941 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 discloses a document information input method and system based on big data processing, and belongs to the technical field of document information processing. According to the method, deep risk feature screening is performed on document data by combining big data mining and a knowledge graph, potential sensitive elements can be accurately identified, risk categories and influence ranges are analyzed based on association rules, automatic early warning and real-time risk notification are realized, a dynamic rule knowledge base is adopted, multi-dimensional compliance verification is supported, and the risk assessment efficiency is improved. Service constraint conditions can be updated in real time, the flexibility and adaptability of rules are ensured, the accuracy and timeliness of compliance check are improved, pattern matching and multi-level check are performed on compliance data objects through an intelligent check process and a recheck rule set trained by big data, the preciseness and reliability of data check are ensured, and the data check efficiency is improved. Through comprehensive quantitative calculation of the business complexity, the risk coefficient and the timeliness priority, the task scheduling weight parameter is generated, the receipt auditing path is optimized, and the auditing efficiency is 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 relates to the technical field of network security, and discloses an LLM-based network threat flow detection rule automatic generation method and system. According to the system, an automatic rule generation framework based on a large language model is constructed for network threat traffic, and the system comprises a cue word module, a large language model module, a rule engine module and a rule base management module; the four modules are executed in sequence to form a complete link from traffic information input to rule automatic generation and application verification; according to the method, domain knowledge is fused into a pre-training model through LoRA fine tuning, small sample cue word engineering and a correction mechanism based on engine feedback, verification is carried out through an actual detection engine, and the effectiveness of a rule automatically generated by the model is verified. According to the method, the network threat traffic sample can be fully and effectively analyzed, meanwhile, the requirement for training data is reduced, the rule set capable of being practically applied is generated, and the network defense capability is 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 provides a self-adaptive decision execution method for a rule engine agent of the Internet of Things and related equipment, and belongs to the field of the Internet of Things, and the method comprises the steps: determining real-time information containing multi-source data; based on a context awareness model, performing context fusion awareness on the real-time information to obtain context awareness information; determining a scene demand mode based on the scene awareness information; determining a target rule set in a rule base according to the scene demand mode; based on the target rule set and a decision algorithm, determining a target decision and executing a corresponding action; and receiving an execution feedback result, judging whether the rule base or the decision algorithm needs to be optimized according to the execution feedback result, and if the rule base or the decision algorithm needs to be optimized, determining an optimization object and optimizing the optimization object according to the execution feedback result. By applying the method provided by the invention, scene fusion perception and scene demand mode identification can be carried out through the scene perception model in various environments, so that rule matching and decision making are completed, and the environment adaptability and response capability of a platform are improved.

The invention relates to an intelligent verification code generation and anti-refreshing method and system and computer equipment. The method comprises the following steps: performing multi-modal generation processing on verification code content to obtain original verification code data; performing multi-dimensional feature extraction on the user interaction behavior to obtain user behavior feature data; inputting the original verification code data and the user behavior feature data into a double attention network for fusion processing to obtain fused feature data; performing dynamic difficulty assessment calculation based on the fused feature data to obtain verification code difficulty adjustment parameters; performing difficulty adjustment on the original verification code data according to the verification code difficulty adjustment parameter to obtain target verification code data; and based on the fused feature data and the target verification code data, performing multi-dimensional anti-refreshing rule analysis, and generating a self-adaptive anti-refreshing rule set. According to the method, the multi-modal data can be comprehensively utilized, the difficulty is dynamically adjusted, and the intelligent verification code of the anti-refreshing rule is adaptively generated.

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.

The invention relates to a malicious script detection method and device, equipment and a storage medium, and the scheme not only can generate a static analysis result through the analysis of a script text, but also can generate a dynamic analysis result according to an operation event of a target script in a running process. And a comprehensive risk assessment result is determined through a machine learning model and a heuristic rule set. Through the mode, malicious behaviors executed by the target script in the running process can be discovered, so that the scheme can effectively detect the novel malicious script; in addition, according to the scheme, the target script is operated through the sandbox container, and the detection efficiency of the malicious script can be improved.

The invention discloses a system for constructing a medical accelerator quality control knowledge base by using a large language model. The system comprises an acquisition module, a two-stage preprocessing module, an abnormal value detection module, a large language training module, a knowledge graph module and a knowledge base indexing and querying module. According to the method, related data of the medical accelerator are collected through multiple channels, information of the medical accelerator in multiple aspects of equipment performance, faults, quality control and the like can be covered, and the constructed knowledge base has comprehensiveness. Intelligent screening and hierarchical processing are performed on the data, so that more accurate processing of the data in different states is facilitated; the preset rule set comprises an equipment parameter range rule, a data format and integrity rule, a data frequency rule, a historical data statistical rule and the like, various characteristics of the medical accelerator data are comprehensively considered, and the accuracy of data screening and blocking is improved; and the method can better adapt to the complexity and specialty of medical accelerator data.

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 relates to a data cleaning method based on rule data driving and a data cleaning engine, and belongs to the technical field of data processing. The data cleaning method comprises the following steps: receiving original data streams of a plurality of heterogeneous service systems; extracting a multi-source data feature and a cross-system field association feature, and constructing to obtain a data feature fingerprint; similarity matching is carried out on the data feature fingerprints, rules with the matching degree higher than a preset threshold value are screened, and a candidate rule set is obtained; converting the candidate rule set into a directed acyclic graph; marking conflict rule pairs to obtain a conflict early warning list; a virtual isolation layer is inserted into the directed acyclic graph, and a path identification table is generated; performing topological sorting according to the directed acyclic graph, generating a rule execution sequence, and performing cleaning processing on the original data stream; distributing a data execution channel according to the path identification table; and outputting the cleaned clean data stream and the cleaning log containing the rule execution path. According to the invention, data from different business systems can be efficiently and accurately processed.

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 material data quality evaluation method based on a large model Agent, and relates to the technical field of material science and intelligent data management. According to the method, firstly, based on a rule set and through a real-time feedback and cyclic detection mechanism, original material data is corrected, and it is ensured that data entering follow-up evaluation has high quality; and secondly, based on the knowledge base, deep analysis is performed on the speciality, logic consistency and rationality of the to-be-evaluated material data by utilizing an RAG technology and an LLMs model, and a data quality evaluation result based on the knowledge base is generated. And meanwhile, various machine learning algorithms are used for carrying out multi-aspect detailed detection on the to-be-evaluated material data to obtain a plurality of data quality evaluation results based on the machine learning algorithms, and visual and quantitative evaluation results are provided through a visual method. And then, multi-dimensional evaluation information is integrated through a large model Agent, and a high-interpretation and structured comprehensive quality evaluation result is generated.

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 relates to the technical field of database auditing, and particularly discloses a database security auditing method and system.The database security auditing method comprises the steps that after an SQL statement access event is extracted from DPI database log data, semantic analysis is conducted on the SQL statement access event and a preset user behavior rule set by adopting a deep learning-based data processing technology, and a database security auditing result is obtained; the semantic feature representation of the SQL statement access event and the semantic feature representation of each user behavior rule are extracted, and then the query matching between the SQL statement access event and the user behavior rule is guided based on the global significant semantic feature of the preset user behavior rule set, so that the intelligent audit of the SQL statement access event is realized, and the audit efficiency is improved. According to the method, access events and behavior rules can be deeply understood, the accuracy and comprehensiveness of auditing results are effectively improved, the risk of misinformation and missing report is reduced, and therefore the safety protection capacity of a database is improved.

The invention provides a professional online collaborative editing method and system for hydropower project cost schemes. The professional online collaborative editing method comprises a standard management process, an intelligent collaborative editing process and a release state synchronous updating process. The standard management process comprises the steps of performing automatic analysis and data standardization conversion on an industrial standard file based on a deep learning model, generating a system rule set and storing the system rule set in a database; and based on the system rule set, automatically generating a standardized filling template of each specialty. According to the micro-service architecture-based intelligent online collaborative editing method and system for the hydropower engineering, a natural language processing NLP technology, a deep learning algorithm and a transaction management mechanism are combined, and the system has the functions of real-time grammar and spelling inspection of filled content, intelligent recommendation, semantic understanding, real-time difference detection, multi-user conflict processing and version control; the problems that communication cost is high, version control is difficult, data verification is not strict and multi-user cooperation is not smooth in water-power engineering cost scheme compiling are solved.

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.

Methods and apparatuses are described for generating parallel synthetic training data for a machine learning model. A server computing device generates a model training dataset from a baseline dataset comprising a plurality of sentences labeled as noncompliant with a ruleset. The server trains a conditional autoregressive language model using the model training dataset as input to generate a corpus of synthetic predicted to be noncompliant with the rulesets. For each synthetic sentence, the server executes a compliance classification model to generate a compliance label for the synthetic sentence. The server identifies a plurality of the synthetic sentences labeled as noncompliant that are semantically similar to sentences from the baseline dataset to generate a first parallel corpus of synthetic training data. The server executes a language suggestion model using the identified synthetic sentences to generate a second parallel corpus of synthetic training data.

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.