1147 results about "Business rule" patented technology

The invention provides a multi-modal enterprise credit risk assessment method based on a knowledge graph, which integrates data such as enterprise relationships, industry policies and supply chain information by constructing an enterprise financial knowledge graph, processes entity static attributes and associated information by using a multi-modal embedding technology, captures the associated information in combination with a heterogeneous graph neural network, and evaluates the credit risk of an enterprise. And the dynamic space-time attention mechanism mines time and space features of the time series data, identifies a core risk conduction path based on an attention weight, and finally fuses graph-level features, dynamic space-time features and business rules to output a structured evaluation result. According to the method, multi-modal data is effectively integrated, the problem of incidence relation modeling deficiency is solved, deep fusion of enterprise multi-source data and accurate extraction of risk features are realized, and the accuracy and interpretability of enterprise credit risk assessment can be effectively improved.

The invention discloses a construction method and device of a multi-component data agent, and the construction method specifically comprises the following steps: obtaining sample data, and constructing a vector knowledge base; extracting a knowledge service flow according to the sample data, determining an ontology through knowledge representation, and forming a knowledge graph; training an initial language model based on the vector knowledge base, and giving a data processing model; and performing modularization arrangement and linking on the knowledge graph and the data processing model in combination with a logic tool chain and a thinking graph of the data intelligent agent, and constructing to obtain the multi-component data intelligent agent. According to the method, the knowledge graph is fused to construct the business rule engine, training data support is provided, the thinking chain process is modeled into the thinking graph of a graph structure, and the data processing model and the knowledge graph associated with the workflow node are executed, so that the accuracy of result reasoning of the data agent is enhanced, and the knowledge boundary is expanded; and particularly for financial auditing data, key information can be better extracted, and the working efficiency of power grid auditing personnel is improved.

The invention discloses a data quality monitoring method and system based on a knowledge graph. According to the method, data tables and fields in multi-source heterogeneous data are constructed into graph nodes, graph edges are constructed through main and foreign key constraint relationships and field value overlapping analysis, and a data blood relationship knowledge graph is formed; business rules are coded into the atlas to execute data constraint check, and an abnormal data influence range is traversed and tracked through a directed graph; and dynamic monitoring is realized based on the time change of the abnormal score sequence. The invention further provides system implementation including data acquisition and preprocessing, knowledge graph construction, quality monitoring, problem diagnosis and quality evaluation. The method solves the problems that a traditional method is difficult to track the influence range of abnormal data and lacks systematic evaluation, and improves the automation level and accuracy of data quality management.

The invention discloses a report verification batch parallel execution optimization method based on a pluggable rule engine framework, and relates to the technical field of report verification, and the method comprises the following steps: receiving compliance rules, converting the compliance rules into a uniform data structure model, analyzing to generate metadata indexes, and supporting business rule set configuration; loading the matched business rule set based on the meta-information of the to-be-verified report, and registering the matched business rule set to an execution container; s3, receiving a to-be-verified report, analyzing a report structure, and generating a report field index table; performing field dependency analysis based on the report field index and the loaded business rule set, and constructing a rule field dependency graph; graph partition optimization and topological sorting are executed according to the dependency graph, and rule batches capable of being executed in parallel are generated; and uniformly collecting and outputting all rule execution results, hit states, abnormal records and performance indexes, and generating a structured verification report and an audit chain log. The financial statement compliance check execution efficiency can be improved.

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

The invention discloses a low-code platform based on a component library and a form engine, and relates to the field of data processing. The platform comprises a design system, an application system and a service system. The design system is provided with a form designer and a template manager, the form designer can configure a form component to generate a description file, and the template manager supports separate storage of a page template; the application system comprises a form analysis engine, a UI rendering engine and a reusable resource management component, the analysis engine analyzes configuration generation logic, the rendering engine loads a resource rendering page, and the reusable resource management component stores resources and generates configuration; the service system comprises a metadata analysis engine and an SQL assembler, the metadata analysis engine analyzes business rules to generate SQL and filtering strategies, and the SQL assembler generates operation statements adaptive to multiple data sources. Through the modular architecture design and the dynamic analysis mechanism, the problem that a low-code platform is insufficient in flexibility and usability is solved.

The invention belongs to the technical field of air traffic control and conduction, and particularly relates to an intelligent question answering system method for air traffic control and conduction business knowledge. According to the method and the system, the refined and context-aware information retrieval capability of the air traffic control communication service knowledge and the expert-level air traffic control communication service knowledge question and answer capability are provided, the knowledge management and application efficiency is effectively improved, and powerful support is provided for digital transformation of related services. Comprising the following steps: S1, a document content extraction and storage process; s2, a content retrieval and generation process; s3, content slice optimization is carried out through extraction and similarity calculation of adjacent paragraphs or contents, complete contents are in the same slice, and the content retrieval efficiency is improved; according to the method, the label library and the business rule library are continuously iteratively optimized, so that the provided service is always in the optimal state.

The invention relates to the technical field of code security analysis, and discloses a control flow analysis and retrieval enhancement-based large model code security review method, which comprises the following steps of: analyzing an incremental code to generate an abstract syntax tree and data flow analysis information; retrieving local knowledge base association business rules and historical vulnerabilities based on grammatical features, and generating a context enhancement prompt; calling a large language model to jointly analyze codes and contexts, identifying vulnerabilities and outputting a structured report; and combining with a historical false alarm data optimization result and then integrating to a development assembly line. Multi-source information is fused through an RAG technology to enhance semantic understanding, and control flow node tracking and cross-version semantic association are combined, so that the problems of a traditional tool business logic vulnerability detection blind area, incremental code analysis failure and high false alarm rate are solved, synchronous improvement of security examination accuracy and efficiency is realized, and the method is adaptive to an agile development scene.

The invention provides a dynamic order making method and device based on multiple rounds of dialogues and electronic equipment, and belongs to the technical field of intelligent interaction. The method comprises the steps of guiding a user to gradually fill in a form through a multi-round dialogue mechanism, generating a question based on a business logic dependency relationship, and mapping a natural language answer of the user into a structured field value in combination with semantic analysis and field dependency management; integrating a business rule engine in a multi-round dialogue mechanism process, executing field-level, logic-level and process-level multi-dimensional verification in real time in a user input process, and dynamically adjusting a verification strategy based on context; constructing and adaptively adjusting a form structure in real time based on a dynamic form generation engine, multi-round dialogue contents and business rules; based on a low-code configuration development platform, in combination with a preset intelligent interaction component and a visual tool, an intelligent filling system supporting natural language interaction is constructed. And the intelligence, flexibility and accuracy of form filling are obviously improved.

The invention relates to a large language model construction method and system fused with spatial semantic understanding, and the method comprises the steps: obtaining a multi-source heterogeneous corpus, and extracting an entity, an attribute and a business rule; extracting a semantic feature vector set based on the multi-source heterogeneous corpus, and constructing an entity relationship network and an enhanced knowledge graph; generating an enhanced training sample, and training the general large language model to obtain a primary large language model; generating a verification sample set and performing verification; identifying a specific weakness pattern, and generating a corresponding confrontation sample and a knowledge enhancement sample; training the primary large language model to obtain an optimized large language model; in conclusion, the enhanced knowledge graph fusing the spatial semantic features and the business rules is constructed, and the gradient training samples are generated based on the graph to perform multi-stage model training and optimization, so that the method has the effects of improving the internalized understanding ability of the model for the spatial semantics and the business rules and enhancing the reliability of multi-step spatial reasoning.

The invention relates to the technical field of automatic arrangement, and discloses a method and device for automatically arranging a business relation chain and a storage medium, and the method comprises the steps: constructing a business rule base based on supplier information, customer information and business information, and carrying out the standardization processing of customer demand data, and obtaining the standardized business demand information; performing multi-dimensional matching operation on the standardized business demand information based on the business rule base to obtain an initial supplier set, and performing dynamic weight adjustment on the initial supplier set to obtain an optimal supplier sequence; constructing a chain node graph according to the optimal supplier sequence, calculating and verifying a business constraint relationship between nodes, and generating a business relationship chain; the business relation chain is converted into a standard order format, order data synchronization is completed and an order synchronization result is output through encryption signature and a concurrent distribution mechanism, and the accuracy and efficiency of business relation chain arrangement are improved.

The invention discloses a multi-source business data collaborative acquisition method for subcompartment change, and relates to the technical field of forest resource management, and the method comprises the following steps: S1, capturing subcompartment boundary, ownership and operation attribute change events in real time through a dynamic topology perception layer, and generating a dynamic topology map; according to the multi-source business data collaborative acquisition method oriented to the subcompartment change, a dynamic topology sensing mechanism and a standardized acquisition process are constructed, so that the problem of multi-source data splitting caused by frequent subcompartment change is solved, and real-time collaborative acquisition and consistency guarantee of multi-source heterogeneous data are realized. A dynamic topological graph accurately senses boundary adjustment and ownership change events, and a double-channel analyzer is combined to automatically map service rules into equipment instructions, so that the data association error rate is remarkably reduced; the bimodal verification mechanism effectively eliminates version conflicts and business rule contradictions through time sequence alignment and sandbox simulation, and improves the accuracy of forest resource assessment.

The invention provides a man-machine cooperation method and system based on robot process automation, and relates to the technical field of automation, and the method comprises the steps: building a data perception processing network, collecting a user operation instruction, a system operation state and task environment data, obtaining a task feature vector through semantic analysis, and matching a business model to generate a task processing strategy. A man-machine cooperative processing framework is constructed based on the strategy, a robot unit processes a structured task, a manual operation unit processes an unstructured task and is connected through an information interaction channel, and transmission of a data analysis result and decision suggestions and updating of processing regulation information when deviation exceeds a threshold value are achieved. Besides, a knowledge migration processing network is constructed, business rules and expert experience data features are extracted, a scene migration model is trained, business feature vectors are mapped to a preset scene feature space, and after validity verification, parameters of the data perception processing network and the man-machine cooperative processing framework are updated.

The invention discloses a data warehouse intelligent construction method and device based on a large model. The invention provides a data warehouse intelligent construction method and device based on a large model, and belongs to the technical field of big data processing. The method comprises the following steps: a user registers to-be-collected business system data source information through a data source registration entry, and submits the information as a data processing task; receiving a data processing task through a task executor, calling a large model to carry out feature analysis on a data table, and identifying a table type, a service attribute and an acquisition parameter; executing data acquisition according to an analysis result, including identifying a temporary table, determining an acquisition period, selecting an increment field and the like; performing intelligent exploration on the collected data, and identifying field features and data quality; performing data conversion based on the probing result, wherein the data conversion comprises automatically binding a business rule, associating a data standard and generating a conversion rule; and finally, executing data fusion, intelligently matching the target table and the field mapping relation through a large model, generating a fusion rule, and executing fusion.

The invention relates to the technical field of science and technology operation management and control, and discloses a science and technology operation intelligent management and control method and system based on big data. According to the method, firstly, heterogeneous data sources in the scientific and technological operation process are collected, and a standardized operation data set is generated through multi-modal fusion processing; performing dynamic feature classification on the key operation indexes, extracting time sequence features and spatial correlation features of the key operation indexes, and constructing a multi-level operation state graph based on feature importance weights; then matching a service rule base with the atlas, identifying abnormal nodes and resource conflict paths, and generating an optimization instruction set containing node repair priorities and conflict resolution strategies; an executable management and control operation sequence is generated; and finally, collecting a feedback data stream, and updating the service rule base and the feature importance weight through an incremental learning mechanism to form a closed-loop optimization link. According to the method, heterogeneous data can be effectively integrated, the operation problem can be accurately identified, the optimization strategy can be quickly generated, and intelligent management and control and continuous optimization of scientific and technological operation can be realized.

A method, system, computer program product of providing a FiniteStateMachine (FSM) with embedded business rules, processing data & events, where each state is individually saved to blockchain ledger, a data record including a unique identifier / name, a set of input field(s) or input data slots, state rules in a rule language, and set of output field names or output data slots, method including: executing of a Smart Contract by identifying state by id or name, loading corresponding Component State from blockchain, adding / executing code for processing the State's behavior, where the code includes rules for the state in the rule language (e.g., but not limited to. Open Policy Agent, Rego, Prolog, DataLog, RuleML, Drools Rule Language, NRules, and JavaScriptObjectNotation(JSON), etc.). System includes processor(s), application program interface (API(s)) for communicating, blockchain, rule processor, and memory. This enhances transparency, immutability, and secure execution of business rules within FSM architecture.

The invention discloses a customer data mining exploration method and system based on big data, particularly relates to the technical field of data mining, and is used for solving the problems of feature conflicts and analysis distortion caused by semantic inconsistency of multi-source data in the prior art. A potential semantic conflict path is identified by constructing a cross-channel rule interaction entropy evaluation model, dynamic mapping and weight adjustment of conflict indexes are realized in combination with a domain knowledge base, and finally a customer behavior analysis result with consistent cross-channel semantics is generated. Multi-source behavior indexes are extracted based on business rule definition, a conflict source is accurately positioned through semantic similarity calculation and difference dimension analysis, and a weight fusion strategy is dynamically optimized according to real-time scene features, so that semantic ambiguity between channels is effectively eliminated, and the accuracy and decision support capability of customer portraits are improved.

The invention relates to an enterprise multi-source data intelligent association analysis method based on artificial intelligence and a large model, and the method comprises the steps: introducing time sequence dynamic analysis, a business rule base and statistical correlation test, carrying out the multi-dimensional and automatic cross verification and consistency test of an association pair outputted by a semantic association engine, and carrying out the analysis of the association pair. Screening out a high-confidence correlation set conforming to the business logic, the time sequence evolution rule and the statistical significance; and packaging to form a reusable business insight analysis model based on the enterprise data knowledge graph, receiving a business query request by the model, automatically generating a deep analysis report for business process optimization and potential risk early warning through graph reasoning, path discovery or an abnormal sub-graph detection algorithm, and pushing a result to a decision support system.

The invention provides a power grid safety supervision method and system, and relates to the technical field of information, and the method comprises the steps: carrying out the grade classification and hidden danger rating of hidden dangers according to a preliminary hidden danger analysis result, a construction site business rule, and the severity of the hidden dangers, and obtaining a hidden danger grade classification result; hidden danger grades are judged according to the hidden danger grade classification result, the hidden danger grades are divided into grade I (major hidden danger), grade II (larger hidden danger), grade III (serious hidden danger) and grade IV (general hidden danger), and a corresponding response instruction sequence is issued to a construction equipment controller based on the hidden danger grades; generating a summary report of power grid supervision according to the fused multi-modal feature vectors; and the generated standardized construction report is stored in a database and pushed to related management personnel, so that the management personnel can know the construction site and the power grid state in time. The risk of safety accidents is reduced, and powerful support is provided for construction safety management.

The invention provides a supply chain product traceability system, method and device based on a block chain, and a medium, and belongs to the technical field of block chains. The system comprises a data acquisition module which is arranged in a plurality of information terminals in a supply chain and is used for collecting product information, carrying out preliminary format conversion and encryption processing, and sending the product information to a block chain network; the block chain network is used for realizing data consistency through a consensus algorithm and storing product information by adopting a distributed account book technology; the data query module is used for providing a product traceability information query service by initiating a query request to a block chain network, and obtaining product supply chain information according to the authority of a query terminal; and the intelligent contract module is deployed on the block chain network and is used for automatically executing the business rules in the supply chain according to predefined intelligent contract code logic. According to the invention, real and reliable recording and efficient query of the full life cycle information of the product are realized by using the block chain technology, and accurate product traceability information is provided for consumers.

The invention discloses a semantic recognition and task scheduling method and system for digital employees, and relates to the field of artificial intelligence. The method comprises the following steps: preprocessing a natural language text input by a user to generate a preprocessed text; performing semantic analysis on the preprocessed text by adopting a semantic analysis model, and extracting text key information; performing intention recognition on the text key information by using a deep learning model to generate an intention recognition result; according to the intention recognition result, mapping the user request to a specific business process based on a preset business rule base, and generating a business classification result; and according to a service classification result, dynamically allocating resources required by task execution by adopting a task scheduling algorithm, and adjusting a task scheduling strategy in real time according to monitoring data. According to the scheme, through accurate semantic recognition, intelligent business process mapping and dynamic task scheduling, digital employees can efficiently understand user intentions and quickly and accurately complete task execution, and the intelligent level and the response capability are improved.

The invention provides a Prompt prompt template automatic construction method for an intelligent reimbursement system, and belongs to the technical field of intelligent financial management. Comprising the steps of obtaining form structure information of a current reimbursement receipt, extracting field attributes, control configurations and business rules in a main table and a sub-table, and constructing a field semantic model; collecting context information in a user filling process, and generating a context semantic model; identifying a user intention in a current form filling stage, and determining a corresponding prompt task type; matching the target template structure from a preset template library, and generating a structured natural language Prompt prompt template; and transmitting the generated Prompt prompt template as input to a large language model agent, and driving the large language model agent to generate user-oriented intelligent prompt content. By constructing the field and context bilingual model, automatic and personalized generation of the Prompt prompt template is realized, and the accuracy, adaptability and user experience of large model prompt in an intelligent reimbursement system are remarkably improved.

The invention provides a business demand confirmation method and system based on a cloud platform, and relates to the technical field of cloud platforms, and the method comprises the steps: building a business process engine on the cloud platform according to a business scene and a business rule, carrying out the visual modeling of a business demand through the business process engine, and generating a standardized business demand model; deploying a demand verification service on the cloud platform, and through a formalized verification method of control flow analysis or data flow analysis and in combination with the business domain ontology model, performing automatic verification on the business demand logic represented in a formalized manner, and generating a demand verification report; and according to the demand verification report, judging whether the business demand logic has semantic contradictions or logic vulnerabilities, and when the semantic contradictions or logic vulnerabilities are detected, correcting the business demand logic which does not pass the verification in combination with the business domain ontology model. And deploying a demand collaboration service on the cloud platform, and publishing the corrected business demand logic in the form of a standardized interface for the collaboration party to subscribe.

The invention discloses an automatic service orchestration method, system and device and a storage medium, belongs to the technical field of computer science and information, and solves the technical problem that when an existing system processes complex queries and decisions, manual intervention is often needed, and the automation level is limited. The method comprises the steps that an intelligent agent obtains natural language query information input by a user and sends the natural language query information to the intelligent agent; preprocessing the natural language query information through the intelligent agent, wherein the preprocessing comprises word segmentation, part-of-speech tagging and syntactic analysis; the intent of the user is understood through the intelligent agent according to the preprocessing result, and a corresponding service process is planned in combination with a business rule and historical data; evaluating different service process options through the intelligent agent, making a decision, selecting an optimal scheme, and determining a specific execution path of the service process; starting to execute the service process through the intelligent agent according to the specific execution path; and confirming that all the steps of service orchestration are successfully executed through the intelligent agent.

System and methods for generating cohesive product recommendations are disclosed. In some embodiments, a disclosed method includes: storing, in a database, historical customer data associated with a customer, receiving an indication of a customer's selection of a first product, parsing and extracting first product description data from catalog description data, generating summary data of the first product description data, the summary data being a subset of the first product description data, and generating a plurality of recommended products based on the summary data, the historical customer data, and at least one business rule.

The invention belongs to the technical field of power file rule extraction and change comparison, and particularly relates to a policy document intelligent rule extraction and change comparison method for electricity charge checking, which comprises the following steps: analyzing, reconstructing and optimizing an electricity charge related policy document, and retaining original context and logic level information; designing a core statement recognition Prompt template and a rule extraction and conversion Prompt template, constructing a policy and rule extraction agent, guiding a large language model to recognize core statements, extracting rule element information and forming standardized policy and rule description; according to the method, similar policy rules are retrieved from a historical policy rule description knowledge base, a policy rule comparison agent is constructed, and deep semantic comparison and change analysis are performed on current standardized policy rule description and historical similar policy rule description, so that automatic identification of newly added and modified contents of current rules and historical rules is realized; and a key technical support is provided for automation and intelligentization of electricity charge checking business rule generation.

The invention discloses a data processing system based on an artificial intelligence algorithm, and relates to the technical field of artificial intelligence and data processing, and the system comprises a data input interface which is used for receiving a multi-source heterogeneous data stream; and the preprocessing engine is connected with the data input interface and comprises a dynamic metadata sensing unit, an incremental quality evaluation unit, a bidirectional verification self-repairing unit and a closed-loop feedback optimization unit. According to the data processing system based on the artificial intelligence algorithm, through dynamic metadata perception and an incremental quality evaluation mechanism, the manual intervention requirement of a multi-source heterogeneous data preprocessing stage is reduced, and the problem of adaptation stiffness caused by the fact that a traditional method depends on a static rule is solved; by combining the bidirectional verification design of the business rule and the model feedback, the dual reliability of the data recovery strategy in logic rationality and algorithm compatibility is ensured, the risk of overfitting or scene mismatching caused by a single verification mechanism is avoided, and the input data quality and decision accuracy of a downstream artificial intelligence model are improved.

The invention provides an intelligent form processing method based on multi-modal verification and large model feedback, and relates to the technical field of artificial intelligence and data processing, and the method comprises the steps: obtaining a corresponding business rule from a relational database through a rule extraction and matching request based on user input data; error verification, service logic verification and cross-field and cross-method verification are carried out on the user input data and the corresponding service rules, and verification results are obtained; based on the verification result, natural language feedback is generated through the large model to provide error prompts and improvement suggestions.

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 provides a full-life-cycle dynamic monitoring system and method for global land comprehensive improvement, and the method comprises the steps: firstly collecting and fusing multi-source heterogeneous data related to global land comprehensive improvement through a multi-source data fusion module, and constructing a data bottom plate with unified space-time reference and semantic association; then the intelligent monitoring module realizes land utilization type change identification, project progress tracking and index dynamic monitoring by utilizing a preset business rule and a machine learning model based on the data base plate, obtains monitoring data in real time and generates early warning information; and finally, the decision support module visually displays the monitoring data and the early warning information, and provides interaction service and decision support for the user at the same time. According to the invention, multi-source heterogeneous data can be efficiently integrated, whole-process dynamic monitoring, intelligent effect evaluation and risk early warning of a global land comprehensive improvement project are realized, and powerful support is provided for management decision.