3D visualization operation and maintenance system and method for industrial field data center

Abstract

This invention provides a 3D visualization operation and maintenance system and method for industrial data centers. A data acquisition module collects a first set of parameters relating multiple basic devices within a centralized data center infrastructure equipment set, and a second set of parameters relating multiple subsystems within a centralized IT business system set. A data processing module, based on the first and second parameter sets, derives multiple independent device modules and multiple independent business modules, and then constructs a module relationship diagram. A 3D visualization display module, based on the module relationship diagram, provides a 3D visualization display of the operational relationship information of the multiple independent device modules and multiple independent business modules. This system can divide the data center infrastructure equipment set and the IT business system set into multiple independent device modules and multiple independent business modules, respectively, and transform them into an intuitive module relationship diagram. Through 3D visualization, it facilitates the understanding of complex indicators by operation and maintenance personnel, improving the monitoring efficiency of the data center.

Description

Technical Field

[0001] This invention relates to the field of data center operation and maintenance technology in the industrial sector, and in particular to a 3D visualization operation and maintenance system and method for industrial data centers. Background Technology

[0002] In the current context of accelerating industrial digitalization, the importance of industrial data centers as core hubs of enterprise operations is self-evident. Industrial data centers integrate data center infrastructure and IT business systems such as data middleware platforms. The broad classification of industries leads to significant differences in IT business systems across various sectors. Currently, most data centers rely on third-party operations and maintenance, a model with several drawbacks. On the one hand, third-party maintenance personnel often lack sufficient understanding of specific industrial business systems, resulting in high training costs, and the specialized and unique nature of IT business systems increases the difficulty of acquiring knowledge. On the other hand, data center monitoring is often limited to the data center infrastructure level, making it difficult to delve into core business systems. In summary, existing technologies suffer from difficulties in enabling maintenance personnel to understand complex indicators and inefficient data center monitoring. Summary of the Invention

[0003] The purpose of this invention is to provide a 3D visualization operation and maintenance system and method for data centers in the industrial field, so as to help operation and maintenance personnel understand complex indicators and improve the monitoring efficiency of data centers.

[0004] This invention provides a 3D visualization operation and maintenance system for industrial data centers. The industrial data center includes a set of data center infrastructure equipment and a set of IT business systems. The 3D visualization operation and maintenance system includes a data acquisition module, a data processing module, and a 3D visualization display module. The data acquisition module is used to collect a first set of parameters associated with multiple basic devices in the data center infrastructure equipment set, and a second set of parameters associated with multiple subsystems in the IT business system set. The data processing module is used to divide the multiple basic devices into multiple independent device modules and the multiple subsystems into multiple independent business modules based on the first and second parameter sets; and to construct a module relationship diagram based on the multiple independent device modules and multiple independent business modules. The 3D visualization display module is used to perform 3D visualization display of the operational association information of the multiple independent device modules and multiple independent business modules based on the module relationship diagram; the operational association information includes operational parameters and operational status.

[0005] Furthermore, the data processing module is also used to: split multiple subsystems in the IT business system according to preset division dimensions to obtain multiple smallest cluster units; wherein, the preset division dimensions include at least one of the following: function type, deployment level, and operation and maintenance affiliation; based on the second parameter set, the multiple smallest cluster units are clustered using a preset clustering method to obtain multiple independent business modules.

[0006] Furthermore, the data processing module is also used to: divide multiple basic devices into core support devices and auxiliary support devices according to the physical relationship between them; and to further split the core support devices and auxiliary support devices based on the first parameter set and the preset support priority to obtain multiple independent device modules.

[0007] Furthermore, the data acquisition module is also used to: acquire multiple preset evaluation indicators; for each target independent module, use the analytic hierarchy process (AHP) to determine the weight of the first indicator corresponding to each evaluation indicator of the target independent module; wherein the target independent module is an independent device module or an independent business module; acquire module operation data of multiple evaluation indicators within a preset historical time period; based on the module operation data, use the entropy weight method to determine the weight of the second indicator corresponding to each evaluation indicator of the target independent module; for each evaluation indicator, calculate the weighted average of the first and second indicator weights corresponding to the evaluation indicator according to a preset weight coefficient to obtain the final indicator weight of the evaluation indicator in the target independent module; and determine the module weight of the target independent module based on the final indicator weight of each evaluation indicator in the target independent module.

[0008] Furthermore, the data acquisition module is also used to: construct an initial module hierarchy diagram based on the business logic relationships between multiple independent device modules and multiple independent business modules; for each pair of adjacent modules in the initial module hierarchy diagram, determine the dependency strength between the two adjacent modules based on the interaction frequency and fault propagation probability between them; if the dependency strength is greater than or equal to a preset first strength threshold, determine that the two adjacent modules are in a superior-subordinate relationship; if the dependency strength is less than a preset second strength threshold, determine that the two adjacent modules have no direct relationship; wherein, the first strength threshold is greater than the second strength threshold; if the dependency strength is greater than or equal to the second strength threshold and less than the first strength threshold, determine that the two adjacent modules are in a peer relationship.

[0009] Furthermore, the data processing module is also used to: determine the dependency classification result based on the dependency strength between two adjacent modules that have a hierarchical relationship; determine the influence intensity corresponding to the dependency classification result based on the dependency classification result, dependency strength, module weight of the upper-level module in the two adjacent modules, and module safety coefficient of the upper-level module; determine the edge weight based on the influence intensity; and construct a module relationship graph corresponding to multiple independent device modules and multiple independent business modules based on the edge weight and the pre-acquired edge attribute annotation information.

[0010] Furthermore, at least some of the modules in the multiple independent device modules and multiple independent business modules are configured with crash thresholds for specified parameter items; the 3D visualization module is also used to: for each of the at least some modules, when the parameter value of the specified parameter item of the module reaches or exceeds the corresponding crash threshold, display the crash effect produced by the module; and in response to touch operation on the module, display the physical location of the module and the running status of other modules that are dependent on the module.

[0011] Furthermore, at least some of the modules in the multiple independent device modules and multiple independent business modules are configured with warning thresholds for specified parameter items; the 3D visualization display module is also used to: for each of the at least some modules, when the parameter value of the specified parameter item of the module reaches or exceeds the corresponding warning threshold, display the warning effect of the module according to the preset warning method.

[0012] Furthermore, the 3D visualization module is also used to: based on the module relationship diagram, and according to the preset presentation format, to perform 3D visualization of the operational association information of multiple independent equipment modules and multiple independent business modules; wherein, the preset presentation format is: output type presentation format, transmission type presentation format or construction type presentation format.

[0013] This invention provides a 3D visualization operation and maintenance method for industrial data centers. The method operates within the 3D visualization operation and maintenance system of any of the aforementioned industrial data centers. The industrial data center includes: a set of data center infrastructure equipment and a set of IT business systems. The 3D visualization operation and maintenance system includes: a data acquisition module, a data processing module, and a 3D visualization display module. The method includes: the data acquisition module acquiring a first set of parameters associated with multiple basic devices in the data center infrastructure equipment set, and acquiring a second set of parameters associated with multiple subsystems in the IT business system set; the data processing module, based on the first and second parameter sets, dividing the multiple basic devices into multiple independent device modules and the multiple subsystems into multiple independent business modules; constructing a module relationship diagram based on the multiple independent device modules and multiple independent business modules; and the 3D visualization display module, based on the module relationship diagram, performing 3D visualization display of the operational association information of the multiple independent device modules and multiple independent business modules. The operational association information includes: operational parameters and operational status.

[0014] This invention provides a 3D visualization operation and maintenance system and method for industrial data centers. The industrial data center includes a set of data center infrastructure equipment and a set of IT business systems. The 3D visualization operation and maintenance system includes a data acquisition module, a data processing module, and a 3D visualization display module. The data acquisition module collects a first set of parameters associated with multiple basic devices in the data center infrastructure equipment set, and a second set of parameters associated with multiple subsystems in the IT business system set. The data processing module, based on the first and second parameter sets, divides the multiple basic devices into multiple independent device modules and the multiple subsystems into multiple independent business modules; it then constructs a module relationship diagram based on the multiple independent device modules and multiple independent business modules. The 3D visualization display module, based on the module relationship diagram, performs 3D visualization of the operational association information of the multiple independent device modules and multiple independent business modules. The operational association information includes operational parameters and operational status. This system can divide the data center infrastructure equipment set and the IT business system set into multiple independent device modules and multiple independent business modules, respectively, and transform them into an intuitive module relationship diagram. Through 3D visualization, it facilitates the understanding of complex indicators by operation and maintenance personnel, improving the monitoring efficiency of the data center. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 A schematic diagram of a 3D visualization operation and maintenance system for an industrial data center provided in an embodiment of the present invention; Figure 2 A schematic diagram of another 3D visualization operation and maintenance system for an industrial data center provided in an embodiment of the present invention; Figure 3 A flowchart illustrating a 3D visualization operation and maintenance method for an industrial data center, provided as an embodiment of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0018] With the accelerating pace of industrial digitalization, the importance of industrial data centers as the core hub of enterprise operations is self-evident. Industrial data centers integrate data center infrastructure equipment with IT business systems such as data middleware. Data center infrastructure equipment, such as servers, network switches, air conditioners, and UPS (Uninterruptible Power Supply), has relatively fixed operating status and monitoring indicators, making it familiar and easy to monitor for maintenance personnel.

[0019] However, the broad categorization within the industrial sector leads to significant differences in IT business systems across various sectors. For example, data center IT business systems in manufacturing focus on metrics related to production process monitoring and supply chain management; while the energy industry focuses on metrics related to energy production and transmission. This diversity makes the monitoring metrics for IT business systems complex and specialized, making it extremely difficult for data center operations personnel, who are only familiar with the operation and maintenance of data center infrastructure equipment, to understand and effectively monitor these metrics.

[0020] Currently, most data centers rely on third parties for operation and maintenance. This model has many drawbacks. On the one hand, third-party maintenance personnel often lack sufficient understanding of specific industrial business systems, resulting in high training costs. The specialized and unique nature of these systems further complicates knowledge acquisition. On the other hand, data center monitoring is often limited to the infrastructure level of the server room, making it difficult to delve into core business systems. For example, while server hardware status can be monitored in real time, the operational status and performance indicators of critical business applications hosted on those servers cannot be effectively monitored. This not only leads to low overall operational efficiency and difficulty in detecting and resolving potential problems in core business systems, but can also cause serious consequences such as business interruptions and data loss, resulting in significant economic losses for enterprises. Furthermore, the inability to comprehensively and efficiently monitor data centers limits the refined management of production and operations, hindering the full realization of the data center's role in enhancing competitiveness. Therefore, this invention provides a 3D visualization operation and maintenance system and method for industrial data centers. This technology can be applied to scenarios requiring operation and maintenance monitoring of industrial data centers.

[0021] To facilitate understanding of this embodiment, a 3D visualization operation and maintenance system for an industrial data center, as disclosed in this embodiment, will first be introduced. The industrial data center includes: a set of data center infrastructure equipment and a set of IT business systems; such as... Figure 1 As shown, the 3D visualization operation and maintenance system includes: a data acquisition module, a data processing module, and a 3D visualization display module; The data acquisition module is used to collect the first set of parameters associated with multiple basic devices in the centralized data center infrastructure, and the second set of parameters associated with multiple subsystems in the centralized IT business system. In actual implementation, the centralized data center infrastructure typically includes multiple basic devices. The data acquisition module can use various sensors, intelligent management interfaces, etc., to collect the parameters associated with each basic device to obtain the first set of parameters. For example, it can obtain information such as server temperature, CPU utilization, and memory usage through the server's built-in management interface; collect data such as temperature setpoint and operating status from the air conditioning intelligent control system; and obtain parameters such as power consumption and output voltage through the UPS's own monitoring module. This ensures that the data is collected in real time and accurately, providing a foundation for subsequent analysis. Similarly, IT business systems typically include multiple subsystems. The data acquisition module can use diverse acquisition methods to obtain parameters associated with each subsystem for different types of data, thus obtaining a second parameter set. For example, it can use database probes to obtain performance indicators such as database query response time and throughput; use network packet capture tools to collect network application traffic data and connection numbers; and use the API (Application Programming Interface) provided by the business system itself to obtain key business indicators such as order processing quantity and task completion rate. The data acquisition module can send the collected first and second parameter sets to the data processing module, or it can aggregate them to the data storage center for centralized processing.

[0022] The data processing module is used to divide multiple basic devices into multiple independent device modules and multiple subsystems into multiple independent business modules based on a first set of parameters and a second set of parameters. It then constructs a module relationship diagram based on these independent device modules and business modules. In actual implementation, the data processing module can analyze the received first set of parameters to divide the multiple basic devices into multiple independent device modules, where each independent device module may include only one basic device or at least a portion of multiple basic devices. Similarly, it can analyze the received second set of parameters to divide the multiple subsystems into multiple independent business modules, where each independent business module may include only one subsystem or at least a portion of multiple subsystems. Based on the divided independent device modules and independent business modules, a module relationship diagram reflecting the interdependencies and influences between the modules can be constructed. For example, in a data center for industrial automated production, the production order processing module and the inventory management module have business relationships; their dependencies are determined through analysis and clearly presented in the module relationship diagram.

[0023] The 3D visualization module is used to visualize the operational relationships between multiple independent device modules and multiple independent business modules based on a module relationship diagram. This operational relationship information includes operational parameters and operational status. In actual implementation, the 3D visualization module can import the aforementioned multiple independent device modules, multiple independent business modules, and the module relationship diagram. Through physically-based rendering technology, it dynamically displays the operational relationship information of each module, including its operational parameters, operational status, and relationships.

[0024] The aforementioned 3D visualization operation and maintenance system for industrial data centers comprises a set of data center infrastructure equipment and an IT business system set. The system includes a data acquisition module, a data processing module, and a 3D visualization display module. The data acquisition module collects a first set of parameters associated with multiple basic devices within the data center infrastructure equipment set, and a second set of parameters associated with multiple subsystems within the IT business system set. The data processing module, based on the first and second parameter sets, divides the multiple basic devices into multiple independent device modules and the multiple subsystems into multiple independent business modules, and constructs a module relationship diagram based on these modules. The 3D visualization display module, based on this module relationship diagram, provides a 3D visualization of the operational association information of the multiple independent device modules and multiple independent business modules. This operational association information includes operational parameters and operational status. This system can divide the data center infrastructure equipment set and the IT business system set into multiple independent device modules and multiple independent business modules, respectively, and transform them into an intuitive module relationship diagram. Through 3D visualization, it facilitates the understanding of complex indicators by operation and maintenance personnel, improving the monitoring efficiency of the data center.

[0025] Furthermore, the data processing module is also used to: split multiple subsystems in the IT business system according to preset division dimensions to obtain multiple smallest cluster units; wherein, the preset division dimensions include at least one of the following: function type, deployment level, and operation and maintenance affiliation; based on the second parameter set, the multiple smallest cluster units are clustered using a preset clustering method to obtain multiple independent business modules. The aforementioned functional types can include: IT computing, storage, network transmission, and infrastructure support, such as industrial servers, disk arrays, industrial switches, and UPS power supplies. The aforementioned deployment layers can include the physical layer of the data center, rack layer, network layer, computing layer, and storage layer, adapting to the deployment architecture of industrial data center equipment. The aforementioned operation and maintenance (O&M) affiliation can include IT equipment O&M groups, infrastructure O&M groups, and special support groups, clearly defining the boundaries of equipment O&M responsibility. In actual implementation, multiple subsystems of the IT business system can be decomposed into multiple minimum cluster units according to preset division dimensions. For example, they can be classified according to "computing, storage, network, and security," decomposing multiple subsystems into multiple minimum cluster units, such as industrial application server clusters, real-time database storage clusters, industrial firewall clusters, and OPC (Open Platform) clusters. Communications (open platform communication) server clusters, etc.; the above-mentioned preset clustering methods can be set according to actual needs. For example, you can refer to the parameters in the second parameter set above and cluster multiple smallest cluster units according to the clustering method of "same function, same technical specifications, same operation and maintenance cycle" to obtain multiple independent business modules. For example, you can obtain "real-time control server module for steelmaking workshop", "data storage module for chemical industrial park", "cross-plant industrial network transmission module", etc. through clustering.

[0026] After obtaining the aforementioned multiple independent business modules, the independence of each independent business module is usually verified. Specifically, this can be done through "single-module device isolation testing" to confirm that the shutdown of devices within a module does not affect the core functions of other modules, such as the expansion of storage modules without interrupting application server operations. At the same time, 3D visualization adaptability is also verified, that is, each independent business module is mapped to an independent device cluster model in a 3D scene, supporting real-time display of device-level parameters (such as CPU load, storage capacity, network bandwidth, etc.).

[0027] Furthermore, the data processing module is also used to: divide multiple basic devices into core support devices and auxiliary support devices according to the physical relationship between them; and to further split the core support devices and auxiliary support devices based on the first parameter set and the preset support priority to obtain multiple independent device modules.

[0028] In practical implementation, based on the physical relationships between multiple basic devices and the functions of each device, these devices can be divided into two main categories: core support devices and auxiliary support devices. For example, core support devices may include UPS power supplies, precision air conditioners, diesel generators, industrial PDUs (Power Distribution Units), fire alarm systems, etc.; auxiliary support devices may include computer room monitoring cameras, temperature and humidity sensors, access control systems, fresh air systems, etc. The preset support priorities mentioned above usually affect the granularity of the breakdown. For example, the support priority of core support devices is usually higher than that of auxiliary support devices, so the granularity of the breakdown of core support devices is usually finer than that of auxiliary support devices. In practical implementation, based on the parameters in the first parameter set and the preset support priorities, core support devices and auxiliary support devices can be broken down into multiple independent device modules. For example, the power system can be broken down into "main power supply module (industrial-grade dual-input)," "UPS redundant cluster module (N+1 configuration)," and "emergency power generation module (15-minute rapid start)," and the cooling system can be broken down into "precision air conditioning cluster module (computer room zoned cooling)," "heat dissipation redundancy module (high-temperature emergency start)," etc.

[0029] After obtaining the above-mentioned multiple independent device modules, device operation and maintenance responsibilities can be bound. Specifically, each independent device module is associated with a 3D visualization operation and maintenance panel, which clarifies the four-dimensional mapping of "device number - 3D model - operation and maintenance personnel - maintenance record". For example, the 3D model of the UPS redundant cluster module is directly associated with the person in charge of the infrastructure operation and maintenance group and the most recent maintenance time.

[0030] In actual implementation, after obtaining the above independent business modules and independent equipment modules, cross-dimensional verification can be performed. For example, the following verification can be performed using the "independent business module-independent equipment module-3D visualization" three-dimensional adaptation matrix: (1) Each independent business module corresponds to a separate independent equipment module (such as the application server module which only depends on the main power supply + precision air conditioning module and does not occupy the emergency power generation module resources); (2) Each independent business module and independent equipment module can independently generate a 3D equipment ledger (including equipment model, installation location, operating years, fault history, etc.), and can also support equipment-level positioning and operation in 3D scenes.

[0031] Furthermore, the data acquisition module is also used to: acquire multiple preset evaluation indicators; for example, these indicators may include core indicators and auxiliary indicators. Core indicators may include: equipment operational criticality (core business capacity, equipment irreplaceability), fault impact scope (number of associated IT devices, data center downtime area, production interruption duration), equipment operation and maintenance costs (maintenance costs, spare parts procurement cycle, downtime losses), resource utilization efficiency (IT equipment computing power utilization rate, infrastructure energy consumption ratio), etc. Auxiliary indicators may include: equipment compliance requirements (IEC 62443 industry standard, data center design specifications), equipment redundancy capabilities (backup device switching efficiency, fault self-healing time), 3D visualization granularity requirements (display of equipment internal structure, real-time parameter monitoring dimensions), etc. For each target independent module, the Analytic Hierarchy Process (AHP) is used to determine the weight of the first indicator for each evaluation metric corresponding to that target independent module. Here, the target independent module can be an independent equipment module or an independent business module. In actual implementation, for each independent equipment module and each independent business module, the AHP can be used to determine the weight of the first indicator for each evaluation metric. Specifically, for each target independent module, when using AHP, the corresponding hierarchical structure is first determined as follows: Target Layer (target independent module weights determined) → Criterion Layer (core indicators + auxiliary indicators) → Solution Layer (independent business module / independent equipment module). Then, an expert team is assembled, which can include technical consultants from equipment manufacturers, data center design engineers, and industrial IT operation and maintenance experts. The "1-9 scale" is used to score the importance of each evaluation metric for that target independent module (e.g., "equipment operation criticality"). The weight is higher than that of "resource utilization efficiency"), and the first indicator weight of each indicator to be evaluated for the target independent module is calculated; considering the dependence of equipment operation and maintenance experience, the consistency ratio threshold can be set to CR < 0.12 to ensure that subjective judgment is consistent with the actual operation scenario of the equipment. That is, when the consistency index result of the matrix obtained based on the scoring result is less than 0.12, it can be considered that the first indicator weight of each indicator to be evaluated for the target independent module is reasonable and reliable; it can be seen that the first indicator weight determined by the analytic hierarchy process is a subjective weight.

[0032] Obtain module operation data corresponding to multiple evaluation indicators within a preset historical time period; for example, it can collect module operation data corresponding to multiple evaluation indicators for the target independent module within the past 12 months (such as the scope of failure impact is counted by the number of associated IT equipment downtime, and equipment operation and maintenance costs are calculated by the sum of annual maintenance costs and downtime losses, etc.). Other data can also be supplemented as needed, such as unique data such as peak computing power of IT equipment, fluctuations in infrastructure energy consumption, and frequency of equipment failures.

[0033] Based on the module's operational data, the entropy weight method is used to determine the weight of the second indicator corresponding to each evaluation indicator of the target independent module. In practical implementation, the module operation data obtained above can be normalized to eliminate the influence of equipment model differences. Based on the module operation data corresponding to each evaluation indicator, the entropy value and the difference coefficient are calculated. The higher the difference coefficient, the stronger the differentiation of the evaluation indicator on the target independent module. Based on the entropy value and the difference coefficient, the weight of the second indicator corresponding to each evaluation indicator of the target independent module can be further calculated. It can be seen that the weight of the second indicator determined by the entropy weight method is an objective weight.

[0034] For each indicator to be evaluated, the weights of the first and second indicators corresponding to that indicator are weighted according to a preset weighting coefficient to obtain the final indicator weight in the target independent module. The preset weighting coefficient can be set according to actual needs. For example, the final indicator weight = 0.55 × first indicator weight (AHP) + 0.45 × second indicator weight (entropy weight method). Considering the actual application needs, the proportion of the second indicator weight can be increased, that is, the weight proportion of the actual operation data of the equipment can be increased to better meet the needs of equipment operation and maintenance.

[0035] Based on the final indicator weight corresponding to each evaluation indicator in the target independent module, the module weight corresponding to the target independent module is determined. In actual implementation, the performance of each evaluation indicator in the target independent module can be pre-scored based on actual module operation data or expert scores. The scoring results are then normalized, and the normalized score result of each evaluation indicator is weighted and calculated with the corresponding final indicator weight to obtain the module weight corresponding to the target independent module.

[0036] After obtaining the module weights corresponding to each target independent module, the rationality of the module weights can be verified. For example, the matching degree of "weight-equipment support resources" can be used: if the maintenance resource allocation ratio of high-weight modules (such as real-time control server modules and UPS redundant cluster modules) is less than 30%, or if 3D visualization monitoring does not cover the core parameters of the equipment (such as UPS battery capacity and server CPU temperature), then the evaluation indicators and expert scores can be readjusted.

[0037] Furthermore, the data acquisition module is also used to: construct an initial module hierarchy diagram based on the business logic relationships between multiple independent device modules and multiple independent business modules; for each pair of adjacent modules in the initial module hierarchy diagram, determine the dependency strength between the two adjacent modules based on the interaction frequency and fault propagation probability between them; if the dependency strength is greater than or equal to a preset first strength threshold, determine that the two adjacent modules are in a superior-subordinate relationship; if the dependency strength is less than a preset second strength threshold, determine that the two adjacent modules have no direct relationship; wherein, the first strength threshold is greater than the second strength threshold; if the dependency strength is greater than or equal to the second strength threshold and less than the first strength threshold, determine that the two adjacent modules are in a peer relationship.

[0038] In practical implementation, the hierarchy can be organized based on the "resource supply flow + data transmission link" of multiple independent equipment modules and multiple independent business modules to draw an initial module hierarchy diagram. For example, "diesel generator → UPS power supply → PDU → industrial server → database storage" corresponds to the module hierarchy "emergency power generation module → UPS redundant cluster module → power distribution module → application server module → data storage module". For each pair of adjacent modules with a relationship in the initial module hierarchy diagram, the dependence strength between the two adjacent modules can be quantified by interaction frequency and fault propagation probability. Interaction frequency can be statistically analyzed based on data transmission volume between modules, power load, etc. (such as the data packet transmission frequency between the server and the switch). Fault propagation probability can be calculated as "the probability of the lower-level module going down after the failure of the upper-level module" (e.g., if the application server goes down 100% after the failure of the UPS redundant cluster, then the fault propagation probability = 1).

[0039] The first and second strength thresholds mentioned above can be set according to actual needs. For example, the first strength threshold can be 0.9 and the second strength threshold can be 0.4. When the dependency strength between two adjacent modules is ≥0.9, the two modules can be determined to be in a superior-subordinate relationship. When the dependency strength is <0.4, the two modules can be determined to have no direct relationship. When 0.9 > dependency strength ≥0.4, the two modules can be determined to be in a peer-to-peer relationship. For two modules with a superior-subordinate relationship, the superior module can provide core operational support and resource supply to the subordinate module; the subordinate module cannot work normally without the superior module (e.g., the "UPS redundant cluster module" is the superior of the "application server module", and the "industrial switch module" is the superior of the "database storage module"). Usually, according to the priority order of protection, the superior module provides priority protection for core protection equipment. For two modules with a peer-to-peer relationship, there is only data interaction or collaborative work, and no direct resource dependency (e.g., the "temperature and humidity sensor module" and the "access control system module").

[0040] In practical applications, hierarchical correction can also be performed, focusing on verifying the hierarchical continuity of the core support equipment links. For example, the power distribution module and all IT equipment modules must be hierarchical to ensure uninterrupted power supply. Additionally, extreme scenario verification can be performed, such as simulating scenarios unique to industrial data centers (e.g., equipment overload, power fluctuations, cooling failure, network attacks) to verify the stability of hierarchical relationships. For instance, when the cooling system module degrades, the server module must synchronously trigger a load reduction mechanism, maintaining the hierarchical relationship unchanged, and the real-time changes in device status should be displayed in the 3D visualization scene (e.g., air conditioning failure → server temperature rise → 3D model highlighted in red, etc.).

[0041] Furthermore, the data processing module is also used to: determine the dependency classification result based on the dependency strength between two adjacent modules that have a hierarchical relationship; determine the influence intensity corresponding to the dependency classification result based on the dependency classification result, dependency strength, module weight of the upper-level module in the two adjacent modules, and module safety coefficient of the upper-level module; determine the edge weight based on the influence intensity; and construct a module relationship graph corresponding to multiple independent device modules and multiple independent business modules based on the edge weight and the pre-acquired edge attribute annotation information.

[0042] The above dependency classification results can include strong dependencies and weak dependencies. Strong dependencies represent "irreplaceable," such as server → UPS power supply, while weak dependencies represent "temporarily replaceable," such as ordinary office terminal → backup network. The module safety factor can be preset according to the severity of the consequences of module failure. For example, the impact strength can be calculated using the following formulas: Impact strength of strong dependency = Dependency strength × Module weight of the parent module × 1.6 (module safety factor); Impact strength of weak dependency = Dependency strength × Module weight of the parent module × 0.8 (module safety factor). Based on the calculated impact strength, the edge weight between two adjacent modules can be determined. In one embodiment, the calculated impact strength can be directly used as the edge weight. The edge attribute annotation information can include: device failure response time, 3D visualization alarm threshold (e.g., triggering device location + maintenance dispatch when impact strength ≥ 0.95), etc., which can be set according to actual needs. The above module relationship graph can be constructed based on the edge weight and edge attribute annotation information.

[0043] This module relationship diagram typically includes the following core elements: (1) Node: that is, multiple independent device modules and multiple independent business modules. The module name, module weight, 3D device model ID, module running status, etc. can be marked in the module relationship diagram; (2) Directed edges: Reflect module dependencies (arrows point from the dependent side to the dependent side, such as "application server module → UPS redundant cluster module"); (3) Edge weights; (4) Dynamic attributes: can mark module dependency type (such as power supply dependency, network dependency, cooling dependency, data dependency, etc.), module redundancy status (main module, backup module), 3D visualization linkage rules (such as 3D model flashing + parameter pop-up alarm when module fails).

[0044] The process of constructing the module relationship diagram mentioned above leverages artificial intelligence technology, enabling in-depth analysis and visualization of complex business and equipment relationships, helping operations and maintenance personnel quickly understand the system architecture and key influencing factors.

[0045] Furthermore, at least some of the modules in the multiple independent device modules and multiple independent business modules are configured with crash thresholds for specified parameter items; the 3D visualization module is also used to: for each of the at least some modules, when the parameter value of the specified parameter item of the module reaches or exceeds the corresponding crash threshold, display the crash effect produced by the module; and in response to touch operation on the module, display the physical location of the module and the running status of other modules that are dependent on the module.

[0046] In practical implementation, when the parameter value of a specified parameter of a module reaches or exceeds the corresponding crash threshold, it means that the module has failed or is on the verge of irreversible failure. For example, an interruption in the mains power input may cause the power distribution module in the data center to be unable to supply power. At this time, the crash effect produced by the module can be displayed through a 3D visualization module. For example, the 3D visualization module can intuitively present module damage, as well as reduced or stopped output; the tilting or collapse of the construction system caused by module damage; maintenance personnel can click on the module in the 3D scene to automatically locate the physical location of the module and display the operating status of other related and dependent modules, assisting maintenance personnel in quickly troubleshooting faults; for example, if maintenance personnel click on the faulty switch module, all connected servers and their corresponding power supply modules can be highlighted in the 3D scene.

[0047] Furthermore, at least some of the modules in the multiple independent device modules and multiple independent business modules are configured with warning thresholds for specified parameter items; the 3D visualization display module is also used to: for each of the at least some modules, when the parameter value of the specified parameter item of the module reaches or exceeds the corresponding warning threshold, display the warning effect of the module according to the preset warning method.

[0048] The aforementioned warning methods may include: high-brightness flashing, audible and visual alarms, etc., which can be set according to actual needs. In actual implementation, by setting warning thresholds, potential problems of the module can be detected, and timely alarms can be issued before the module crashes so that intervention can be carried out. For example, if the compressor current of a precision air conditioner fluctuates more, a warning alarm will be generated. At this time, the maintenance personnel can handle it and eliminate the warning in time.

[0049] In practical applications, to further ensure the accuracy of module crash detection, a comprehensive judgment can be made by combining multiple other parameters. For example, if module A stops working, the initial judgment might be that the cause is a power outage due to an abnormal power supply. However, if other modules are still working normally, in this case, it is usually assumed that the relevant power supply data for module A is incorrect, and only a warning can be issued. If other modules also experience abnormal power supply conditions, it can further confirm that module A has indeed crashed. In this case, the crash effect produced by module A can be displayed.

[0050] Furthermore, the 3D visualization module is also used to: based on the module relationship diagram, and according to the preset presentation format, to perform 3D visualization of the operational association information of multiple independent equipment modules and multiple independent business modules; wherein, the preset presentation format is: output type presentation format, transmission type presentation format or construction type presentation format.

[0051] The above output type representation is applicable to scenarios involving power, gas, and liquid output. For example, when the parameter value of a specified parameter item in module B reaches the corresponding failure threshold, the 3D visualization visually presents the damage to module B and the reduced or stopped output, allowing maintenance personnel to quickly perceive the impact of module B's failure on the output. For instance, if the power distribution system in workshop A fails, a burst of orange electric sparks will appear inside the corresponding module, and the entire workshop A area will turn red on the 3D map. Meanwhile, workshops B and C, powered by other switchgear, will remain green, indicating normal operation and uninterrupted energy flow.

[0052] The aforementioned transmission types are designed for scenarios such as power mechanical transmission and equipment processing. For example, when the parameter value of a specified parameter in module B reaches the corresponding failure threshold, 3D visualization can be used to show that module B is damaged, the transmission speed decreases or stops, helping maintenance personnel to intuitively judge the impact of the fault on the transmission system. For instance, in the case of congestion on an electronic product assembly conveyor belt, a normal conveyor belt module is a green high-speed streamline; when congestion occurs, the conveyor belt module changes to a yellow medium-speed streamline; and when completely blocked, the conveyor belt module turns red and stationary.

[0053] The aforementioned construction type representation can be used in scenarios such as product assembly. For example, when the parameter value of a specified parameter item of module B reaches the corresponding collapse threshold, 3D visualization can be used to present the damage to module B, the tilting or collapse of the construction system, and vividly demonstrate the destruction of the construction process by the failure of module B. This approach can be applied to large-scale industrial equipment manufacturing systems such as heavy equipment and automobile manufacturing.

[0054] It is understandable that, depending on the characteristics of different industrial sectors, other forms of representation may be included, not limited to the three preset forms mentioned above. In the 3D visualization demonstration phase, the independent equipment modules, multiple independent business modules, and the constructed module relationship diagrams are visualized using a 3D visualization module, clearly presenting the data interaction and collaborative working mechanisms between each module.

[0055] The aforementioned 3D visualization module can use Vue 3.0 as the basic framework for the web page, combined with the more reliable TypeScript programming language, and call various functions of the Babylon.js 3D engine.

[0056] The 3D visualization module utilizes mainstream 3D formats supported by Babylon.js, such as GLB (GL Transmission Format Binary File), glTF (GL Transmission Format), OBJ (a 3D model file format), and STL (Stereolithography), to import 3D models and module relationship diagrams of independent business modules and independent device modules generated by the data processing module. Through physically based rendering technology, each module presents realistic material effects; real-time lighting and shadow functions enhance the realism of the scene; particle systems are used to display special effects such as heat dissipation and data flow; and post-processing effects are used to highlight key information. For example, the server model uses physically based rendering technology to present a metallic texture, combined with lighting and shadow effects, making it more realistic in the scene.

[0057] This 3D visualization module uses an industrial-grade 3D visualization platform (such as Unity Industrial Edition, ThingWorx) + DCIM (Data Center Infrastructure Management) system data interface to achieve a 1:1 restoration of "equipment module relationship - 3D computer room scene" (such as the physical location and connection lines of independent equipment modules in the 3D scene are consistent with the actual computer room, the line width changes with the intensity of influence, and the dependency type is distinguished by color). This 3D visualization module can also dynamically display various indicators and their relationships. As data updates in real time, the various models and indicators in the 3D scene change synchronously. For example, when network traffic increases, the network line model displays the traffic change as a flowing light strip, and the relevant server and network device models react accordingly. It also provides multiple interaction methods for operations and maintenance personnel, supporting gestures, voice, keyboard, and mouse operations; for example, operations and maintenance personnel can view detailed server operating indicators through gesture operations, and when indicators are abnormal, the device model flashes a warning color. Simultaneously, it supports multi-user collaboration, allowing multiple operations and maintenance personnel to simultaneously enter the virtual scene to jointly view and analyze the data center's operational status.

[0058] The 3D visualization module also establishes a low-latency linkage process of "real-time acquisition of equipment status (once per second) - dynamic adjustment of dependencies - synchronous update of 3D scene". During peak industrial production periods, the incremental update frequency is increased to once every 3 minutes to ensure that the visualization of equipment status is lag-free. In practical applications, appropriate representation methods can be selected for 3D visualization based on the characteristics of different industrial sectors. For example, in the energy sector, data centers can use output type representation methods. When a failure indicator of a power generation equipment module is detected, the 3D visualization shows that the power generation equipment model is damaged, and the power output decreases or stops. In the manufacturing sector, data centers can use transmission type representation methods. When a failure indicator of a production equipment module is detected, the production equipment model is damaged, and the transmission speed decreases or stops.

[0059] In one implementation, three levels of alarms (yellow / orange / red) can be set in the 3D scene. When the edge weight is ≥0.98 (high-risk threshold for equipment), the 3D equipment model is highlighted and flashed, the computer room audio and visual alarms are triggered, and the operation and maintenance personnel's mobile APP accurately dispatches orders. At the same time, the fault propagation path is drawn in the 3D scene (such as the red alarm line from the UPS module to the server module, marking the number and location of affected equipment).

[0060] In terms of equipment operation and maintenance applications, the results can be displayed through 3D visualization to identify over-dependent core devices (such as a core switch connecting all production servers), optimize equipment deployment (add redundant switches), and preview the optimized dependencies and equipment load distribution in a 3D scene. In terms of equipment resource scheduling, the 3D visualization platform can accurately allocate operation and maintenance resources according to module weight and influence intensity (e.g., high-weight real-time control server modules are given priority in allocation of maintenance resources, and equipment maintenance plans and responsible persons are displayed in the 3D scene). In terms of equipment emergency drills, the results can be displayed using 3D visualization to simulate core equipment failure scenarios (such as UPS power outages or precision air conditioner failures), verify the effectiveness of emergency plans, and simultaneously assess the impact of failures on other equipment and industrial production in the 3D scenario.

[0061] For ease of understanding, see Figure 2The diagram illustrates another 3D visualization operation and maintenance system for industrial data centers. First, modules are divided. Multiple subsystems within the IT business system are divided into independent business modules, and multiple basic devices within the data center infrastructure are divided into independent device modules. Next, weights are assigned. The analytic hierarchy process (AHP) and entropy weighting are used to determine the weights of each independent business module and each independent device module. Relationships between modules are also determined by analyzing service dependencies and identifying redundant relationships to establish a relationship matrix. Then, indicators are classified. Key indicators (corresponding to the specified parameter items) for each independent business module and each independent device module are selected, and a crash threshold is set for each, resulting in a set of crash indicators. Based on the weight assignment results, relationship determination results, and indicator classification results, a relationship graph can be constructed. Weighted nodes are created, directed edges represent dependencies, and crash indicators are associated as state triggers. An algorithm is used to generate the module relationship graph.

[0062] Through the above improvements, this solution can solve the problems of operation and maintenance personnel having difficulty understanding complex indicators and the inefficiency of core business system monitoring, and achieve efficient 24 / 7 monitoring of business systems and data center systems, thus comprehensively improving the operation and maintenance level of data centers in the industrial field.

[0063] This innovative solution divides IT business systems and data center infrastructure into independent modules as subsystems and devices, assigns weights to modules through scientific evaluation, determines module relationships based on business logic, and constructs a module relationship diagram based on module characteristics, providing a brand-new perspective for data center operation and maintenance.

[0064] This solution can be tailored to different characteristics of the industrial sector by using three easy-to-understand expression methods. Through 3D model visualization modules and data interaction and collaborative working mechanisms between physical devices, it can achieve accurate and intuitive operation and maintenance display.

[0065] In this solution, the data acquisition module accurately collects data, the data processing module deeply analyzes and constructs a module relationship diagram, and the 3D visualization module vividly presents the results. All modules work closely together to form a complete and efficient operation and maintenance system.

[0066] The 3D visualization operation and maintenance system for industrial data centers proposed in this solution has several significant benefits, as detailed below: 1. Enhance the comprehension skills of operations and maintenance personnel By using AI-based monitoring metric transformation, complex IT business system monitoring metrics are converted into intuitive module relationship diagrams. Combined with 3D visualization, operations and maintenance (O&M) personnel can easily understand the architecture of business systems and data center infrastructure, the dependencies between modules, and key influencing factors. Whether it's the module weight of independent business modules, the module weight of independent equipment modules, or the hierarchical and peer relationships between modules, everything is presented visually, reducing the cognitive difficulty for O&M personnel and enhancing their control over the entire data center. Even O&M personnel without an O&M background can quickly get started.

[0067] 2. Improve monitoring efficiency Enables efficient 24 / 7 monitoring of business systems and data center systems. Leveraging dynamic indicators and relationships displayed through 3D visualization, operations and maintenance personnel can gain a comprehensive, real-time understanding of the data center's operational status. In the event of an anomaly, changes in device models within the 3D scene, warning colors for indicators, and specific animation effects allow for rapid problem identification and resolution, significantly reducing troubleshooting time and improving overall data center operational efficiency.

[0068] 3. Assist in enterprise operation and management Effective monitoring of core business systems is crucial for enterprise production and operations. This solution enables enterprises to manage production processes more precisely, promptly identify and resolve issues in business systems, and avoid economic losses caused by business interruptions and data loss. For example, in industrial manufacturing enterprises, it ensures smooth processing of production orders, improves production efficiency and product quality, and enhances the enterprise's market competitiveness. Simultaneously, optimizing data center operation and maintenance management helps enterprises rationally plan resources and reduce operating costs.

[0069] 4. Strengthen internal collaboration within the enterprise 3D visualization provides an efficient platform for internal communication and collaboration within enterprises. Personnel from different departments can use this visualization system to clearly understand the data center's operational status, promoting information sharing and collaborative work, breaking down departmental barriers, and driving the overall efficient operation of the enterprise's business.

[0070] This invention provides a 3D visualization operation and maintenance method for industrial data centers. The method operates within a 3D visualization operation and maintenance system for industrial data centers as described above. The industrial data center includes: a set of data center infrastructure equipment and a set of IT business systems. The 3D visualization operation and maintenance system includes: a data acquisition module, a data processing module, and a 3D visualization display module. Figure 3 As shown, the method includes the following steps: Step S302: The data acquisition module collects the first set of parameters associated with multiple basic devices in the computer room infrastructure equipment set, and the second set of parameters associated with multiple subsystems in the IT business system set. Step S304: The data processing module divides multiple basic devices into multiple independent device modules and multiple subsystems into multiple independent business modules based on the first parameter set and the second parameter set; and constructs a module relationship diagram based on the multiple independent device modules and multiple independent business modules. Step S306: The 3D visualization module, based on the module relationship diagram, performs 3D visualization of the operational association information of multiple independent device modules and multiple independent business modules; the operational association information includes: operational parameters and operational status.

[0071] The aforementioned 3D visualization operation and maintenance method for data centers in the industrial field can divide the data center infrastructure equipment set and IT business system set into multiple independent equipment modules and multiple independent business modules, and transform them into an intuitive module relationship diagram. Through 3D visualization, it is easier for operation and maintenance personnel to understand complex indicators and improve the monitoring efficiency of the data center.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A 3D visualization operation and maintenance system for data centers in the industrial field, characterized in that, The industrial data center includes: a set of computer room infrastructure equipment and an IT business system set; the 3D visualization operation and maintenance system includes: a data acquisition module, a data processing module, and a 3D visualization display module. The data acquisition module is used to collect a first set of parameters associated with multiple basic devices in the data center infrastructure equipment set, and to collect a second set of parameters associated with multiple subsystems in the IT business system set; The data processing module is used to divide the multiple basic devices into multiple independent device modules and the multiple subsystems into multiple independent business modules based on the first parameter set and the second parameter set; and to construct a module relationship diagram based on the multiple independent device modules and the multiple independent business modules. The 3D visualization module is used to perform 3D visualization of the operational association information of multiple independent device modules and multiple independent business modules based on the module relationship diagram; the operational association information includes: operational parameters and operational status.

2. The system according to claim 1, characterized in that, The data processing module is also used for: According to a preset division dimension, the multiple subsystems of the IT business system are split into multiple smallest cluster units; wherein, the preset division dimension includes at least one of the following: function type, deployment level, and operation and maintenance affiliation; Based on the second parameter set, multiple smallest cluster units are clustered using a preset clustering method to obtain multiple independent business modules.

3. The system according to claim 1, characterized in that, The data processing module is also used for: Based on the physical relationships between the various basic devices, the various basic devices are divided into core support devices and auxiliary support devices; Based on the first parameter set and the preset protection priority, the core protection equipment and the auxiliary protection equipment are respectively split to obtain multiple independent equipment modules.

4. The system according to claim 1, characterized in that, The data acquisition module is also used for: Obtain multiple preset evaluation indicators; For each target independent module, the analytic hierarchy process (AHP) is used to determine the weight of the first indicator corresponding to each evaluation indicator for that target independent module; wherein, the target independent module is an independent equipment module or an independent business module. Obtain module operation data corresponding to multiple evaluation indicators within a preset historical time period; Based on the module's operational data, the entropy weight method is used to determine the weight of the second indicator corresponding to each evaluation indicator of the target independent module. For each of the indicators to be evaluated, the weight of the first indicator and the weight of the second indicator corresponding to the indicator to be evaluated are calculated by weighting according to the preset weight coefficient to obtain the final indicator weight corresponding to the indicator to be evaluated in the target independent module. Based on the final indicator weight corresponding to each indicator to be evaluated in the target independent module, the module weight corresponding to the target independent module is determined.

5. The system according to claim 1, characterized in that, The data acquisition module is also used for: Based on the business logic relationships between the multiple independent device modules and the multiple independent business modules, an initial module hierarchy diagram is constructed; For each pair of adjacent modules in the initial module hierarchy diagram, the dependency strength between the two adjacent modules is determined based on the interaction frequency and fault propagation probability between them. If the dependency strength is greater than or equal to a preset first strength threshold, the two adjacent modules are determined to be in a hierarchical relationship. If the dependency strength is less than a preset second strength threshold, it is determined that the two adjacent modules have no direct relationship; wherein, the first strength threshold is greater than the second strength threshold; If the dependency strength is greater than or equal to the second strength threshold and less than the first strength threshold, the two adjacent modules are determined to be of the same level.

6. The system according to claim 5, characterized in that, The data processing module is also used for: For the two adjacent modules that have the aforementioned hierarchical relationship, the dependency classification result is determined based on the dependency strength between the two adjacent modules; The influence intensity corresponding to the dependency classification result is determined based on the dependency classification result between the two adjacent modules, the dependency strength, the module weight of the parent module in the two adjacent modules, and the module safety coefficient of the parent module. Determine the edge weights based on the intensity of the influence. Based on the edge weights and the pre-acquired edge attribute annotation information, a module relationship diagram corresponding to multiple independent device modules and multiple independent business modules is constructed.

7. The system according to claim 1, characterized in that, At least a portion of the multiple independent device modules and multiple independent business modules have specified parameter items configured with crash thresholds; the 3D visualization module is also used for: For each of the at least some modules, when the parameter value of a specified parameter item of the module reaches or exceeds the corresponding crash threshold, the crash effect produced by the module is displayed; In response to touch input to this module, the system displays the physical location of the module and the operating status of other modules that depend on it.

8. The system according to claim 7, characterized in that, At least a portion of the multiple independent device modules and multiple independent business modules have specified parameter items configured with warning thresholds; the 3D visualization module is also used for: For each of the at least some modules, when the parameter value of a specified parameter item of the module reaches or exceeds the corresponding warning threshold, the warning effect of the module is displayed according to a preset warning method.

9. The system according to claim 1, characterized in that, The 3D visualization module is also used for: Based on the module relationship diagram, the operational association information of multiple independent equipment modules and multiple independent business modules is displayed in 3D according to a preset presentation format; wherein, the preset presentation format is: output type presentation format, transmission type presentation format, or construction type presentation format.

10. A 3D visualization operation and maintenance method for data centers in the industrial field, characterized in that, The method operates on the 3D visualization operation and maintenance system for industrial data centers as described in any one of claims 1-9; The industrial data center includes: a set of data center infrastructure equipment and an IT business system set; the 3D visualization operation and maintenance system includes: a data acquisition module, a data processing module, and a 3D visualization display module; the method includes: The data acquisition module collects a first set of parameters associated with multiple basic devices in the data center infrastructure equipment set, and a second set of parameters associated with multiple subsystems in the IT business system set; The data processing module divides the multiple basic devices into multiple independent device modules and the multiple subsystems into multiple independent business modules based on the first parameter set and the second parameter set; and constructs a module relationship diagram based on the multiple independent device modules and the multiple independent business modules. The 3D visualization module, based on the module relationship diagram, performs 3D visualization of the operational association information of multiple independent device modules and multiple independent business modules; the operational association information includes: operational parameters and operational status.

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