1488 results about "Rule engine" patented technology

The invention relates to the technical field of financial data processing, in particular to a multi-source heterogeneous fund data processing method and system, and the method comprises the steps: recording source information through building a data source registry, and marking a unique identifier for data; performing differential analysis on the fund data in different formats to generate standardized column type storage data; traversing column type storage data to extract statistical characteristics, performing field classification through metadata analysis and financial dictionary matching, analyzing business connotations of fields difficult to classify in combination with a localized large language model, and mapping the business connotations to a header fusion knowledge graph; generating a mapping rule from the source field to the enterprise-level data model by applying a rule engine template on the basis of field classification and semantic recognition results; converting the data structure according to the mapping rule and executing standardization processing; the quality is further optimized through data cleaning; and finally, the data are verified, standardized fund data supporting data traceability are output, and the strict requirements of financial supervision application are met.

The systems and methods disclosed herein orchestrate task execution among autonomous (or semi-autonomous) AI agentic models (“agents”) using a gateway router that dynamically coordinates the agents based on prompt characteristics, user context, and / or real-time operational factors. Received inputs (e.g., prompts) are segmented into subcomponents (e.g., sub-queries), which are routed / mapped to candidate agents based on the output parameters of the subcomponent (e.g., performance thresholds, cost thresholds) and operational parameters (e.g., cost, performance metric values, user access restrictions, timing restrictions) of each agent. The gateway router maintains dynamic routing data structures for each agent that are continuously updated based on environmental stimuli (e.g., geo-political stimuli, sensor stimuli, agent stimuli). For example, the gateway router causes agents to dynamically switch between rule engines identified by the routing tables in response to detecting environmental stimuli. Responses from the candidate agents are aggregated into an output that is responsive to the input.

The invention relates to the technical field of computers, and discloses a computing power resource multi-dimensional scheduling method and system based on dynamic weight, and the method comprises a data perception step, a weight generation step, an intelligent decision-making step and a scheduling optimization step. The system corresponds to the method. The method comprises the following steps: a data sensing step: collecting multi-dimensional state parameters of computing power nodes and carrying out feature modeling to construct a global feature space; a weight generation step: dynamically adjusting the weight of each dimension based on a machine learning model and a rule engine; an intelligent decision-making step of screening candidate nodes in the global feature space and evaluating priorities, generating an optimal node cluster and performing resource dynamic slice distribution; and a scheduling optimization step: monitoring an execution effect and performing closed-loop feedback so as to iteratively optimize a weight strategy and decision logic. The problems that in the prior art, the sensing dimension is single, and decision-making weight is rigid are solved, and multi-dimensional accurate sensing, dynamic weight decision making and elastic resource allocation of computing power resources are achieved.

The invention discloses a computer big data information processing system, which comprises a data acquisition layer, a data processing layer and a data processing layer, wherein the data acquisition layer is used for accessing structured, unstructured and streaming data by using a multi-source adapter and Apache NiFi, executing format standardization, and extracting basic metadata and semantic tags through a rule engine and an NLP model; the metadata intelligent management layer integrates four modules, namely a federal learning framework for realizing cross-domain dynamic classification labels, an intelligent contract for real-time uplink storage evidence blood relationship change, a Neo4j combined graph neural network for constructing a knowledge graph for mining implicit association, and a reinforcement learning engine for optimizing a storage strategy based on frequency and risk indexes; the distributed storage calculation layer is used for processing batch and real-time metadata by adopting a Cassander + MinIO mixed framework and Spark / Flink, and dynamic partition balance performance is realized; and the application service layer is used for outputting functions of blood relationship query, classified browsing, compliance report and the like through a Vue.js portal and a Spring Cloud micro-service API (Application Program Interface) to form a full-link closed loop.

The invention discloses a heterogeneous data conversion method and system based on a multi-modal large model, and aims to solve the problems of single-modal limitation, high migration cost and the like in traditional heterogeneous data processing. The method comprises the following steps: firstly, carrying out format adaptation and classification preprocessing on multi-source heterogeneous data such as images, texts, audios and videos; then, through a ViT, BERT and Transform cross attention mechanism, deep analysis and cross-modal semantic association of visual, text and audio features are realized, and visual-text-audio semantic mapping is established; and based on a dynamic prompt project, guiding the multi-modal large model to generate structured data according to a preset Schema zero sample. And then error correction and format normalization are carried out through a Drools rule engine to ensure that the data meet the business standard. And finally, supporting multi-format output of JSON / XML / database table and the like, and meanwhile, associating original data storage to realize data traceability. According to the method, the limitation of single-modal processing is broken through, and cross-modal data deep association is realized.

A predictive incident management system, consisting of: a sensor input module configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical and cyber sources; a temporal correlation control unit operationally coupled to the sensor input module, wherein the temporal correlation control unit is configured to normalize received data into a uniform time series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context markers, and is further configured to compute sliding window-cross-sensor correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities; a scalable rule processor that is communicatively linked to the control unit for temporal correlation, wherein the rule engine includes an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules that reference primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; A historical repository that is communicatively connected to both the temporal correlation control unit and the rule engine. The repository is configured to store tagged event histories, correlation motif dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions; and An incident response interface is operationally connected to the rule engine. The incident response interface is configured to trigger automated workflows, including the generation of tickets for IT service management, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols. the system is configured to perform predictive analyses based on temporal correlations between sensors and to execute context-aware, rule-based incident management in real time.

The invention provides a marine equipment drawing recognition method and system based on a large model, and is applied to the field of intelligent analysis and knowledge management of ship engineering. The method comprises the following steps: receiving a marine equipment engineering drawing image, extracting global features through character and line detection after preprocessing so as to identify a drawing type, and combining symbol classification and topological graph construction to determine an equipment connection relationship, performing multi-source information reasoning on the target component by fusing a rule engine and a large language model, and outputting a high-confidence identification result; according to the scheme, the accuracy and the automation level of recognition of the equipment parts in the complex marine drawing can be remarkably improved, and the technical bottlenecks of a traditional method in the aspects of insufficient cross-modal information fusion, limited semantic understanding depth, poor heterogeneous drawing adaptability and the like are effectively solved.

The invention discloses an electrical work ticket intelligent generation method and device, a medium and equipment in the technical field of power systems, and aims to solve the problems of serious module decoupling, dependence on manual connection and insufficient intelligent generation capability in the prior art. The method comprises the following steps: generating a required digital knowledge base and an electrical work task description generation work ticket based on a pre-constructed work ticket by utilizing a multi-model collaborative generation architecture; the multi-model collaborative generation architecture comprises a rule driving module used for calling a corresponding template based on task element matching; the task execution unit is used for executing a specific subtask according to a pre-deployed lightweight model or rule engine; and the large language model is used for performing semantic understanding and reasoning according to the electrical work task description, calling the rule driving module and the task execution unit according to a semantic understanding and reasoning result, and generating a work ticket in combination with a digital knowledge base required by work ticket generation. The method has both the accuracy of structured knowledge recognition and the flexibility of natural language generation.

The invention relates to the technical field of information and data processing, in particular to an enterprise digitalization-oriented data management and application method, which comprises the following steps of: acquiring multi-source heterogeneous data and recording metadata; establishing a cross-department management system to standardize the treatment process; building an asset catalog based on the business section and the data field classification; formulating unified data description of a multi-level data standard system; designing a quality control rule to filter low-quality data; constructing a business index system and embedding the business index system into a business system; identifying a cross-system and cross-type association mode through a metadata association technology and a rule engine; and dynamically adjusting the treatment process by adopting a multi-objective optimization algorithm in combination with service feedback and treatment parameters. According to the method, the defects of existing data governance in comprehensiveness, depth and business adaptability are overcome, standardized governance and potential value mining of multi-source data are achieved, and effective data support is provided for intelligent decision making of enterprises.

The invention discloses an archive data management labeling method and device based on computer application, and relates to the technical field of archive information management and intelligent labeling, and the device comprises a semantic analysis module, an allogenic character normalization module, a context modeling module, a historical semantic alignment module, a context feedback module, a processing module and a model optimization module. The device can automatically monitor file input, clean and segment texts, quickly and normatively replace foreign characters and intelligently convert simple and traditional Chinese characters; a semantic vector is generated based on a Transform model, semantic consistency is detected, and if semantic drift is found, rollback adjustment is carried out; the processing module is combined with a rule engine and a depth model to complete entity labeling and multi-label reasoning; and the optimization module dynamically records and feeds back annotation data, and supports self-learning and continuous improvement of the model. By continuously optimizing the judgment logic, the system has self-learning and evolutionary capabilities, so that the archive labeling process is more and more intelligent, and the labeling result is more and more accurate.

The invention discloses a complex task structured decomposition method, system and device based on a large language model and a medium, and the method comprises the steps: converting natural language query into a standardized atomic task flow through the combination of a dynamic prompt template and a strong type verification mechanism; the core innovation comprises the steps that a structured prompt template is adopted to guide a large language model to generate a subtask description object meeting the JSON Schema specification, it is ensured that an output result meets the expected structure requirement in combination with a format constraint mechanism, and the logic completeness of a task flow is ensured through a dependency relationship consistency verification mechanism; the system realizes an end-to-end processing process from natural language input to structured task output through task processing link series prompt template construction, model reasoning and result analysis links; according to the method, the problems of adaptability limitation, difficulty in unstructured output docking and weak dependency relationship verification of a traditional rule engine are effectively solved, and efficient and reliable task decomposition support is provided for a complex business scene.

The invention relates to a production abnormality automatic identification and recovery process control method, which comprises the following steps of S1, realizing second-level synchronization of multi-source heterogeneous data, constructing a real-time data flow pipeline and generating a total-factor production situation data flow through a distributed message queue by adopting a Modbus / TCP protocol analysis algorithm based on industrial Internet of Things edge calculation; through the cooperative effect of industrial protocol analysis and distributed message queues, second-level synchronization of multi-source heterogeneous data is realized, a real-time data flow pipeline is constructed, data acquisition delay is effectively eliminated, the timeliness of anomaly detection is ensured, a dynamic weight distribution mechanism of a rule engine and a long and short-term memory network prediction model is adopted, and the real-time performance of the system is improved. By combining sliding window threshold detection, the accuracy and coverage of anomaly recognition are improved, false alarm and missing alarm caused by a single detection mechanism are reduced, and multi-dimensional root cause tracing is performed by combining a fault mode knowledge base through combined application of time sequence correlation analysis and a causal diagram inference engine.

The invention relates to the technical field of front ends, can be applied to the field of science and technology finance / digital medical treatment, and discloses an activity landing page intelligent building method and device, electronic equipment and a storage medium. The method comprises the following steps: analyzing activity demand data through a large language model, and matching the activity demand data with features of components in a component library according to an analysis result to obtain candidate components; determining a target component and component attributes according to the user operation instruction to generate an initial page structure; configuring a business association rule between the target components based on the rule engine, and converting the natural language description of the business rule into a form which can be recognized by the rule engine through a large language model; generating a preview page of the activity landing page according to the initial page structure and the business association rule, and updating the page structure and the business association rule according to the adjustment instruction; and compiling and packaging the updated page structure and the business association rule. According to the method, intelligent construction of the landing page from demand to deployment is realized, and the efficiency and normalization are improved.

The invention relates to the technical field of data management and information, and discloses a data asset identification system and method based on multi-dimensional rule engine blood relationship analysis, and the method comprises the steps: obtaining static code information and dynamic operation tracking information of to-be-analyzed data; constructing a theoretical data blood relationship map based on the static code information; constructing a real-time data blood relationship map based on the dynamic operation tracking information; generating a fusion data consanguinity map by adopting a time window alignment strategy; constructing a five-dimensional data asset identification rule model; starting a blood relationship driven multi-dimensional rule engine; executing the multi-dimensional rule engine, and evaluating and determining the nature of the to-be-analyzed data; in the evaluation process, a conflict resolution mechanism is started. According to the method, the multi-dimensional rule engine is driven through dynamic and static combined blood relationship analysis, and conflict judgment is performed by using blood relationship information.

The invention relates to the technical field of data governance, and discloses a data governance method based on an intelligent decision engine, which comprises the following steps: carrying out business semantic classification and marking on preliminarily processed real-time streaming data, and constructing a data portrait library; constructing a dynamic topological graph, learning an abnormal propagation rule based on a graph neural network, analyzing an influence range and establishing an influence grading mechanism; performing multi-dimensional quality evaluation on the data, and generating a dynamic data quality score and a grading strategy; constructing a data quality historical problem and reason case library, and generating a quality anomaly root cause judgment and influence quantification report by using a large language model agent; generating a candidate strategy set, and selecting an optimal governance strategy from the candidate strategy set by establishing a multi-objective optimization model; and performing compliance test and conflict identification on the optimal governance strategy by using a large language model agent, and dynamically adjusting the decision weight of a rule engine by using a reinforcement learning algorithm to realize a closed loop of data governance and dynamic learning.

The invention discloses an automatic official document generation method based on a dynamic rule, and relates to the technical field of official document generation, and the method comprises the following steps: receiving official document writing demand data input by a target user, carrying out semantic analysis on the official document writing demand data, extracting structured parameter data based on a semantic analysis result, and generating an official document according to the structured parameter data. Performing official document type identification on the structured parameter data to confirm an official document type identification code; a dynamic rule engine is used for matching the corresponding format verification rule base and the content logic knowledge graph, and an official document writing framework is generated; receiving business data corresponding to the target user, extracting a key information triple from the business data, inputting the key information triple into a preset intelligent mapping model for semantic level association analysis, and generating a preliminary official document manuscript in combination with an official document writing framework; and verifying the preliminary official document manuscript, and adjusting and optimizing the preliminary official document manuscript according to a verification result to generate a final official document manuscript. The method has the effect of improving the document generation efficiency.

The invention relates to the technical field of data disaster recovery, and provides a disaster recovery drill and disaster switching method and system, and the method comprises the steps: automatically recognizing multi-source heterogeneous database assets through a rule engine and a machine learning algorithm, and constructing a disaster recovery topological graph; a disaster recovery strategy is generated and optimized by adopting an intelligent algorithm based on the disaster recovery topological graph, and the disaster recovery strategy is dynamically adjusted in combination with real-time service requirements; fusing the historical disaster recovery data and the real-time monitoring data to reconstruct a disaster recovery strategy; executing data are collected in real time in the drilling process, and a disaster recovery strategy is optimized through a closed-loop feedback mechanism; monitoring a whole process state through a visual interface and displaying key indexes; based on the multi-dimensional evaluation model, quantitatively analyzing the drilling result, and determining a target disaster recovery strategy; and a security verification mechanism is adopted to authorize the key operation, a disaster recovery scene scheme is matched according to the fault type, and an automatic execution unit is controlled to complete disaster switching based on the target disaster recovery strategy. According to the invention, the disaster recovery switching accuracy in a complex scene is improved.

The invention discloses a transformer substation detection report automatic generation method based on artificial intelligence, and the method comprises the following steps: obtaining primary detection data through multi-modal data collection and preprocessing; performing standard verification on the primary detection data by using a rule engine, completing trend analysis and anomaly detection, and generating structured detection data; inputting the structured detection data into an automatic summarization and suggestion generation module to obtain an analysis abstract and a professional suggestion; filling the structured detection data, the analysis abstract and the suggestion into a standardized detection report template for automatic matching; a standard detection report is generated by adopting automatic typesetting and is pushed to a multi-role cooperation module, and the standard detection report is audited by related auditing personnel; and after checking and confirming, filing the detection report, and distributing the detection report to a related management system or a specified terminal according to a set rule. According to the invention, intelligent acquisition, anomaly detection, automatic report generation and whole-process closed-loop management of multi-modal data are realized, and the detection efficiency, accuracy and report standardization level are greatly improved.

The invention discloses an elevator generation type health state assessment method and system based on knowledge enhancement retrieval, and the method comprises the following steps: 1) collecting multi-source operation and maintenance data of an elevator, constructing an elevator health state knowledge graph containing equipment, parts, faults, health indexes and maintenance standards, and forming a structured health assessment knowledge base; 2) performing semantic analysis on operation indexes and natural language task description based on a large language model, and generating a health evidence sub-graph in combination with knowledge graph retrieval; 3) carrying out quantitative calculation on the component risk value and the complete machine comprehensive risk value under a rule engine and multi-agent framework, and constructing a memory graph to carry out historical trend analysis and similar case retrieval; and 4) generating an interpretable elevator health state evaluation report in combination with the logical reasoning graph and the standard knowledge graph. According to the method, the accuracy and traceability of elevator health assessment can be improved, and technical support is provided for predictive maintenance of the elevator.

The invention discloses an industry knowledge graph automatic construction method oriented to multi-source heterogeneous data, and relates to the technical field of knowledge engineering. Structured, semi-structured, unstructured and distributed data are supported, and high-concurrency data are cached through Kafka; preprocessing and standardizing, and executing cleaning, field unification, text conversion and format conversion; a pre-training model and a rule engine are adopted to cover entities and relations of multiple industries; designing a dynamic Schema, storing the dynamic Schema in a graph database, and constructing an index; evaluating and correcting from multiple dimensions; and dynamic updating and maintenance, incremental updating based on data change, and support of version management and knowledge service. According to the method, the knowledge graph is automatically constructed through multi-source heterogeneous data, the construction efficiency and quality are improved, cross-domain fusion is supported, dynamic updating is adapted, and multi-industry intelligent application is assisted.

The invention provides a multi-tenant data pushing method, and relates to the technical field of computer software and information, and the method comprises the steps: achieving unified identity authentication through single sign-on, and dynamically distributing a pushing authority based on an authority strategy; defining conversion rules and tasks by utilizing a visual interface of the low-code configuration center, and generating a standardized data packet; and centralized scheduling is carried out in a multi-tenant management and control center, and the stability of the system is guaranteed by combining a current limiting and dynamic retry mechanism. Source system data are collected in real time, and format conversion is completed through a rule engine after grammar, permission and event state verification. And pushing in an asynchronous mode, and recording full-link information including a tracking ID, a timestamp, a data fingerprint and a response state. And when pushing fails, a compensation mechanism is triggered, degradation or retry is carried out according to a preset rule, final consistency of data is ensured, and the reliability and maintainability of the system are improved.

The invention relates to a method and system for identifying a highway event by using a video large model, and the method comprises the steps: employing a three-stage processing architecture, firstly carrying out the real-time target detection and preliminary event judgment of a highway monitoring video stream through employing a YOLO algorithm, and generating an event candidate set; inputting the candidate events and the video clips thereof into a specially trained visual large model for deep semantic analysis and secondary reasoning; and finally, a reasoning result is rechecked through a rule engine, and false alarms are filtered by applying illusion suppression and a space-time association rule. According to the method, the real-time performance of traditional target detection and the deep reasoning capability of a visual large model are fused, so that the problems of high false alarm rate and high missing report rate of a traditional method are effectively solved, the accuracy and reliability of event identification in a complex traffic scene are remarkably improved, and meanwhile, the real-time processing capability of a system on multiple paths of high-definition video streams is ensured.

The invention is suitable for the technical field of intelligent customer service, and provides an intelligent question-answering method and system for public rental housing and computer equipment, and the method comprises the steps: receiving a query request input by a user, recognizing a query intention and key parameters through analyzing the query request, and generating an action framework containing the query intention, the key parameters and a preset retrieval strategy; at least one retrieval path is selected and executed according to the action framework, information is retrieved in parallel from heterogeneous data sources at least comprising a vector database, a search engine and a knowledge graph database, and all recalled source information is integrated into an evidence chain capable of being verified; and constraining a generation process of the large language model based on an evidence chain to generate a preliminary answer, calling a rule engine to perform policy compliance verification on the preliminary answer, and after the verification is passed, outputting a final answer with a traceable source label. According to the method and the system, the problem that defects exist when business scenes which are complex, dynamic and high in compliance requirement in the public rental housing field are processed in the prior art is solved.

The invention discloses a dynamic portrait construction method and system fusing a large model and user behavior data, belongs to the technical field of artificial intelligence and big data analysis, and aims to solve the problems of insufficient real-time performance, difficulty in multi-source data integration and high privacy risk in the traditional technology. The method comprises the following steps: collecting basic attributes, behavior sequences and unstructured data by burying points, and processing through an Apache Flink session window and a dynamic watermark; multi-modal feature extraction (discrete feature embedding, bidirectional LSTM coding behavior sequence and BERT coding text) is carried out, and joint embedding is generated through cross-modal contrast learning; generating three types of labels, namely a static label (rule engine), a dynamic label (1.3 B parameter quantity Nano-vLLM) and a predictive label (XGBoost), and dynamically adjusting weights; and realizing global model updating through federated learning and differential privacy. The system comprises a data acquisition layer, a feature extraction layer and a label generation updating layer. The real-time performance and accuracy of the portrait are improved, the privacy of the user is protected, and the commercial value in e-commerce, finance and other scenes is remarkable.

The invention discloses a power system data access management and control system based on risk dynamic assessment, and particularly relates to the technical field of power system information security and access control. Comprising an access request interception module, a multi-dimensional risk data acquisition module, a dynamic risk assessment engine module, a self-adaptive access control decision module and a security audit and learning module. Multi-dimensional dynamic context information such as an access subject, an object, an environment and a behavior sequence is collected in real time, a rule engine and machine learning hybrid model is combined to carry out real-time risk calculation, a quantitative comprehensive risk value is output, and an access control decision is dynamically generated according to a risk level threshold. The system has self-learning and self-adaptive optimization capabilities, continuously optimizes the model through an audit log, improves the security and flexibility of data access of the power system, and balances service availability and security protection requirements.

The invention discloses a method for dynamically monitoring and automatically processing abnormity of an entrance and an exit of a parking lot, relates to the technical field of intelligent parking lot management, and aims to solve the problems that existing unattended parking lot entrance and exit management depends on manpower, abnormity processing is passive, and the misjudgment rate is high. The method is based on an information acquisition and execution layer-edge processing layer-cloud platform three-layer architecture, the core realizes high-precision scene understanding through a multi-algorithm fused visual perception module, multi-modal data weighted fusion is combined with a ground induction coil, a radar and the like, and the misjudgment rate is reduced; a sensing result is converted into an automatic processing action through a configurable intelligent rule engine; and meanwhile, the cloud platform stores passing data and an evidence chain, optimizes an AI model through incremental training and returns the AI model to an edge layer, so that closed-loop evolution is realized.

The invention belongs to the technical field of electric power engineering digitization and intelligent drawing auditing, and particularly relates to a dynamic rule engine and multi-target auditing task decomposition method oriented to electric power drawings. Obtaining a power rule file, analyzing and verifying to generate qualified rules, and screening the qualified rules to obtain an effective rule pool; the method comprises the following steps: reserving candidate areas by preprocessing an electric power engineering drawing, generating structured cells through line segment classification and combination and table detection, extracting and cleaning a text, and affiliating the text to the corresponding cells to form a structured result; screening candidate rules from the effective rule pool, extracting auditing objects from the structured result, generating auditing points, calculating the priority of the auditing points, and packaging the auditing points into an assignment unit set capable of being executed in parallel; on the basis of the dispatching unit set, optimizing task distribution by adopting a dynamic model and a greedy algorithm in an execution environment with the maximum concurrency, and doubly optimizing task execution by quantifying benefits and verifying rules; and performing rule judgment and result measurement on a task execution result.

The invention provides an official document auditing method, device and equipment based on a large model and a medium, and the method comprises the following steps: preprocessing an official document to realize format and language standardization; using a retrieval enhancement generation (RAG) technology to complete vectorization conversion and fragmentation processing; then grammar and semantic analysis is carried out through a text analysis module fused with the large model, and matching detection is carried out according to a preset rule base in combination with an automatic rule engine module; then, deep auditing is conducted by means of a machine learning auditing model, content problems are recognized, manual rechecking is triggered in case of high uncertainty or key content, and an auxiliary tool is provided; receiving an audit opinion optimization model and a rule base; and finally outputting a result, and feeding back the problem by generating a report without annotation. The document auditing efficiency and accuracy can be improved, continuous optimization is supported, and the method is suitable for multiple scenes.

The invention provides a match alliance platform management system and method, and the system comprises a match authentication module constructed by a block chain, an AI-driven qualification auditing module, a process management module, a convolutional neural network, a big data analysis module, and an RBAC authority management module. Wherein the match authentication module dynamically analyzes and stores authenticated match rule data through an intelligent contract, and realizes rule version tracing by using an encrypted hash chain; the AI-driven qualification auditing module integrates a natural language processing engine to analyze the selection standard and construct a multi-dimensional evaluation model, and adopts a machine learning algorithm to analyze historical data of athletes to generate a visual qualification report; the process management module supported by cloud computing automatically generates an application process based on a rule engine, adopts a Monte Carlo algorithm to execute multi-constraint drawing, and constructs a directed graph model to dynamically generate a schedule topological structure containing a three-person rotation time sequence.

The invention belongs to the technical field of artificial intelligence, is applied to the field of financial science and technology and the field of medical health, and discloses a rule engine configuration method, device and equipment based on machine learning and a storage medium. The method comprises the steps that historical calculation data and performance indexes in historical calculation tasks are collected, and a training data set is constructed; a machine learning model is trained, and algorithm parameters of the rule engine are dynamically optimized through the training data set; configuring an expression and a user-defined function of a rule engine, and realizing analysis and execution of scripts through JEXL (JavaScript Exchange Library); establishing a dynamic mapping relationship between the algorithm configuration library and the business variables, mapping algorithm variable names and business variable names, and generating configurable algorithm rules; and analyzing task load and resource requirements based on a pre-trained machine learning model, distributing system resources, and repairing abnormal behaviors in a calculation process. According to the invention, the problems of low processing efficiency, high maintenance cost, unreasonable resource allocation and poor expandability in the prior art are solved.