219 results about "Rule engine" patented technology

The embodiment of the application discloses an automatic online quality inspection method and device based on AI image recognition, a medium and a product; the method comprises the following steps: in the automatic script execution process, the application interface screenshot is collected at the preset node, and the context metadata corresponding to the screenshot is obtained; the screenshot and the context metadata are input into a multimodal model to obtain a structured semantic representation for the screenshot, wherein the structured semantic representation at least includes type information, text content, spatial position information and hierarchical relationship of interface elements; based on the structured semantic representation, the interface elements are uniformly semantically modeled to form a standardized representation; a rule engine is called to perform rule matching calculation on the standardized representation to obtain a matching result, a deviation value and a confidence; according to the matching result, the deviation value and the confidence, an inspection judgment result is generated, and structured evidence information containing a rule hit path is output.

This application relates to the field of fintech and discloses a dynamic hybrid parsing and deterministic verification system and method for fuzzy bond instructions. The system constructs structured data through an instruction preprocessing module; a routing decision module calculates complexity scores based on structure, semantics, and business characteristics, and generates path selection instructions using a resource-aware dual-threshold strategy; a hybrid parsing module responds to instructions, employing a rule engine to handle low-complexity tasks and a large-scale model with enhanced retrieval to handle highly ambiguous tasks; a fallback verification module performs confidence-weighted integration and logical consistency checks, and repairs anomalies through issuer set operations and edit distance matching; a mapping generation module constructs standard query statements using an abstract syntax tree; and a closed-loop optimization module dynamically updates parameters based on user interaction feedback. This invention effectively balances computational efficiency and parsing accuracy, eliminates model illusion risks, and achieves adaptive evolution of the system.

The application discloses a kind of based on distributed rule engine's things connection area autonomy and collaborative management method and system, the method includes: things connection system is configured as " cloud-edge-end " things connection architecture, that is cloud edge-edge device end-things connection device end;And cloud rule engine is deployed in cloud, edge rule engine is deployed on the edge device of each area, and edge device is as the host computer of things connection device end PLC and issues instruction;Cloud rule engine is responsible for the formulation and update of rule base, and rule base is issued to edge rule engine;During normal operation of things connection system, edge rule engine is responsible for generating alarm and executing disposal, and cloud rule engine is responsible for global alarm receiving and execution monitoring;In the case where cloud rule engine appears to be down or failure, edge rule engine continues to control things connection device based on local rule base through edge-edge collaborative mechanism.Thereby, cloud dependence is reduced, system resilience is improved, and cross-regional collaborative control capability is enhanced.

The application discloses a kind of based on user behavior portrait's layered content propaganda push effect quantitative evaluation method and system, including the construction fusion basic attribute, behavior, demand and effect data multidimensional user portrait system, dynamic update user rule of law literacy label;Design double-layer push algorithm, first round is matched group scene content through rule engine, second round is generated personalized content set through weighted cosine similarity and knowledge weak point incentive factor;Build three-level evaluation system, combine entropy weight method and subjective adjustment coefficient to calculate quantitative score of effect of popularization of law;The evaluation result is inputted push algorithm and portrait update in reverse, realize the closed-loop optimization of push strategy;And based on TFIDF and DBSCAN algorithm, identify regional high-frequency legal risk points, generate early warning report and supporting popularization of law content;The application solves the technical problems, such as single portrait dimension, low push matching degree, effect is difficult to quantify, regional governance support is insufficient, and is suitable for multi-terminal popularization of law scene.

The application discloses a host system security emergency response method and device, equipment and medium, and relates to the technical field of network security. The method comprises the following steps: receiving attack trace encrypted data packets; the attack trace encrypted data packets are obtained by performing format conversion and encryption compression on target collected data through a local portable collection program of a target host; the target collected data are obtained by performing attack trace extraction in an offline or online manner based on a preset attack trace collection knowledge graph through the portable collection program; after the attack trace encrypted data packets are decrypted, data cleaning and formatting are performed to obtain attack trace data dictionaries; a preset data analysis model is used to identify security abnormal behaviors according to the attack trace data dictionaries, and an attack behavior relationship graph is obtained by reconstructing an attack chain timeline; the preset data analysis model comprises a preset rule engine and a preset large language model reasoning engine; and a security analysis report is generated based on the attack behavior relationship graph, so that security emergency response operations can be performed.

Provided in the present invention is a rule-engine-based intelligent configuration method for a power transaction settlement strategy. The method comprises: acquiring transaction data and external factors from a power transaction market, and after pre-processing operations, such as data cleaning, standardization and normalization, are performed, inputting pre-processed data into a machine learning model for training, in order to generate a machine-learning-based strategy; performing weighted summation on the machine-learning-based strategy and an expert strategy, which is formed by service rules and experience in an expert knowledge base, in order to generate a final power transaction settlement strategy; and writing the generated power transaction settlement strategy into a constructed rule engine library, in order to form a dynamically adjustable settlement strategy rule library.

The application belongs to the technical field of digitalization and intelligent application in nuclear power industry, and provides an intelligent diagnosis and compliance automatic checking system and method for maintenance regulations of a nuclear power plant, the system comprising a rule base management module, an intelligent analysis module, a rule engine module and a user interaction module, the method creating and maintaining a machine-readable rule base for checking maintenance regulations of a nuclear power plant, acquiring and analyzing a maintenance regulation file to be checked, generating a structured regulation object model, matching and evaluating the regulation object model with rules in the rule base, generating a diagnosis result set, displaying the diagnosis result set to a user, and providing interactive modification guidance.The application performs automatic and real-time compliance checking and logic diagnosis on maintenance regulations of a nuclear power plant, so as to replace most repetitive and rule-based manual review work with machine automatic checking.

The application discloses a kind of intelligent fusion terminal heterogeneous protocol conversion method and system, the method includes the following steps: first, the three-layer system architecture of perception layer, protocol analysis layer and intelligent conversion layer is constructed, realizes from data acquisition, semantic understanding to the whole process of intelligent conversion.Through the construction of uniform protocol description model based on JSON Schema, centralized metadata registration and version management are carried out on protocol template, the standardization of heterogeneous protocol is realized;Intelligent identification of protocol type and analysis field is realized by using convolutional neural network, and mapping rules consistent with semantics are automatically generated by combining knowledge graph and rule engine;Model pruning and quantization technology are used to optimize deep learning model, and priority scheduling and cache mechanism are designed.The application effectively solves the interoperability problem caused by the heterogeneous communication protocol of power equipment, and improves the protocol conversion efficiency and system expansibility of intelligent fusion terminal.

This invention discloses a data verification method, apparatus, and related equipment. The method includes: configuring data verification rules that support multiple data sources based on business requirements, wherein the multiple data sources include at least two data source types among file, single table, cross-table, and cross-database; acquiring the data change trend of the business data within a preset historical time period, and dynamically adjusting the data verification rules based on the data change trend using an adaptive rule engine; loading the adjusted data verification rules to perform data verification on the data to be verified, and generating data verification results.

This invention discloses an intelligent self-healing system and method for operation and maintenance based on an LLM Agent, belonging to the fields of information technology and operation and maintenance automation technology. The system includes a data acquisition and preprocessing module, a rule scheduling module, a large language model intelligent agent module, a work order generation module, and an approval and execution module. It collects and standardizes multi-source monitoring data through dual channels, and the rule scheduling module intelligently distributes data according to single alarm or multi-alarm aggregation conditions: simple scenarios directly trigger the automatic operation and maintenance mechanism; complex scenarios submit the alarm context and predefined operation types to the LLM Agent to output optimal execution parameters. The work order generation module automatically generates work orders; the execution engine automatically completes the repair operation. This invention solves the problems of rigidity in traditional rule engines and the uncontrollability and difficulty in integration of pure AI models, achieving an end-to-end self-healing closed loop that balances security and intelligence, significantly improving the automation coverage and decision-making accuracy in complex scenarios.

The invention provides a rules engine that manages user requests within an interface integrating multiple AI platforms and AI Models. Upon receiving a query, the engine assigns scores based on factors like management rules, user preferences, and contextual information. Determinative scores, such as those enforcing strict enterprise policies, can override others, leading the engine to block or reroute the query. If no score is determinative, the engine forwards the query and associated prompts to a specialized routing AI Model for contextual analysis. Based on this analysis, the rules engine directs the query to the most appropriate AI Model or defaults to the user-designated AI Model. This system balances user intent with rule enforcement, optimizing query processing across various AI platforms while ensuring compliance with enterprise guidelines.

The application relates to the field of artificial intelligence and natural language processing technology, in particular to a document intelligent review method and system based on a multi-modal artificial intelligence and a rule engine, which solves the problems that an existing review scheme driven by a pure rule engine lacks semantic understanding and cannot understand context, a review scheme model based on a traditional natural language processing model has limited model generalization ability and still needs a large amount of feature engineering, a scheme based on a pure large language model for direct review has untraceable and unverifiable results, and the scheme has high delay and high cost, and the application comprises a document preprocessing and analysis module, a rule intelligent analysis module, a key information intelligent extraction module and a collaborative intelligent review engine. The application positions a large language model LLM as an enhanced component of a rule engine, utilizes the capability of the large language model LLM to understand natural language rules, extract complex information and perform deep semantic reasoning, and the framework of the whole review process, the final output of decision and the accurate positioning of results are dominated by the controllable and explainable rule engine.

The application discloses a livestock data search method and system based on multi-source heterogeneous fusion. The application adopts a four-layer architecture of data acquisition, data processing, retrieval service and user interaction: through adaptive acquisition integration of active grabbing and passive access, covering public and non-public data; using rule engine and machine learning to clean data and establish meta model, standardizing and converting structured, semi-structured and unstructured data, constructing livestock field knowledge graph and using hybrid storage of relational database, non-relational database and graph database; providing personalized recommendation through user portrait construction and collaborative filtering algorithm; using RBAC permission management and data desensitization to ensure compliance. The application realizes efficient integration, accurate retrieval and intelligent service of multi-source livestock data.

This invention discloses an automated optimization method for operational processes that integrates data analysis and a rule engine, applied to the solder paste printing process in a high-precision electronic component surface mount production line. The method includes: real-time acquisition of squeegee pressure, squeegee travel speed, demolding speed, and corresponding average solder paste volume, average solder paste height, and defect rate, while simultaneously acquiring ambient temperature and humidity values; constructing a response surface experimental matrix using a sliding window period to obtain sample data; fitting and solving a multivariate quadratic response surface model to obtain the optimal combination of process parameters that optimizes quality indicators; injecting the optimal parameters as dynamic optimization rules into the rule engine and assigning them the highest execution priority, returning the parameters after matching with environmental parameters; and the programmable logic controller (PLC) of the solder paste printing equipment receiving and executing the parameters to form an automated closed-loop control.

This invention discloses an automated rule engine execution system and method for communication events, belonging to the field of communication technology. The system includes: collecting multi-source heterogeneous communication events; obtaining standard communication event objects through a dedicated rule meta-model for each communication event; loading a rule set; generating an abstract syntax tree through syntax parsing and pre-compilation; constructing a rule dependency graph and performing intersection and mutual exclusion detection to form a scheduling strategy containing priority, mutual exclusion whitelists, and execution order; locating a subset of candidate rules based on system context; determining the target rule through condition matching; encapsulating automated operations into atomic action units; and executing them concurrently or sequentially according to orchestration logic; simultaneously generating a unique execution trace ID to record the entire link log; monitoring anomalies in real time; triggering a circuit breaker if the anomaly rate exceeds the limit or execution times out; taking down abnormal rules and demoting their execution; evaluating the rule effectiveness and providing optimization suggestions after the session ends; this invention improves the efficiency of communication event processing and the stability of rule execution, ensuring reliable system operation.

This invention relates to sales activity management and digital risk control technology, and discloses an intelligent early warning and control system for sales activity risks, enabling real-time, accurate, and automated insight and intervention in sales activity risks. The system includes: a data acquisition and processing layer, used to access multi-source heterogeneous sales-related data, perform standardized processing based on a predefined ontology library, extract entities and relationships, and construct and maintain a dynamic risk knowledge graph; a rule engine and intelligent analysis layer, used to perform graph neural network anomaly detection, adaptive threshold rule judgment, and risk diffusion simulation based on the dynamic risk knowledge graph, and output risk analysis results; an early warning triggering and grading layer, used to generate tiered early warning information based on the risk analysis results; and a visualization and interaction layer, used to visualize information and provide risk control configuration and query interaction entry points.

The present application relates to the technical field of Fintech, and discloses a baffle generation method, which comprises the following steps: obtaining a standard interface document of a terminal, and respectively performing rule configuration on request parameters and return parameters in the standard interface document through a rule engine; analyzing the request parameters that have been subjected to rule configuration through the rule engine to obtain target mock code corresponding to the request parameters, and analyzing the return parameters that have been subjected to rule configuration through the rule engine to obtain simulation mock code corresponding to the return parameters; and determining a target simulation server mock server baffle based on the target mock code and the simulation mock code. The present application also discloses a baffle generation device, equipment and a computer storage medium. The present application improves the accuracy of test results of mock testing.

The application discloses a kind of based on dynamic editable rule engine's Internet of Things alarm implementation method, including in Internet of Things server end setting data transceiver module, the interface in data transceiver module is acquired and sends the Internet of Things equipment data collected;Rule configuration module is set in Internet of Things client, and rule configuration module is open to user, and user can set alarm rule according to actual demand;Rule configuration module connects dynamic rule pool, and dynamic rule pool receives the Internet of Things equipment data sent by data transceiver module, and judges whether data generates alarm, in the application, four-dimensional rule conditions are set in dynamic rule, which are time interval, frequency, numerical logic, time window, and multiple sub-conditions can be connected into new rule, and various complexity alarms can be dynamically edited, these rule conditions can be edited and executed at any time through client.

The application discloses a PCB intelligent manufacturing quality prevention and diagnosis method, comprising the following steps: collecting and structuring defect data of key processes in the whole PCB manufacturing process to form a knowledge base; collecting real-time production data corresponding to the associated parameters in the knowledge base; based on the collected real-time production data, using a pre-trained defect prediction model or / and a rule engine to perform risk prediction and early warning; when defects are actually detected by online detection equipment or the system triggers a risk warning, intelligent diagnosis of defect root causes and improvement measures are recommended; a feedback mechanism is established to record and evaluate the diagnosis results and improvement measures each time, and feedback to the knowledge base, defect prediction model and rule engine for data optimization and updating. The PCB intelligent manufacturing quality prevention and diagnosis method can realize intelligent prediction, rapid diagnosis and measure recommendation of defects, and significantly improve the quality stability and problem solving efficiency. The application also provides a PCB intelligent manufacturing quality prevention and diagnosis system.

This invention relates to the field of insurance data processing and claims management technology, and discloses an insurance claims configuration system and method based on event perception and dual-track rule execution. This invention achieves unified access to multi-source data by pre-setting a standardized perception entry point covering all insurance claims scenarios, and constructs a configuration management layer that supports business entities in independently configuring perception scenarios, rule conditions, and dual-track execution methods. It combines a rule engine unit and a large model skill unit to execute structured and unstructured rule judgments respectively. When the judgment result meets the trigger conditions, customized data extraction and management actions are automatically executed. This solves the problems of traditional claims management systems, such as difficulty in meeting personalized needs, excessively long demand response cycles, wasted functional development resources, suppressed enthusiasm of management entities, and lagging functional iteration. It improves the flexibility and business response speed of claims management, reduces system development and maintenance costs, fully leverages the management initiative of business personnel, and maximizes the value of claims management.

This invention provides a system for real-time dynamic adjustment of grouping across devices, including an identity authentication module, a distributed synchronization module, a rule engine module, a trigger engine module, and an algorithm engine module. These modules are coupled and interact through standardized data interfaces. By establishing a three-element association relationship between users, devices, and groups, unified management and authentication of user identities are achieved in a multi-device environment. A dynamic adjustment strategy is employed to achieve static allocation and dynamic adjustment of user permissions. Multi-dimensional grouping rules are formulated to filter users meeting certain conditions into the same group. A multi-dimensional trigger strategy is used to dynamically adjust groups. Intelligent calculation and optimization of dynamic grouping are performed based on the K-means clustering algorithm combined with a greedy strategy. Data synchronization between devices is achieved based on a distributed architecture, enabling real-time dynamic adjustment and efficient collaboration of grouping across devices.

This invention discloses a method for supporting individual educational development based on innate biometric assessment. The method first collects fingerprint image data, which is considered lifelong and unchanging, from individuals. Second, it preprocesses the fingerprint images and extracts quantified feature vectors. Then, it inputs these feature vectors into a pre-trained biometric-educational trait association model to obtain quantified assessment scores for the individual across multiple preset educational trait dimensions. Next, combining the assessment scores, individual age, and developmental goals, a personalized initial educational development plan is generated by matching rules through an educational plan generation rule engine. This invention uses stable biometrics as an objective benchmark and, through a computable model and rule engine, achieves a complete technical process from innate trait assessment to the generation and optimization of long-term, dynamic, and personalized educational support plans, solving the technical problems of traditional methods that rely on unstable acquired assessments and lack a long-term reference system.

This invention provides a human-machine collaborative method and system for short- and medium-term hydrological forecasting enhanced by a large language model, relating to the fields of hydrological forecasting and artificial intelligence. The method includes the following steps: constructing a hydrological forecasting knowledge base for the target watershed; obtaining natural language forecasting requirements and generating structured forecasting task descriptions; recommending solutions and completing parameters based on the knowledge base; executing business rule constraints and verifying solutions; calling the short- and medium-term hydrological forecasting system to perform forecast calculations; and intelligently interpreting forecast results and accumulating experience. The system includes six modules: user interaction, large language model interaction, hydrological forecasting knowledge base, business rule engine, system interface calls, data storage, and logs. Without changing the core architecture of existing short- and medium-term hydrological forecasting systems, this invention introduces a large language model to achieve semantic parsing of forecasting tasks, intelligent construction of forecasting solutions, and intelligent interpretation of forecast results, lowering the system's usage threshold and improving the interpretability of forecast results and the structured reusability of forecasting experience.

This application discloses a modeling method, apparatus, and equipment for a flight early warning center, relating to the field of civil aviation early warning technology, and is used to solve the technical problems of poor early warning accuracy, consistency, and centralized control capabilities in existing technologies. The method includes: constructing a rule management center using dynamic rule management services and dynamic rule loading services; the rule management center having standardized early warning event models and early warning rule distribution protocols; constructing a dynamic early warning rule library and an early warning processing center using the Drools rule engine; constructing an early warning calculation center using the Drools rule engine and the Disruptor asynchronous processing framework; and constructing a flight early warning center based on the dynamic early warning rule library, rule management center, early warning processing center, and early warning calculation center. Therefore, this application can effectively improve early warning accuracy and consistency through "Drools rule engine, Disruptor asynchronous processing framework, standardized early warning event models, and early warning rule distribution protocols."

The application discloses a rule and deep learning model-based illegal content detection method and device and medium, relates to the technical field of content security, and comprises the following steps: obtaining to-be-detected data; analyzing the data content based on a preset rule engine to obtain a rule determination result and a rule confidence; determining a target deep learning model corresponding to the rule confidence and system load, inputting the data content into the target deep learning model, and obtaining a model determination result output by the target deep learning model; performing weighted summation processing on the rule determination result, the model determination result and user information to obtain a final determination result, and generating indicating content indicating that the to-be-detected data is illegal in the case that the final determination result represents illegality. The application is used to solve the problems of low accuracy and slow detection efficiency in the prior art when a single mode is used for illegal content detection, and realizes fast and accurate illegal content detection.

The application discloses a kind of based on big model's data security intelligent evaluation method, belong to data security and artificial intelligence cross technical field.The method includes the following steps: multi-source data asset acquisition and structured processing, extract metadata and construct data portrait;Classification and grading are carried out to data based on big model, and the classification and grading results are cross-validated in combination with rule engine;Management class data and technical class data are collected, and management dimension risk value and technical dimension risk value are respectively quantified;Weight coefficient is configured, and comprehensive risk value is calculated;Evaluation report is automatically generated and disposal suggestion is output.The method solves the problems of low efficiency, strong subjectivity and incomplete coverage in traditional manual evaluation, realizes the automation, precision and dynamic adaptation of data security evaluation, and provides reliable protection for organizational data asset security.

Interactive event streaming can be provided in real-time and selectively transmitted to data consumers based on a rules engine. For example, a computer-implemented method described herein can include receiving a plurality of interaction events from at least one processing application. The method can include modifying the plurality of interaction events to produce a plurality of secured interaction events, wherein personal information of at least one data publisher in each secured interaction event of the plurality of secured interaction events is obscured. The method can further include selecting, based at least in part on a rules engine, at least one particular interaction event from the plurality of secured interaction events. Additionally, the method can include notifying a data consumer of the at least one particular interaction event from the plurality of secured interaction events.

The application discloses an asynchronous account reconciliation processing method and device, equipment and a storage medium. The method comprises the following steps: obtaining first to-be-reconciled data; performing data cleaning on the first to-be-reconciled data to obtain second to-be-reconciled data in a unified data format and store the second to-be-reconciled data in a database; configuring a preset reconciliation rule based on a rule engine drools; matching the second to-be-reconciled data with the preset reconciliation rule, performing asynchronous reconciliation processing according to a preset reconciliation task multi-thread, and obtaining a reconciliation result. Through the asynchronous multi-thread mode, the reconciliation speed is greatly improved, the to-be-reconciled data in different formats is cleaned in a unified data format, and the technical problems of the current reconciliation platform, such as only a database mode, complex reconciliation rules and low single-thread reconciliation efficiency, are solved.

An alpha network of a Rete network used by a rule engine can be dynamically constructed and modified in some examples. As one particular example, a system can dynamically construct an alpha network for a Rete network of a rule engine. The alpha network is dynamically constructed in memory during an execution time of the rule engine. The alpha network is dynamically constructed based on data objects propagating through alpha nodes of the alpha network. The alpha nodes correspond to constraints defined in a predefined set of rules for the rule engine. After dynamically constructing the alpha network, the system can propagate a data object through the alpha network to a beta network of the Rete network.

The application belongs to the technical field of intelligent management of enterprise policy, and discloses an intelligent management system for enterprise policy matching and planning, wherein a policy coding acquisition unit is used to analyze implicit conditions by means of NLP semantic analysis and industry rule engine, and a double-dimensional model of industry risk and policy effect is used to optimize thresholds in combination with historical execution data, so as to generate a unique policy code containing information such as region and valid period, and to form a traceability chain by connecting a government affairs block chain; both quantitative constraints of implicit conditions and coding authenticity are ensured, the situation of report rejection is greatly reduced, and the efficiency and reliability of automatic verification of policy matching are improved; a multi-dimensional matching unit driven by the code can monitor dynamic changes of the code, automatically recalculate the matching degree, and load tax preferential and R&D subsidy priority codes for small and micro enterprises and high-tech enterprises respectively, so as to strengthen the corresponding implicit condition verification.