244 results about "Enterprise resource planning" patented technology

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.

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 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 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.

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.

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.

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.

The invention provides a stereoscopic warehouse and a management method thereof, and relates to the technical field of intelligent warehousing. The stereoscopic warehouse comprises an enterprise resource planning system used for sending order information to a manufacturing execution system; the manufacturing execution system is used for making a production plan according to the order information and sending a warehouse-out request according to the production plan; the warehouse management system is used for generating a warehouse-in instruction according to the warehouse-back request and generating a warehouse-out instruction according to the warehouse-out request; the warehouse control system is used for controlling the stacking machine and the conveying line to operate according to the warehouse-in instruction and the warehouse-out instruction so that materials can be carried to the warehouse location or the warehouse-out opening. And the vehicle scheduling system is used for controlling the operation of the automatic guided vehicle according to the warehouse-in instruction and the warehouse-out instruction, so that the materials are carried to a warehouse-in opening, a line-side warehouse or a production line. According to the invention, high-efficiency automation of material storage and transfer is realized.

The invention provides a project-customized adaptive data query and display method, system, device and medium, and belongs to the technical field of data query, the method comprises the following steps: determining the business meaning of each business entity in a key field in a form scene, and solidifying the business meaning in ERP (Enterprise Resource Planning) system configuration; carrying out association relation configuration on the key fields according to the requirements of the business entities; the association relationship comprises an association table, an association field and an association condition; configuring a target field which needs to be brought out by each service entity in the form scene; and in response to a service demand, determining an association relationship of the key fields according to a service entity type, constructing a dynamic SQL query statement by using the determined association relationship, obtaining target data, and filling the target data into a target field of the form scene. According to the method, the personalized requirements of different projects or departments are met, the data query and display content is flexibly configured, dynamic configuration adjustment is supported, the method adapts to the change of service requirements, and the secondary development workload is reduced.

The invention relates to the technical field of intelligent warehouse logistics, and particularly discloses a warehouse logistics AI path planning system and method based on multi-modal dynamic optimization. The system comprises a multi-source data layer which aligns inventory data, traffic flow data and meteorological data of a warehouse management system (WMS) in real time through a sliding window synchronization mechanism, and ensures time-space consistency of multi-modal input; the dynamic fusion engine responds to a traffic interruption event within 3 seconds based on a space-time alternative path algorithm and generates an anti-interference optimized path scheme; according to the path decision core, a two-stage reinforcement learning architecture formed by a graph convolutional network (GCN) and a deep Q network (DQN) is adopted, static topology analysis and dynamic real-time characteristics are fused, and a final transportation path is output; the decision auditing module is used for recording Q value distribution of decision nodes, constructing a traceable path decision tree and automatically detecting regional distribution deviation through a quantization formula; and the zero-code compatible layer is used for presetting a standardized API template library and a rule compiler so as to realize plug-and-play integration with an enterprise resource plan (ERP) or a warehouse management system (WMS). Through multi-source data collaborative optimization, dynamic anti-interference decision and whole-process audit tracing, the real-time performance, robustness and transparency of warehouse logistics path planning are remarkably improved.

The invention provides a configurable ERP (Enterprise Resource Planning) system, method, equipment and medium for automatically saving certificates, and belongs to the technical field of ERP system management, and the ERP system comprises the following steps: a certificate editing module receives certificate information input and editing operation of a user; the non-strict checking module monitors whether the voucher information contains a core accounting element or not in real time; the strict detection module performs financial rule verification on the voucher information containing the core accounting elements; the automatic storage module automatically stores the voucher information passing the financial rule verification when a preset automatic storage triggering condition is met; the incomplete voucher management module allocates temporary numbers to the incomplete vouchers and performs financial rule verification again after the user supplements the incomplete vouchers; and if the user storage priority module receives a manual storage request of the user, the current automatic storage process is interrupted, and manual storage is executed preferentially. The method prevents data loss, improves the working efficiency, adapts to multiple scenes, optimizes the user experience, and guarantees the safety of financial data.

An artificial intelligence-based business management system for real-time decision optimization, consisting of: a data acquisition unit configured to ingest structured, semi-structured and unstructured data streams from enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, Internet of Things (IoT) devices, external market feeds and financial transaction systems, encrypting, timestamping and verifying the data prior to further processing; a graph processing unit that is communicatively connected to the said acquisition layer, wherein the unit is configured to encode heterogeneous data into dynamic graph structures comprising nodes representing business entities and edges representing transaction or relationship dependencies, wherein the unit is further configured to perform deduplication, metadata tagging and real-time data synchronization; a decision optimization unit operationally linked to the graph processing unit, wherein the decision optimization unit includes modules for reinforcement learning, modules for Bayesian optimization and multi-objective solvers configured to simulate multiple alternative decision paths and select an optimal path based on performance indicators such as cost efficiency, resource utilization, customer satisfaction and risk minimization; a real-time inference control unit with specialized hardware cores, including at least one graphics processing unit (GPU), a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC), wherein the accelerator performs inference tasks of the decision optimization unit with a latency in the millisecond range; a diagnostic processing unit configured to generate causal diagrams, feature mapping maps, and interpretable result summaries according to the optimization outputs; and a control interface unit configured to transmit optimized decisions to process controls within the enterprise, robot actuators, planning systems or interactive dashboards, with the interface supporting bidirectional communication for higher-level interventions, error feedback and triggers for re-optimization.

The invention relates to the technical field of enterprise resource management, in particular to an enterprise management optimization method and system based on big data, and the method comprises the following steps: capturing data of employee skills, a project execution process and customer interaction according to enterprise resource planning in an enterprise, analyzing the captured data, and carrying out the multi-dimensional information classification processing; and constructing an enterprise resource comprehensive data set. According to the method, the flow and utilization efficiency of the resources is improved by establishing the graphic database to integrate the enterprise resources and converting the data elements into the network nodes and the connecting edges, and the resource flow and utilization efficiency is improved by identifying and analyzing the association strength of the nodes and the weights of the edges in the resource network. Through detailed flow path analysis, an enterprise can formulate a scientific resource allocation strategy according to market requirements and current resource situations, resource allocation and use are optimized after dynamic resource allocation is implemented, the strategy is adjusted in time by comparing and analyzing resource efficiency and cost effectiveness before and after optimization, and the market response speed and competitiveness of the enterprise are remarkably improved.

The invention provides a dynamic inventory scheduling method and system based on an ERP system, and relates to the technical field of inventory scheduling. The inventory and order information is obtained from the ERP system in real time, it is ensured that the data basis of scheduling decision is newest and accurate, scheduling errors caused by information lag are effectively reduced, meanwhile, the cargo correlation degree is comprehensively evaluated by applying a multi-dimensional correlation analysis and weighted decision model, and the scheduling efficiency is improved. Optimization of inventory layout and reduction of transportation time and cost are realized, intelligence of inventory scheduling is realized through a graph neural network and an adaptive clustering algorithm, high-correlation cargo combinations can be automatically identified, differentiated scheduling schemes can be generated, scheduling efficiency and accuracy are improved, and the method has flexibility and expandability and is suitable for popularization and application. According to the technical scheme, adjustment and optimization can be carried out according to actual conditions to adapt to different service requirements and scene changes, and then inventory resource configuration is effectively optimized through implementation of collaborative scheduling and differential scheduling schemes.

A computer-implemented method for improving test data troubleshooting in an enterprise resource planning (ERP) system is disclosed. The method can receive an error message generated by an application programming interface of the ERP system when processing a set of test data, embed the error message into a message vector, search a vector database comprising a plurality of object vectors to identify a target object vector matching the message vector, retrieve a target object document associated with the target object vector, and generate an answer including instructions to modify the set of test data based on the error message and the target object document. Related systems and software for implementing the method are also disclosed.

The invention discloses a data processing method and system based on an ERP (Enterprise Resource Planning) interface, and the method comprises the steps: obtaining time sequence features of historical data, carrying out fluctuation analysis and dynamic window clustering, dynamically generating a time window range, carrying out data mapping and grading according to a hierarchical structure, and generating a dynamic access control strategy; according to the dynamic access control strategy, double-target optimization iteration is carried out, an equilibrium threshold range is obtained, conflict prediction and dynamic locking are carried out, and a data processing result is obtained; according to a data processing result, performing fragment-copy collaborative adjustment to obtain a balance scheme, and performing knowledge graph modeling and priority scheduling to obtain an optimized balance scheme; and according to a time window range and the equilibrium threshold range, through collaborative optimization of a routing rule and reinforcement learning, obtaining an elastic scheduling result, and according to a fluctuation condition, carrying out multi-level early warning and strategy linkage to obtain a stable scheduling result. According to the invention, dynamic parameter adjustment can be realized so as to adapt to data fluctuation and business change.

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 invention belongs to the technical field of block chain ERP (Enterprise Resource Planning), and discloses an enterprise ERP management system based on a block chain, which realizes credible sharing through an alliance chain architecture of a block chain base layer, ensures sensitive information privacy through a zero-knowledge proof technology, and can establish cooperative trust without endorsement of a third-party mechanism; the intelligent contract of the supply chain cooperation unit automatically executes cooperation rules such as purchase and acceptance, and trust disputes caused by manual intervention are avoided; the intelligent contract engine drives the unit processes of supply chain, production, finance and the like to be automated, so that end-to-end linkage of purchase-production-finance is realized, and manual operation links are reduced; the dynamic contract updating function enables the enterprise to quickly adapt to the business rule change, and the online period of a new business scene is shortened; the digital twinborn technology of the production management unit is fused with the AI model of the data analysis center, so that the decision accuracy of production scheduling and inventory management is improved, the resource configuration is optimized, and the problems of solidified process and slow response of the traditional ERP (Enterprise Resource Planning) are solved.

The invention belongs to the field of enterprise resource planning (ERP) systems and cost accounting, and particularly relates to a data chain construction method and system for supporting product material cost accounting based on project numbers, and the method comprises the steps: S1, generating a to-be-delivered product project number according to an order; s2, constructing an order-production plan relation mode; s3, constructing a production plan-purchase order relation mode; s4, constructing a purchase order-inventory relation mode; s5, constructing a production plan-material picking list relation mode; s6, constructing an order-delivery order relation mode; step S7, constructing an order-settlement order relation mode; according to the method, key data breakpoints are connected in series to support full-process material cost accounting, each direct material cost quota and actual cost expenditure are accurately tracked, finally, refined cost control is achieved, and decision-making quality is improved.