A seamless steel pipe manufacturing process ERP intelligent collaborative management and control system

Abstract

This invention provides an intelligent collaborative management and control system for the entire seamless steel pipe manufacturing process using ERP, relating to the fields of seamless steel pipe manufacturing and ERP management and control technology. It fundamentally solves the problems of poor collaboration, data silos, imprecise management, and unwise decision-making. The system includes a data acquisition layer, a data storage layer, a core management and control layer, an application layer, an interaction layer, and a security protection layer. The data acquisition layer enables real-time data acquisition throughout the entire process; the data storage layer employs hybrid storage and standardized data models to ensure data quality; the core management and control layer integrates nine modules: procurement, production, quality, warehousing, sales, finance, equipment, intelligent decision-making, and system collaboration, achieving intelligent collaboration and closed-loop management throughout the entire process; the application and interaction layers provide personalized services and convenient operation across multiple terminals. This invention breaks down information silos, unifying order flow, material flow, and data flow, improving production efficiency, ensuring product quality, and reducing operating costs.

Description

Technical Field

[0001] This invention relates to the intersection of seamless steel pipe manufacturing and enterprise resource planning (ERP) management technology, and particularly to an intelligent collaborative management system for the entire process of seamless steel pipe manufacturing using ERP. Background Technology

[0002] Seamless steel pipes, as a key industrial basic component, are widely used in petroleum, chemical, electric power, machinery, aerospace and other fields. Their manufacturing process is complex, with many steps, strict process parameters, and high requirements for multi-departmental collaboration. It covers business segments such as raw material supply, production and processing, quality inspection, warehousing and logistics, and financial management, requiring close cooperation among departments such as production, procurement, quality inspection, warehousing, finance and sales.

[0003] The current management and control models of seamless steel pipe manufacturers in China have significant shortcomings: First, existing ERP systems are mostly limited to basic business operations such as financial accounting and inventory management, lacking deep integration with core production processes such as billet piercing, continuous rolling, sizing, and heat treatment. This leads to a disconnect between production planning and resource allocation, with macro-planning failing to match actual workshop capacity and equipment status, resulting in poor plan implementation. Second, data from various business modules is stored in a scattered manner, forming information silos. Data such as material consumption, quality inspection results, and inventory status cannot be exchanged in real time, easily leading to problems such as material shortages, production rework, and delivery delays. Third, the collection of key process parameters is lagging and the control is crude, lacking linkage analysis with the ERP system, making it difficult to ensure product quality stability and making quality traceability difficult. Fourth, there are many manual intervention links. Data statistics and plan adjustments rely on manual operation, resulting in low efficiency, large errors, and a lack of intelligent decision support, making it impossible to scientifically predict and dynamically adjust production plans, resource allocation, and process optimization based on real-time and historical data. Fifth, system integration is poor. Most companies' ERP systems have inconsistent interfaces with Manufacturing Execution System (MES), Warehouse Management System (WMS), and Supplier Relationship Management (SRM). Data exchange relies on manual import and export, which results in delays and errors, further exacerbating the management and control difficulties.

[0004] Existing technologies mostly involve digital transformation of single links, failing to achieve intelligent collaborative management and control of the entire process and all links through ERP. This fails to fundamentally solve problems such as poor collaboration, data sharing, imprecise management and control, and unwise decision-making, making it difficult to meet the high-quality, high-efficiency, and low-cost management and control needs of modern seamless steel pipe manufacturing enterprises, thus hindering the digital transformation of enterprises and the improvement of their core competitiveness. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent collaborative management and control system for the entire process of seamless steel pipe manufacturing via ERP. This system addresses the problem that existing technologies often involve digital transformation of single links, failing to achieve intelligent collaborative management and control of the entire process and all links in seamless steel pipe manufacturing via ERP. Consequently, it cannot fundamentally solve the problems of poor collaboration among multiple departments, data sharing barriers, inaccurate management and control, and unintelligent decision-making.

[0006] To achieve the above objectives, the present invention provides an ERP intelligent collaborative management and control system for the entire process of seamless steel pipe manufacturing, the system comprising:

[0007] The data acquisition layer is used to collect data from the entire process of seamless steel pipe manufacturing.

[0008] The data storage layer is used to store the entire process data uploaded by the data acquisition layer, and to clean, verify, and encrypt the entire process data, and to build a unified data model.

[0009] The core control layer includes modules for collaborative control of procurement, production, quality, warehousing, sales, finance, equipment, intelligent decision-making, and system collaboration, to achieve collaborative control over the entire seamless steel pipe manufacturing process.

[0010] The application layer is used to provide personalized application services for various departments and positions, and supports access from multiple terminals.

[0011] The interaction layer provides interface operation, permission management, and early warning prompts.

[0012] Compared with existing technologies, the seamless steel pipe manufacturing process ERP intelligent collaborative management and control system provided by this invention has the following beneficial effects: By constructing an integrated system architecture comprising a data acquisition layer, a data storage layer, a core management and control layer, an application layer, and an interaction layer, this invention enables real-time acquisition, standardized storage, and efficient interoperability of data throughout the entire seamless steel pipe manufacturing process, breaking down information silos between different business segments. Relying on a multi-module collaborative management and control mechanism, it achieves closed-loop management and intelligent collaboration across the entire business chain, including procurement, production, quality, warehousing, sales, finance, and equipment, significantly improving cross-departmental collaboration efficiency and production operation stability. Through a unified data model and standardized interfaces, it effectively breaks down data barriers between ERP and external systems, reducing manual intervention and data errors, while providing enterprises with precise decision support and a convenient operating experience, comprehensively improving production efficiency, ensuring product quality, reducing operating costs, and powerfully promoting the digital and intelligent transformation of seamless steel pipe manufacturing enterprises.

[0013] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 The diagram shows a structural schematic of an ERP intelligent collaborative management and control system for the entire process of seamless steel pipe manufacturing provided by an embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] In this embodiment, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Words such as "exemplary" or "for example" are used to indicate examples, illustrations, or explanations, intended to present related concepts in a specific manner, and should not be construed as superior or more advantageous than other embodiments or designs.

[0018] Example 1

[0019] This invention provides an ERP intelligent collaborative management and control system for the entire process of seamless steel pipe manufacturing. Figure 1 This diagram illustrates the structure of an ERP intelligent collaborative management and control system for the entire seamless steel pipe manufacturing process provided by an embodiment of the present invention. Figure 1 As shown, the system includes:

[0020] Data acquisition layer 1 is used to collect data from the entire process of seamless steel pipe manufacturing.

[0021] It should be noted that data acquisition layer 1, as the system's data input source, is used to collect data from the entire seamless steel pipe manufacturing process. This data includes production data, procurement data, quality inspection data, warehousing data, sales data, financial data, equipment data, and environmental data. Specifically, standardized interfaces are used to connect with various acquisition devices and terminal systems to ensure the real-time nature, completeness, and accuracy of data acquisition.

[0022] Specifically, the procurement data can include the specifications, material, quantity, and supplier information of the billet raw materials. The production data can include process parameters (heating temperature, rolling force, cooling rate, elongation coefficient, etc.), production progress, labor hours, and work-in-process quantity for production processes such as billet piercing, continuous rolling, sizing, heat treatment, and finishing. The quality inspection data can include inspection items, standards, results, and non-conforming items for raw material inspection, process inspection, and finished product inspection. The warehousing data can include the quantity of raw materials, work-in-process, and finished products entering and leaving the warehouse, inventory location, and inventory turnover rate. The sales data can include product sales orders, sales quantity, sales price, customer information, and delivery schedule. The financial data can include procurement payments, production costs, sales revenue, and profit calculation. The equipment data can include the operating status, fault information, maintenance records, and energy consumption data of the production equipment. The environmental data can include the temperature, humidity, and dust concentration of the production workshop.

[0023] The data acquisition layer adopts a composite acquisition mode of "IoT sensors + PLC + manual data entry". Process parameters, equipment operation data, and environmental data are automatically acquired in real time through IoT sensors and PLC. Data that cannot be automatically acquired, such as purchase orders, sales orders, and manual inspection results, are entered manually through a terminal. The acquisition frequency can be dynamically adjusted according to the needs of different stages. It also supports data anomaly alarms. When the acquired data exceeds the preset threshold, an alarm signal is automatically triggered and pushed to the relevant responsible person.

[0024] Furthermore, the data acquisition layer includes IoT temperature sensors, pressure sensors, speed sensors, displacement sensors, PLC controllers, barcode scanners, and manual data entry terminals. All acquisition devices use standardized interfaces, which can flexibly connect to devices of different models and manufacturers, facilitating system upgrades and expansions. At the same time, it supports multiple transmission methods such as 5G / industrial Ethernet to ensure the real-time performance and stability of data transmission.

[0025] Data storage layer 2 is used to store the full-process data uploaded by the data acquisition layer, and to clean, verify, and encrypt the full-process data, and to build a unified data model.

[0026] It should be noted that data storage layer 2 is used to store all data collected by the data acquisition layer. It adopts a hybrid storage architecture of "relational database + non-relational database". The relational database is used to store structured data, such as purchase orders, sales orders, financial data, quality inspection results, etc., to ensure data consistency and integrity. The non-relational database is used to store unstructured data, such as equipment failure pictures, scanned copies of inspection reports, process drawings, etc., to improve the flexibility and scalability of data storage.

[0027] The data storage layer 2 incorporates data cleaning, data verification, and data encryption modules. The data cleaning module removes redundant, erroneous, and missing data from the collected data, and corrects or marks abnormal data. The data verification module verifies the format, range, and logical consistency of the data to ensure compliance with system control requirements. The data encryption module uses symmetric encryption algorithms to encrypt and store sensitive data (such as customer information, financial data, and core process parameters) to prevent data leakage, tampering, and loss. Simultaneously, the data storage layer supports data backup and recovery functions, automatically backing up data periodically and performing full or incremental backups as needed. In the event of a system failure, data can be quickly restored to ensure normal system operation. Furthermore, the data storage layer builds a unified data model based on the ISA-95 international standard, unifying data fields across ERP, MES, WMS, SRM, and other systems, laying the foundation for cross-system data collaboration.

[0028] The core control layer 3 includes a procurement collaborative control module, a production collaborative control module, a quality collaborative control module, a warehousing collaborative control module, a sales collaborative control module, a financial collaborative control module, an equipment collaborative control module, an intelligent decision-making module, and a system collaboration module, to conduct collaborative control over the entire seamless steel pipe manufacturing process.

[0029] It should be noted that the core control layer 3 is the core of the system. Based on the ERP core architecture, it integrates multi-module collaborative logic and intelligent control algorithms to achieve collaborative control, intelligent decision-making, and process optimization throughout the entire seamless steel pipe manufacturing process. It includes the following functional modules:

[0030] 1. Procurement Collaboration and Management Module

[0031] This system enables end-to-end control of seamless steel pipe raw material procurement. Based on production plans, inventory data, and market demand, it intelligently generates procurement plans, automatically matches qualified suppliers (prioritizing them based on parameters such as supplier qualifications, quotations, delivery cycles, and historical cooperation quality), generates purchase orders, and pushes them to suppliers. It tracks the progress of purchase orders in real time, including raw material production, transportation, and arrival. When abnormal situations such as delayed arrival or substandard quality occur, it automatically triggers alerts and synchronizes with the production planning module to adjust the production plan. After raw materials arrive, they are inspected by the quality inspection module. Upon successful inspection, the system automatically processes warehousing procedures, updates inventory data, and synchronizes with the finance module to generate accounts payable vouchers. Simultaneously, it establishes supplier files and an evaluation system, regularly assesses suppliers, optimizes supplier resources, reduces procurement costs, and achieves supply chain collaborative optimization by referencing the core functions of an SRM system.

[0032] 2. Production Collaborative Management and Control Module

[0033] For the full-process control of the core production processes in seamless steel pipe manufacturing, based on sales orders, inventory data, and the status of enterprise resources (equipment, manpower, raw materials), it intelligently generates the main production plan and process plans, decomposes production tasks to each production workshop, each team, and each piece of equipment, and clarifies the production quantity, production cycle, process parameter standards, and responsible persons for each process. It receives the production progress data and process parameter data uploaded by the data acquisition layer in real time, compares and analyzes them with the preset standards. When abnormal situations such as production progress lag and process parameter deviation occur, it automatically triggers an alarm and pushes it to relevant management personnel for handling. It supports the dynamic adjustment of the production plan. When situations such as sales order changes, raw material shortages, and equipment failures occur, it automatically recalculates the resource allocation and adjusts the production plan to ensure the feasibility and flexibility of the production plan. At the same time, it interfaces with the MES system to achieve refined control of production processes, decomposes the ERP macro plan into process tasks executable by the workshop, synchronizes the production progress data to the ERP system, and realizes the closed-loop control of "planning - execution - feedback - adjustment"; in addition, it statistics the working hours consumption and material consumption in the production process to provide data support for production cost accounting.

[0034] 3. Quality collaborative control module

[0035] For the quality control of the full process of seamless steel pipe manufacturing, it establishes the full-process inspection standards and inspection processes from raw material inspection, process inspection to finished product inspection, interfaces with the data acquisition layer to obtain various inspection data, and automatically judges whether the inspection results are qualified. It classifies non-conforming products (raw material non-conformity, process non-conformity, finished product non-conformity), analyzes the reasons for non-conformity, pushes them to relevant responsible modules (procurement module, production module) for rectification, tracks the rectification effect, and forms a quality rectification closed loop. It establishes a product quality traceability system, based on the product batch number, associates raw material information, production information, inspection information, equipment information, etc., realizes the full-process traceability of product quality, and can quickly locate the problem link and trace the problem cause when there are quality complaints or quality problems, reducing quality losses. At the same time, it statistics quality indicators such as the quality pass rate and non-conformity rate, generates a quality analysis report, and provides data support for process optimization and supplier screening.

[0036] 4. Warehouse collaborative control module

[0037] This system enables end-to-end warehouse management of raw materials, work-in-process, and finished goods. It integrates with a WMS (Warehouse Management System) for refined inventory management, providing real-time updates of inventory data, including quantity, location, and status (available, occupied, unqualified). It supports inventory alerts, automatically triggering alerts when raw material inventory falls below safety stock or finished goods inventory exceeds warning levels, and pushing alerts to the purchasing and sales modules for processing. It achieves precise material inbound and outbound management, automatically linking purchase orders and inspection reports upon receipt and sales orders and production plans upon issuance, ensuring accurate specifications and quantities of materials. It optimizes inventory layout by intelligently allocating inventory locations based on material turnover rate and size, improving warehouse space utilization and inbound / outbound efficiency. It tracks inventory turnover rate and inventory backlog time, providing data support for inventory optimization and procurement plan adjustments, reducing inventory backlog and lowering inventory costs.

[0038] 5. Sales Collaboration and Management Module

[0039] This system enables end-to-end control of seamless steel pipe sales, managing sales data such as sales orders, customer information, sales prices, and delivery schedules. It establishes customer profiles and a customer credit rating system, implementing tiered credit management to reduce sales risks. It integrates with the production and warehousing modules to query production progress and inventory status in real time, providing customers with delivery updates to ensure on-time delivery. It handles sales order changes and returns, adjusting production plans and inventory data synchronously to minimize losses from order changes. It also compiles sales revenue, sales profit, market share, and other sales indicators, generating sales analysis reports to provide data support for sales strategy development and market expansion. Furthermore, it integrates with the finance module to synchronize sales data, generate accounts receivable vouchers, track accounts receivable collection progress, and reduce bad debt risks.

[0040] 6. Financial Collaborative Management and Control Module

[0041] This system enables comprehensive financial management throughout the seamless steel pipe manufacturing process. It integrates procurement, production, sales, and warehousing data to automatically calculate production costs (raw material costs, labor costs, equipment costs, energy costs, etc.), sales revenue, and profits. It interfaces with the procurement and sales modules to generate accounts payable and accounts receivable vouchers, track payment and collection progress, and manage corporate cash flow. It generates financial statements such as balance sheets, income statements, and cash flow statements to support management's financial decision-making. It establishes a cost control system, compares actual costs with budgeted costs, analyzes the reasons for cost discrepancies, and pushes data to relevant modules for cost optimization, reducing operating costs. Simultaneously, it supports compliance management of financial data, ensuring its authenticity, accuracy, and completeness, and meeting the company's financial management and tax reporting needs.

[0042] 7. Equipment Collaborative Management and Control Module

[0043] This system enables full lifecycle management of production equipment, establishing equipment files that record specifications, purchase date, installation and commissioning details, maintenance records, and fault information. It receives real-time equipment operation data from the data acquisition layer, analyzes equipment operating status, predicts equipment failure risks, triggers early maintenance warnings, and pushes them to maintenance personnel for preventative maintenance, minimizing downtime. It manages equipment maintenance plans, developing routine, periodic, and specialized maintenance schemes, tracking maintenance progress and effectiveness to ensure normal equipment operation. It also tracks indicators such as equipment utilization, failure rate, and maintenance costs, providing data support for equipment upgrades and maintenance strategy optimization, thereby improving equipment management efficiency and extending equipment lifespan.

[0044] 8. Intelligent Decision-Making Module

[0045] Based on historical and real-time data from the data storage layer, and employing intelligent algorithms such as big data analytics and machine learning, the system scientifically predicts and makes intelligent decisions regarding production planning, resource allocation, process optimization, cost control, and sales strategies. It generates production optimization suggestions, cost optimization suggestions, inventory optimization suggestions, and sales strategy suggestions, pushing them to enterprise management and relevant responsible modules to assist management in making scientific and reasonable decisions. Simultaneously, it constructs an intelligent decision-making dashboard, displaying the enterprise's core indicators such as production, quality, finance, and sales in real time, intuitively reflecting the enterprise's operational status and providing management with real-time and comprehensive decision support. Furthermore, by analyzing historical production and quality data, it optimizes production process parameters, improving product quality stability and production efficiency.

[0046] Furthermore, the algorithms in the intelligent decision-making module include regression analysis algorithms, clustering analysis algorithms, neural network algorithms, etc., allowing for the selection of appropriate algorithms for data analysis and prediction based on different decision-making needs. Simultaneously, it supports autonomous learning and optimization of the algorithms, continuously accumulating data and optimizing the algorithm model as the system runs, thereby improving the accuracy and reliability of intelligent decision-making.

[0047] 9. System Collaboration Module

[0048] This system enables collaborative operation between various functional modules and between itself and external systems (MES, WMS, SRM, CRM, OA, etc.). It utilizes standardized interfaces such as RESTful APIs to achieve real-time data sharing and seamless business process integration. It addresses the challenges of system integration and inconsistent interface standards, eliminating information silos. Supporting multi-department collaborative work, changes in any step are automatically synchronized to related steps, ensuring consistency across departments and improving efficiency. Furthermore, it supports multi-factory and multi-organizational structure modeling, meeting the needs of cross-plant collaborative manufacturing in large enterprise groups.

[0049] The core control layer adopts a modular design among its functional modules, which can be flexibly added, removed, or adjusted according to the actual needs of the enterprise, and has good scalability and compatibility. At the same time, the system adopts a low-code development platform, which supports non-technical personnel to quickly configure business processes and adapt to the control needs of seamless steel pipe manufacturing enterprises of different sizes and with different processes.

[0050] Application layer 4 is used to provide personalized application services for various departments and positions, and supports access from multiple terminals.

[0051] It's important to note that the application layer, based on the core control layer's functionality, provides personalized application services to users in different departments and positions. These include applications for purchasing, production, quality inspection, warehousing, sales, finance, equipment, and management. Each application corresponds to specific job requirements, providing functions such as data querying, business processing, report generation, and early warning handling. For example, purchasing personnel can use the purchasing department application to process purchase orders, track procurement progress, and manage suppliers. Production personnel can use the production department application to receive production tasks, report production progress, and view process parameters. Management can use the management application to view core enterprise operation indicators, intelligent decision-making suggestions, and various analytical reports. The application layer supports access from multiple terminals, including computers and mobile devices, allowing users to handle business and view data anytime, anywhere, thus improving work efficiency.

[0052] Interaction layer 5 is used to provide interface operation, permission management and early warning prompts.

[0053] It should be noted that the interaction layer, as the interface between the system and the user, adopts a simple and intuitive interface design, providing functions such as data display, business operations, information query, early warning prompts, and access control. It supports customized interface layouts and report formats to meet the personalized needs of different users. Multi-level access control is implemented, assigning different operation permissions based on user roles and responsibilities to ensure system data security and operational standardization, preventing unauthorized operations. An early warning prompt function is provided, centrally displaying early warning information triggered by various system modules (data anomalies, progress delays, quality defects, inventory warnings, etc.) to remind users to handle them promptly; simultaneously, system help and operation guides are provided to assist users in quickly familiarizing themselves with system operations.

[0054] Furthermore, the seamless steel pipe manufacturing end-to-end ERP intelligent collaborative management and control system also includes a security protection layer. This security protection layer is deployed between all layers to ensure the system's operational security and data security, including functions such as firewalls, intrusion detection, data encryption, access control, and operation log auditing. The firewall defends against external network attacks and prevents unauthorized access. Intrusion detection monitors the system's operational status in real time, promptly alerting and intercepting intrusion attempts. Data encryption encrypts data during transmission and storage to prevent data leakage and tampering. Access control strictly controls user operation permissions, ensuring that users in different positions can only access and operate functions and data within their corresponding permission scope. Operation log auditing records all user operations, facilitating subsequent querying, tracing, and accountability, ensuring the compliance and security of system operation.

[0055] Example 2

[0056] This invention provides an ERP intelligent collaborative management and control system for the entire seamless steel pipe manufacturing process, applicable to medium-sized seamless steel pipe manufacturing enterprises. It covers the entire process including raw material procurement, billet preparation, piercing, continuous rolling, sizing, heat treatment, finishing, inspection, warehousing, sales, finance, and equipment management. The specific structure is as follows:

[0057] 1. Data Acquisition Layer

[0058] The data acquisition layer deploys various acquisition devices, including: barcode scanners at the billet raw material receiving point (collecting data such as raw material specifications, material, supplier, and purchase batch); IoT temperature sensors, pressure sensors, and speed sensors deployed in the piercing workshop, continuous rolling workshop, and heat treatment workshop (collecting real-time process parameters and equipment operation data such as heating temperature, rolling force, cooling rate, and equipment speed); PLC controllers (collecting production data such as production progress, man-hour consumption, and work-in-process quantity); manual data entry terminals in the quality inspection workshop (collecting quality inspection data such as raw material inspection, process inspection, and finished product inspection results and non-conforming items); barcode scanners and inventory sensors in the warehousing workshop (collecting warehousing data such as material entry and exit quantities, inventory location, and inventory status); terminal equipment in the sales and purchasing departments (collecting data such as sales orders, purchase orders, customer information, and supplier information); terminal equipment in the finance department (collecting financial accounting-related data); and environmental sensors in the production workshop (collecting environmental data such as workshop temperature and humidity).

[0059] All data acquisition devices are connected to the data storage layer via industrial Ethernet. The acquisition frequency is set as follows: process parameters and equipment operation data are acquired every 10 seconds; production progress and inventory data are acquired every 30 seconds; and manually entered data from purchasing, sales, and quality inspection are uploaded in real time. When the acquired process parameters exceed the preset threshold (e.g., heating temperature deviation ±5℃) or the equipment operation status is abnormal (e.g., rotation speed is lower than the preset value), an audible and visual alarm is automatically triggered, and the alarm signal is pushed to the terminals of the relevant responsible persons in the production module and equipment module.

[0060] 2. Data storage layer

[0061] Structured data, including purchase order tables, sales order tables, production plan tables, quality inspection result tables, inventory data tables, financial data tables, and equipment file tables, is stored using a MySQL relational database to ensure data consistency and integrity. Unstructured data, including equipment failure images, scanned inspection reports, process drawings, and scanned customer contracts, is stored using a MongoDB non-relational database to enhance data storage flexibility. A built-in data cleaning module automatically removes redundant data (such as duplicate order data) and erroneous data (such as incorrectly formatted process parameters), marks missing data, and prompts for manual entry. A data validation module verifies the logical consistency of the data (such as the matching of purchase order quantities with inbound quantities). Sensitive data such as customer information, financial data, and core process parameters are encrypted using the AES symmetric encryption algorithm. A full data backup is performed daily at 2 AM, and an incremental data backup is performed every 6 hours. Backup data is stored on a dedicated backup server to ensure rapid recovery in case of data loss. A unified data model is built based on the ISA-95 standard to unify data fields between ERP, workshop MES, and warehouse WMS systems, such as material codes, process names, and work order numbers, so as to avoid data confusion.

[0062] 3. Core Control Layer

[0063] The core management layer is based on the ERP core architecture and deploys various functional modules. The specific operation process is as follows:

[0064] (1) Procurement Collaboration and Management Module

[0065] After the production collaborative management module generates a production plan, the procurement collaborative management module automatically reads the raw material requirements (specifications, quantity, delivery time) and inventory data from the production plan. When raw material inventory falls below safety stock, the system intelligently generates a procurement plan, prioritizing suppliers based on parameters such as supplier qualifications, quotations, delivery cycles, and historical cooperation quality from the supplier profile, and recommending the best supplier. After confirmation by procurement personnel, the system automatically generates a purchase order, pushes it to the supplier's terminal, and tracks the purchase order progress in real time. Through supplier feedback and logistics data, the system monitors the production and transportation of raw materials. Upon arrival of raw materials, the system automatically reminds quality control personnel to inspect them. The quality control module feeds back the inspection results to the procurement collaborative management module. After passing inspection, the system automatically processes the warehousing procedures, updates inventory data, and synchronizes it to the financial collaborative management module to generate accounts payable vouchers. Suppliers are evaluated monthly, with performance indicators including on-time delivery rate, raw material qualification rate, and price reasonableness. Unqualified suppliers are eliminated, and supplier resources are optimized.

[0066] (2) Production Collaborative Management and Control Module

[0067] After receiving a sales order, the sales collaboration management module reads the sales order information (product specifications, quantity, delivery time), inventory data, and equipment and human resource status. It intelligently generates a master production plan and process plans, decomposing production tasks to various production workshops such as the piercing workshop, continuous rolling workshop, and heat treatment workshop. It clearly defines the production quantity, production cycle, process parameter standards, and responsible personnel for each process. It receives production progress data and process parameter data uploaded from the data acquisition layer in real time and compares them with preset standards. If the production progress of a certain process lags behind by more than one hour or the process parameters deviate from the preset threshold, an alert is automatically triggered and pushed to the production management personnel's terminal. In case of sales order changes, raw material shortages, equipment failures, etc., managers can manually trigger production plan adjustments through the system. The system automatically recalculates resource allocation, adjusts the production tasks and time nodes of each process, and synchronizes them to each production workshop and related modules. Simultaneously, it connects to the MES system, breaking down the ERP master production plan into process tasks for each piece of equipment and each shift. After the production workshop completes its processes, it reports the production progress through the MES system, synchronizing it to the ERP production collaboration management module, achieving closed-loop management of the production plan. The system tracks and records labor and material consumption during the production process, uploading the data in real time to the financial collaborative management module for production cost accounting.

[0068] (3) Quality Collaborative Control Module

[0069] Establish a full-process inspection standard. The raw material inspection standard includes material composition, specification deviation, etc. The process inspection standard includes piercing accuracy, rolling thickness, etc. The finished product inspection standard includes dimensional accuracy, mechanical properties, etc. After the quality inspection personnel enter the inspection data through the terminal, the system automatically judges whether the inspection result is qualified. The unqualified products are automatically marked and classified (raw material unqualified, process unqualified, finished product unqualified). For raw material unqualified, the system automatically pushes it to the procurement collaborative control module, notifying the procurement personnel to communicate with the supplier for return or replacement. For process unqualified, it is pushed to the production collaborative control module, notifying the production personnel to analyze the reasons and rectify. After rectification, re-inspection is carried out. For finished product unqualified, it is pushed to the warehousing collaborative control module, marked as unqualified products, and prohibited from leaving the warehouse. At the same time, analyze the reasons for unqualified and optimize the production process parameters. Based on the product batch number, associate raw material procurement information, production process information, inspection information, equipment operation information, etc., to achieve quality traceability. The management personnel input the batch number to query the full-process data of this batch of products. Generate a quality analysis report monthly, count indicators such as quality pass rate and unqualified rate, and push it to the management level and production and procurement modules for process optimization and supplier screening.

[0070] (4) Warehousing Collaborative Control Module

[0071] Receive the inbound and outbound data and inventory data uploaded by the data acquisition layer in real time, update the inventory ledger, and clarify the inventory quantity, inventory location and inventory status of raw materials, work-in-progress and finished products. Set the safety inventory threshold (such as the safety inventory of a certain specification of billet is 50 tons). When the inventory is lower than the safety inventory, an alarm is automatically triggered and pushed to the procurement collaborative control module to remind the procurement personnel to replenish the purchase. When the finished product inventory is higher than the warning inventory (such as the inventory of a certain specification of finished products exceeds 200 tons), it is pushed to the sales collaborative control module to remind the sales personnel to increase the promotion efforts. When the material is put into storage, the system automatically associates the purchase order and inspection report. After scanning the code to confirm that the material specifications and quantities are correct, the inventory location is automatically allocated (materials with fast turnover speed are allocated to positions close to the outbound port). When the material is out of storage, the system automatically associates the sales order or production plan. After scanning the code to confirm that the material information is correct, the outbound procedures are handled and the inventory data is updated. Count indicators such as inventory turnover rate and inventory backlog time monthly, generate an inventory analysis report, and push it to the management level and procurement and sales modules to optimize the inventory configuration and reduce inventory backlog.

[0072] (5) Sales Collaborative Control Module

[0073] After managers enter sales orders (product specifications, quantity, delivery time, customer information, etc.), the system automatically links to customer files, checks customer credit ratings, and reminds managers to confirm payment methods for customers with low credit ratings. It integrates with the production and warehousing collaborative management modules to query production progress and inventory status in real time, providing delivery progress feedback to customers. If a customer requests order changes (such as adjusting product quantity or delivery time), the system automatically synchronizes with the production collaborative management module to adjust the production plan, while simultaneously updating inventory data and sales order information. For product returns, after inspection by the quality control module, returned products are synchronized to the warehousing collaborative management module for warehousing procedures, inventory data is updated, and the data is pushed to the financial collaborative management module for refund processing. Monthly statistics on sales revenue, sales profit, market share, and other indicators are compiled, generating sales analysis reports that are pushed to management to assist in developing sales strategies. Sales data is synchronized to the financial collaborative management module to generate accounts receivable vouchers, track accounts receivable collection progress, and automatically trigger alerts for overdue payments, reminding finance personnel to follow up.

[0074] (6) Financial Collaborative Management and Control Module

[0075] It automatically integrates procurement data (raw material costs) from the procurement collaboration management module, production data (labor costs, equipment costs, energy costs) from the production collaboration management module, warehousing data (inventory costs) from the warehousing collaboration management module, and sales data (sales revenue) from the sales collaboration management module to perform production cost accounting, sales revenue accounting, and profit accounting. It generates accounts payable and accounts receivable vouchers, tracks payment and collection progress, and manages corporate cash flow. It generates monthly financial statements such as balance sheets, profit and loss statements, and cash flow statements, and pushes them to management. It compares actual costs with budgeted costs, analyzes the reasons for cost differences (such as raw material costs exceeding the budget, excessive equipment energy consumption), and pushes these analyses to the procurement and production collaboration management modules, proposing cost optimization suggestions (such as changing to more cost-effective suppliers, optimizing production processes to reduce energy consumption). It ensures the authenticity and accuracy of financial data, meeting the company's financial management and tax reporting needs.

[0076] (7) Equipment collaborative management module

[0077] Establish equipment files, recording the specifications, model, purchase time, installation and commissioning status, maintenance records, and fault information for each piece of equipment. Receive real-time equipment operation data (speed, temperature, vibration, etc.) uploaded from the data acquisition layer, analyze equipment operating status through intelligent algorithms, predict equipment failure risks, and trigger maintenance alerts in advance when abnormal operating trends are observed, pushing them to the equipment maintenance personnel's terminals. Develop equipment maintenance plans: daily routine maintenance, monthly scheduled maintenance, and quarterly special maintenance. After maintenance personnel complete maintenance, they enter maintenance records, and the system tracks maintenance effectiveness. Compile statistics on equipment utilization, failure rate, maintenance costs, and other indicators, generating monthly equipment management reports and pushing them to management to assist in developing equipment update and maintenance strategies, improving equipment management efficiency and extending equipment lifespan. When equipment malfunctions, the system automatically records fault information and pushes it to equipment maintenance personnel. After maintenance personnel handle the issue, they update the fault status, forming a closed loop for equipment fault handling.

[0078] (8) Intelligent Decision Module

[0079] Regression analysis algorithms are used to predict production cycles and costs, clustering analysis algorithms to optimize production plans and inventory allocation, and neural network algorithms to predict equipment failures and product quality. Based on historical production, quality, and financial data in the data storage layer, the system generates production optimization suggestions (such as adjusting process parameters to improve efficiency), cost optimization suggestions (such as optimizing raw material procurement ratios to reduce costs), inventory optimization suggestions (such as reducing the purchase of certain types of stockpiled materials), and sales strategy suggestions (such as increasing promotional efforts for certain best-selling products), which are then pushed to management and relevant responsible modules. An intelligent decision-making dashboard is built to display the company's core indicators such as production progress, quality pass rate, inventory turnover rate, and sales revenue in real time, allowing management to intuitively understand the company's operational status. By analyzing historical production and quality data, production process parameters are continuously optimized to improve product quality stability. For example, by analyzing the correlation data between heat treatment temperature and product mechanical properties, the range of heat treatment temperature parameters is optimized to reduce product defect rates.

[0080] (9) System Collaboration Module

[0081] Utilizing standardized RESTful API interfaces, the system enables real-time data exchange and seamless business integration between core management modules and between this system and external MES, WMS, and SRM systems. For example, the MES system synchronizes workshop process execution data to the ERP production collaboration management module, while the ERP system synchronizes production plans to the MES system. The WMS system synchronizes inventory data to the ERP warehouse collaboration management module, and the ERP system synchronizes inbound and outbound instructions to the WMS system. The SRM system synchronizes supplier information and purchase order progress to the ERP purchase collaboration management module, and the ERP system synchronizes supplier evaluation results to the SRM system. When purchase orders change, the system automatically synchronizes with the warehousing, production, and finance modules, ensuring consistency across departments. Multi-factory collaboration is supported; if an enterprise has multiple production plants, the system can facilitate cross-plant production planning collaboration, resource allocation collaboration, and data sharing.

[0082] 4. Application Layer

[0083] Personalized application services are provided for users in different departments and positions: The purchasing department application offers functions such as purchase order management, supplier management, and purchase progress inquiry. The production department application offers functions such as production task reception, production progress reporting, process parameter inquiry, and anomaly handling. The quality inspection department application offers functions such as inspection standard inquiry, inspection data entry, and quality inspection report generation. The warehousing department application offers functions such as inventory inquiry, inbound and outbound processing, and inventory warning viewing. The sales department application offers functions such as sales order management, customer management, and delivery progress inquiry. The finance department application offers functions such as financial accounting, voucher management, and financial statement generation. The equipment department application offers functions such as equipment file inquiry, maintenance plan management, and fault handling. The management level application offers functions such as viewing core indicators, viewing intelligent decision suggestions, and generating various analysis reports. The application layer supports access from desktop (Windows, Linux systems) and mobile (Android, iOS systems). Mobile devices, through an app, enable business processing and data inquiry, allowing users to work anytime, anywhere.

[0084] 5. Interaction Layer

[0085] The system features a clean and intuitive interface design, with the main screen divided into a function navigation area, a data display area, and an alert area. The function navigation area provides quick access to each module, allowing users to switch quickly to the corresponding function interface. The data display area shows real-time core data of interest to users (such as production progress, inventory quantity, and sales revenue). The alert area centrally displays alert information triggered by various modules of the system; users can click on the alert information to view details and take action. Customizable interface layout and report formats are supported, allowing users to adjust the displayed content and report styles according to their needs. Multi-level access control is implemented, with three levels: super administrator, department administrator, and ordinary user. Super administrators have full system operation permissions, department administrators have operation permissions related to their department's functions, and ordinary users only have operation permissions related to their specific roles (e.g., production workers can only view their assigned production tasks and report production progress). System help and operation guides are provided; users can click the help button to view the operation steps for each function module, assisting them in quickly familiarizing themselves with system operation.

[0086] 6. Safety protection layer

[0087] Deploy firewalls to defend against external network attacks and prevent unauthorized access to the system. Deploy an intrusion detection system to monitor system operation in real time, promptly alerting and intercepting intrusion attempts (such as unauthorized logins or malicious data tampering). Encrypt data during transmission using SSL encryption to prevent leakage and tampering. Establish an operation log auditing system to record all user actions (such as login time, operation content, and modified data) for easy subsequent querying, tracing, and accountability. Regularly scan and patch system vulnerabilities to ensure system security.

[0088] After the system in this embodiment is put into operation, it realizes intelligent collaborative management and control of the entire seamless steel pipe manufacturing process, breaks down information silos, improves collaborative efficiency and management intelligence, increases production efficiency by 22%, inventory turnover rate by 35%, product quality pass rate by 8%, shortens production cycle by 26%, and reduces operating costs by 18%. It effectively solves the pain points of the existing management and control model, meets the enterprise's production management and control needs for high quality, high efficiency and low cost, and helps the enterprise achieve digital and intelligent transformation.

[0089] Compared with the prior art, the seamless steel pipe manufacturing process ERP intelligent collaborative management and control system provided by the embodiments of the present invention has the following beneficial effects:

[0090] 1. This invention realizes intelligent collaborative management and control of the entire seamless steel pipe manufacturing process through ERP, covering all aspects such as raw material procurement, production and processing, quality inspection, warehousing and logistics, sales settlement, after-sales traceability, equipment management, and financial management. It breaks down information silos between various business links, and achieves deep integration of ERP with MES, WMS, SRM and other systems through system collaboration modules and standardized interfaces, as well as real-time data exchange and seamless business connection between departments and modules. It solves the problems of existing ERP systems being disconnected from the production process and having poor collaboration, thereby improving the collaborative efficiency of enterprises.

[0091] 2. The data acquisition layer enables real-time, complete, and accurate collection of various data throughout the seamless steel pipe manufacturing process. Combined with the data cleaning, verification, and encryption functions of the data storage layer, data quality is ensured. Furthermore, based on a unified data model, reliable data support is provided for subsequent data analysis and intelligent decision-making. A full-process quality traceability system is established through the quality collaborative management module, enabling product quality traceability, reducing quality losses, and ensuring product quality stability.

[0092] 3. Introduce an intelligent decision-making module, employing intelligent algorithms such as big data analysis and machine learning. Based on historical and real-time data, it makes scientific predictions and intelligent decisions on production planning, resource allocation, process optimization, and cost control, assisting management in making reasonable decisions. At the same time, it enables dynamic adjustment of production plans, preventive maintenance of equipment, and optimized allocation of inventory, reducing manual intervention, minimizing human error, and improving the level of intelligent management and control.

[0093] 4. Through the synergistic effect of various collaborative management modules, the system optimizes business processes, improves production efficiency, shortens production and delivery cycles, reduces procurement, inventory, production, and operating costs, minimizes inventory backlog and rework, and simultaneously enhances product quality and customer satisfaction, thereby strengthening the company's core competitiveness. According to industry practice data, after implementing this system, companies can improve production plan achievement rate by over 20%, inventory turnover rate by over 30%, order delivery cycle by over 25%, and product quality pass rate by 5%-10%.

[0094] 5. Adopting a modular and standardized design, it boasts excellent scalability, compatibility, and flexibility. Functional modules can be flexibly added, removed, or adjusted according to the actual needs of seamless steel pipe manufacturing enterprises of different sizes and with different processes, adapting to the enterprise's development requirements. It also supports multi-terminal access and group-wide collaboration, meeting the long-term needs of enterprise digital transformation. Implementation is simple, cost-effective, and easy to promote and apply.

[0095] 6. Establish a comprehensive security protection layer and access control mechanism. Through functions such as firewalls, data encryption, and operation log auditing, ensure system operation security and data security, prevent data leakage, tampering, and unauthorized access, and ensure the compliant and stable operation of the system.

[0096] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An ERP intelligent collaborative management and control system for the entire process of seamless steel pipe manufacturing. Its characteristics It lies in, including: The data acquisition layer is used to collect data from the entire process of seamless steel pipe manufacturing. The data storage layer is used to store the entire process data uploaded by the data acquisition layer, and to clean, verify, and encrypt the entire process data, and to build a unified data model. The core control layer includes modules for collaborative control of procurement, production, quality, warehousing, sales, finance, equipment, intelligent decision-making, and system collaboration, to achieve collaborative control over the entire seamless steel pipe manufacturing process. The application layer is used to provide personalized application services for various departments and positions, and supports access from multiple terminals. The interaction layer provides interface operation, permission management, and early warning prompts.

2. The seamless steel pipe manufacturing process ERP intelligent collaborative management and control system according to claim 1, characterized in that, The seamless steel pipe manufacturing process data includes: production data, procurement data, quality inspection data, warehousing data, sales data, financial data, equipment data, and environmental data.

3. The seamless steel pipe manufacturing process ERP intelligent collaborative management and control system according to claim 1, characterized in that, It also includes a security protection layer deployed between each layer to ensure the operational safety and data security of the seamless steel pipe manufacturing ERP intelligent collaborative management and control system. The security protection layer includes firewalls, intrusion detection, data encryption, access control, and operation log auditing.

4. The seamless steel pipe manufacturing process ERP intelligent collaborative management and control system according to claim 1, characterized in that, The data storage layer uses a hybrid storage architecture of relational databases and non-relational databases for storage; The data storage layer has a built-in data cleaning module, data verification module, and data encryption module to clean, verify, and encrypt the data throughout the entire process.

5. The seamless steel pipe manufacturing process ERP intelligent collaborative management and control system according to claim 4, characterized in that, The relational database is MySQL, used to store structured data, and the non-relational database is MongoDB, used to store unstructured data.

6. The seamless steel pipe manufacturing process ERP intelligent collaborative management and control system according to claim 1, characterized in that, The procurement collaboration and management module is used to realize closed-loop management of the entire procurement process of seamless steel pipe raw materials. Based on production plans, inventory data and market demand, it intelligently generates procurement plans, automatically selects suppliers, tracks order progress and automatically issues warnings for anomalies, automatically puts goods into the warehouse after they pass inspection and synchronizes inventory and financial data, and realizes supply chain collaboration optimization through the supplier evaluation system. The production collaboration and control module is used to realize full-process control of the core production process of seamless steel pipes. It intelligently schedules production and decomposes tasks based on orders, inventory, and equipment, manpower, and raw material resources, and monitors progress and process parameters in real time, and automatically issues early warnings for anomalies. It supports dynamic adjustment of production plans when orders, materials, and equipment change, and connects with the Manufacturing Execution System to achieve closed-loop management of planning-execution-feedback-adjustment. At the same time, it counts labor and material consumption to provide data for cost accounting.

7. The seamless steel pipe manufacturing process ERP intelligent collaborative management and control system according to claim 1, characterized in that, The quality collaborative management module is used to realize the quality control of the entire process of seamless steel pipes. It establishes inspection standards for raw materials, processes and finished products and automatically judges the results. It classifies and manages non-conforming products, pushes rectification and forms a closed loop. Based on the product batch number, it builds a full-process quality traceability system, statistically analyzes quality indicators and generates analysis reports, and provides data support for process optimization and supplier selection. The warehouse collaborative management module is used to realize the full-process warehouse management of raw materials, work-in-process and finished products. It connects with the warehouse management system to realize refined inventory control and real-time updates. It has functions such as inventory early warning, accurate inbound and outbound, and intelligent warehouse location allocation. It also calculates inventory operation indicators to provide data support for inventory optimization and procurement adjustments, thereby reducing inventory costs.

8. The seamless steel pipe manufacturing process ERP intelligent collaborative management and control system according to claim 1, characterized in that, The sales collaboration and management module is used to realize the full-process management of seamless steel pipe sales, manage order and customer information and establish a credit rating system, connect with the production and warehousing modules to synchronize delivery progress in real time, and support order changes and return processing; Calculate sales targets and generate analysis reports, and simultaneously manage accounts receivable in the finance module. The financial collaborative management module is used to realize the financial management of seamless steel pipes throughout the entire process. It integrates business data from multiple stages to automatically complete cost, revenue, and profit accounting, generate accounts receivable and payable vouchers and manage cash flow; output financial statements, establish a cost control system, and ensure the compliance of financial data.

9. The seamless steel pipe manufacturing process ERP intelligent collaborative management and control system according to claim 1, characterized in that, The equipment collaborative management module is used to realize the full life cycle management of production equipment, establish equipment files and monitor the operating status in real time, predict faults through intelligent algorithms and trigger preventive maintenance warnings, manage equipment maintenance plans, and collect equipment operating indicators to improve equipment utilization and service life.

10. The seamless steel pipe manufacturing process ERP intelligent collaborative management and control system according to claim 1, characterized in that, The intelligent decision-making module is used to make intelligent predictions and decisions on production planning, resource allocation, process optimization, cost control, and sales strategies based on historical and real-time data from the data storage layer, using big data analysis and machine learning algorithms. It generates production optimization suggestions, cost optimization suggestions, inventory optimization suggestions, and sales strategy suggestions, and pushes them to the enterprise management and relevant responsible modules. The intelligent decision dashboard visually displays the company's core operational metrics and optimizes production process parameters by analyzing historical production and quality data. The system collaboration module is used to enable real-time data exchange and seamless business integration between various functional modules of the core control layer, as well as between the seamless steel pipe manufacturing full-process ERP intelligent collaborative control system and external manufacturing execution systems, warehouse management systems, and supplier relationship management systems, based on the RESTful API standardized interface.

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