22 results about "Enterprise resource planning" patented technology

A system may include a data classification module configured to classify data into classified data based on predefined sensitivity levels and regulatory compliance requirements. A system may include an access control module configured to manage permissions for different user roles within an enterprise, granting access to the classified data in accordance with the sensitivity levels and regulatory compliance requirements. A system may include a data formatting module configured to format classified data into formatted data with customized presentations for various enterprise departments. A system may include an integration module configured to interface with at least one of an Enterprise Resource Planning (ERP) system or a Customer Relationship Management (CRM) system to retrieve and classify the data. A system may include a user interface module configured to present the formatted data within the host application, providing a seamless user experience for accessing the embedded marketplace.

The application provides an enterprise resource planning system data flow conversion monitoring method, relates to the technical field of abnormal cause analysis in an enterprise resource planning system, and aims to solve problems such as difficult extraction of abnormal propagation causality, unexplainable traceability path, and low efficiency of quasi-real-time cause analysis in a multi-node complex business process. The core technical scheme includes the following steps: collecting and standardizing time series data of business key state indicators, establishing a local causal correlation cache by using a sliding window and a causal reasoning algorithm, recursively tracing back to an abnormal node as a terminal to form a preliminary causal path, screening high-confidence causal links through multi-dimensional time consistency evaluation, generating a simplified dynamic path by using a node semantic aggregation mechanism, and finally embedding the path in an operation and maintenance interface in the form of a visual trajectory to realize interactive cause analysis verification. The scheme improves the accuracy and explainability of abnormal cause analysis, realizes full-process automation and man-machine fusion optimization from data to root cause links, and significantly enhances the intelligent level of system abnormality handling.

The application discloses an AI-enhanced knowledge graph quality anomaly tracing method based on industrial big data, obtains historical production data and real-time production data corresponding to multiple data sources in an industrial production line, the multiple data sources including a manufacturing execution system, an enterprise resource planning system, a sensor collection system and a quality detection system; the historical production data is preprocessed based on preset data cleaning rules and data alignment rules, an industrial big data set reflecting production elements in the whole life cycle of a product is constructed, the production elements including equipment parameters, process parameters, material properties, environmental conditions and operation specifications; multi-source heterogeneous data in the industrial big data set is subjected to feature extraction and entity alignment operations by using a deep graph attention network, and an initial quality knowledge graph containing multiple nodes and multiple edges is generated, wherein the nodes represent production entities, and the edges represent process correlation relationships and material flow direction relationships between the production entities.

This invention belongs to the field of enterprise resource planning (ERP) technology and discloses an intelligent matching method and system for policy application conditions based on enterprise resource analysis. By constructing a dedicated extraction model for policy application elements, it performs structured parsing and industry-customized four-level hierarchical decomposition of policy texts, transforming natural language application conditions into executable logical expressions. It accurately handles complex rules such as AND, OR, non-nested logic, numerical thresholds, and temporal constraints. A four-level progressive matching and verification mechanism is adopted, prioritizing matching according to the order of prohibition (one-vote veto), rigid access, core scoring, and auxiliary bonus points. It incorporates industry-specific rules and enterprise lifecycle adaptation logic to avoid invalid matching and screening biases, upgrading policy matching from superficial textual association to deep compliance verification, thus improving matching accuracy and adapting to the application and screening needs of various complex preferential policies for enterprises.

The application discloses a kind of packaging robot monitoring processing method and device, comprising: obtaining user information in user interface, and the user information is bound to the printing information of packing label of packaging robot;Obtain the model of current slitting film roll, customer information and order information, generate the order list of enterprise resource planning system;According to the order list and the packaging rule configured, execute film roll packaging task, and print label according to the printing information, and paste the printed label on the packaging snap-on paper cover.The application can automatically execute film roll packaging task according to the configuration and information of user interface and packaging interface, simplify the operation and interface of film roll packaging, and facilitate monitoring processing of packaging robot.

This invention relates to the field of enterprise resource planning (ERP) systems, specifically disclosing an ERP material specification interpretation and selection assistance system based on a large language model. The system includes a specification information collection and preprocessing module, a specification semantic parsing module driven by a large language model, a multi-dimensional selection decision assistance module, and a design result compliance and inventory optimization verification module. By automatically parsing unstructured specification data, intelligently matching candidate materials, and comprehensively evaluating technical parameters, cost-effectiveness, and inventory availability, it achieves efficient and accurate material selection and supply chain resource optimization.

This application discloses a method, apparatus, computer device, computer-readable storage medium, and computer program product containing instructions or computer programs for generating business declaration rules, aiming to improve the accuracy, efficiency, and timeliness of business declaration rule generation. The method includes: acquiring target metadata entities related to changes in business policies; inputting the target metadata entities into a large model; and having the large model perform the following steps: determining initial declaration rule information from a preset knowledge base whose matching degree with the target metadata entities reaches a preset matching degree threshold; and outputting a first target declaration rule corresponding to the target metadata entities based on the initial declaration rule information. This application can be applied to various information management systems, such as Enterprise Resource Planning (ERP) systems, tax systems, financial systems, human resource systems, and supply chain systems.

This invention relates to the field of enterprise business data processing technology, specifically a business collaborative management system based on multi-dimensional data fusion. The system includes modules for data extraction, data cleaning, correlation fusion, analysis cube, and collaborative decision-making. The data extraction module extracts business master data from enterprise resource planning systems, customer relationship management tools, and supply chain management platforms to form corresponding datasets. The data cleaning module removes duplicate records and outliers to purify the data. The correlation fusion module establishes logical mapping relationships between production work orders, sales orders, and purchase contracts in the business data lake, generating a business object correlation graph. The analysis cube module constructs a multi-dimensional data cube with specified dimensions, and the collaborative decision-making module extracts key indicators through multi-dimensional operations to form an indicator set. This system breaks down the isolation of cross-source data, achieving full-domain correlation of business data and targeted extraction of collaborative decision-making indicators.

A computer-implemented system and method for analyzing quality-event information through standardized representations, rule-based logic, and adaptive training. The system receives free-text quality-event descriptions and stores them in a historical database. A standardization module converts narratives into structured representations using domain terminology normalization and calibrated severity indicators. A rule-logic engine generates quality-rule constructs identifying primary, secondary, and systemic causes. A root-cause clustering module classifies events into related causal clusters. Based on identified clusters, the system generates training modules and control -measure templates for preventive or corrective actions. A simulation engine executes adaptive, branching scenarios presenting role-specific decision points and collecting performance metrics. A benchmarking engine computes comparative indicators across organizational units using anonymized data. An update module refines quality-rule constructs based on simulation performance and external system data. The system integrates with quality management, enterprise resource planning, and training platforms, enabling continuous organizational improvement through objective, data-driven analysis and dynamic, personalized training delivery.

The embodiment of the application provides a data processing method, device and equipment of an enterprise resource planning system and a medium, comprising: obtaining system data to be processed; performing semantic analysis on the system data to be processed through a large language model to generate a data sensitivity grading result; performing algorithm rating on the system data to be processed to generate an algorithm score; performing strategy matching on the system data to be processed according to the data sensitivity grading result and the algorithm score to generate a strategy description file; performing algorithm matching according to the strategy description file to obtain a data privacy protection algorithm; and encrypting the system data to be processed according to the data privacy protection algorithm to complete privacy protection of the enterprise resource planning system data, thereby improving the accuracy of the data processing effect of the enterprise resource planning system.

This invention belongs to the field of risk warning technology, specifically relating to a method and system for early warning of automotive supply chain disruption risks based on multi-source data fusion. The method includes the following steps: acquiring and fusing enterprise resource planning system data, real-time logistics data, and external event data from each node in the automotive supply chain network to construct a corresponding cell for each node in the supply chain network; calculating the risk-bearing coefficient of each cell based on its own buffer inventory level and substitutability; defining the state of each cell as a multi-dimensional state vector containing operating status, material fulfillment rate, and risk accumulation value; and calculating the correlation strength between cells based on historical transaction frequency and real-time logistics data. This invention provides managers with intuitive and actionable early warning information by generating risk heatmaps, high-risk node lists, and risk propagation subchains, thereby enhancing the practical value of the early warning results.

A unified, AI-driven system for automating business processes for seamless integration with one or more enterprise resource planning (ERP) platforms, wherein the system comprises: • an ERP integration and data synchronization engine (101) configured to establish secure bidirectional communication with a variety of heterogeneous ERP systems via standardized application programming interfaces and real-time data exchange protocols, and maintains a centralized, synchronized enterprise data repository containing transactional and master data;• a workflow optimization module (102) that is functionally coupled with the ERP integration and data synchronization engine (101) and configured to analyze historical and real-time business process data using machine learning models to automatically identify inefficiencies and generate optimized workflow execution paths; • a natural language interaction agent (103) configured to receive natural language input from a user, perform intent and command detection, and convert the natural language input into executable ERP workflow instructions; • a predictive analytics module (104) configured to process business data records from the centralized, synchronized business data repository and generate demand forecasts and resource planning predictions;• An anomaly detection and compliance monitoring agent (105) configured to monitor ERP transaction flows and user access activities, detect irregular events based on anomaly detection models and compliance rules, and generate alerts and audit logs; • A process orchestration engine (106) configured to execute and coordinate multi-stage automated business processes across multiple ERP modules based on outputs generated by the workflow optimization module (102), the predictive analytics module (104), and the anomaly detection and compliance monitoring agent (105); • A reporting and monitoring dashboard (107) configured to generate real-time performance indicators, workflow status reports, and exception reports via role-based visualization interfaces;and • an adaptive learning module (108) configured to continuously retrain and update at least one in the workflow optimization module (102), the predictive analytics module (104), and the anomaly detection and compliance monitoring agent (105) based on feedback data on process results collected during the execution of enterprise workflows, wherein the process orchestration engine (106) is further configured to autonomously trigger the execution of at least one ERP transaction or workflow action in response to natural language input and machine learning model outputs, thereby enabling automated optimization and execution of enterprise business processes across a multitude of heterogeneous ERP systems.

A computer-implemented method receives a request to explain a configuration key which represents a set of rules for controlling a process flow of an entity of an enterprise resource planning (ERP) system, the set of rules defining a data operation scheme based on a plurality of tables stored in a database of the ERP system. The method generates a data object from the plurality of tables, the data object including a group of key-value pairs which collectively define the set of rules, preserving the hierarchy of the involved tables. The method generates a prompt based on the data object generated from the plurality of tables, prompts a large language model using the prompt, receives a response from the large language model, and based on the response, outputs an explanation of the configuration key summarizing the set of rules in natural language. Related computing system and software are also disclosed.

This invention discloses a method for intelligent generation, dynamic maintenance, and closed-loop restoration of spot contracts based on coil hash values, belonging to the field of Enterprise Resource Planning (ERP) technology. This invention enables batch selection of coils and one-click contract generation. The coil hash values ​​are generated and automatically grouped based on four core attributes: material, thickness, width, and zinc layer. Unit prices and freight are set in batches by hash group. Coil materials are dynamically added and deleted before financial review. After a contract is deleted, the associated coil materials automatically restore their original inventory state. This invention focuses on the core need for "rapid order completion" in spot trading, with ≤3 operation steps and a 95% improvement in order completion efficiency. It solves the problems of cumbersome operation, error-prone manual calculation, inconvenient adjustment, and data corruption after deletion in traditional spot contracts. The operation is extremely simple, the calculation is accurate, the dynamic adjustment is possible, and the closed-loop system is secure. It is suitable for high-frequency, high-volume business scenarios in spot trading such as steel coils. This invention has been fully implemented in a real industrial ERP system and can run stably in a real production environment for a long time, possessing mature engineering value and industrial promotion capabilities.

This invention provides an information processing method and program for collecting financial and non-financial data and disclosing and reporting them appropriately. [Solution] In a sustainability ERP (Enterprise Resource Planning) system, the information processing method involves a computer server that obtains data items that are required to be disclosed to the user organization in accordance with non-financial information disclosure standards, refers to a storage device that stores non-financial information related to the user organization, determines whether or not the storage device stores data corresponding to the data items, and outputs the result of the determination.

The invention relates to a system and method for optimizing task assignments in an enterprise environment using real-time biometric data collected from wearable devices. The system comprises a wearable device for capturing biometric signals such as heart rate, EEG data, and other physiological metrics, a data processor to filter and validate the data, a cognitive analyzer to calculate cognitive metrics including cognitive load, stress levels, and focus, and a workflow optimizer to assign tasks based on these metrics. The workflow optimizer leverages machine learning algorithms to analyze the cognitive state of users, compare it with historical performance data, and adjust workload distribution dynamically. The system integrates with enterprise resource planning (ERP) systems to synchronize optimized task assignments across the organization. By monitoring cognitive metrics in real time, the invention ensures efficient workload management, reduces employee fatigue, and enhances overall productivity.

The application discloses a kind of production line ERP-MES fusion's whole-process production report intelligent collection and unmarked material statistics method, and relates to the technical field of industrial manufacturing execution system and enterprise resource planning system data fusion.The application builds whole-process standardization data base, adopts contract number+contract hash value+plan number cross-system unique identification, realizes whole-link data and reverse trace-through;Through lightweight temporary table extremely simple reuse, CTE one-step calculation and batch direct change main table strategy, realize large data volume report second-level response;Establish 100% accurate identification and whole node statistics mechanism of unmarked material, adapt to the unified standard output of multiple business types.The application solves the pain points such as data dispersion, system barrier, unmarked material control loss, report generation slow, etc., and the statistical efficiency is improved by more than 95%, which has been landed in actual industrial production line MES system, with mature engineering value and full-industry promotion ability.

This invention provides an information processing method, an information processing program, and an information processing device that output the power procurement methods necessary for companies to achieve their greenhouse gas emission targets. [Solution] In a server that is a component of a Sustainability ERP (Enterprise Resources Planning) system, the control unit acquires the company ID and target time for achieving the target of the company to be processed, and from a storage unit that stores usage information including site IDs and power consumption in association with the company ID, it acquires the usage information corresponding to the acquired company ID, and also acquires the renewable energy procurement method and constraints related to the procurement of renewable energy, and executes a process to determine the renewable energy procurement method for achieving the target at the target time for each site ID based on the usage information, procurement method and constraints, and outputs the procurement method for each site ID that was determined.

The present invention relates to a method for automating the generation of business procedures for automatically generating a business procedure input process. The present invention includes a data collection step of collecting payment slips and supporting files from an Enterprise Resource Planning (ERP) system, and a business procedure ontology generation step of setting the payment slips as upper-level tasks and the supporting files as lower-level tasks, and generating a business procedure ontology for the upper-level tasks by assigning sequence numbers to the lower-level tasks in reverse order of the creation date of the supporting files.

A large language model data analysis method and apparatus, a computer device, and a storage medium, relating to the technical field of intelligent data processing, and being applied to an enterprise resource planning (ERP) system, etc. The method comprises: acquiring query information to be analyzed, and matching a target data indicator in a preset data indicator set on the basis of said query information; when the matching of the target data indicator fails, matching a corresponding target data entity on the basis of said query information; when the matching of the target data entity succeeds, acquiring a data indicator template, and determining the target data indicator on the basis of the target data entity and the data indicator template; and acquiring target data on the basis of the target data indicator, and obtaining a data analysis result on the basis of the target data. Using the present method can improve the accuracy of intelligent query for complex forms.

This invention discloses an integrated industrial IoT system and method for teaching and prototyping based on a control agent fusion architecture. It includes: a physical control layer comprising multiple educational programmable controllers (PLCs) for controlling production equipment and collecting data; a communication coordination layer, which is a dedicated controller independently configured and not connected to external devices, with a built-in message broker service serving as the system's sole message hub; a data intermediary layer for subscribing to specific topics of the message hub and completing protocol conversion; an enterprise application layer deploying an enterprise resource planning (ERP) system to achieve business management and interact with equipment commands; and a visualization layer for real-time monitoring. This invention, by adding a dedicated controller as an independent message broker, achieves complete decoupling of equipment control and communication functions, and employs differentiated data stream transmission, reducing hardware costs and complexity while realizing end-to-end automatic synchronization from physical production to the enterprise business system.

A biometric scanner for processing live seafood harvests streamlines regulation compliance workflows and produces verifiable catch data that integrates with enterprise resource planning (ERP) systems. The biometric scanner captures individual images and weights of a live catch and applies an inference pipeline to the images and weights to determine one or more physical characteristics. Based on the one or more physical characteristics, the biometric scanner generates a unique fingerprint, determines compliance statuses, and generates a processing priority indicia for each individual unit of the live catch. Determining the compliance status requires factoring in all relevant certification and regulation-related policies and is intended to serve as a proxy for onboard oversight or auditing requirements. The processing priority indicia may comprise one more visual, auditory, or haptic feedback cues signaling a recommendation for how to prioritize the contents of a live harvest.