quality of the large model is effectively improved.

The invention discloses a document proofreading system and method based on artificial intelligence, and relates to the technical field of natural language processing, and the method comprises the steps: analyzing a document, dividing semantic units, and obtaining a document with a context label based on context type library labeling; performing initial error detection by using the corresponding rule set, and comparing the format of the document and the text quality characteristics with a standard sample vector space to obtain an initial error set; constructing a defect conduction chain network, and locating root cause error nodes causing a plurality of secondary errors by tracing the defect conduction chain network; generating an intelligent proofreading report; receiving a new-version document revised by the user, comparing the new-version document with the original-version document, positioning a change area, and analyzing a quality difference of the change area on a related quality dimension; based on the quality difference, an incremental proofreading report is output, and the incremental proofreading report comprises newly introduced errors and unsolved root cause errors.

The invention discloses a water affair engineering construction drawing review method, which comprises the steps of obtaining a building information model file corresponding to a to-be-reviewed water affair engineering construction drawing, the building information model file comprising component information of each component and a project type; generating a semantic model based on the component information; based on the project type, screening out a target rule set in a preset water affair specification provision library; based on the target rule set, extracting to-be-examined parameters from the semantic model; and performing review processing on the to-be-reviewed parameters based on the target rule set to obtain a review result corresponding to the building information model file. The BIM model file corresponding to the construction drawing is subjected to semantic processing, and the target rule set is screened in combination with the project type, so that the accuracy of compliance review can be improved while redundant information interference in manual review can be reduced.

The invention discloses a business state data storage method and device, computer equipment and a storage medium, belongs to the technical field of big data, and is applied to an insurance intelligent loss assessment scene. Firstly, transaction records and user behavior data are analyzed to generate business state characteristic parameters; secondly, by obtaining a dynamic rule set from a preset rule base and constructing an initial rule matching model, the system can perform automatic screening, standardized analysis and logic mapping on related rules according to service state characteristics; then, a hierarchical rule index structure is constructed based on the initial matching model, and an initial matching path is determined; furthermore, by acquiring a flow mode corresponding to the data flow and dynamically adjusting the weight of the index node, the system can generate an optimization rule matching path, so that the high-frequency access node is processed preferentially; and finally, based on the optimized matching path, extracting storage configuration and generating a storage strategy allocation scheme, and combining consistent hash fragmentation and an encryption transmission mechanism.

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 an engineering building construction quality analysis system and method based on data processing, and belongs to the technical field of commercial data processing and intelligent decision, and the system comprises a causal relationship graph construction module which is used for fusing multi-source monitoring data to construct a causal relationship graph; the risk profile construction module is used for performing reverse deduction and forward simulation and constructing a prospective quality risk profile; the multi-target collaborative optimization module is used for solving a Pareto optimal equilibrium solution set; the quality pre-control instruction generation module is used for instantiating a dynamic quality pre-control instruction; the efficiency evaluation and attribution correction module is used for updating the causal relationship graph; and the evolution auditing and causal knowledge distillation module is used for auditing historical updating of the atlas and distilling a core causal rule set. According to the method, risk deduction and simulation are carried out by constructing the dynamic causal relationship graph, and multi-objective optimization and closed-loop feedback correction are combined, so that prospective identification and collaborative intelligent pre-control of the project quality risk can be realized.

The invention provides a management service operation session processing method and system based on an AI digital human, and the method comprises the steps: firstly receiving a management service operation session flow between the AI digital human and a user, and obtaining a management service system operation item list and a historical session service feedback data set; establishing a dynamic association rule set of service appeal expressions and operation item lists based on a session service association algorithm and historical data, generating a management service operation execution scheme according to the rule set and session flow context information, adjusting the scheme after system feedback is obtained, correcting algorithm parameters, generating an optimization algorithm and session response content, and generating a session response result; and finally, the optimization algorithm is output to the AI digital human algorithm storage module, and the voice response content is output to the display module, so that the performance of the AI digital human in management service session processing is improved.

The invention relates to the technical field of computer data processing and network security, in particular to an illegal character automatic cleaning system, which comprises a rule compiling module for monitoring rule change, performing semantic fusion and topological mapping on a rule set, constructing a deterministic finite automaton and mapping the deterministic finite automaton into a state transition table; the state switching module is used for constructing a double-buffer context and operating lock-free switching through an atomic pointer to realize hot updating; the speculation execution module is used for carrying out vectorization pre-scanning based on a state transition table by utilizing single-instruction multi-data stream parallel loading, identifying a walk path, falling into a safe state for releasing and falling into a trap state for triggering external verification; the self-adaptive feedback module is used for counting trap state triggering frequency, generating a rule allergy report and dynamically adjusting the size of a read fragment; according to the method, the contradiction between rule flexibility and execution efficiency is solved, and high-performance cleaning based on speculative execution is realized.

The invention provides an attack technology and tactical identification method oriented to honey array linkage. The method comprises the following steps: coding according to malicious sample data and associated TTP labels, and constructing a training data set; constructing an interpretable discrimination model for binary classification modeling to obtain a behavior triggering weight vector and an offset item of the malicious sample; extracting dominant trigger rules to construct a mapping inference rule base; obtaining alarm data, extracting a behavior clue based on a preset standard behavior dimension set, converting the behavior clue to obtain a trigger identifier, and mapping the alarm data into an alarm behavior vector; constructing a prediction TTP label set of the alarm behavior vector and recording a trigger path; and encapsulating the behavior semantic data, constructing an execution strategy response rule set, performing mapping in the execution strategy response rule set according to the behavior semantic data to obtain an execution strategy, and adjusting honey array deployment according to the execution strategy. By applying the method, accurate identification and transparent reasoning of the potential TTP in the attack activity can be constructed and realized, and linkage scheduling of a defense system is supported.

The invention provides an AI auxiliary code generation method and system applied to a low-code platform, and relates to the technical field of low-code platforms.The AI auxiliary code generation method comprises the steps that firstly, component demand scenarized information is obtained and analyzed to obtain a scenario feature set, and a dynamic evolution rule set is generated in combination with platform real-time metadata; calling an AI code generation module, inputting the scene feature set and the rule set, and generating an initial component code; then deploying the initial code to a test environment, collecting and analyzing operation data, generating an iterative optimization instruction adjustment code and a rule set, and obtaining a target component code; and finally, embedding the target code into a platform specified function module to complete code generation. The method can adapt to diversified scene requirements, has a self-optimization capability in combination with the real-time state of the platform, and can improve the development efficiency and code quality of the low-code platform.

The invention discloses a SaaS financial software artificial intelligence voucher auditing method based on an expert database, and relates to the field of voucher auditing, and the method comprises the steps: receiving to-be-audited financial voucher data uploaded by a user through SaaS financial software; integrity verification and logic conflict detection are performed on the structured data, text information in the unstructured data is extracted, and authenticity feature identification is performed on the bill image; constructing a uniform feature vector; obtaining an auditing rule set, calculating a matching degree between the feature vector and the auditing rule set, and outputting an initial auditing confidence coefficient; if the confidence coefficient is higher than a preset threshold value, adopting, and if the confidence coefficient is lower than the preset threshold value, triggering multiple rounds of iterative verification; and updating the expert database, and returning a structured auditing result to the client through the API gateway. The method has the advantages that the AI technology and the expert database are integrated, efficient and accurate financial voucher auditing is achieved, the rules are dynamically updated, manual intervention is reduced, and automation and applicability are improved.

The invention discloses a document strategy engine method, and the method comprises the following steps: S1, uploading to-be-processed document data, and recognizing an original text of the document data; s2, analyzing the recognized original text, extracting a preset specific field, and performing standardization processing to generate a standardized text; s3, constructing a hierarchical rule set based on the standardized text and a preset rule base, and executing rule verification on the standardized text according to the priority of the hierarchical rule set to generate a verification result; s4, performing data completion and risk prediction through an AI large model based on the original text, the standardized text and the verification result, and outputting a risk analysis result containing a confidence score; and S5, judging whether the risk analysis result meets an artificial early warning condition or not, if so, triggering artificial early warning, performing artificial composite correction based on the risk analysis result, and then outputting an engine verification result, otherwise, directly outputting the engine verification result, thereby remarkably improving the coverage capability for a complex document scene.

The invention provides a cross-file semantic association analysis system based on a hierarchical rule engine. The cross-file semantic association analysis system comprises a plurality of modules, wherein a code compilation analysis module is used for converting an input code file into an AST object and executing cross-file dependency and reference analysis; the semantic fact construction module generates an initial fact set according to the data output by the code compiling and analyzing module; the rule interpreter receives the rule set of the hierarchical rule base and constructs a corresponding hierarchical rule network; the rule matcher injects the initial fact set into the hierarchical rule network in a streaming mode to execute rule matching; the condition verification module verifies a rule matching condition according to the input fact data and returns a result; when rule matching succeeds, the rule executor executes corresponding processing logic and updates the fact base; and the result analyzing and processing module analyzes and integrates the final reasoning fact data and writes a result into a conclusion library. The method can be used for constructing a universal cross-file code context analysis system, and the code static analysis performance is improved.

The invention discloses a code quality verification method and device, equipment and a storage medium. The method comprises the steps that a to-be-verified target code file and verification context information corresponding to the target code file are received; based on the verification context information, screening out a verification rule set meeting a preset requirement from a rule base, and arranging the verification rule set to generate a rule set arrangement task; calling a corresponding verification execution engine to perform automatic verification on the target code file according to the characteristics of each rule in the rule set arrangement task, and generating an initial verification result; and carrying out fusion processing on the initial verification result to obtain a fusion result, and carrying out risk grading on the fusion result based on a preset priority strategy to generate a standardized verification result. According to the code quality verification method and device, the verification rule set meeting the preset requirement is screened out from the rule base based on the verification context information, the target code file is automatically verified according to the rule set arrangement task, and the reliability and the verification efficiency of code quality verification are improved.

The invention relates to the field of life insurance management, and discloses a life insurance risk management system based on an AI large model, which is used for constructing an intelligent, dynamic and closed-loop optimized life insurance anti-money laundering risk management framework. Comprising the following steps: automatically analyzing an external unstructured supervision text by utilizing a large language model, and generating a structured rule set which can be dynamically updated; jointly inputting the rule set and real-time transaction data into a network analysis model based on multi-body system dynamics, and constructing a transaction network situation map to identify abnormal cooperative behaviors; a suspicious transaction report draft meeting supervision requirements is automatically generated by a large language model based on the graph; carrying out cross validation and confidence rating by fusing the offline data extracted by the OCR technology; and finally generating a standard report file and submitting the standard report file to a bank system. According to the invention, automation and intelligentization of the whole process from rule interpretation, risk identification, report generation to supervision report are realized, and the anti-money laundering risk management efficiency of the life insurance industry is improved.

A data processing system and method include receiving a set of documents from which to generate at least one topic, creating at least one information extraction model for the set of documents, executing the at least one information extraction model to extract a plurality of text segments from the set of documents by applying one or more rule sets defined for at least one entity, word list, or grammatical pattern and extracting a plurality of text portions from the set of documents based on the one or more rule sets as the plurality of text segments, inputting at least a subset of the plurality of text segments into an unsupervised machine learning model, and executing the unsupervised machine learning model to output the at least one topic for the set of the documents.

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.