103 results about "Rule system" patented technology

The invention discloses an intelligent approval rule modeling method oriented to process automation, and relates to the technical field of business process management, and the method comprises the following steps: S100, in a process of constructing a rule candidate set, extracting scene features, field semantic hierarchy and participation role information of each piece of historical approval data, generating a context semantic tag set, and establishing a rule candidate set; the method is used for subsequent rule difference modeling. According to the method, context semantic tags are introduced to be aligned with ternary features, so that the semantic boundary recognition capability of the rule is enhanced; constructing a rule feature matrix and a differentiation candidate set, and realizing accurate classification and processing of ambiguity rules; in combination with expression sensitivity enhancement and simulation verification, approval offset and risk are identified in advance; finally, the dynamic optimization of the rule model is realized through backtracking correction, the stability and accuracy of the rule model in multiple scenes are improved, and a closed-loop credible intelligent approval rule system is constructed.

The invention provides a supply chain purchase bid invitation project multi-mode intelligent auditing method, and belongs to the technical field of electronic purchase. Comprising the following steps: defining a review rule system comprising rule contents, review points and review logic; in the bidding stage, bidding files and quotation information uploaded by suppliers are received, and after bid opening, bid invitation files, bidding files of entered suppliers and corresponding review rule systems are input into a multi-mode large model server; performing unstructured data analysis on the bidding document and the bidding document by using a multi-modal large model in combination with an OCR (Optical Character Recognition) technology, and identifying and matching response contents in the bidding document through intention; pushing the extracted content to a corresponding business auditing agent, and performing judgment according to an auditing point and auditing logic; and summarizing the review results, and generating an intelligent review report containing the number of conformity items, the number of non-conformity items, conclusions of the review points and judgment bases. The examination efficiency and accuracy are improved, and human errors and compliance risks are reduced.

The invention provides a standardized data processing method based on a large model, electronic equipment, a storage medium and a computer program product, and the method comprises the steps: generating a standard field semantic vector through large model coding field name character string input, a field value sample list and context structure information of a field; calculating a semantic offset degree between the semantic vector of the field to be standardized and the semantic vector of the standardized field, and generating a successfully matched field pair set and a normalized failed field set; obtaining a historical standard version, and generating an optimal field and maximum score version mapping set; semantic positioning is carried out in the maximum score version according to the optimal field, and a matching field is calculated; and obtaining standard mapping field information under the current version based on the mapping rule table of the matching field, and outputting the standard mapping field information. The problem that a traditional rule system depends on a fixed field and is not sensitive to version change is solved.

The invention discloses a privacy protection effect evaluation method and system, and the method comprises the steps: carrying out the hierarchical analysis of a privacy protection effect evaluation index system, defining the definition and calculation boundaries of four first-level dimensions, i.e., data features, compliance, availability and safety, and subordinate second-level indexes, carrying out the adaptation of a calculation algorithm for different types of indexes, and carrying out the weighted integration of the second-level indexes through employing an analytic hierarchy process, thereby achieving the evaluation of the privacy protection effect. Multi-dimensional quantitative evaluation is realized; an index association rule base and a matrix are constructed through static analysis and dynamic mining, and linkage analysis is achieved; determining an index fluctuation reference according to scene characteristics and historical data, converting user demands into a variation amplitude of a fluctuation reference unit, and realizing index dynamic linkage updating based on an incidence matrix; and finally, the optimization rule is verified through simulation testing and historical data backtracking, and a dynamically adaptive and self-iterative optimization relation rule system is formed, so that the limitation of traditional evaluation is broken through, a scientific basis is provided for privacy protection strategy optimization, and the privacy governance efficiency is improved.

The invention discloses an edge end multi-modal perception and decision cooperation method and system, and relates to the edge calculation and artificial intelligence cross technology field, and the system comprises a multi-modal perception unit used for collecting heterogeneous perception data of an environment; the data processing unit is in communication connection with the multi-mode sensing unit; wherein the data processing unit comprises a dynamic cognitive kernel, and the dynamic cognitive kernel sequentially comprises a modal credibility evaluation layer, a context reasoning layer and a decision verification layer; and the modal credibility evaluation layer is configured to receive the heterogeneous sensing data. According to the edge-end multi-modal sensing and decision-making collaboration method and system, by introducing a dynamic cognitive kernel, real-time evaluation and dynamic weight adjustment of the reliability of multi-modal sensing data are achieved, the defect that the collaboration efficiency of a traditional fixed rule system is reduced when the environment changes is effectively overcome, and the collaborative performance of the system is improved. And the adaptability and decision accuracy of the system in a complex scene are improved.

The invention discloses an industry operation permission intelligent approval system based on Java and an implementation method, and belongs to the technical field of computer application. The system adopts a four-layer architecture including a presentation layer, a business logic layer, a data access layer and a data storage layer. The core of the system is that the business logic layer integrates an intelligent auditing engine, a rule engine and a workflow engine; the intelligent auditing engine automatically analyzes and extracts key information in the application material through NLP and OCR technologies; a rule system based on industry classification is built in the rule engine, three-dimensional automatic verification of subject qualification, material integrity and condition conformity is executed, and regulatory terms can be mapped into executable logic; and the workflow engine automatically triggers and manages the approval process according to the verification result. According to the invention, full-process automation and intellectualization of industry operation permission approval are realized, the approval efficiency, accuracy and dynamic adaptation capability to policies and regulations are greatly improved, and the problems of low efficiency, different standards, lagging and the like existing in a traditional manual approval mode are effectively solved.

The invention provides a work fund compliance auditing method and system based on a big language model, and relates to the technical field of compliance auditing, the method comprises the following steps: extracting an unstructured text, and using the big language model to carry out data cleaning, privacy desensitization and standardization processing; based on a large language model and a retrieval enhancement generation technology, constructing a work fund compliance knowledge graph, performing deep auditing analysis on standardized data, and identifying income and expenditure anomalies, asset vulnerabilities and compliance risks; building a compliance rule base by using a large language model, and performing automatic compliance judgment; generating a differentiated improvement suggestion, a rectification path and an audit report first draft, and continuously optimizing a model and a rule; the system comprises a multi-source data acquisition and preprocessing module, a deep audit analysis module, a compliance analysis module, a decision support and report generation module, and a learning and optimization module. According to the invention, through automation and intelligentization of the work fund auditing process, the auditing efficiency, the auditing accuracy and the self-adaptive capability are remarkably improved.

The invention provides an intelligent time sequence security analysis method and device based on a large language model, and relates to the technical field of information security, and the method comprises the steps: collecting an original log from a multi-source heterogeneous system to obtain a standardized log event; inputting the standardized log event into a preset large language model to generate a historical semantic log event; constructing a user digital portrait based on the historical semantic log event, and establishing a dynamic behavior baseline model associated with the user digital portrait by using a long-short term memory neural network model; obtaining a semantic log event stream output by the large language model in real time, and querying a dynamic behavior baseline model of a user corresponding to the semantic log event stream; and evaluating whether the current semantic log event is an abnormal event: if the current semantic log event is an abnormal event, calling a large language model to generate a semantic alarm, and injecting the semantic alarm into a rule engine. The technical problems that a traditional static rule system is poor in adaptability, high in false alarm rate and high in maintenance cost are effectively solved.

A water level data anomaly detection method based on rule and neural network fusion comprises the following steps that S10, upstream and downstream water level stations associated with a target water level station are determined, and rainfall stations influencing the water level of the water level stations are analyzed; s20, converting the collected and processed data into a time sequence format suitable for the input of the LSTM model; s30, constructing an anomaly judgment rule system of the target monitoring station, converting a rule into a differentiable mathematical constraint term, and embedding a training target loss function of the LSTM model; s40, a rule-constrained LSTM model is constructed and trained, whether the model is converged or not is judged based on the change trend of a target loss function, and model parameters and rule weight coefficients are corrected; and S50, using the trained rule to constrain the LSTM model, and performing water level abnormal data detection and result output. According to the method, the rule parameters are fused into the LSTM, two normal forms of data driving and knowledge driving are combined, the abnormal water level data are accurately recognized, and meanwhile, the interpretability and engineering applicability of an abnormal detection result are improved.

The invention discloses an unformatted scale bill intelligent conversion method and system based on image recognition, relates to the technical field of character intelligent recognition, and solves the technical problems that the accuracy of subsequent OCR recognition is unstable due to the fact that the inclination angle detection precision is low and noise filtering and contrast enhancement effects are limited by scenes. According to the method, table line detection and text line analysis are adopted for the structured / no-table scale bill, the inclination angle detection precision is improved, layered denoising and adaptive contrast enhancement are achieved, the text recognition accuracy in a complex scene is improved, PaddleOCR is dynamically switched based on the Chinese proportion, a low-confidence region recheck mechanism is combined, the recognition error rate is reduced, and the recognition efficiency is improved. According to the technical scheme, a scale document field dictionary and a relation graph are constructed, intelligent term matching and error correction are achieved, the recognition problem of uncommon words and industry exclusive vocabularies is solved, a hierarchical rule system of basic verification, association verification and compliance verification is adopted, format, logic and industry / enterprise compliance full dimensions are covered, and the verification coverage rate is increased.

The invention discloses a sheet metal part manufacturability reasoning method based on space-semantic map alignment, and relates to the field of manufacturing-oriented design evaluation and industrial knowledge reasoning, and the method comprises the following steps: carrying out geometric analysis on a CAD geometric model of a sheet metal part to be evaluated; abstracting the geometric features and the topological / metric spatial relationship thereof into a computable spatial semantic graph; performing semantic analysis on the process specification described by a natural language, converting the process rule into formalized logic check expression by using a large language model through context learning, and generating an executable domain-specific language check script; executing the script on a spatial semantic graph, realizing deterministic reasoning through graph matching and attribute verification, and completing accurate mapping and violation detection of text rules and geometric features; and outputting an interpretable diagnosis result containing violation feature positioning, triggering rules and numerical evidence. In order to solve the problems that a process rule'natural language-geometric model 'has a semantic gap, a traditional rule system is poor in adaptability, and an end-to-end learning method is high in data dependence and cannot be explained, a new rule can be quickly adapted under the condition that a large amount of data does not need to be labeled and a model does not need to be retrained; the method can accurately identify the violation of the micro-size and spatial relationship, and has reasoning preciseness, interpretability and engineering availability.

The invention discloses a personal data value management system and method. The system comprises a main body collaboration layer which is used for defining roles and responsibilities of multiple main bodies and constructing a multi-main-body collaboration framework; the process management and control layer is used for constructing a full-process closed-loop design and establishing linkage rules of all links; the rule guarantee layer is used for constructing a rule system and dynamically adjusting the rule system according to scenes, policies and markets; the value distribution layer is used for carrying out quantitative processing on the individual contribution degree and the enterprise contribution degree and carrying out income distribution according to the individual contribution degree and the enterprise contribution degree; and the personal participation layer is used for constructing a personal participation mechanism so as to obtain personal data dynamics and rights and interests in real time. By adopting the system provided by the embodiment of the invention, a five-layer cooperative personal data valued management system is constructed, each layer not only defines the core function, but also realizes cooperation through data synchronization, rule linkage and a subject counterbalance mechanism, and a management closed loop covering the full life cycle of personal data is formed.

The invention discloses an intelligent classification and grading method and system for structured data, and relates to the technical field of artificial intelligence and data security, and the method comprises the steps: obtaining to-be-classified structured data, and carrying out the data preprocessing; performing preliminary identification based on the rule system, and outputting rule system credibility; inputting the preprocessed structured data into the model system to obtain the credibility of the model system; the credibility of the rule system and the credibility of the model system are subjected to weighted fusion, credibility fusion scores are obtained through calculation, and the type with the maximum fusion score is selected as a fusion classification result of the structured data; dynamically adjusting the weight of the fusion score according to the credibility difference; outputting a grading label of the to-be-classified structured data; and after user feedback is received, the rule system rule and the model system parameters are updated. The method supports different types of structured data sources and constantly changing business requirements, and has wide application prospects and industrial popularization values.

The invention discloses an intelligent form processing method and system based on rule tree optimization and large model screening, and relates to the technical field of artificial intelligence and data automation. The problems that the processing efficiency is low due to static solidification of a rule system, redundancy of execution paths and dispersion of homogeneous rules in a traditional form processing method, and the screening accuracy is low and the labor cost is high due to the lack of effective judgment capability for complex, fuzzy or implicit condition data of semantic understanding are solved. By constructing and optimizing the rule tree structure and combining with the semantic comprehension ability of the large language model, automatic grading processing and intelligent screening of the form are realized. Firstly, deterministic rule matching and filtering are completed by a rule tree, then semantic judgment is carried out in a logic context by utilizing a large model for uncertainty data, and finally, a screening result is fused and output. According to the method, the efficiency, the accuracy and the automation level of form processing in a complex business scene are remarkably improved.

This invention belongs to the field of artificial intelligence technology, specifically disclosing an intelligent rule conflict detection method, system, and program product. By transforming the traditionally tedious and time-consuming manual rule review into intelligent rule conflict detection, it greatly simplifies the verification process for business personnel in rule design and significantly improves the efficiency of rule review. It can comprehensively and deeply analyze the rule system, automatically identifying logical errors, overlaps, and contradictions that are difficult for the human eye to detect, effectively reducing the risk of rule defects and potential financial losses caused by human negligence. It deeply embeds artificial intelligence technology into the business operation process of rule configuration, ensuring the logical validity of rules once they are created, and promoting the upgrade of enterprise rule management from reliance on manual experience to intelligent automatic detection. Furthermore, it can intuitively display potential problems and conflicts between rules, making the entire rule verification process clearer and easier for users to understand and make decisions.

The invention discloses a new pipeline planning path analysis method and system based on artificial intelligence. The method comprises the following steps: S1, constructing a multi-source data fusion platform; s2, establishing a dynamic constraint rule system which comprises a mandatory specification library, planning control line conflict detection, a safety spacing matrix and an elastic economic index; s3, developing an intelligent migration and modification decision model, and dynamically calculating a pipeline migration and modification priority threshold value through the five-dimensional evaluation matrix; s4, realizing multi-modal demand analysis and scheme generation, supporting mixed input and outputting a main and standby scheme comparison table; and S5, constructing an adaptive optimization module, and carrying out compliance and economy balance adjustment on the initial scheme based on the new constraint condition. According to the method, on the basis of the input demand, the space constraint, the engineering specification and the economical efficiency of bypassing or relocating the existing pipeline are comprehensively considered, the new pipeline planning scheme and a plurality of alternative schemes are provided, and the problems that traditional planning depends on artificial experience, efficiency is low, and the pipeline conflict risk is likely to be caused are solved.

The invention discloses a production scheme selection method, device and system, a storage medium and a computer program product. The method comprises the steps of obtaining multi-dimensional production data in a production process; processing the multi-dimensional production data to remove invalid data and unify data formats; target machine type information is received, and at least one corresponding initial production scheme is called from a preset production scheme library according to the target machine type information; based on a preset rule system which can be dynamically adjusted independent of a business process and contains inter-rule association logic and scenarized rule grouping, performing matching screening on the multi-dimensional production data and the initial production scheme to obtain a candidate production scheme; performing quantitative evaluation on the candidate production schemes by adopting a preset multi-dimensional evaluation index system, and determining an optimal production scheme; and verifying the optimal production scheme, and outputting the scheme after the verification is passed. According to the scheme, the flexibility and maintainability of the scheme selection technology are improved, and the scheme selection accuracy is improved.

The invention relates to the technical field of artificial intelligence, in particular to a papermaking employee virtual practical training method and system combined with a large model. According to the papermaking employee virtual practical training method and system combined with the large model, immersive interaction with a high-fidelity virtual papermaking production line is achieved through data collection, virtual simulation and large model interaction and reasoning. According to the method, a large-scale language model is introduced, so that the method has strong natural language understanding, deep reasoning and multi-modal information fusion capabilities, complex and changeable virtual environment states can be analyzed in real time, and intelligent diagnosis and guidance are performed in combination with natural language questions of employees; and even non-preset or multi-factor coupled fault scenes which cannot be processed by a traditional rule system can be processed, man-machine dialogue interaction similar to experienced experts is realized, and the intelligent level of the system on solving complex problems is remarkably improved.

The application discloses a kind of comprehensive data analysis system and method applied to intelligent audit, it is related to big data analysis technical field, the present application is by obtaining audit object whole-link business data, constructs full audit data set by standardization processing;Extract static and dynamic risk characteristics, build the comprehensive rule system containing static and dynamic required verification data type library;Analysis single risk and associated business link causality, generate audit associated link mapping table and ordered risk set;Calculate corresponding risk determination threshold;Real-time monitoring audit business based on threshold, data characteristics are intercepted by sliding window, after audit cycle ends, iteration updates threshold.The system includes data full collection, audit rule construction, associated link analysis, determination threshold calculation, audit threshold iteration module and subordinate unit.The present application realizes full data coverage and real-time monitoring, accurately traces risk transmission, improves the confidence and timeliness of audit compliance judgment.

Disclosed in the present description are a multivariate decision processing method and apparatus, a storage medium, and an electronic device. The method comprises: acquiring an initial multivariate decision rule system configured by a user end on the basis of standard DSL grammar rule information, wherein the standard DSL grammar rule information is DSL grammar rule information preset for a multivariate transaction decision development scenario; determining all multivariate decision rule combinations corresponding to the initial multivariate decision rule system, and determining reference transaction workflows corresponding to the multivariate decision rule combinations; constructing a multivariate combination instance library for the initial multivariate decision rule system, and on the basis of the multivariate combination instance library, performing engineering language conflict detection processing on the initial multivariate decision rule system, so as to obtain a target multivariate decision rule system after conflict detection; and using the target multivariate decision rule system to perform transaction workflow decision processing on transaction data.

Embodiments of the present application provide a user property safety risk early warning method, device, medium and product, relating to the technical field of communication. The method comprises: based on the user's operator network behavior data, obtaining the user's communication behavior data, location data and application usage data; generating target features according to the communication behavior data, the location data and the application usage data, and constructing an abnormal feature matrix according to the target features; according to the abnormal feature matrix, using a preset statistical generation model, a deep learning discriminant model and a knowledge-driven rule system, respectively generating a first generation result, a second generation result and a third generation result containing the user's risk state, and determining the user's predicted state result based on the three results, and performing corresponding property safety warning behavior based on the predicted state result. The scheme of the present application solves the problem of limitations in the security attack risk of the old people's telecommunication mode.

Systems and methods include causing presentation of a first cluster in association with an event of the first cluster, the first cluster from a first set of clusters of events. Each event includes a time stamp and event data. Based on the presentation of the first cluster, an extraction rule corresponding to the event of the first cluster is received from a user. Similarities in the event data between the events are determined based on the received extraction rule. The events are grouped into a second set of clusters based on the determined similarities. Presentation is caused of a second cluster in association with an event of the second cluster, where the second cluster is from the second set of clusters.

A system can establish a database that includes a plurality of atomic units from documents relating to one or more clinical actions. The system can receive a request for authorization of a clinical action and determine, using a rules engine, one or more rules for a type of the clinical action. The system can generate a query to the relational database to retrieve, from the relational database, a group of atomic units dynamically identified as corresponding to the type of chunk and based on the one or more filters. The system can determine, by the rules engine using the one or more rules, that the group of atomic units resulting from the query satisfy the one or more rules. The system can authorize the clinical action responsive to the determination.

The embodiment of the invention provides an intelligent ship and cargo matching method, device and equipment. The method comprises the following steps: in response to received rule constraint data predefined by a user, constructing and initializing a rule base about ship and cargo matching based on the rule constraint data; wherein the rule base comprises a multi-level rule system which is divided into a hard constraint and a soft constraint; in response to the received ship transport capacity data and cargo demand data, performing preliminary matching screening on the ship and cargo combination based on a rule base, and generating a candidate matching set; based on the candidate matching set, a Pareto optimal solution set for ship and cargo matching is solved and generated through a multi-objective optimization algorithm; in response to the received real-time shipping data, updating a matching scheme in the Pareto optimal solution set through a dynamic feedback mechanism, and generating an updated matching scheme; and determining a final ship and cargo matching result based on the updated matching scheme. By using the method provided by the embodiment of the invention, the decision-making efficiency of shipping logistics can be comprehensively improved.

The invention discloses a data quality auditing and scoring model, which relates to the technical field of data processing and comprises the following steps of demand acquisition and scheme formation, rule system construction, data auditing execution, comprehensive quality scoring and problem closed-loop processing. According to the method, management and control objects, core data items, six data quality dimension standard-reaching requirements and differentiated management and control thresholds are defined in a demand acquisition stage, a data quality scheme is formed, a quality rule system covering beforehand, during and after beforehand is constructed, a verification logic module is formed according to a six-dimension disassembling rule, and a verification result is obtained. A data quality auditing model is built to verify target data, deep linkage of demand-rule-auditing is achieved in the whole process, a rule system and a management and control target are precisely matched, the data quality auditing model executes verification according to a standardized dimension, the dimension problems of data integrity, uniqueness and the like can be precisely positioned, and the verification efficiency is improved. And clear data quality control direction and efficient execution are ensured.

The invention relates to the technical field of interpretable reasoning, in particular to an enterprise AI ethical compliance management platform combined with a knowledge graph, and the platform comprises the steps: analyzing an enterprise ethical file to generate a clause node set, comparing nodes, reconstructing responsibility conflicts to establish a responsibility level graph, calling the graph to carry out semantic dependency mapping on enterprise behaviors and clauses, constructing a behavior ethical mapping set, evaluating the transparency of a mapping path, adjusting a display scheme, generating a reasoning transparent path set, evaluating the compliance according to the path, carrying out the classification identification, and outputting an enterprise ethical compliance management result. According to the method, ordered arrangement of conflict relationships and differentiated definition of rule boundaries are realized through precise disassembly of constraint terms and layered construction of responsibility relationships, structured mapping with hierarchy and dependency features is formed through semantic path matching, the verifiability of logic trajectories is enhanced by using a transparency grading mechanism, and finally, differentiated risk presentation is performed. And the rule system is coherent, explainable and controllable.

The invention discloses a business document cost allocation method and device, equipment and a medium, and the method comprises the steps: obtaining the document type of a business document; based on a plurality of preset extraction fields corresponding to the receipt type, extracting cost information from the business receipt; based on the cost information, determining an apportionment rule of the business document; and based on the apportionment rule and the cost information, generating apportionment result data of the business document. By integrating allocation scenes of different business receipts and constructing a universal and configurable allocation rule system, various business receipts can directly quote a unified rule to realize cost allocation, and the problems that a traditional manual processing mode is low in efficiency and easy to make mistakes are effectively solved; while the allocation accuracy and the execution efficiency are remarkably improved, the workload of repeated development and customization is greatly reduced, and the manpower and cost investment is reduced; and by supporting flexible rule configuration of multiple service scenes, the application flexibility and universality of pre-allocation of the bill cost are further enhanced.

The invention belongs to a text data processing system or method in the field of administrative penalty. The invention discloses an administrative auxiliary quantity penalty method based on a large model, and the method comprises the steps: 1, inputting core data, and providing an environment field case original text, law articles, a case vector database and related basic data; 2, text preprocessing is conducted, non-target domain texts are intercepted through a domain verification-multistage cleaning-structured regularization three-order framework, data noise is eliminated, and structured analysis is completed; 3, quantity penalty core elements are extracted, model context limitation is broken through by adopting a fragmentation-extraction-aggregation framework, and accuracy of quantity penalty elements is ensured by combining multi-round iterative extraction and verification optimization; step 4, quantity penalty amount calculation: depending on an expert rule system and a layered random forest regression model, realizing scene-based quantity penalty amount calculation; and step 5, outputting a result, generating an administrative penalty decision draft, a quantity penalty basis and an amount detail, and attaching a confidence label to ensure that the result is traceable.

According to the semantic rule association system and method based on the accounting knowledge graph, an accounting element three-dimensional semantic modeling system and a rule association degree quantitative evaluation model are introduced, and the problems that accounting elements are isolated and stored, change is complex, and evaluation is difficult in a traditional method are solved; and the universality and high self-evolution of the rule system are realized. The method is suitable for a financial sharing center, a transnational group tax management system, an intelligent auditing platform and other scenes needing to process complex accounting rule association, and is particularly beneficial to prevention of finance and tax compliance risks.

A vehicle management system comprising a computer system and an agent. The agent comprises a machine learning model and a rule system. The machine learning model system is trained to receive observations for the vehicle system and select a behavior for the vehicle system in response to receiving the observations. The rule system is configured to select a set of actions to execute the behavior for the vehicle system in response to a selection of the behavior by the machine learning model system.