320 results about "Enterprise resource planning" patented technology

The invention discloses an intelligent financial statement automatic generation platform, and relates to the technical field of financial statement generation, and the platform comprises a data integration module which is used for connecting and synchronizing a data source, the data source comprises a bank transaction record, an enterprise resource planning system and an electronic invoice system, and data is grabbed through connection configuration of a data interface. According to the intelligent financial statement automatic generation platform, classification rules of financial subjects are customized by a user, so that the platform can adapt to specific financial statement requirements and business characteristics of different enterprises, the flexibility and applicability of the system are improved, statement customization is supported through the platform, financial statements of different versions are provided, and the user experience is improved. According to the method, different requirements of different users on financial information are met, the flexibility and applicability of the system are improved, a temporary version exclusive to the current requirement is formulated based on a customizable mechanism of a classification rule, and more targeted financial support is provided for decision and management of enterprises.

A computer-implemented method for intelligent query response in an enterprise resource planning (ERP) system is disclosed. The method can receive, from a user interface of the ERP system, a natural language user query for processing data of a target object and invoke a function call through an application programming interface of the ERP system based on the user query. Invoking the function call includes extract denormalized data from one or more database tables, generating a prompt using a prompt template and the denormalized data, prompting a large language model using the prompt, and receiving a response generated by the large language model. The method can generate an output on the user interface based on the response generated by the large language model. Related systems and software for implementing the method are also disclosed.

A real-time inventory management system that includes: a centralized control platform module configured to receive, consolidate, and process inventory data from multiple sources, including warehouses, retail stores, and distribution centers; an IoT and sensor module with RFID tags, barcode scanners, and smart sensors configured to track inventory items, monitor environmental conditions, and update inventory records in real time; a machine learning and demand forecasting module configured to analyze historical inventory data, market trends, and external factors to predict future demand and optimize inventory levels; an automatic replenishment and replenishment module configured to dynamically generate and execute replenishment orders based on real-time inventory levels and forecasted demand; a data integration and synchronization module configured to enable seamless communication and data synchronization with external systems, including Enterprise Resource Planning (ERP) platforms and supplier networks; a user interface and dashboard module configured to display real-time inventory status, actionable insights, and key performance indicators (KPIs) for decision-making.

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The invention discloses an equipment data acquisition and query optimization method based on a large model enabling ERP-MES system, and relates to the technical field of integrated enterprise resource planning and monitoring management systems, and the method comprises the following steps: carrying out the dynamic adaptive data acquisition and preprocessing of equipment: employing an edge node to deploy a lightweight noise detection model, identifying an electromagnetic interference mode in sensor data collected by the equipment in real time, and dynamically compensating and correcting by combining environment temperature and humidity data; according to the method, an edge node lightweight noise detection model is combined with environmental data compensation and correction, and a BERT large model is utilized to analyze a protocol document design middleware, so that accurate acquisition and efficient processing functions of dynamic self-adaptive data of equipment are realized; preprocessed data are mapped and fused through a multi-modal large model to construct a unified data warehouse, and a mapping strategy is optimized through reinforcement learning, so that the equipment data quality is improved, and the data fusion and utilization efficiency is enhanced while heterogeneous data fusion is achieved.

An AI-based automated pricing and inventory management system (100) for retail marketplaces, including: a data collection module configured to collect real-time data from sales transactions, competitor pricing, market trends, inventory levels, and customer behavior; an AI engine configured to process the collected data using machine learning algorithms for demand forecasting, price optimization, and customer segmentation; a price optimization module that is communicatively connected to the AI ​​machine and configured to dynamically adjust product prices based on demand patterns, competitive strategies and predefined business rules in order to maximize profitability and maintain competitiveness in the market; an inventory management module configured to predict optimal stock levels, generate replenishment alerts, and recommend inventory reduction strategies, thereby minimizing overstocking and stockouts; a dashboard interface configured to display actionable insights, pricing strategies, inventory levels, and key performance indicators in real time; an integration layer configured to interface with existing retail systems (100), including point-of-sale (POS), enterprise resource planning (ERP) and e-commerce platforms, enabling seamless operation and data exchange; the system dynamically coordinates (100) decisions on pricing and inventory management based on real-time data analysis, thereby improving operational efficiency, profitability and customer satisfaction.

The invention relates to the technical field of cloud computing, and discloses an enterprise resource planning data efficient processing method based on cloud computing, and the method comprises the steps: collecting system data of finance, supply chain, production management and the like through a multi-source interface module, and carrying out the preprocessing through a hierarchical fusion framework, thereby obtaining a standardized data set; the method comprises the following steps of: selecting a multi-target scheduling model, inputting the multi-target scheduling model into a task decomposition model, generating task decomposition parameters based on a block processing mechanism and a service association rule, constructing the multi-target scheduling model aiming at the shortest processing duration and the minimum resource occupation, and outputting an optimal scheduling scheme by adopting a dynamic allocation algorithm. A hierarchical execution control model comprising a strategy layer, a coordination layer and an execution layer is established, the strategy layer performs global task planning, the coordination layer performs local resource adjustment, and the execution layer tracks tasks based on a load balancing algorithm and outputs a processing control instruction to realize efficient processing. The method improves the data processing efficiency and the resource utilization rate, and provides support for enterprise decision making.

The invention discloses a reinforcement learning-based dynamic production scheduling optimization method and an ERP (Enterprise Resource Planning) integrated system, relates to the technical field of intelligent manufacturing and information technologies, and is used for solving the problem that the rapid development of the modern manufacturing industry puts forward higher requirements on production scheduling optimization. The flexibility and the adaptability are insufficient in the face of a real-time changing production environment; the intelligent level of the production process is improved through the multi-dimensional data processing capacity, the multi-dimensional quantization coding technology is adopted, dimension raising or dimension reduction processing is carried out based on the income-cost ratio of cross-domain technical parameters, and it is ensured that a calculation task is transferred to edge equipment and the PLC-level soft real-time requirement is met through edge calculation and lightweight framework deployment. On-time process completion and delay penalty are integrated, a global optimization item is combined, a meta-learning algorithm is used for dynamically adjusting the weight, and balance of delay, energy consumption and a safety target is ensured.

A system (100) for autonomous inventory optimization and resilient supply chain management in the healthcare and medical device sector, comprising: (a) a data ingestion module configured to collect structured and unstructured data from a variety of sources, including hospital enterprise resource planning (ERP) systems, RFID scanners, electronic health records (EHR), supplier application programming interfaces (APIs), and Internet of Things (IoT) sensors; b) an artificial intelligence / machine learning (AI / ML) module configured to process the ingested data using deep learning and time series prediction models to predict item-level inventory requirements, with the models continuously trained with new data to improve prediction accuracy; c) an optimization module configured to apply operations research algorithms to generate inventory management strategies that minimize inventory costs, reduce expired inventory, and maintain predefined service levels; (d) a supply chain risk module configured to analyze external data sources, including news feeds, weather reports, and supplier performance metrics, to identify potential risks and generate mitigation strategies; and e) a user interface module configured to provide real-time dashboards, alerts and recommendations tailored to clinicians, procurement managers and supply chain analysts.

A system for migrating master data to a target system, comprising: at least one data processor; and at least one memory result in operations comprising: extracting the master data from a source database; validating the extracted master data at a database layer; mapping the validated master data to the target database specific datasets, and inserting the mapped master data into a target database, wherein the extraction, validation, mapping, and insertion are performed at the database layer.

The invention relates to a big language model data analysis method and device, computer equipment, a computer readable storage medium and a computer program product, and is applied to an enterprise resource planning system (ERP) and the like. The method comprises the following steps: acquiring to-be-analyzed question number information, and matching a target data index in a preset data index set according to the to-be-analyzed question number information; matching a corresponding target data entity according to the to-be-analyzed question number information under the condition that the matching of the target data index fails; under the condition that the target data entity is successfully matched, acquiring a data index template, and determining a target data index according to the target data entity and the data index template; and obtaining target data according to the target data index, and obtaining a data analysis result according to the target data. By adopting the method, the intelligent number asking accuracy of the complex form can be improved.

The invention relates to the technical field of data management, and discloses an automatic collaborative decision-making method, system and equipment for enterprise resource planning, a medium and a product, so as to solve the problem of low automatic decision-making efficiency of an existing enterprise resource planning system. The method comprises the following steps: constructing a multi-agent reinforcement learning model, designing a global reward function and a local reward function, constructing a potential function of the local reward function, and training based on training data to obtain a collaborative decision model; collecting work information of each collaborative workflow to obtain business data; calling a collaborative decision model to perform collaborative decision on the business data according to the global reward function and the local reward function, and performing reward shaping on the local reward function based on the potential function to generate a business strategy; generating a decision instruction based on the business strategy according to the labor division content of each actuator in the enterprise resource system; and each actuator executes the corresponding decision instruction, monitors the execution process in real time, records the execution result, generates feedback information, and updates the work information.

A computer-implemented method can run an application associated with an intelligent scenario deployed on an enterprise resource planning (ERP) system. The application receives input values for one or more parameters from a tenant user through a user interface of the ERP system. The method can select a prompt template defined in the intelligent scenario, generate a prompt using the prompt template by replacing the one or more parameters included in the prompt template with respective input values, prompt a large language model (LLM) specified by the intelligent scenario using the prompt, receive a response generated by the LLM, and present the response on the user interface of the ERP system.

The invention discloses an intelligent optimization system for enterprise resource planning, and relates to the technical field of enterprise resource management, and the system comprises a data collection and preprocessing layer, a knowledge construction and reasoning layer, a risk early warning and decision support layer, and a resource scheduling and execution layer. According to the invention, a knowledge graph covering various resources of an enterprise and association relationships thereof is constructed by using preprocessed data through a knowledge construction and reasoning layer, a causal relationship network among the data is established, and meanwhile, an intervention effect evaluation unit combines an anti-factual reasoning result of a causal reasoning unit to evaluate the intervention effect of the enterprise. Quantitative evaluation is carried out on different resource pre-allocation intervention schemes, and a detailed evaluation report is output, so that an enterprise can perceive and take corresponding measures in advance before a risk occurs, and can select an optimal scheme according to a scientific evaluation result when a resource planning decision is made, thereby effectively ensuring stable operation of enterprise resources, and improving the resource planning efficiency. And the operation risk caused by the resource problem is reduced.

The invention relates to the technical field of ERP (Enterprise Resource Planning) data processing, in particular to an ERP data exception analysis method and system based on big data. Extracting time sequence features and frequency domain features of the ERP potential abnormal data, inputting the time sequence features and the frequency domain features into a fluctuation detection model, carrying out fluctuation recognition on the ERP potential abnormal data to obtain ERP fluctuation data, and carrying out multi-dimensional linkage analysis on the ERP fluctuation data through a covariance analysis method to obtain linkage strength among different types of ERP fluctuation data; according to the linkage strength and the time sequence characteristics and the frequency domain characteristics of the ERP potential abnormal data, a service category target threshold value is obtained through a dynamic threshold value adjustment model, abnormal judgment is conducted on the ERP fluctuation data according to the service category target threshold value, when the ERP fluctuation data exceed the target threshold value range, the ERP fluctuation data are marked as first target abnormal data, and a data abnormal early warning mechanism is triggered. And more accurate data anomaly analysis is realized.

The invention relates to the technical field of medical information, and particularly discloses an ERP (Enterprise Resource Planning) integration-based medicine transaction data synchronization system, which comprises a data acquisition module, a mapping analysis module, a data fusion module, a trend analysis module and a center console module. According to the scheme, the multi-dimensional feature space is constructed, the field structures in different service systems are identified and mapped to the uniform target field set to construct the dynamic mapping table, and the adaptability to different service systems is enhanced; a dynamic updating mechanism based on event driving is designed, complex business logic is flexibly handled, the system load is reduced, the system operation efficiency is improved, the compliance of a business process is ensured through cooperation of a rule engine, and the problem of data conflict possibly occurring in the data synchronization process of multiple business departments is solved; and a center console is arranged to monitor efficient circulation of medicine transaction data, so that the stability and safety of data exchange are ensured, a reliable tracing basis is provided for medicine supervision, and subsequent analysis and maintenance are facilitated.

The invention discloses an ERP (Enterprise Resource Planning) data intelligent supervision method based on the Internet of Things, which relates to the technical field of enterprise informatization, and comprises the following steps: constructing a time sequence integrity observation layer in an Internet of Things data acquisition link, embedding a mirror image time anchor in each sensor sampling point, recording electromagnetic interference, time service offset and queuing delay, generating a phase fingerprint time baseline, and obtaining a phase fingerprint time baseline; constructing a unified time reference framework; operating a causal coherent analysis mechanism based on a unified time reference frame, comparing instantaneous offsets of a mirror time anchor and a real-time timestamp, identifying a phase inversion region, extracting information of a corresponding sampling node, a production line position and an energy consumption unit, generating a high-risk time slice list, and determining a time correction boundary. According to the method, a time sequence observation layer and a time reference frame are constructed, phase abnormity is identified, a data trend is reconstructed, a regulation and control instruction draft is generated, sampling time sequence recovery and scheduling convergence are realized through time inversion control, data consistency and decision accuracy are improved, and a closed-loop control system is constructed.

The invention relates to the field of information technology application, and discloses a product manufacturing whole process BOM data structured construction method and related equipment, and the method comprises the steps: obtaining defect BOM data of an order output result of an ERP system; according to the defect BOM data, data features of ABOM, MBOM and EBOM are reversely analyzed and traced, and an analysis result is obtained; according to the analysis result, the ABOM, the MBOM and the EBOM are reconstructed to obtain a BOM data structure; the BOM data structure transmits the obtained data features to an ERP system, and BOM data structured construction work in the whole product manufacturing process is completed. According to the method, result defect BOM data is output through an enterprise resource planning system, BOM data characteristics in various BOM data driving business operation are reversely analyzed, difference removal, same storage and combination are carried out, a product design source EBOM model is reconstructed, and finally a'three-in-one 'simplified new BOM reconstruction model for driving enterprise business operation based on the source EBOM as a product digital main line is realized.

Isolated environments for development of modules of a software system, such as an enterprise resource planning (ERP) system, can be generated using a container image generated from a copy of a central development environment. A graph-based machine learning model can be trained and applied to a graph of the software system to predict dependencies between the modules of the software system. An isolation forest machine learning model can be trained and applied to a selected module to verify its integrity. The container image can be modified based on the predicted dependencies and the integrity verification, among other factors. The modified container image can be executed to generate an isolated environment for the selected module. A version management utility and a transport system can be used during subsequent development in the isolated environment to manage and register repositories and objects associated with the isolated environment.

The invention provides an intelligent inventory management method, device and equipment and a storage medium, and the method comprises the steps: carrying out the data collection and preprocessing of an enterprise resource planning system, a supplier management system and a point-of-sale system of a target enterprise, obtaining an integrated data set, constructing a plurality of time-varying autoregression demand prediction models, and carrying out the prediction of the demands of the target enterprise. Combining a plurality of prediction results for constructing a plurality of time-varying autoregression demand prediction models in combination with an ensemble learning method to obtain a dynamic demand prediction result; according to the deep Q network model, evaluating an inventory replenishment decision in combination with the dynamic demand prediction result and the integrated data set to obtain an inventory optimization strategy; and performing multi-target Pareto optimization on the inventory management of the target enterprise according to the inventory optimization strategy to obtain and execute an intelligent inventory management scheme. According to the method, the comprehensiveness and the dynamism of demand prediction are remarkably improved by comprehensively utilizing data of all systems in an enterprise and applying an advanced time-varying prediction model and an integrated learning technology.

The invention relates to the technical field of digital intelligent management, in particular to a digital intelligent management system and method for an unmanned aerial vehicle research, development and manufacturing enterprise, and the system comprises a knowledge management module, a data embedding processing module, an LLM center model, an AI agent, an enterprise resource planning module, a user interaction feedback module, and a security scale module. The knowledge management module is used for providing an internal knowledge base, encrypting authority management and real-time updating; the data embedding processing module is used for vector database and multi-mode embedding; the system realizes operations such as data input, storage, learning, calling, updating and the like to realize an intelligent working system, staff in a company exchange information with zero delay, and by combining intelligent auxiliary analysis of data, misjudgment caused by redundant and repeated invalid delay and information mismatching is avoided, and the working efficiency is improved. According to the method, internal information circulation of a company is effectively improved, the information island problem of each department is solved, the data secrecy capability in an enterprise is improved, and the management risk early warning capability is enhanced.

Example methods and systems relate to the modeling of production processes to include intermediate products. An enterprise resource planning (ERP) system receives user input to define an intermediate product, and generates a master data record for the intermediate product. The ERP system further receives user input to include the intermediate product in a production model within the ERP system. The production model is associated with a real-world production process. The production model is updated based on the master data record to integrate the intermediate product into a sequence of activities. The sequence of activities comprises a first activity that produces the intermediate product and a second activity that consumes the intermediate product. The ERP system executes a production order based on the production model. Execution of the production order includes tracking state information of the intermediate product within the ERP system.

A machine learning based (ML-based) computing method for identifying transaction categories from enterprise resource planning (ERP) data, is disclosed. The machine learning based computing method includes steps of: receiving inputs from electronic devices associated with first users; retrieving first data associated with first transactions from databases, based on the inputs received from the electronic devices associated with the first users; generating features associated with the transaction categories based on the first data associated with the first transactions; identifying first transaction categories based on at least one of: the first data associated with the first transactions and the features associated with the transaction categories, by clustering based machine learning models; identifying second transaction categories based on pre-configured rules; and providing an output of at least one of: the first transaction categories and the second transaction categories, to the first users on user interfaces associated with the electronic devices.

The invention relates to the technical field of carbon emission, in particular to an on-chain enterprise carbon emission accounting method and system. The method comprises the following steps: acquiring energy consumption, process flow and supply chain collaborative data of an enterprise in links of raw material acquisition, production processing, logistics transportation, equipment operation and recovery processing through Internet of Things equipment, and generating a carbon emission data set; based on a life cycle evaluation theory, boundaries of direct emission, indirect energy emission and supply chain associated emission are divided, and enterprise-level, product-level and supply chain-level carbon emission accounting models are constructed. A machine learning algorithm is utilized to analyze production fluctuation data in real time, carbon emission factor weights and accounting boundary parameters are dynamically adjusted, and an accounting result is optimized. And outputting a result to a green purchase decision-making system, a carbon asset management system and a supply chain collaboration platform. The system integrates industrial Internet of Things equipment, block chain nodes and enterprise resource planning system interfaces, collects and verifies full-link data in real time, solves the problem of data collection, and improves the accounting reliability.

The embodiment of the invention discloses a data query method based on a natural language, related equipment and a storage medium, and is used for the field of language processing. In the method, based on a business scene to which a natural language statement belongs, a target business document corresponding to the natural language statement is positioned, and a target business field in the natural language statement is split; recalling field metadata of the target business field from a document knowledge base corresponding to the target business document; generating a database statement based on the field metadata of the target business field; the field metadata recalled from the receipt knowledge base is the field metadata of the business fields of the same business receipt in the business scene, mutual interference of the metadata is effectively avoided during receipt knowledge base recall, the accuracy of receipt knowledge base recall is improved, the accuracy of database statements is improved, and the accuracy of data query is improved. The method can be applied to various business systems, such as an enterprise resource planning (ERP) system, an enterprise management system, a manpower system and the like.

The invention discloses a 360-degree image compression method and a 360-degree image compression device for adaptive latitude perception transform coding, which have obvious advantages in code rate saving and can effectively solve the problem of distortion redundancy of ERP (Enterprise Resource Planning) images. The method comprises the following steps: (1) designing a self-adaptive latitude sensing module; (2) constructing a multi-scale gated convolutional neural network; (3) transforming spatial features guided by the modulation importance feature activation graph; and (4) constructing a learned 360-degree image compression overall framework.

The invention discloses a cord fabric dipping glue solution preparation management system, and relates to the technical field of glue solution production, the cord fabric dipping glue solution preparation management system comprises an order interaction unit, an order sequence making unit, a glue solution preparation management and control unit and a glue solution preparation monitoring unit; the order interaction unit is used for establishing connection between the enterprise resource planning system and the client to obtain a glue solution production order; the order sequence formulating unit is used for carrying out priority ranking on the glue solution production orders based on the client demand responsivity; and the glue solution preparation management and control unit is used for generating a raw material process formula based on a predefined raw material proportioning rule, and preparing a glue solution required by the glue solution production order according to the working procedures in the raw material process formula and the raw material combination. Through docking with an enterprise communication platform, important production information, abnormal alarms or task instructions can be pushed in real time, so that the response speed and the unmanned level of production management are greatly improved.

Documents and other process area events in an Enterprise Resource Planning (ERP) system are extracted according to a knowledge table. The extracted events are stitched together in a timeline order, placed in a transport container (or otherwise transmitted), and input to, for example, a process mining or process intelligence tool. The tool utilizes any of AI, Machine learning, or other techniques to evaluate processes and / or documents represented by the extracted events, and triggering alerts or messages based on the evaluation. The tool may be a task mining tool and / or provide information or suggestions for improving the document or process represented by the document(s) and may prepare and display (or provide for display) a visual model of the evaluated process or document and may highlight anomalies therein.

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.

The invention discloses a method and device for extracting a bill of material and title information in CAD and a medium, and the method comprises the steps: extracting an INSERT insertion object and an ATTRIB attribute object from a drawing object list of a CAD file, and taking the INSERT insertion object and the ATTRIB attribute object as filtering data; the BOM and the title information are extracted from the filtered data through a preset rule matching algorithm, and if extraction succeeds, the BOM and the title information are output; and if the extraction fails, extracting the BOM and the title information from the filtered data according to a preset dictionary matching algorithm, and outputting the BOM and the title information. According to the method, a dictionary matching and rule matching algorithm in NLP (Natural Language Processing) and text analysis is fully utilized to perfectly solve the pain point of manually inputting the BOM in the use process of an ERP (Enterprise Resource Management) system.