Excavation-anchor integrated machine remanufacturing visual management method

Abstract

This invention belongs to the field of mining equipment remanufacturing management technology. It aims to solve the problems of disjointed collaboration, rigid resource allocation, and delayed anomaly response in the remanufacturing process of integrated tunneling and anchoring machines. Simultaneously, it addresses industry pain points in traditional management, such as data incompatibility across multiple systems, architecture limitations imposed by network terminals, and inefficient management at each stage without intelligent support. This invention provides a visualized management method for integrated tunneling and anchoring machine remanufacturing. This method integrates a multi-system heterogeneous data fusion architecture specific to integrated tunneling and anchoring machine remanufacturing with an industrial-grade lightweight dynamic deployment architecture. Through real-time data interaction with CAPP, VMS, and SAP systems, it upgrades the traditional management model to an intelligent model of proactive perception, intelligent computing, digital twins, and end-to-end collaboration. It breaks the limitations of isolated modules in traditional management, improves the collaborative efficiency of integrated tunneling and anchoring machine remanufacturing, and is applicable to the entire process of visualized dynamic management of heavy mining equipment remanufacturing. It can also be extended to the field of engineering machinery equipment remanufacturing, making its application even more valuable.

Description

Technical Field

[0001] This invention belongs to the field of mining equipment remanufacturing management technology, specifically relating to a visual management method for the remanufacturing of an integrated tunneling and anchoring machine. Background Technology

[0002] As a core heavy equipment for tunneling and anchoring in coal mines, the remanufacturing process of the integrated roadheader involves multiple complex and interconnected stages, including equipment disassembly, component repair, parts replacement, and assembly. This places extremely high demands on the coordinated management and control of each stage. Current remanufacturing management often adopts a modular, independent control model, with data on equipment status, project schedule, parts supply, and personnel allocation stored in a scattered manner, lacking a real-time linkage mechanism, leading to a disconnect between different stages.

[0003] Meanwhile, traditional management methods lack intuitive visualization support, making it difficult for managers to grasp full-process data in real time. Resource allocation is based on static planning and cannot be adjusted in a timely manner according to dynamic changes in each stage. The detection of anomalies is delayed and the response efficiency is low, which can easily lead to project delays and resource waste. With the development of intelligent coal mines, there is an urgent need for a management method that can realize real-time data linkage between each stage and visualize the control process, break the isolated module pattern, and improve the collaborative efficiency and control accuracy of the remanufacturing of integrated tunneling and anchoring machines.

[0004] In addition, traditional management systems suffer from various industry pain points, such as inconsistent data standards across multiple systems, interfaces that can only be read and transmitted directly, high requirements for network and terminal configuration in B / S architecture, low efficiency and error-prone parameters due to manual entry of equipment status, static comparison for schedule management and inaccurate work hour calculation, inefficient parts matching and lack of intelligent support for missing parts allocation, personnel and process matching based on experience and single traceability dimension, cumbersome process document search and chaotic version management, fixed allocation of permissions based on job positions and lack of abnormal operation warnings. Summary of the Invention

[0005] In order to solve at least one of the above-mentioned technical problems in the prior art, the present invention provides a visual management method for the remanufacturing of integrated tunneling and anchoring machines.

[0006] This invention employs the following technical solution: a visual management method for the remanufacturing of an integrated tunneling and anchoring machine, which builds a visual dynamic control platform based on a B / S architecture, establishes real-time data interaction interfaces with CAPP, VMS, and SAP systems, and achieves collaborative control through data linkage and visual display, including the following steps: S1. By connecting to CAPP, VMS, and SAP systems through the data interface of the visual dynamic management and control platform, real-time collection of comprehensive data including equipment basic parameters, process data, parts supply and demand information, personnel skill files, and equipment maintenance progress is achieved. A dedicated data dictionary for the remanufacturing field of tunneling and anchoring integrated machines is used to automatically clean, convert, and semantically complete heterogeneous data in the comprehensive data to establish a remanufacturing dynamic database. The database includes, but is not limited to, sub-libraries of equipment parameter industry standards, sub-libraries of maintenance personnel skill levels, sub-libraries of intelligent matching of parts-processes-equipment models, sub-libraries of three-dimensional association of personnel-equipment-parts, and sub-libraries of process document version management. The data of each type in the database is mapped according to association rules, and all collected data is synchronized to the platform's visual interface for categorized display. S2. Obtain theoretical schedule data from the CAPP system and input it into the visual dynamic management and control platform. Input the actual progress data of each process in real time into the visual dynamic management and control platform, or automatically collect the working time data through the intelligent working time check-in function and synchronize it to the remanufacturing dynamic database. Use the dynamic linkage algorithm to call the theoretical schedule data stored in the database, the parts supply status data synchronized in real time from the VMS and SAP systems, and the current personnel configuration data to calculate the schedule deviation value. The schedule data and deviation value are presented in the visual interface. S3. Real-time retrieval of spare parts inventory, in transit, and arrival data synchronized between VMS and SAP systems in the remanufacturing dynamic database. Combined with equipment maintenance progress data recorded in the database, a linkage and matching mechanism between spare parts supply and process progress is established. Based on the intelligent matching sub-library of spare parts-process-equipment model, intelligent spare parts push is realized. When spare parts are in short supply, a dispatch recommendation plan is automatically generated. When a spare parts shortage or arrival delay is detected, alternative spare parts information is filtered from the database or a process adjustment plan is generated. The relevant results are synchronously updated to the visualization interface and the remanufacturing dynamic database. S4. Establish digital skill files for maintenance personnel in the remanufacturing dynamic database, including skill level, proficient processes, maintenance experience, and historical working time efficiency. Incorporate these files into the maintenance personnel skill level sub-database. The file information is synchronized to the visual dynamic management and control platform in real time. The platform calls the real-time progress requirement data of each process in the database and generates the optimal scheduling plan through intelligent matching of personnel and processes and scheduling optimization algorithms. The allocation results are displayed on the visual interface and fed back to the equipment schedule management link. S5. The maintenance process documents are digitized and broken down into process-level execution units, which are then linked to the corresponding process nodes in the remanufacturing dynamic database. Maintenance personnel can retrieve the associated process documents through the visual dynamic management platform and provide real-time feedback on the process execution status. The visual dynamic management platform enables intelligent push of process documents based on the current process. New or updated process documents are synchronized to the process document version management sub-database and the corresponding process nodes after review and are displayed on the visual interface. S6. Based on job functions, set differentiated collaborative permissions in the visual dynamic management and control platform, adopt a role + scenario dynamic fine-grained permission control mechanism, add operation scenario permissions, and dynamically allocate temporary operation permissions according to the user's login terminal, location, and time.

[0007] Preferably, in step S1, the remanufacturing dynamic database sets up data classification indexes for equipment, schedule, parts, personnel, and process. The data association rules are established based on the process logic of the remanufacturing of the tunneling and anchoring machine, including but not limited to the mapping relationship between equipment model and compatible parts, process and skill requirements, schedule nodes and parts requirements, and personnel skills and process difficulty. The data in each sub-database and the main database are linked and updated synchronously in real time, and all data supports full-link traceability, retrieval, and export.

[0008] Preferably, in step S2, the dynamic linkage algorithm uses an eight-hour workday as the calculation benchmark and calculates the project duration deviation value using the formula: actual project duration - theoretical project duration. The actual project duration is obtained by summing the differences between the actual start time and end time of each process, and the theoretical project duration is obtained by summing the standard project durations of each process obtained from the CAPP system.

[0009] Preferably, in step S2, the visualization interface presents the project schedule data and deviation values ​​through differentiated color progress bars and horizontal bar charts. The green progress bar corresponds to the project schedule being ahead of schedule, with a corresponding deviation value < 0; the blue progress bar corresponds to the project schedule being on time, with a corresponding deviation value = 0; the deviation value corresponding to the project schedule being behind schedule is > 0, and is divided into slight delay and severe delay. Slight delay is represented by a yellow progress bar, and severe delay is represented by a red progress bar. The delayed process automatically triggers a graded warning and pushes it to the relevant responsible persons.

[0010] Preferably, in step S3, the linkage and matching mechanism between the supply of parts and the progress of the process includes a part demand time window prediction function, which is calculated by the formula demand time window = process start time + part installation lead time. When the delivery time of the parts exceeds the window, it is determined to be a supply abnormality.

[0011] Preferably, in step S3, the selection criteria for alternative parts information include functional compatibility, installation adaptability, supply cycle and cost, and the process adjustment plan meets the constraints that it does not affect the priority of core processes and the overall remanufacturing schedule deviation does not exceed a preset threshold.

[0012] Preferably, in step S4, the personnel-process intelligent matching and scheduling optimization algorithm schedules shifts based on the skill requirements of the current maintenance process, the matched maintenance personnel, the current workload of the maintenance personnel, and the process duration requirements. It sets corresponding weights for each of the above factors, quantifies the indicators of each factor, multiplies each factor by its corresponding weight, and sums the results. The optimal shift is the one with the smallest weighted combination of all factors. The three-dimensional association sub-library of personnel-equipment-parts is used to record the operation content, operation time, and operation results of each maintenance personnel. The association data of the other two dimensions can be traced with one click through any one dimension.

[0013] Preferably, in step S5, when maintenance personnel enter the operation interface of a certain process through the platform, the platform automatically pushes the process document of that process to the operation terminal, and supports the synchronous linkage of video screen and process steps; at the same time, the platform supports workers to mark questions online on the process document page, and technicians can reply in real time, realizing intelligent push of process guidance, interactive learning, and online Q&A, and all interaction records are synchronized to the remanufacturing dynamic database; The newly added process document version management sub-library in the remanufacturing dynamic database enables intelligent version management and compliance verification of process documents. The platform automatically records the upload time, version number, updated content, and reviewer of process documents, and supports retrospective viewing of old versions of documents in a visual interface. At the same time, an industry compliance database for coal mine equipment remanufacturing processes is constructed. The platform automatically verifies the compliance of uploaded process documents. If any content does not comply with industry standards, it is immediately marked on the visual interface and the direction of modification is suggested, thus achieving version control, compliance verification, and full traceability of process documents.

[0014] Preferably, in step S6, an intelligent behavior analysis and abnormal permission warning function for system operations is added. The platform collects and analyzes the system operation behavior of all users in real time, builds a normal user operation behavior model, and if abnormal operation occurs, including but not limited to logging in outside of working hours, cross-permission operation, and batch modification of data, an abnormal permission warning is immediately triggered. A reminder is pushed on the visual interface and the user's operation permission is temporarily frozen, which needs to be unlocked after administrator review. At the same time, the platform logs and archives all permission operations and synchronizes them to the remanufacturing dynamic database, which supports traceability and viewing on the visual interface, realizing intelligent monitoring of operation behavior, abnormal warning, and permission fallback.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention relies on a visual dynamic management and control platform to present all process data, including equipment status, project progress, parts supply, personnel allocation, and process execution, in an intuitive format. Managers can monitor the entire process in real time and make rapid decisions based on the visualized data, solving the problems of scattered data and delayed decision-making in traditional management. Simultaneously, by designing a multi-system heterogeneous data fusion architecture and an industrial-grade lightweight dynamic deployment architecture, it addresses industry pain points such as inconsistent data standards across multiple systems and the limitations of B / S architecture on networks and terminals, achieving precise cross-system data fusion and multi-terminal, full-scenario adaptation. The design of a digital twin visualization screen and a personalized data dashboard upgrades the entire process management from "data display" to "digital twin management," and multi-terminal collaborative interaction further improves the timeliness and accuracy of decision-making.

[0016] This invention achieves deep linkage between equipment status and resource allocation, project schedule and spare parts / personnel, and process execution and equipment status. It enables rapid response to anomalies such as spare parts shortages, effectively reducing project delays and improving resource utilization. The invention integrates intelligent algorithms and intelligent linkage functions across all stages. Equipment status is intelligently updated and parameters are intelligently verified; project schedule management features dynamic prediction, intelligent timekeeping, and tiered root cause warnings; spare parts management offers intelligent matching, immersive visualization, and full lifecycle traceability; personnel management enables intelligent scheduling and 3D correlation traceability; and process management provides intelligent push notifications, interactive guidance, and version compliance management. This deep data linkage and intelligent collaboration across all stages significantly improves anomaly response speed and resource allocation efficiency, further reducing the probability of project delays and increasing the utilization rate of human and spare parts resources.

[0017] This invention is applicable to the remanufacturing management of integrated tunneling and anchoring machines, coal mining machines, scraper conveyors, and other heavy mining equipment. It can also be extended to the remanufacturing of engineering machinery equipment, demonstrating its wide applicability and high industry promotion value. Furthermore, this invention incorporates dynamic, fine-grained access control and intelligent operational behavior analysis functions, ensuring the security and standardization of platform use. The system's intelligent operation and maintenance and self-optimization module enables continuous iteration of the platform's core algorithms and operational processes, adapting to the continuous development of enterprise business. The integration of cutting-edge technologies such as intelligent sensing, algorithm modeling, and digital twins allows this method to not only be applicable to heavy mining equipment but also provide an intelligent upgrade solution for the remanufacturing management of engineering machinery equipment, further expanding its industry promotion value and application scenarios. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments 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.

[0019] Figure 1 This is a schematic diagram of the overall process of the present invention; Figure 2 This is a schematic diagram of the main interface of the visual dynamic management and control platform of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described 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 implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that the structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportional relationships, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should fall within the scope of the technical content disclosed in the present invention. It should be noted that in this specification, relational terms such as "first" and "second" are only used to distinguish one entity from several other entities, and do not necessarily require or imply any actual relationship or order between these entities.

[0022] This invention provides an embodiment: like Figure 1 , Figure 2 As shown, a visualization management method for the remanufacturing of tunneling and anchoring integrated machines is proposed. Based on a B / S architecture, a visualization dynamic control platform is built, establishing real-time data interaction interfaces with CAPP, VMS, and SAP systems. This interface is a bidirectional interaction interface based on industrial data semantic mapping. A dedicated data dictionary for the remanufacturing of tunneling and anchoring integrated machines is constructed. It integrates a multi-system heterogeneous data fusion architecture and an industrial-grade lightweight dynamic deployment architecture for tunneling and anchoring integrated machine remanufacturing, constructing a full-link collaborative intelligent visualization management system. Collaborative control is achieved through data linkage and visualization display, including the following steps: S1. Through the data interface of the visual dynamic management and control platform, it connects with CAPP, VMS, and SAP systems to collect real-time data on equipment basic parameters, process data, spare parts supply and demand information, personnel skill files, and equipment maintenance progress. A dedicated data dictionary automatically cleans, converts formats, and completes semantics for heterogeneous data such as CAPP process time data, VMS spare parts storage data, and SAP material coding data. This solves the problems of inconsistent data standards and data redundancy / missing information across multiple systems, achieving accurate cross-system data fusion, real-time synchronization, and two-way feedback. It also supports the subsequent addition of industrial systems by the enterprise. The system enables rapid integration and adaptation; a dynamic remanufacturing database is established, with main indexes for equipment, schedule, parts, personnel, and process. Sub-databases are added, including industry standards for equipment parameters, skill levels for maintenance personnel, intelligent matching of parts, processes, and equipment models, three-dimensional association between personnel, equipment, and parts, and version management of process documents. The industry standards for equipment parameters sub-database stores the industry standard values ​​and reasonable error ranges for core parameters such as the length, width, and height of the tunneling and anchoring machine and the cutting height / width. The skill level for maintenance personnel sub-database comprehensively records personnel's professional skills, proficient processes, maintenance experience, historical working hours, and efficiency. The database establishes mapping relationships for different types of data according to association rules, and all collected data is synchronized to the platform's visual interface for categorized display. The visual dynamic management and control platform adopts a dual data storage mode of local caching + cloud synchronization. For complex network environments that switch between workshop LAN and offline network, it enables local operation and offline data caching in the offline state, and automatically completes cloud data synchronization after connecting to the network. Furthermore, the platform's front end has been optimized for industrial-grade lightweight design, adapting to smooth operation on multiple terminals such as office computers and workshop industrial control computers, breaking through the dual limitations of traditional B / S architecture on network and terminal configuration. The database's data association rules are established based on the process logic of remanufacturing the tunneling and anchoring machine, including but not limited to the mapping relationship between equipment models and compatible parts, process and skill requirements, project schedule nodes and parts requirements, and personnel skills and process difficulty. Data in each sub-database is linked and updated synchronously with the main database in real time, and all data supports full-chain traceability, retrieval, and export.

[0023] S2. Obtain theoretical schedule data from the CAPP system and input it into the visual dynamic management and control platform. Input the actual progress data of each process in real time into the visual dynamic management and control platform, or automatically collect the working time data through the intelligent working time check-in function and synchronize it to the remanufacturing dynamic database. Use the dynamic linkage algorithm to call the theoretical schedule data stored in the database, the parts supply status data synchronized in real time from the VMS and SAP systems, and the current personnel configuration data to calculate the schedule deviation value. The schedule data and deviation value are presented in the visual interface. This step, based on traditional schedule deviation calculation, designs a dynamic schedule prediction model that integrates process complexity, staffing, and spare parts availability. The platform collects multi-dimensional data in real time, including the current maintenance process of the equipment, the actual number and skill level of staff, and the status of spare parts arrival / shortage. This data is then fed into the pre-trained dynamic schedule prediction model to automatically predict the completion time of subsequent processes and the overall schedule (this is existing technology, and the specific model is not described in detail). If situations such as spare parts shortages or personnel changes occur, the schedule is recalculated in real time, and adaptive schedule adjustment suggestions are automatically generated (such as increasing staff or prioritizing spare parts allocation). At the same time, schedule risk points are prominently displayed on the visual interface. The dynamic linkage algorithm uses an eight-hour workday as the calculation benchmark and calculates the project duration deviation value using the formula: Actual Project Duration - Theoretical Project Duration. The actual project duration is obtained by summing the differences between the actual start and end times of each process, while the theoretical project duration is obtained by summing the standard project durations of each process obtained from the CAPP system. A new intelligent time clock function linked to process nodes has also been added. Workers clock in / out of a specific process through the platform. The platform automatically calculates the actual effective working hours for that process and synchronizes them to the remanufacturing dynamic database based on the remanufacturing working hour calculation rules for the tunneling and anchoring integrated machine (eight-hour workday, automatic deduction of rest time between processes, and separate marking of overtime hours). No manual data entry is required, and it supports parallel clocking in for multiple processes, automatically distinguishing the working hours of different workers, and achieving automatic collection, accurate calculation, and statistics by person / process for working hour data. The visualization interface presents schedule data and deviation values ​​through differentiated color progress bars and horizontal bar charts. A green progress bar indicates that the schedule is ahead of schedule, with a deviation value < 0; blue indicates that the schedule is on time, with a deviation value = 0; and red indicates that the schedule is behind schedule, with a deviation value > 0. Delayed processes automatically trigger tiered warnings and are pushed to relevant responsible persons. The upgraded schedule warning mechanism is an intelligent warning mechanism combining tiered warnings and root cause analysis. This includes setting yellow warnings (minor delays) and red warnings (serious delays) based on the duration of delays, with different warning levels corresponding to differentiated display styles (progress bar color, pop-up reminders, and sound prompts). Simultaneously, it analyzes the core reasons for schedule delays (such as insufficient staffing, missing parts, and excessively long process operation times), displaying the percentage of each reason's impact on the schedule on the visualization interface, enabling early warning, tiered handling, and root cause tracing of schedule issues.

[0024] S3. Real-time retrieval of spare parts inventory, in transit, and arrival data synchronized between VMS and SAP systems in the remanufacturing dynamic database. Combined with equipment maintenance progress data recorded in the database, a linkage and matching mechanism between spare parts supply and process progress is established. This mechanism realizes intelligent spare parts push based on a smart matching sub-library of spare parts-process-equipment models. When a spare part shortage or arrival delay is detected, alternative spare parts information is filtered from the database or a process adjustment plan is generated. At the same time, the platform automatically generates a recommended spare parts allocation plan, and the relevant results are synchronously updated to the visualization interface and the remanufacturing dynamic database. Based on equipment maintenance process nodes and parts SAP codes, an intelligent matching sub-library of parts-process-equipment models is built in the remanufacturing dynamic database. When the platform identifies the current maintenance process, it automatically pushes all the parts information required for the process to the visual interface and marks the parts arrival status and inventory quantity. If a part is missing, the platform not only displays a part shortage warning, but also automatically generates a recommended part allocation plan (such as allocation from a certain workshop, estimated time / cost of emergency procurement) based on the warehouse data of the WMS system and the parts allocation records of other workshops, and synchronizes it to the visual interface, realizing the integration of intelligent parts matching, part shortage warning, and allocation decision-making. The platform's original equipment structure display has been upgraded to a lightweight 3D structural model of the tunneling and anchoring integrated machine, which is deeply linked to the project schedule and component assembly status. In the visual interface, clicking on any structural part of the 3D model allows users to view all component information (SAP code, quantity, arrival status, assembly progress) for that part with one click. The 3D model supports rotation, scaling, and disassembly. The assembly part of the current process is dynamically highlighted in the 3D model, and missing parts are marked with flashing red, realizing immersive interaction, visual positioning, and accurate identification of missing parts between component assembly and equipment structure. The linkage and matching mechanism between spare parts supply and process progress includes a spare parts demand time window prediction function, calculated using the formula: Demand Time Window = Process Start Time + Spare Part Installation Lead Time. When the spare parts arrival time exceeds this window, a supply anomaly is identified. A unique electronic traceability code is established for each spare part and linked to the remanufacturing dynamic database. The traceability code binds to the entire lifecycle data of the spare part, including procurement, arrival, warehousing, requisition, assembly, and repair / replacement. Simultaneously, spare part quality information is linked to the operational status of the remanufactured equipment. If a quality problem occurs with a spare part, the platform can quickly trace the entire process information of that spare part through a visual interface and automatically mark other tunneling and anchoring machines using the same spare part, achieving full lifecycle traceability of spare parts and linked early warning of quality problems. The selection criteria for alternative spare parts include functional compatibility, installation adaptability, supply cycle, and cost. Furthermore, the process adjustment plan must meet the constraints of not affecting the priority of core processes and ensuring that the overall remanufacturing schedule deviation does not exceed a preset threshold.

[0025] S4. Establish digital skill files for maintenance personnel in the remanufacturing dynamic database, including skill level, proficient processes, and workload. Improve the recording of personnel's professional skills, maintenance experience, historical working hours and efficiency, and incorporate them into the maintenance personnel skill level sub-database. The file information is synchronized to the visual dynamic management and control platform in real time. The platform calls the real-time progress requirement data of each process in the database, allocates suitable maintenance personnel based on the maintenance personnel skill digital files, and displays the allocation results on the visual interface while feeding back to the equipment schedule management link. Based on the skill level sub-database of maintenance personnel in the remanufacturing dynamic database, an intelligent matching and scheduling optimization algorithm for personnel and processes is designed (existing technology, not elaborated here). After the platform identifies the skill requirements of the current maintenance process of the equipment, it automatically recommends matching maintenance personnel and generates the optimal scheduling plan on the visual interface according to the current workload of personnel and the process time requirements. At the same time, the platform calculates the working efficiency of the current personnel in real time and compares it with the theoretical efficiency. If the efficiency is low, it automatically analyzes the reasons (such as mismatch of personnel skills or insufficient number of personnel) and puts forward adjustment suggestions on the visual interface, so as to realize intelligent matching of personnel and processes, scheduling optimization, and efficiency monitoring. A three-dimensional sub-database linking personnel, equipment, and parts is established within the remanufacturing dynamic database. This database records the operation content (such as assembling a part of a piece of equipment or inspecting a process of a piece of equipment), operation time, and operation results of each maintenance worker. All data is synchronized to a visual interface, allowing for one-click traceability of the related data in the other two dimensions through any one dimension (personnel / equipment / parts). This enables full-dimensional traceability of the remanufacturing process and precise identification of responsibilities.

[0026] S5. The maintenance process documents are digitized and broken down into process-level execution units, which are then linked to the corresponding process nodes in the remanufacturing dynamic database. Maintenance personnel can retrieve the associated process documents through the visual dynamic management platform or obtain the corresponding process documents through the platform's intelligent push function and provide real-time feedback on the process execution status. Newly added or updated process documents are synchronized to the process document version management sub-database and corresponding process nodes in the remanufacturing dynamic database after review, and are displayed on the visual interface. The maintenance process documents support video, image, PDF, and document formats. The visual dynamic management platform provides preview and download access for different file formats. Videos, images, and PDFs support online preview, while documents can be downloaded and viewed. Process execution feedback is provided in the form of digital forms.

[0027] When maintenance personnel access the operation interface of a certain process through the platform, the platform automatically pushes the process documents (videos / images / PDFs / documents) for that process to the operator's terminal. It also supports the synchronous linkage between video footage and process steps (clicking on a certain time point in the video automatically jumps to the corresponding text description of the process step; clicking on the process step automatically jumps to the corresponding screen in the video). At the same time, the platform allows workers to mark questions online on the process document page, and technicians can reply in real time, realizing intelligent push of process guidance, interactive learning, and online Q&A. All interaction records are synchronized to the remanufacturing dynamic database. A sub-database for process document version management has been added to the remanufacturing dynamic database to achieve intelligent version management and compliance verification of process documents. The platform automatically records the upload time, version number, updated content, and reviewer of process documents, and supports retrospective viewing of old versions of documents in a visual interface. At the same time, an industry compliance database for coal mine equipment remanufacturing processes has been built. The platform automatically verifies the compliance of uploaded process documents. If any content does not comply with industry standards, it is immediately marked on the visual interface and the direction of modification is suggested, so as to achieve version control, compliance verification, and full traceability of process documents. The platform is designed with an intelligent parsing function for industrial multimedia process documents. It can automatically extract key operation steps from maintenance videos and key parameters / operation requirements from process images. The extracted information is then converted into structured text content, bound to the original process document, and displayed on a visual interface. It also supports the retrieval of extracted key information, realizing intelligent parsing, information structuring, and rapid retrieval of process documents.

[0028] S6. Based on job functions, set differentiated collaborative permissions in the visual dynamic management and control platform, adopt a role + scenario dynamic fine-grained permission control mechanism, add operation scenario permissions, and dynamically allocate temporary permissions according to login terminal, location, and time.

[0029] In the past, team leaders received process progress warnings and adjusted personnel configurations through a visual interface. Technicians were responsible for adjusting process parameters and schedule data based on the process execution feedback data and equipment status data displayed on the interface. Supervisors and company leaders obtained full-process collaborative management information and anomaly warnings through the visual interface. Administrators coordinated data interaction permissions and collaborative rule configurations.

[0030] The traditional fixed-position permissions have been upgraded to a dynamic, fine-grained permission control mechanism based on roles and operational scenarios. In addition to the original four-level role permissions of team leader, technician, supervisor / company leader, and administrator, operational scenario permissions (such as on-site operation in the workshop, remote management in the office, and offline emergency operation) have been added. The platform dynamically allocates temporary operation permissions based on the user's login terminal (industrial control computer / office computer / mobile tablet), login location (workshop / office), and operation time. For example, when a technician is operating on-site in the workshop, he / she is temporarily granted permission for quick entry of parts, while when remotely managing from the office, he / she is only granted permission to view and modify. This achieves fine-grained control, dynamic adaptation, and security of permissions. The platform adds intelligent behavior analysis and abnormal permission warning functions for system operations. It collects and analyzes the system operation behavior of all users in real time, builds a model of normal user operation behavior, and immediately triggers an abnormal permission warning if abnormal operation occurs (such as logging in outside of working hours, cross-permission operation, batch modification of data). The platform pushes a reminder on the visual interface and temporarily freezes the user's operation permissions, which can only be unlocked after administrator approval. At the same time, the platform logs and archives all permission operations and synchronizes them to the remanufacturing dynamic database, which can be traced and viewed in the visual interface, realizing intelligent monitoring of operation behavior, abnormal warning, and permission fallback.

[0031] The above description is merely a preferred 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. A visual management method for the remanufacturing of an integrated tunneling and anchoring machine, characterized in that, A visual dynamic management and control platform is built based on a B / S architecture, establishing real-time data interaction interfaces with CAPP, VMS, and SAP systems. Collaborative management and control are achieved through data linkage and visualization, including the following steps: S1. By connecting to CAPP, VMS, and SAP systems through the data interface of the visual dynamic management and control platform, real-time collection of comprehensive data including equipment basic parameters, process data, parts supply and demand information, personnel skill files, and equipment maintenance progress is achieved. A dedicated data dictionary for the remanufacturing field of tunneling and anchoring integrated machines is used to automatically clean, convert, and semantically complete heterogeneous data in the comprehensive data to establish a remanufacturing dynamic database. The database includes, but is not limited to, sub-libraries of equipment parameter industry standards, sub-libraries of maintenance personnel skill levels, sub-libraries of intelligent matching of parts-processes-equipment models, sub-libraries of three-dimensional association of personnel-equipment-parts, and sub-libraries of process document version management. The data of each type in the database is mapped according to association rules, and all collected data is synchronized to the platform's visual interface for categorized display. S2. Obtain theoretical schedule data from the CAPP system and input it into the visual dynamic management and control platform. Input the actual progress data of each process in real time into the visual dynamic management and control platform, or automatically collect the working time data through the intelligent working time check-in function and synchronize it to the remanufacturing dynamic database. Use the dynamic linkage algorithm to call the theoretical schedule data stored in the database, the parts supply status data synchronized in real time from the VMS and SAP systems, and the current personnel configuration data to calculate the schedule deviation value. The schedule data and deviation value are presented in the visual interface. S3. Real-time retrieval of spare parts inventory, in transit, and arrival data synchronized between VMS and SAP systems in the remanufacturing dynamic database. Combined with equipment maintenance progress data recorded in the database, a linkage and matching mechanism between spare parts supply and process progress is established. Based on the intelligent matching sub-library of spare parts-process-equipment model, intelligent spare parts push is realized. When spare parts are in short supply, a dispatch recommendation plan is automatically generated. When a spare parts shortage or arrival delay is detected, alternative spare parts information is filtered from the database or a process adjustment plan is generated. The relevant results are synchronously updated to the visualization interface and the remanufacturing dynamic database. S4. Establish digital skill files for maintenance personnel in the remanufacturing dynamic database, including skill level, proficient processes, maintenance experience, and historical working time efficiency. Incorporate these files into the maintenance personnel skill level sub-database. The file information is synchronized to the visual dynamic management and control platform in real time. The platform calls the real-time progress requirement data of each process in the database and generates the optimal scheduling plan through intelligent matching of personnel and processes and scheduling optimization algorithms. The allocation results are displayed on the visual interface and fed back to the equipment schedule management link. S5. The maintenance process documents are digitized and broken down into process-level execution units, which are then linked to the corresponding process nodes in the remanufacturing dynamic database. Maintenance personnel can retrieve the associated process documents through the visual dynamic management platform and provide real-time feedback on the process execution status. The visual dynamic management platform enables intelligent push of process documents based on the current process. New or updated process documents are synchronized to the process document version management sub-database and the corresponding process nodes after review and are displayed on the visual interface. S6. Based on job functions, set differentiated collaborative permissions in the visual dynamic management and control platform, adopt a role + scenario dynamic fine-grained permission control mechanism, add operation scenario permissions, and dynamically allocate temporary operation permissions according to the user's login terminal, location, and time.

2. The visual management method for remanufacturing of integrated tunneling and anchoring machines according to claim 1, characterized in that, In step S1, the remanufacturing dynamic database sets up data classification indexes for equipment, schedule, parts, personnel, and process. The data association rules are established based on the process logic of the remanufacturing of the tunneling and anchoring machine, including but not limited to the mapping relationship between equipment model and compatible parts, process and skill requirements, schedule nodes and parts requirements, and personnel skills and process difficulty. The data in each sub-database and the main database are linked and updated in real time, and all data supports full-link traceability, retrieval, and export.

3. The visual management method for remanufacturing of integrated tunneling and anchoring machines according to claim 1, characterized in that, In step S2, the dynamic linkage algorithm uses an eight-hour workday as the calculation benchmark and calculates the project duration deviation value using the formula: actual project duration - theoretical project duration. The actual project duration is obtained by summing the differences between the actual start time and end time of each process, and the theoretical project duration is obtained by summing the standard project durations of each process obtained from the CAPP system.

4. The visual management method for remanufacturing of integrated tunneling and anchoring machines according to claim 1, characterized in that, In step S2, the visualization interface presents the project schedule data and deviation values ​​through differentiated color progress bars and horizontal bar charts. Green progress bars correspond to project schedule ahead of schedule with a deviation value < 0; blue progress bars correspond to project schedule on time with a deviation value = 0; and project schedule lag corresponds to a deviation value > 0, which is divided into slight lag and severe lag. Slight lag is represented by a yellow progress bar, and severe lag is represented by a red progress bar. The lagging process automatically triggers a graded warning and pushes it to the relevant responsible persons.

5. The visual management method for remanufacturing of integrated tunneling and anchoring machines according to claim 1, characterized in that, In step S3, the linkage and matching mechanism between the supply of parts and the progress of the process includes a part demand time window prediction function, which is calculated by the formula demand time window = process start time + part installation lead time. When the delivery time of the parts exceeds the window, it is determined to be a supply abnormality.

6. The remanufacturing visualization management method for integrated tunneling and anchoring machines according to claim 5, characterized in that, In step S3, the selection criteria for alternative parts information include functional compatibility, installation adaptability, supply cycle and cost, and the process adjustment plan meets the constraints that it does not affect the priority of core processes and the overall remanufacturing schedule deviation does not exceed a preset threshold.

7. The remanufacturing visualization management method for integrated tunneling and anchoring machines according to claim 1, characterized in that, In step S4, the personnel-process intelligent matching and scheduling optimization algorithm schedules shifts based on the skill requirements of the current maintenance process, the matched maintenance personnel, the current workload of the maintenance personnel, and the process duration requirements. It sets corresponding weights for each of the above factors, quantifies the indicators of each factor, multiplies each factor by its corresponding weight, and sums the results. The optimal shift is the one with the smallest weighted combination of all factors. The three-dimensional association sub-library of personnel-equipment-parts is used to record the operation content, operation time, and operation results of each maintenance personnel. The association data of the other two dimensions can be traced with one click through any one dimension.

8. The visual management method for remanufacturing of integrated tunneling and anchoring machines according to claim 1, characterized in that, In step S5, when maintenance personnel enter the operation interface of a certain process through the platform, the platform automatically pushes the process document of that process to the operation terminal, and supports the synchronous linkage of video screen and process steps; at the same time, the platform allows workers to mark questions online on the process document page, and technicians can reply in real time, realizing intelligent push of process guidance, interactive learning, and online Q&A, and all interaction records are synchronized to the remanufacturing dynamic database. The newly added process document version management sub-library in the remanufacturing dynamic database enables intelligent version management and compliance verification of process documents. The platform automatically records the upload time, version number, updated content, and reviewer of process documents, and supports retrospective viewing of old versions of documents in a visual interface. At the same time, an industry compliance database for coal mine equipment remanufacturing processes is constructed. The platform automatically verifies the compliance of uploaded process documents. If any content does not comply with industry standards, it is immediately marked on the visual interface and the direction of modification is suggested, thus achieving version control, compliance verification, and full traceability of process documents.

9. The visual management method for remanufacturing of integrated tunneling and anchoring machines according to claim 1, characterized in that, In step S6, a new intelligent behavior analysis and abnormal permission warning function for system operations has been added. The platform collects and analyzes the system operation behavior of all users in real time, builds a normal user operation behavior model, and if abnormal operation occurs, including but not limited to logging in outside of working hours, cross-permission operation, and batch modification of data, an abnormal permission warning will be triggered immediately. A reminder will be pushed to the visualization interface and the user's operation permission will be temporarily frozen. The user's operation permission will be unlocked after the administrator's review. At the same time, the platform logs and archives all permission operations and synchronizes them to the remanufacturing dynamic database, which can be traced and viewed in the visualization interface, realizing intelligent monitoring of operation behavior, abnormal warning, and permission fallback.

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