Precise configuration method for centralized control center equipment of steel rolling workshop

Abstract

The invention discloses a precise configuration method for steel rolling workshop centralized control center equipment, and relates to the field of steel rolling workshop centralized control center construction of iron and steel enterprises. According to the precise configuration method for the steel rolling workshop centralized control center equipment, a hyper-converged server is used for replacing a traditional server, and an AI algorithm and a digital twinborn model are introduced; computing resource distribution of a scheduling server is dynamically optimized, task cooperation and resource borrowing between upstream and downstream centralized control centers when an emergency occurs in a production workshop are ensured, closed loop of centralized control instruction issuing and physical entity feedback is realized, and a resource allocation item is integrated according to a resource allocation item integration result in combination with an autonomous learning function of the system. A steel rolling workshop centralized control center construction scheme is constructed by using an optimal optimization method, steel rolling workshop centralized control center equipment is accurately configured, production requirements are met, appropriate redundancy is achieved, the lowest construction cost investment is ensured, and the construction scheme is better.

Description

Technical Field

[0001] This invention relates to the field of construction of centralized control centers in steel rolling mill workshops, and specifically to a method for precise configuration of equipment in a centralized control center for steel rolling mill workshops. Background Technology

[0002] A centralized control center refers to an intelligent, integrated operating platform that coordinates production, improves efficiency, optimizes personnel allocation, and reduces labor costs. Currently, the construction of centralized control centers in steel rolling mills still faces the following challenges: (1) Traditional servers are used more often, while hyperconverged servers are used less often, resulting in problems such as waste of computing resources and lack of horizontal scalability; (2) The centralized control console uses traditional PCs / industrial control computers more often than desktop cloud platforms, resulting in problems such as cluttered consoles, insecure data, unstable business operations, and difficult business operation and maintenance management. (3) The central control console display can only display a single screen and does not have flexible operation functions such as audio and video feedback, keyboard and mouse feedback, and screen switching of the display interface. (4) Most of the construction units of the centralized control center are IT equipment manufacturers, who do not have enough understanding of the steel rolling process, resulting in too much or too little equipment, which leads to excessive costs or insufficient computing and display resources.

[0003] In response to the above situation, this invention proposes a method for precise configuration of equipment in the centralized control center of a steel rolling workshop. During the construction of the centralized control center of the steel rolling workshop, a hyperconverged server is used to replace the traditional server, and the equipment in the centralized control center of the steel rolling workshop is precisely configured. This method meets the computing and display resource requirements while reducing construction costs and achieving a better construction solution. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for precise configuration of equipment in the centralized control center of a steel rolling workshop. By combining a hyper-converged integrated server with a new algorithm model, the production work in the steel rolling workshop can be facilitated.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for precise configuration of equipment in a steel rolling mill control center, the method comprising the following steps: S1. Determine the configuration items of the steel rolling production workshop business system, including: L1 system configuration items, L2 system configuration items, MES system configuration items, and target virtualization client / industrial control computer configuration items; S2. Determine the computing resource configuration of the hyper-converged integrated machine server based on the configuration of the steel rolling production workshop business system; S3. Determine the virtual machine resources required for the operator display and office computers in the steel rolling production workshop; S4. Determine the computing resource configuration of the desktop cloud system server based on the operator display in the steel rolling production workshop and the virtual machine resources required by the office computers; S5. Confirm the configuration items of the KVM seat system, including the configuration parameters of the seat screen in the steel rolling production workshop, seat operation console, seat display services, central control screen, physical construction of the central control center and physical construction of the computer room; S6. Based on the KVM seat system configuration items, determine the resource configuration of the distributed KVM control system, centralized control system, supporting modules, and cabling. S7. Based on the computing resource configuration of the hyper-converged integrated machine server, the computing resource configuration of the desktop cloud system server, and the resource configuration of the KVM seat system, construct a construction plan for the centralized control center of the steel rolling workshop.

[0006] Preferably, the MES system is configured in partitions according to the sequence of processes, upgrading from a single workshop to a group-level management system with multiple processes and multiple control centers. Under each partition, the configuration items of the workshop business system are determined, and the number of personnel required for the target virtualization client / industrial control computer configuration is determined based on the configuration items of the workshop business system.

[0007] Preferably, the computing resource configuration of the hyperconverged integrated machine server is determined, and AI algorithms and digital twin models are introduced to dynamically optimize the allocation of computing resources of the scheduling server, so as to ensure task collaboration and resource sharing between upstream and downstream control centers when emergencies occur in the production workshop, and realize the closed loop of control command issuance and physical entity feedback.

[0008] Preferably, the configuration items in step S1 are specifically: L1 system configuration items: slab warehouse management, heating furnace control, transmission control, high-pressure water descaling control, rolling mill auxiliary equipment control, hydraulic AGC system, looper control, laminar flow cooling control, coiler control, and wire drawing machine control; L2 system configuration items: Initial Data Input (PDI), furnace model setting, material tracking, roughing procedure setting, width control (AWC) model, finishing procedure setting, finishing model setting, coiling temperature control (CTC) model, coiling setting, coil weighing and inkjet printer control, coil conveyor chain control, data upload, and self-learning function. MES system configuration items: warehouse management, quality assessment and quality assurance certificate printing, and shipment management; The client / industrial control computer configuration items are mainly based on: the number of operator stations, the number of operators per shift, and the number of HMI interfaces in the operator station.

[0009] Preferably, the hyperconverged integrated server computing resource configuration in step S2 includes: CPU (processor), memory, system / buffer disk, data disk, and network card.

[0010] Preferably, the virtual machine resources required for the operator display in the steel rolling production workshop in step S3 are: Preferably, step S4 specifically comprises: A typical central control center IT infrastructure consists of the following three core layers: This invention discloses a method for precise configuration of equipment in the centralized control center of a steel rolling workshop, which has the following beneficial effects: This method for precisely configuring equipment in the rolling mill control center utilizes hyperconverged servers instead of traditional servers and introduces AI algorithms and digital twin models to dynamically optimize the allocation of computing resources for scheduling servers. This ensures task collaboration and resource sharing between upstream and downstream control centers in the event of emergencies in the production workshop, achieving a closed loop between the issuance of control commands and physical entity feedback. Based on the integration results of resource configuration items and combined with the system's self-learning function, the method uses optimal optimization to construct a construction plan for the rolling mill control center. This allows for precise configuration of the equipment in the rolling mill control center, meeting production needs while providing appropriate redundancy, ensuring the lowest possible construction cost, and achieving a superior construction plan. Attached Figure Description

[0011] 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.

[0012] Figure 1 This is a complete flowchart of the business systems at all levels and their configuration items in this invention; Figure 2 This is the IT infrastructure of the central control center of this invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, 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.

[0014] This invention discloses a method for precise configuration of equipment in the centralized control center of a steel rolling workshop; Includes the following steps: S1. Determine the configuration items of the steel rolling production workshop business system, including: L1 system configuration items, L2 system configuration items, MES system configuration items, and target virtualization client / industrial control computer configuration items; S2. Determine the computing resource configuration of the hyper-converged integrated machine server based on the configuration of the steel rolling production workshop business system; S3. Determine the virtual machine resources required for the operator display and office computers in the steel rolling production workshop; S4. Determine the computing resource configuration of the desktop cloud system server based on the operator display in the steel rolling production workshop and the virtual machine resources required by the office computers; S5. Confirm the configuration items of the KVM seat system, including the configuration parameters of the seat screen in the steel rolling production workshop, seat operation console, seat display services, central control screen, physical construction of the central control center and physical construction of the computer room; S6. Based on the KVM seat system configuration items, determine the resource configuration of the distributed KVM control system, centralized control system, supporting modules, and cabling. S7. Based on the computing resource configuration of the hyper-converged integrated machine server, the computing resource configuration of the desktop cloud system server, and the resource configuration of the KVM seat system, construct a construction plan for the centralized control center of the steel rolling workshop.

[0015] The upgrade from a single workshop to a group-level management system with multiple processes and multiple control centers involves partitioning the MES system configuration according to the sequence of processes, determining the workshop business system configuration items within each partition, and then determining the required number of personnel for the target virtualization client / industrial control computer configuration based on the workshop business system configuration items.

[0016] The computing resource configuration of the hyperconverged integrated machine server is determined, and AI algorithms and digital twin models are introduced to dynamically optimize the allocation of computing resources of the scheduling server. This ensures task collaboration and resource sharing between upstream and downstream control centers in the event of an emergency in the production workshop, and realizes a closed loop between the issuance of control commands and physical entity feedback.

[0017] The specific configuration items in step S1 are as follows: L1 system configuration items: slab warehouse management, heating furnace control, transmission control, high-pressure water descaling control, rolling mill auxiliary equipment control, hydraulic AGC system, looper control, laminar flow cooling control, coiler control, and wire drawing machine control; L2 system configuration items: Initial Data Input (PDI), furnace model setting, material tracking, roughing procedure setting, width control (AWC) model, finishing procedure setting, finishing model setting, coiling temperature control (CTC) model, coiling setting, coil weighing and inkjet printer control, coil conveyor chain control, data upload, and self-learning function. MES system configuration items: warehouse management, quality assessment and quality assurance certificate printing, and shipment management; The client / industrial control computer configuration items are mainly based on: the number of operator stations, the number of operators per shift, and the number of HMI interfaces in the operator station.

[0018] The computing resources configuration of the hyperconverged integrated machine server in step S2 includes: CPU (processor), memory, system / buffer disk, data disk, and network card.

[0019] CPU (Processor): Includes CPU model, quantity, number of cores, and clock speed. The number of cores and clock speed directly affect the virtual machine's processing performance and multitasking concurrency capabilities.

[0020] Memory: The capacity is usually large, using high-specification DDR4 memory to ensure smooth operation when a large number of virtual machines are running at the same time.

[0021] System / Cache Disk: Typically uses a high-performance SSD solid-state drive to install the host operating system and host virtual machine cache to improve I / O speed.

[0022] Data disk: Stores virtual machine data, and can be either all-flash SSDs or a hybrid SSD / HDD configuration. All-flash SSDs offer higher performance, while the hybrid configuration strikes a balance between capacity and cost.

[0023] Network interface card (NIC): Requires high bandwidth and is usually equipped with 10 Gigabit optical ports and 1 Gigabit electrical ports to meet the network traffic and data synchronization needs of virtual machines.

[0024] When making a selection, pay attention to whether the platform supports heterogeneous expansion, that is, whether it allows the addition of server nodes of different generations or models in the cluster.

[0025] For critical services that cannot tolerate interruptions, it is necessary to ensure that the configuration meets redundancy requirements, and also to pay attention to whether the domestically produced chips and the independently controllable software architecture comply with policy and security requirements.

[0026] Preferably, the virtual machine resources required for the operator display in the steel rolling production workshop in step S3 are: Preferably, step S4 specifically comprises: CPU resource calculation: Total vCPU requirement = (20 seats × 4 vCPUs) + (30 office staff × 2 vCPUs) = 140 vCPUs.

[0027] In a virtualized environment, an important configuration principle is that the ratio of vCPUs to physical CPU cores should not be too high, and it is generally recommended to be between 1:4 and 1:6 to avoid performance degradation caused by scheduling wait.

[0028] Based on a 1:6 ratio, the required number of physical CPU cores = 140 vCPU / 6 ≈ 24 physical cores.

[0029] Considering that servers are typically configured with dual CPUs, this means you will need at least two server-grade CPUs with 12 or more cores (such as Intel Xeon Silver / Gold series or AMD EPYC series).

[0030] Memory resource calculation: Total memory requirement = (20 seats × 8 GB) + (30 office workers × 4 GB) = 280 GB.

[0031] To ensure system stability and reserve buffer space, the total memory usage of all virtual machines should not exceed 80% of the physical memory allocated to the server.

[0032] Required physical memory capacity = 280 GB / 0.8 = 350 GB.

[0033] Therefore, it is recommended to configure 384GB of DDR4 / 5 server memory (e.g., 12 x 32GB memory modules).

[0034] Storage Resource Planning: Capacity Requirements: Production workstations using "thick provisioning" will immediately occupy the full space, while office computers can be allocated on demand using "thin provisioning." Total capacity requirements need to be calculated based on the actual usage model, with sufficient space reserved.

[0035] Performance requirements (IOPS): Total IOPS requirement = (20 seats × 30 IOPS) + (30 office staff × 25 IOPS) = 1350 IOPS.

[0036] To meet these performance requirements and ensure a good user experience, the storage system must consist entirely of high-performance SSDs (Solid State Drives), and a RAID 10 or RAID 5 array configuration is recommended to ensure both performance and data security. Additionally, the number of virtual machines placed on each LUN (Logical Unit) should not be excessive (e.g., fewer than 10 for production applications) to prevent I / O bottlenecks.

[0037] Planning server architecture and high availability For a scale of 50 desktops, it is recommended to use at least 3 physical servers to form a virtualization cluster.

[0038] High Availability (HA): When any server in the cluster experiences a hardware failure, the virtual machines running on it can automatically restart on other servers in the cluster, thereby ensuring the business continuity of production workers. This is an advantage that traditional PCs cannot match.

[0039] Hyperconverged infrastructure: This is a modern deployment approach that integrates computing, storage, and networking functions onto a single server node. Ansteel Group's case study utilizes a hyperconverged solution, which enables the integration of production data, facilitating subsequent intelligent analysis, simplifying management, and providing more flexible scalability.

[0040] A typical central control center IT infrastructure consists of the following three core layers: Hyperconverged all-in-one server configuration Hyperconverged infrastructure is the "heart" of the control center, integrating computing, storage, and network resources.

[0041] Computing resources: It is recommended to configure dual high-performance CPUs (such as Intel Xeon Scalable processors), with each agent server having at least 16 cores to support a large number of virtual machines running concurrently. Sufficient memory is also recommended; depending on the number of virtual machines, the total memory capacity typically needs to range from several hundred GB to several TB.

[0042] Storage resources: A hybrid configuration of SSDs and HDDs or all-flash storage is used. High-performance SSDs are used to install the operating system and host virtual machine caches to improve I / O speed; large-capacity HDDs can be used for data storage. A distributed storage architecture is adopted, directly connecting servers to hard drives to form a storage resource pool, improving reliability and performance.

[0043] High Availability and Networking: Deploy at least 2-3 nodes to form a cluster for high availability. This allows for rapid virtual machine replacement in the event of a business system failure, ensuring business continuity. For the network, both 10 Gigabit fiber optic ports and gigabit Ethernet ports are required to meet the network traffic and data synchronization needs of the dense virtual machines.

[0044] 2. Desktop cloud system server configuration Desktop cloud systems deliver operating systems and applications to operating terminals as services.

[0045] Virtual Desktop Planning: When configuring virtual machine resources for the workstation displays and office computers in the rolling mill workshop, a balance must be struck between high performance and efficient resource utilization. For example, it is recommended that production workstation display terminals (running MES clients, L2 process control systems, real-time monitoring, industrial configuration software, etc.) be configured with 2-4 core vCPUs and 4GB-8GB or higher memory; office computers (running office software, internal management systems, etc.) are recommended to be configured with 1-2 core vCPUs and 2GB-4GB memory. Adopting a cloud desktop solution can save a significant amount of space per workstation and reduce the amount of power cabling.

[0046] Server resource estimation: Similar projects can be referenced for configuration based on the number of concurrent users. For example, when configuring the agent system hardware resources for 24 concurrent users, considering the large resource consumption of the HMI software, three cloud servers are required. The solution uses virtualization technology to centralize computer hardware resources into a virtual resource pool, allowing for elastic resource release and allocation based on demand, thus improving equipment utilization.

[0047] 3. KVM Seat Management System Configuration KVM systems are key to achieving "human-machine separation" and efficient signal scheduling.

[0048] Core functions: Should support one machine for multiple screens (one operator can use one set of keyboard and mouse to control multiple screens on multiple hosts), free mouse scrolling (the mouse can seamlessly switch between different host screens), OSD menu management, user permission management and other functions.

[0049] Deployment Mode: A distributed KVM system can be used to enable collaborative operation among agents and flexible signal scheduling for display on the wall. For example, through a multi-screen KVM multi-computer splitter, each agent can monitor and operate multiple hosts, and display them on a single screen in a multi-screen split mode.

[0050] Value: Compared to traditional methods, KVM seat management significantly reduces equipment investment, saves desktop space, simplifies computer management, improves data security, reduces maintenance workload, and greatly improves the operator's user experience.

[0051] Key Implementation Considerations Network and Security: The network architecture needs to be divided into a core layer, aggregation layer, and access layer. VLAN segmentation and port aggregation technologies should be used to reduce network interference. Security devices should be used for boundary protection between the industrial control network and the core network, including firewalls, and strict operation permission switching mechanisms (such as response switching mechanisms) should be set up to ensure system security.

[0052] High availability and redundancy: Key components such as hyperconverged clusters, desktop cloud management systems, and core network switches must be designed in a redundant mode to ensure that a single point of failure does not affect the overall business continuity.

[0053] Intelligent function integration: Consider introducing a sound pickup system to monitor the operating status of on-site equipment, and using 3D video fusion technology to build a panoramic monitoring system for the workshop, providing operators with a more comprehensive on-site perception capability.

[0054] Summary of the advantages of the solution This construction plan can bring you the following core values: Efficient centralized management and control: Enables centralized deployment and efficient collaboration of operation positions in various processes, breaking down information silos.

[0055] Convenient and reliable operation and maintenance: The system supports high availability and can recover quickly in case of failure. Centralized management and remote operation and maintenance via cloud desktops greatly improve efficiency.

[0056] Flexible and elastic resources: Computing resources can be allocated on demand and scaled elastically to easily cope with future business growth.

[0057] Significant energy savings and cost reductions: Cloud terminals consume far less energy than traditional PCs, and hyperconverged systems can also effectively reduce electricity costs.

[0058] Implementation plan and corresponding tables Suppose that a steel group is implementing a full-process centralized control center upgrade project.

[0059] Step 1: Determine the configuration items of the entire business system. This step corresponds to S1 in the original patent, but the scope is expanded to include the four major processes of iron, steel, casting, and rolling.

[0060] Step 2: Determine the configuration of the intelligent hyperconverged cloud resource pool This step corresponds to S2 and S4 of the original patent, but integrates the business system and desktop cloud resources into a dynamically allocated intelligent resource pool.

[0061] Step 3: Determine the global KVM agent collaboration system configuration This step corresponds to S5 and S6 of the original patent, emphasizing the ability for cross-regional and cross-process collaboration among agents.

[0062] This method achieves intelligent control of the central control center through hierarchical configuration and resource integration. The core steps are as follows: I. Workshop Numbering and System Configuration Item Division (S1) 1. L1 System Configuration Items: This refers to the basic automation layer (equipment-level control), which includes PLCs, sensors, and real-time data acquisition systems for equipment such as rolling mills and heating furnaces. It is responsible for executing process instructions and providing feedback on equipment status.

[0063] 2. L2 system configuration items: This is the process control layer (workshop-level optimization), which covers rolling rhythm control, temperature model calculation, etc., and achieves dynamic parameter adjustment through L1 data.

[0064] 3. MES system configuration items: Production Execution Layer (factory-level management), including functions such as order scheduling and quality traceability, connecting L2 and ERP systems.

[0065] 4. Virtualization Client / Industrial Control Computer: Used to deploy L1 / L2 system virtual machines, which must meet the requirements of real-time performance (such as low-latency network) and high reliability.

[0066] II. Server Resource Allocation (S2-S4) 1. Hyperconverged appliance: Hosts L1 / L2 system virtual machines, requires high-performance CPU (such as multi-core Xeon), large memory (≥128GB) and redundant storage, and supports real-time data processing.

[0067] 2. Desktop Cloud System: Provides operators with a virtualized office environment, requiring the allocation of GPU resources to support multi-screen displays (such as 4K large screens) and graphical interface rendering.

[0068] III. KVM Seat System Configuration (S5-S6) 1. Distributed KVM control: Enables cross-seat signal switching (e.g., pushing rolling mill monitoring screens to the central control screen), and needs to support low-latency video transmission (≤50ms) and multi-protocol compatibility.

[0069] 2. Centralized control module: Integrates alarm management, permission allocation and other functions, and needs to be linked with the MES system to achieve rapid response to anomalies.

[0070] IV. Solution Integration (S7) By leveraging the resources of hyperconverged servers, desktop cloud, and KVM systems, an integrated control center for "data-control-display" is constructed, enabling visualization and remote control of the entire steel rolling process.

[0071] V. Explanation of Key Terms 1. L1 / L2 system: Referring to the intelligent manufacturing standard system of the steel industry, L1 focuses on real-time control at the equipment level, while L2 focuses on workshop-level process optimization.

[0072] 2. MES System: As the core of production control, it needs to exchange data with the L2 system and support quality analysis and equipment efficiency statistics.

[0073] This method solves the problem of isolated systems in traditional steel rolling workshops by using hierarchical configuration and resource virtualization, thereby improving production efficiency and control precision.

Claims

1. A method for precise configuration of equipment in a centralized control center of a steel rolling workshop, characterized in that, The method includes the following steps: S1. Determine the configuration items of the steel rolling production workshop business system, including: L1 system configuration items, L2 system configuration items, MES system configuration items, and target virtualization client / industrial control computer configuration items; S2. Determine the computing resource configuration of the hyper-converged integrated machine server based on the configuration of the steel rolling production workshop business system; S3. Determine the virtual machine resources required for the operator display and office computers in the steel rolling production workshop; S4. Determine the computing resource configuration of the desktop cloud system server based on the operator display in the steel rolling production workshop and the virtual machine resources required by the office computers; S5. Confirm the configuration items of the KVM seat system, including the configuration parameters of the seat screen in the steel rolling production workshop, seat operation console, seat display services, central control screen, physical construction of the central control center and physical construction of the computer room; S6. Based on the KVM seat system configuration items, determine the resource configuration of the distributed KVM control system, centralized control system, supporting modules, and cabling. S7. Based on the computing resource configuration of the hyper-converged integrated machine server, the computing resource configuration of the desktop cloud system server, and the resource configuration of the KVM seat system, construct a construction plan for the centralized control center of the steel rolling workshop.

2. The precise configuration method as described in claim 1, characterized in that: The upgrade from a single workshop to a group-level management system with multiple processes and multiple control centers involves partitioning the MES system configuration according to the sequence of processes, determining the workshop business system configuration items within each partition, and then determining the required number of personnel for the target virtualization client / industrial control computer configuration based on the workshop business system configuration items.

3. The precise configuration method as described in claim 1, characterized in that: The computing resource configuration of the hyperconverged integrated machine server is determined, and AI algorithms and digital twin models are introduced to dynamically optimize the allocation of computing resources of the scheduling server. This ensures task collaboration and resource sharing between upstream and downstream control centers in the event of an emergency in the production workshop, and realizes a closed loop between the issuance of control commands and physical entity feedback.

4. The precise configuration method as described in claim 1, characterized in that: The specific configuration items in step S1 are as follows: L1 system configuration items: slab warehouse management, heating furnace control, transmission control, high-pressure water descaling control, rolling mill auxiliary equipment control, hydraulic AGC system, looper control, laminar flow cooling control, coiler control, and wire drawing machine control; L2 system configuration items: Initial Data Input (PDI), furnace model setting, material tracking, roughing procedure setting, width control (AWC) model, finishing procedure setting, finishing model setting, coiling temperature control (CTC) model, coiling setting, coil weighing and inkjet printer control, coil conveyor chain control, data upload, and self-learning function. MES system configuration items: warehouse management, quality assessment and quality assurance certificate printing, and shipment management; The client / industrial control computer configuration items are mainly based on: the number of operator stations, the number of operators per shift, and the number of HMI interfaces in the operator station.

5. The precise configuration method as described in claim 1, characterized in that: The hyperconverged integrated server computing resource configuration described in step S2 includes: CPU (processor), memory, system / buffer disk, data disk, and network card.

6. The precise configuration method as described in claim 1, characterized in that: The virtual machine resources required for the operator display in the steel rolling production workshop in step S3 are: vCPU (processor), memory, storage (hard disk), operating system, and graphics display.

7. The precise configuration method as described in claim 1, characterized in that: The virtual machine resources required for the agent operation display and office computer in step S4 are specifically: vCPU, memory, storage, IOPS (storage performance), and high availability (HA).

8. The precise configuration method as described in claim 1, characterized in that: The configuration parameters of the middle seat screen in step S5 include the number of seat screens, screen size, screen resolution, and splicing parameters. The configuration parameters of the seat control panel include the number of control panels, the number of keyboards, the number of mice, and voice configuration. The configuration parameters of the centralized control screen include the number of centralized control screens, size, resolution, and splicing parameters (the row and column composition, number, and resolution of the spliced ​​screens).

9. The precise configuration method as described in claim 1, characterized in that: The system configuration resources in step S6 specifically include: input nodes, output nodes, underlying control software, touch screen, control host, power controller, distributor, intelligent human-machine interface editing software, switch, router, and several data cables.

10. The precise configuration method as described in claim 1, characterized in that: By combining the computing resource configurations of hyperconverged integrated machine servers, desktop cloud system servers, and KVM console systems, a centralized control center IT infrastructure is constructed, integrating computing, storage, and network resources to form a unified resource pool.

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