WO2026007600A1 - 一种云边协同1+6+n智慧火电厂智慧基建系统及部署方法 - Google Patents

一种云边协同1+6+n智慧火电厂智慧基建系统及部署方法

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Publication number
WO2026007600A1
WO2026007600A1 PCT/CN2025/099273 CN2025099273W WO2026007600A1 WO 2026007600 A1 WO2026007600 A1 WO 2026007600A1 CN 2025099273 W CN2025099273 W CN 2025099273W WO 2026007600 A1 WO2026007600 A1 WO 2026007600A1
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Prior art keywords
data
layer
smart
power plant
thermal power
Prior art date
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Pending
Application number
PCT/CN2025/099273
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English (en)
French (fr)
Inventor
王林
赵章明
孙德良
王承文
王田
秦斌
王文毓
雷杨祥
查玲
郑小钢
曹军
何康
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian Thermal Power Research Institute Co Ltd
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Xian Thermal Power Research Institute Co Ltd
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Publication of WO2026007600A1 publication Critical patent/WO2026007600A1/zh
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Program-control systems
    • G05B19/02—Program-control systems electric
    • G05B19/04—Program control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Program control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0423—Input/output
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/25—Pc structure of the system
    • G05B2219/25257—Microcontroller
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Definitions

  • This application belongs to the field of smart infrastructure engineering for thermal power units, and relates to a cloud-edge collaborative 1+6+N smart infrastructure system for thermal power plants and its deployment method.
  • thermal power plant infrastructure projects are equipped with corresponding management systems, such as power generation peak-shaving systems and fault repair systems.
  • management systems such as power generation peak-shaving systems and fault repair systems.
  • the limited number of management categories makes it difficult to achieve comprehensive control during the infrastructure construction phase.
  • Implementing comprehensive infrastructure control would inevitably increase the complexity of the system architecture and business processes, leading to more systems and terminal devices involved in data and command interaction. This would place higher demands on the system's hardware and software for data storage, data management, and backend application computing.
  • it could result in low inter-system connectivity, further increasing the difficulty of management during the infrastructure construction phase.
  • a smart thermal power plant smart infrastructure system needs to be proposed, which supports various new technologies for sensing, analysis, modeling, calculation, control, optimization, and decision-making, comprehensively and three-dimensionally meets the various needs of power plant construction and production, and conforms to the new power generation concepts and models that meet the needs of the times. It should establish a brand-new management concept for the power plant infrastructure process and help achieve overall infrastructure control.
  • This application provides a cloud-edge collaborative 1+6+N smart thermal power plant smart infrastructure system and deployment method to address the technical challenges of how to improve the overall infrastructure management and operation and maintenance of thermal power plants, providing efficiency, security, and visualization, and how to significantly improve the infrastructure management level and operation and maintenance efficiency of power plants.
  • This application provides a cloud-edge collaborative 1+6+N smart thermal power plant smart infrastructure system, including:
  • Lightweight IaaS layer, lightweight PaaS layer, and lightweight SaaS layer are Lightweight IaaS layer, lightweight PaaS layer, and lightweight SaaS layer;
  • the lightweight IaaS layer allows users to deploy and run any software or application, enabling basic computing, storage, and network resource access for the smart infrastructure system of a smart thermal power plant.
  • the lightweight PaaS layer is used to set up the application development and deployment environment for any software or application deployed by users, as well as to access the data of terminal sensing devices and control devices of the smart thermal power plant smart infrastructure system.
  • the lightweight SaaS layer is used for applications and services in the smart thermal power plant smart infrastructure system. It is deployed in the form of a microservice architecture to meet the various business needs of the smart thermal power plant smart infrastructure system.
  • the lightweight IaaS layer mainly includes an infrastructure platform, which consists of containers, virtual machines, a software management platform, and a hardware management platform.
  • the lightweight PaaS layer mainly includes: data infrastructure layer, data access layer, data processing layer, data application layer, and data presentation layer.
  • the data infrastructure layer is used to collect and aggregate data from the smart infrastructure system of a smart thermal power plant through device terminals;
  • the data access layer is used to transmit the collected and aggregated data to the data processing layer via the 5G network;
  • the data processing layer is used for the transmission, sharing, computation, analysis, and interactive integration of relevant data, and supports the development and integration of lightweight IaaS layer customized applications.
  • the data transmission methods of the data access layer include remote access, real-time synchronization, and incremental synchronization.
  • the data processing layer extraction module extracts raw data from various business databases, including access control terminal systems, vehicle gate terminal systems, violation identification information, progress data, cost data, design drawings, interactive documents, progressive 3D models, risk control data, and engineering management data, as input for basic data. Then, the transformation and loading module loads the business data into a data warehouse or data mart to generate an OLAP database.
  • the data processing layer uses keyword extraction to automatically form a relational body using data management standards, thereby completing the fusion analysis of multi-source heterogeneous data during the infrastructure construction process.
  • the data processing layer also combines intelligent video analysis algorithm models to analyze the collected video data.
  • a violation event is automatically created, and the violation is broadcast on the scene of the violation, and a stop signal is issued in a timely manner.
  • the management of participating units is realized.
  • the lightweight SaaS layer includes: a data application layer and a data visualization layer;
  • the data application layer is used to establish a progressive, high-precision cloud-rendered 3D information model that precedes the construction progress by improving the KKS coding and identification system, enabling applications such as construction data control, quality management, cost control, and engineering document management; based on the 3D model, it deploys drawing verification and analysis, 3D collision detection, 3D cable laying management, 3D progress analysis, 3D construction simulation, and document management to achieve application visualization; and with the 3D model as the core carrier, the data association body enables the continuous accumulation of data and knowledge.
  • the data presentation layer allows users to view report data, approve workflows, and receive message pushes through a portal interface, and then displays the data.
  • This application also provides a deployment method for a cloud-edge collaborative 1+6+N smart thermal power plant smart infrastructure system. Based on any one of the above method embodiments, the method includes:
  • an environment for application development and deployment is set up for any software or application deployed by the user, as well as the access to data from terminal sensing and control devices of the smart thermal power plant smart infrastructure system.
  • the applications and services of the smart thermal power plant smart infrastructure system are deployed in the form of a microservice architecture to meet the various business needs of the smart thermal power plant smart infrastructure system.
  • the cloud-edge collaborative 1+6+N smart thermal power plant infrastructure system includes a lightweight IaaS layer, which allows users to deploy and run any software or application, enabling basic computing, storage, and network resource access for the smart thermal power plant infrastructure system, effectively reducing data storage requirements; a lightweight PaaS layer, which provides an environment for application development and deployment for any software or application deployed by the user, and facilitates data access for the terminal sensing and control devices of the smart thermal power plant infrastructure system; and a lightweight SaaS layer, which deploys the applications and services of the smart thermal power plant infrastructure system in a microservice architecture to meet the various business needs of the smart thermal power plant infrastructure system.
  • Figure 1 is a schematic diagram of a cloud-edge collaborative 1+6+N smart thermal power plant smart infrastructure system architecture provided in this application;
  • FIG. 2 is a schematic diagram of an infrastructure data processing layer provided in this application.
  • Figure 3 is a step diagram of a cloud-edge collaborative 1+6+N smart thermal power plant smart infrastructure system provided in this application.
  • the smart thermal power plant edge cloud platform of the cloud-edge collaborative 1+6+N smart thermal power plant smart infrastructure system includes a lightweight IaaS layer, a lightweight PaaS layer, and a lightweight SaaS layer.
  • the lightweight IaaS layer allows users to deploy and run any software or application, enabling basic computing, storage, and network resource access for the smart infrastructure system of a smart thermal power plant.
  • system resources of the smart infrastructure system of the smart thermal power plant are deployed in virtual machines or containers in the IaaS layer of the power plant edge cloud platform, so that users can deploy and run any software or application to realize the basic computing, storage and network resource access of the smart infrastructure system of the smart thermal power plant.
  • the lightweight PaaS layer is used to set up the application development and deployment environment for any software or application deployed by users, as well as to access the data of terminal sensing devices and control devices of the smart thermal power plant smart infrastructure system.
  • the computational data required by the smart infrastructure system of the smart thermal power plant is provided by the data access layer of the PaaS layer, and the required computational models and resources are provided by the data analysis layer of the PaaS layer.
  • the PaaS layer can also set up the application development and deployment environment for any software or application deployed by the user, as well as the access of data from the terminal sensing devices and control devices of the smart infrastructure system of the smart thermal power plant.
  • the applications and services of the smart infrastructure system for smart thermal power plants are developed and deployed in the SaaS layer of the power plant edge cloud platform in the form of a microservice architecture to meet the various business needs of the smart infrastructure system for smart thermal power plants.
  • cloud-edge collaborative 1+6+N smart thermal power plant infrastructure system provided in this application fully leverages the advantages of cloud computing, edge computing, and IoT technologies to offer an efficient, secure, and visualized solution for the infrastructure management and operation and maintenance of thermal power plants. Through real-time data acquisition, efficient data processing, and intelligent applications, it can significantly improve the infrastructure management level and operation and maintenance efficiency of power plants.
  • the lightweight IaaS layer mainly includes an infrastructure platform, which consists of containers, virtual machines, a software management platform, and a hardware management platform.
  • the cloud-edge collaborative 1+6+N smart thermal power plant smart infrastructure system proposed in this application mainly includes the following lightweight PaaS layer: data foundation layer, data access layer, data processing layer, data application layer, and data display layer.
  • the data infrastructure layer is used to collect and aggregate data from the smart infrastructure system of a smart thermal power plant through device terminals;
  • the data access layer is used to transmit the collected and aggregated data to the data processing layer via the 5G network;
  • the data processing layer is used for the transmission, sharing, computation, analysis, and interactive integration of relevant data, and supports the development and integration of lightweight IaaS layer customized applications.
  • various intelligent sensors, PLC controllers, smart meters, and surveillance cameras are deployed within the power plant, ensuring that these devices support standard network communication protocols (such as Modbus, OPC UA, MQTT, etc.) to facilitate the transmission of real-time operational data to the Dahua ICC platform.
  • access devices are registered and configured on the Dahua ICC platform, including setting device IDs, IP addresses, ports, and corresponding authentication information.
  • the data access layer can collect and aggregate data through terminal devices and 5G networks using remote access, real-time synchronization, and incremental synchronization.
  • the smart infrastructure of the power plant involves data from various sources and formats, including manually entered data, video-collected data, geographic information data, drawing and document data, and detection data collected by smart terminals. Specific data content includes progress data, cost data, quality data, operational risk data, vehicle information data, special equipment data, emergency plan data, access control data, video surveillance data, warehouse data, storage location data, equipment and material data, contract data, and personnel data.
  • the data access layer integrates and stores detection data from various smart terminals by connecting the data interfaces of various subsystem databases and other related databases, enabling the transmission, sharing, computation, analysis, and interactive fusion of relevant data.
  • a data entry function for on-site infrastructure construction is developed; for offline structured data, a function for batch importing data tables conforming to data standards and template specifications is developed.
  • data access and processing are achieved through services such as data mapping, metadata, and real-time data access.
  • Accessed data is verified and quality inspected according to the Smart Connect infrastructure data management standards, and the accessed datasets are managed according to different dimensions such as business activities, business processes, data sources, and data time flows, forming data assets.
  • the data transmission methods of the data access layer include remote access, real-time synchronization, and incremental synchronization.
  • the data processing layer is designed for efficient identification and keyword extraction of multi-source heterogeneous data generated during the construction of thermal power plants (including drawings, documents, forms, specifications, workflows, video streams, management processes, access control systems, personnel IDs, etc.). It automatically forms related data using data management standards, breaking down barriers between different data structures and solving the problem of fusion analysis of multi-source heterogeneous data during the construction process. It should be noted that the data processing layer mainly involves personnel master data, progress master data, quality master data, cost master data, video master data, access control master data, safety master data, and material master data.
  • the data processing layer extraction module extracts raw data from various business databases, including access control terminal systems, vehicle gate terminal systems, violation identification information, progress data, cost data, design drawings, interactive documents, progressive 3D models, risk control data, and engineering management data, as input for basic data. Then, the transformation and loading module loads the business data into a data warehouse or data mart to generate an OLAP database.
  • the data processing layer extracts multi-source heterogeneous data resources to a temporary intermediate layer through data extraction, transformation, and loading modules. After cleaning, transformation, and integration, the data is finally loaded into a data warehouse or data mart, becoming the foundation for online analytical processing (OLAP) and data mining.
  • the data extraction module extracts raw data from business databases such as access control terminal systems, vehicle gate terminal systems, violation identification information, progress data, cost data, design drawings, interactive documents, progressive 3D models, risk control data, and engineering management data as input for basic data. Then, it extracts and transforms data such as personnel master data, progress master data, quality master data, cost master data, video master data, access control master data, security master data, and material master data, and further cleans and integrates them. This ultimately forms the bottom layer of the data warehouse in the data center, i.e., the data foundation layer, and converges into the OLAP database.
  • the data processing layer uses keyword extraction to automatically form a relational body using data management standards, thereby completing the fusion analysis of multi-source heterogeneous data during the infrastructure construction process.
  • the data processing layer is developed and deployed to provide efficient identification and keyword extraction functions for multi-source heterogeneous data (including drawings, documents, forms, specifications, workflows, video streams, management flows, access control, personnel IDs, etc. generated during the construction period) of thermal power infrastructure. It automatically forms related entities using data management standards, breaks down barriers between different data structures, and solves the problem of fusion analysis of multi-source heterogeneous data during the construction process.
  • the data processing layer also combines intelligent video analysis algorithm models to analyze the collected video data.
  • a violation event is automatically created, and the violation is broadcast on the scene of the violation, and a stop signal is issued in a timely manner.
  • the management of participating units is realized.
  • the data processing layer also combines an intelligent video analysis algorithm model to intelligently analyze video data.
  • a violation event is automatically created, thereby realizing the intelligent collection of violation events.
  • the layer also links the violation to the on-site voice broadcast to prevent the violation in a timely manner and achieve safety risk control.
  • the layer Based on the basic personnel data collected by the access control terminal, the layer enables the management of participating units.
  • the layer realizes on-site safety risk control of infrastructure construction and develops and deploys functions such as self-reporting by construction units, management of infrastructure supervision tasks, visitor management, violation management, operation risk control, personnel distribution, attendance statistics, special personnel management, and safety measure analysis.
  • the lightweight SaaS layer includes: a data application layer and a data visualization layer;
  • the data application layer is used to establish a progressive, high-precision cloud-rendered 3D information model that precedes the construction progress by improving the KKS coding and identification system, enabling applications such as construction data control, quality management, cost control, and engineering document management; based on the 3D model, it deploys drawing verification and analysis, 3D collision detection, 3D cable laying management, 3D progress analysis, 3D construction simulation, and document management to achieve application visualization; and with the 3D model as the core carrier, the data association body enables the continuous accumulation of data and knowledge.
  • the data application layer can deploy functions such as construction data management, participating unit management, quality management, cost management, safety risk management, engineering data management, mobile applications, and warehouse management.
  • the data application layer through a refined KKS coding and identification system, establishes a progressive, high-precision cloud-rendered 3D information model that precedes the construction progress.
  • This enables applications such as construction data control, quality management, cost control, and engineering document management.
  • the modeling granularity includes design objects such as civil engineering, equipment, nozzles, pipelines, pipe fittings (valves, flanges, filters, etc.), supports, underground pipe networks, and cable trays.
  • the boiler model achieves weld joint and measurement point precision.
  • visualization applications such as drawing verification and analysis, 3D collision checking, 3D cable laying management, 3D progress analysis, 3D construction simulation, and document management are deployed.
  • the 2D/3D information model and management platform are used to conduct comprehensive multi-disciplinary collision checks and 2D/3D verification throughout the entire process to ensure the effectiveness of design results.
  • a data association system with the 3D model as the core carrier enables the continuous accumulation of data and knowledge (equipment ledgers, engineering drawings, design attributes, weld joint information, engineering documents, etc.).
  • infrastructure data management and control applications including construction data management, infrastructure project cost control, project quality auxiliary management, infrastructure project data management, and smart warehouse management and control.
  • Project progress data management Develop and deploy comprehensive analysis and management functions for design progress, construction progress, procurement progress and supply progress data, and monitor potential risks to project progress from multiple dimensions.
  • Investment Budget Control Synchronize and categorize investment and budget data, including preliminary design budgets, management budgets, completed investments, estimated final investments, signed contract amounts, completed investments under signed contracts, uncompleted investments under signed contracts, contract taxes, and progress payments. This data assists managers in assessing project budget execution, investment and project schedule deviations, and their impact. By collecting and comparing annual and monthly budget and progress data, project budget execution analysis, review, and deviation warnings are achieved.
  • Procurement Cost Control Synchronize the delivery and payment status of equipment procurement contracts, construction and installation contracts, design/supervision/commissioning contracts, other contracts, and project quantities for each project. View project implementation status, contract amount, settlement status, contract amount minus settled amount, compare with design estimates, and calculate the difference between design estimates, management estimates, and contract estimates.
  • Construction cost control Simultaneously, the execution status of equipment costs, installation engineering costs, construction engineering costs, and other expenses within the factory is statistically analyzed and categorized.
  • Design Change Control Synchronize design change records for each set of drawings, detailing the volume number, volume name, requesting unit, design unit, reason for change, changed location, request date, and cost. Perform statistical analysis on the volume change data.
  • Collision Management After establishing a 3D information model and performing a comprehensive collision check, formulate a process for handling and approving collision issues, view collision points in 3D, intuitively understand the collision situation, and provide feedback on collision handling methods.
  • Cable laying data management Visualizes the cable laying path and generates three-dimensional laying length.
  • Model Center Deploy and publish 2D drawings and 3D digital models. Through 2D-3D association and 3D visualization technology, restore all professional equipment and facilities of the building, and view relevant engineering data based on the selected 3D object.
  • Data delivery verification Generate a statistical table for verifying the integrity of data transfer data by performing statistics on the data released by the data management module.
  • Document Management Batch import all power plant documents, including but not limited to equipment instruction manuals, system layout diagrams, equipment manufacturer drawings, historical maintenance records, and work orders related to equipment and facilities. Documents should support version control. Simultaneously, establish associations between documents and between documents and 3D objects, enabling 2D and 3D linkage within drawings and documents.
  • Materials Management Provides planning and monitoring data for the materials management of infrastructure projects, and provides basic data for a series of interconnected business processes such as equipment delivery demand planning, equipment supervision and delivery, and equipment issuance.
  • Delivery plan early warning management Provides information on the difference between the actual delivery progress of equipment and the plan, automatically identifies and identifies problems before they occur, and issues timely warnings.
  • the data presentation layer allows users to view report data, approve workflows, and receive message pushes through a portal interface, and then displays the data.
  • the data presentation layer can deploy mobile applications and infrastructure collaborative work applications.
  • Data display is completed through services such as portal interface, report data viewing, workflow approval, and message push.
  • the display can integrate video surveillance, construction operation area division, construction personnel management, special work type management, construction task information, construction progress, road occupancy, on-site radiographic operation management, and safety risk management, so as to realize global visual management of infrastructure on-site construction information.
  • a 3D visualization management platform is established to display various monitoring information, environmental parameters, equipment status, etc. in an intuitive way, which is convenient for managers to conduct real-time monitoring and remote operation and maintenance.
  • Mobile applications complete data display through services such as mobile portals, mobile data viewing, message push and workflow approval.
  • Mobile Portal Custom-developed mobile portal supporting user, permission, and content management.
  • the mobile portal displays business data from modules such as personnel profiles, equipment profiles, security measures process profiles, video surveillance footage, push notifications, attendance management, infrastructure project cost control, and on-site personnel distribution.
  • the mobile application supports the retrieval and viewing of data within the system, such as data, charts, curves, and 3D models from various application centers. Customized data monitoring screens are available on the mobile app to meet the needs of displaying data across different dimensions, including progress dashboards, security statistics, and personnel statistics.
  • (3) Message push By integrating information and data after subscription on the mobile terminal, including construction delay warning data, equipment supply delay warning, violation messages, and self-reporting messages from participating units, different users have different subscription permissions. After subscription, the system pushes relevant information according to the subscription status.
  • the portal comprehensively displays the progress, cost, safety management, on-site operation status, personnel management status and other related intelligent analysis results of the infrastructure project. By reasonably dividing the on-site display area, it can intelligently link access control and monitoring video, work task plan, personnel distribution, on-site operation status and other data, and remotely access comprehensive data and on-site video cameras in real time to view the on-site situation.
  • the personal workbench includes to-do items and completed items.
  • the items are automatically identified violation-related processing procedures, including speeding, not wearing a safety helmet, smoking, people falling to the ground, abnormal number of people, etc.
  • the violation events are reviewed, and a safety assessment notice is automatically generated after the review is approved.
  • the authorization center will periodically or in real time synchronize the usernames in the system to the subsystem to ensure that the permission management built into the subsystem is running normally.
  • the cloud-edge collaborative 1+6+N smart thermal power plant infrastructure system provided in this application is a new power generation concept and model that meets the needs of the times under the background of the digital and intelligent transformation of the power industry. Based on a hyper-converged integrated edge cloud platform, it covers six major application scenarios including power plant infrastructure, safety, operation, maintenance, fuel and management. It can well support various new technologies in sensing, analysis, modeling, calculation, control, optimization and decision-making, and comprehensively and three-dimensionally meet the various needs of power plant construction and production. Under the cloud-edge collaborative 1+6+N smart thermal power plant infrastructure system architecture, the power plant establishes a new management concept for the infrastructure process, helping to achieve overall infrastructure control.
  • This application also provides a deployment method for a cloud-edge collaborative 1+6+N smart thermal power plant smart infrastructure system. As shown in Figure 3, the method includes:
  • S1 Deploy and run any software or application on the lightweight IaaS layer to realize the basic computing, storage and network resource access of the smart thermal power plant smart infrastructure system;
  • S2 In the lightweight PaaS layer, set up the application development and deployment environment for any software or application deployed by the user, as well as the access to data from the terminal sensing and control devices of the smart thermal power plant smart infrastructure system.
  • cloud-edge collaborative 1+6+N smart thermal power plant smart infrastructure system deployment method can be used to set up a system that is exactly the same as the cloud-edge collaborative 1+6+N smart thermal power plant smart infrastructure system mentioned above, which will not be elaborated here.
  • the cloud-edge collaborative 1+6+N smart thermal power plant smart infrastructure system reduces the difficulty of data management and business decision-making.
  • it can achieve global collaborative management during the infrastructure construction period. It effectively integrates information such as personnel, location, attendance, operations, and materials, providing decision-making basis for management to conduct personnel scheduling, on-site operations, equipment and material supervision, and overall project progress, quality, safety, and cost management. It also features low business concurrency, high reliability, high real-time performance, flexible expansion, low maintenance difficulty, and high information security level.

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  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

本申请提供了一种云边协同1+6+N智慧火电厂智慧基建系统及部署方法，该系统包括：轻量化IaaS层、轻量化PaaS层和轻量化SaaS层。其中轻量化IaaS层，用于用户自行部署和运行任意软件或应用程序，实现智慧火电厂智慧基建系统基本的计算、存储和调用网络资源；轻量化PaaS层，用于为用户部署的任意软件或应用程序设置应用程序开发和部署的环境以及智慧火电厂智慧基建系统的终端感知设备和控制设备的数据的接入；轻量化SaaS层，用于智慧火电厂智慧基建系统的应用和服务采用微服务架构的形式进行部署，满足智慧火电厂智慧基建系统的各种业务需求。以解如何提高火电厂的基建管理和运维提供了高效、安全、可视化的技术问题以及如何显著提升电厂的基建管理水平和运维效率。

Description

一种云边协同1+6+N智慧火电厂智慧基建系统及部署方法
本申请要求在2024年7月1日提交中国专利局、申请号为202410872650.9、发明名称为“一种云边协同1+6+N智慧火电厂智慧基建系统及部署方法”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请属于火电机组智慧基建工程领域，涉及一种云边协同1+6+N智慧火电厂智慧基建系统及部署方法。
背景技术
由于火电厂基建期与生产期分别隶属于两个行政主体，各环节各阶段的管理相互分立割裂，容易造成工程数据丢失、隐蔽工程无法追溯、设备资产不贯通、劣质供应商难追责等一系列问题。如何通过数字化技术，降低工程造价、提高工程质量、统一工程数据源头、实现基建运维一体化管控，在运维阶段为智慧电厂打造以工程实体对象为基础的数字化底座，是发电企业需要在基建过程统筹考虑的问题。
目前，火电厂基建工程会配套相应的管理系统，例如发电调峰系统、故障检修系统等项目。管理科目单一，难以实现基建期全局管控，电厂若实现基建全局管控，势必会造成系统架构和业务流程复杂程度增大，进行数据交互与指令交互的系统及终端设备增多，系统数据存储、数据管理、后台应用计算等均对系统的软硬件提出了更高的要求，同时，可能导致系统之间关联程度低，进而导致基建期管理难度增大。
为此，一种支撑各种传感、分析、建模、计算、控制、优化、决策新技术，全方位、立体化满足电厂建设与生产的各种需求，以及符合时代需求的新型发电理念和模式的智慧火电厂智慧基建系统有待提出，针对电厂基建过程建立全新的管理理念，助力实现基建全局管控。
发明内容
本申请提供了一种云边协同1+6+N智慧火电厂智慧基建系统及部署方法，以解如何提高火电厂的整体基建管理和运维提供了高效、安全、可视化的技术问题以及如何显著提升电厂的基建管理水平和运维效率。
本申请采用以下技术方案：
本申请提供了一种云边协同1+6+N智慧火电厂智慧基建系统，包括：
轻量化IaaS层、轻量化PaaS层和轻量化SaaS层；
其中轻量化IaaS层，用于用户自行部署和运行任意软件或应用程序，实现智慧火电厂智慧基建系统基本的计算、存储和调用网络资源；
轻量化PaaS层，用于为用户部署的任意软件或应用程序设置应用程序开发和部署的环境以及智慧火电厂智慧基建系统的终端感知设备和控制设备的数据的接入；
轻量化SaaS层，用于智慧火电厂智慧基建系统的应用和服务采用微服务架构的形式进行部署，满足智慧火电厂智慧基建系统的各种业务需求。
可选地，轻量化IaaS层主要包含基础设施平台，基础设施平台由容器、虚拟机、软件管理平台、硬件管理平台构成。
可选地，轻量化PaaS层主要包括：数据基础层、数据接入层、数据处理层、数据应用层和数据展示层。
可选地，数据基础层，用于通过设备终端实现智慧火电厂智慧基建系统的数据采集和汇总；
数据接入层，用于通过5G网络将数据基础成采集和汇总的数据传输至数据处理层；
数据处理层，用于相关数据的传输共享、计算分析与交互融合，并支持轻量化IaaS层定制化应用的开发与集成。
可选地，数据接入层的数据传输方式包含远程访问、实时同步、增量同步。
可选地，数据处理层抽取模块从门禁终端系统、车辆道闸终端系统、违章行为识别信息、进度数据、成本数据、设计图纸、交互文档、渐进式三维模型、风险管控数据、工程管理数据多种业务数据库抽取原始数据作为基础数据的输入，然后通过转换和加载模块将业务数据加载到数据仓库或数据集市中，生成OLAP数据库。
可选地，数据处理层通过关键字提取功能，利用数据管理标准自动形成关联体，完成基建过程中多源异构数据的融合分析。
可选地，数据处理层还结合智能视频分析算法模型对采集的视频数据进行分析，当发现人员违章时，自动创建违章事件，并联动违章现场语音播报违章情况，及时发出阻止信号；根据门禁终端采集的人员基础数据，实现参建单位管理。
可选地，轻量化SaaS层包括：数据应用层和数据展示层；
数据应用层，用于通过完善KKS编码标识系统，建立先于建设进度的渐进式高精度云渲染三维信息化模型，实现施工数据管控、质量管理、成本管控、工程资料管理等应用；基于三维模型部署图纸校验分析、三维碰撞检查、三维电缆敷设管理、三维进度分析、三维模拟施工、文档管理实现应用的可视化；以三维模型为核心载体的数据关联体，实现数据、知识的连续积累；
数据展示层，通过门户界面完成报表数据查看、工作流审批以及消息推送多种服务完成，并进行数据展示。
本申请还提供了一种云边协同1+6+N智慧火电厂智慧基建系统部署方法，基于上述方法实施例中任意一种的云边协同1+6+N智慧火电厂智慧基建系统，该方法包括：
在轻量化IaaS层自行部署和运行任意软件或应用程序，实现智慧火电厂智慧基建系统基本的计算、存储和调用网络资源；
在轻量化PaaS层，为用户部署的任意软件或应用程序设置应用程序开发和部署的环境以及智慧火电厂智慧基建系统的终端感知设备和控制设备的数据的接入；
在轻量化SaaS层，对智慧火电厂智慧基建系统的应用和服务采用微服务架构的形式进行部署，满足智慧火电厂智慧基建系统的各种业务需求。
本申请的有益效果是：
本申请提供的云边协同1+6+N智慧火电厂智慧基建系统，通过设置轻量化IaaS层，用于用户自行部署和运行任意软件或应用程序，实现智慧火电厂智慧基建系统基本的计算、存储和调用网络资源，可以有效降低智慧基建数据存储；轻量化PaaS层，用于为所述用户部署的任意软件或应用程序设置应用程序开发和部署的环境以及所述智慧火电厂智慧基建系统的终端感知设备和控制设备的数据的接入；轻量化SaaS层，用于所述智慧火电厂智慧基建系统的应用和服务采用微服务架构的形式进行部署，满足所述智慧火电厂智慧基建系统的各种业务需求。降低了数据管理以及业务决策的难度，采用多种管理终端互补协同管控，可以实现对基建期的全局协同管理，有效的将人员、位置、考勤、作业、物资等信息进行整合，为管理层进行人员调度、现场作业、设备和物资监管以及项目整体进度、质量、安全、成本管理提供决策依据，并且具备业务并发小、可靠性高、实时性高、扩展灵活、维护难度低、信息安全等级高等特点。
附图说明
图1为本申请提供的一种云边协同1+6+N智慧火电厂智慧基建系统架构示意图；
图2为本申请提供的一种基建数据处理层流程示意图；
图3为本申请提供的一种云边协同1+6+N智慧火电厂智慧基建系统步骤方法步骤图。
具体实施方式
下面结合附图和具体实施方式对本申请进行详细说明。
本申请提供了一种云边协同1+6+N智慧火电厂智慧基建系统，以下将结合图1对该系统进行解释说明，具体该系统包括：
本申请提供的云边协同1+6+N智慧火电厂智慧基建系统的智慧火电厂边缘云平台包括轻量化IaaS层、轻量化PaaS层和轻量化SaaS层；
其中轻量化IaaS层，用于用户自行部署和运行任意软件或应用程序，实现智慧火电厂智慧基建系统基本的计算、存储和调用网络资源；
在一种实施例中，将智慧火电厂智慧基建系统的系统资源部署在电厂边缘云平台IaaS层的虚拟机或者容器中，用于用户自行部署和运行任意软件或应用程序，实现智慧火电厂智慧基建系统基本的计算、存储和调用网络资源。
轻量化PaaS层，用于为用户部署的任意软件或应用程序设置应用程序开发和部署的环境以及智慧火电厂智慧基建系统的终端感知设备和控制设备的数据的接入；
在一种实施例中，智慧火电厂智慧基建系统所需要的计算数据均由PaaS层的数据接入层提供，所需要的计算模型和计算资源均由PaaS层的数据分析层提供，此外PaaS层还可以为用户部署的任意软件或应用程序设置应用程序开发和部署的环境以及智慧火电厂智慧基建系统的终端感知设备和控制设备的数据的接入。
轻量化SaaS层，用于智慧火电厂智慧基建系统的应用和服务采用微服务架构的形式进行部署，满足智慧火电厂智慧基建系统的各种业务需求。
在一种实施例中，智慧火电厂智慧基建系统的应用和服务采用微服务架构的形式在电厂边缘云平台SaaS层中开发部署，满足智慧火电厂智慧基建系统的各种业务需求。
需要说明的是，本申请提供的云边协同1+6+N智慧火电厂智慧基建系统充分利用了云计算、边缘计算和物联网技术的优势，为火电厂的基建管理和运维提供了高效、安全、可视化的解决方案。通过实时数据采集、高效数据处理和智能化应用，能够显著提升电厂的基建管理水平和运维效率。
可选地，轻量化IaaS层主要包含基础设施平台，基础设施平台由容器、虚拟机、软件管理平台、硬件管理平台构成。
需要说明的是本申请所提出的一种云边协同1+6+N智慧火电厂智慧基建系统，轻量化PaaS层主要包括：数据基础层、数据接入层、数据处理层、数据应用层和数据展示层。
可选地，数据基础层，用于通过设备终端实现智慧火电厂智慧基建系统的数据采集和汇总；
数据接入层，用于通过5G网络将数据基础成采集和汇总的数据传输至数据处理层；
数据处理层，用于相关数据的传输共享、计算分析与交互融合，并支持轻量化IaaS层定制化应用的开发与集成。
在一种实施例中，在火电厂内部署各类智能传感器、PLC控制器、智能仪表和监控摄像头等设备，并确保这些设备支持标准的网络通信协议(如Modbus、OPC UA、MQTT等)，以便于将实时运行数据传输至大华ICC平台。此外，在大华ICC平台上注册并配置接入设备，包括设置设备ID、IP地址、端口及相应的认证信息等，数据接入层可通过终端设备和5G网络采用远程访问、实时同步和增量同步的方式将数据进行采集与汇总，火电厂智慧基建涉及数据包括人工输入的数据、视频采集的数据、地理信息数据、图纸文档数据、智能终端采集的检测数据等多种来源和格式，具体数据内容包括进度数据、成本数据、质量数据、作业风险数据、车辆信息数据、特种设备数据、应急预案数据、门禁数据、视频监控数据、仓库数据、仓位数据、设备物资数据、合同数据以及人员数据。
数据接入层，通过打通各分系统数据库、其它相关数据库的数据接口，集成并存储各智能终端的检测数据，实现相关数据的传输共享、计算分析与交互融合。对于必须的历史数据，开发基建现场数据录入功能，对于线下结构化数据，开发批量导入符合数据标准和模板规范的数据表格功能；在数据安全的条件下，通过数据映射、元数据、实时数据接入等服务功能，实现数据的接入、处理，对接入的数据，按照智慧通基建数据管理标准进行校验和质检，并按业务活动、业务流程、数据源、数据时间流程等不同维度分别对接入的数据集进行管理，形成数据资产。
可选地，数据接入层的数据传输方式包含远程访问、实时同步、增量同步。
数据处理层开发针对火电基建多源异构数据(涵盖基建期产生的图纸、文档、表单、规格参数、工作流、视频流、管理流、门禁、人员ID等)的高效识别、关键字提取功能，利用数据管理标准自动形成关联体，突破不同结构数据之间的壁垒，解决基建过程中多源异构数据的融合分析，需要说明的是，数据处理层主要涉及的数据有人员主数据、进度主数据、质量主数据、成本主数据、视频主数据、门禁主数据、安全主数据、物资主数据。
可选地，数据处理层抽取模块从门禁终端系统、车辆道闸终端系统、违章行为识别信息、进度数据、成本数据、设计图纸、交互文档、渐进式三维模型、风险管控数据、工程管理数据多种业务数据库抽取原始数据作为基础数据的输入，然后通过转换和加载模块将业务数据加载到数据仓库或数据集市中，生成OLAP数据库。
在一种实施例中，如图2所示，数据处理层通过数据抽取、转换和加载模块将多源异构数据资源抽取到临时中间层后进行清洗、转换、集成，最后加载到数据仓库或数据集市中，成为联机分析处理、数据挖掘的基础。数据抽取模块从门禁终端系统、车辆道闸终端系统、违章行为识别信息、进度数据、成本数据、设计图纸、交互文档、渐进式三维模型、风险管控数据、工程管理数据等业务数据库抽取原始数据，作为基础数据的输入，然后将人员主数据、进度主数据、质量主数据、成本主数据、视频主数据、门禁主数据、安全主数据、物资主数据等数据进行抽取转换、并进一步进行清洗集成，最终构成数据中心中数据仓库的最底层即数据基础层，并汇聚至OLAP数据库。
可选地，数据处理层通过关键字提取功能，利用数据管理标准自动形成关联体，完成基建过程中多源异构数据的融合分析。
在一种实施例中，数据处理层开发部署针对火电基建多源异构数据(涵盖基建期产生的图纸、文档、表单、规格参数、工作流、视频流、管理流、门禁、人员ID等)的高效识别、关键字提取功能，利用数据管理标准自动形成关联体，突破不同结构数据之间的壁垒，解决基建过程中多源异构数据的融合分析。
可选地，数据处理层还结合智能视频分析算法模型对采集的视频数据进行分析，当发现人员违章时，自动创建违章事件，并联动违章现场语音播报违章情况，及时发出阻止信号；根据门禁终端采集的人员基础数据，实现参建单位管理。
在一种实施例中，数据处理层还结合智能视频分析算法模型对视频数据进行智能分析，当发现人员违章时，自动创建违章事件，从而实现违章事件的智能采集，并联动违章现场语音播报违章情况，及时阻止，实现安全风险管控；根据门禁终端采集的人员基础数据，实现参建单位管理，通过系统数据集成，实现基建现场安全风险管控，开发部署施工单位自主申报、基建督办任务管理、访客管理、违章管理、作业风险管控、人员分布、出勤统计、特种人员管理、安全措施分析等功能。
可选地，轻量化SaaS层包括：数据应用层和数据展示层；
数据应用层，用于通过完善KKS编码标识系统，建立先于建设进度的渐进式高精度云渲染三维信息化模型，实现施工数据管控、质量管理、成本管控、工程资料管理等应用；基于三维模型部署图纸校验分析、三维碰撞检查、三维电缆敷设管理、三维进度分析、三维模拟施工、文档管理实现应用的可视化；以三维模型为核心载体的数据关联体，实现数据、知识的连续积累；
在一种实施例中，数据应用层可部署施工数据管控、参建单位管理、质量管理、成本管控、安全风险管控、工程资料管理、移动应用和仓储管理等功能。
数据应用层通过完善KKS编码标识系统，建立先于建设进度的渐进式高精度云渲染三维信息化模型，实现施工数据管控、质量管理、成本管控、工程资料管理等应用，建模颗粒度包括土建、设备、管嘴、管道、管件(阀门、法兰、过滤器等)、支吊架、地下管网、桥架等设计对象，锅炉模型精度达到焊口级、测点级；基于三维模型部署图纸校验分析、三维碰撞检查、三维电缆敷设管理、三维进度分析、三维模拟施工、文档管理等可视化应用；利用二/三维信息模型及管理平台开展全过程多专业综合碰撞检查和二三维校验，确保设计成果的有效性；以三维模型为核心载体的数据关联体，实现数据、知识(设备台账、工程图纸、设计属性、焊口信息、工程文档等)的连续积累。
基于数据的连续积累开发基建数据管控应用，包括施工数据管理、基建工程成本管控、工程质量辅助管理、基建工程资料管理、智慧仓储管控等应用。
(1)施工数据管理
1)项目进度数据管理：开发部署设计进度、施工进度、采购进度及供货进度的数据的综合分析管理等功能，从多维度对项目进度可能存在的风险进行监查。
2)现场施工作业可视化协同：导入现场分层的平面布置图作为施工现场的展示基础，通过采集现场视频监控数据、施工进度数据，将施工信息、工业视频信息标签定位到图纸上，实现通过包含实际位置信息的可视化二维地图上，查看上报的施工进度报表。通过施工点位、作业点位获取工业视频摄像头图像，实现对现场施工进度的可视化管理。
3)施工现场大型机械管控：建立大型机械台账，台账中包含器械入场申请、审核、登记备案、定期检查和维护、购销合同、制造许可证、产品合格证、制造监督检验证明、安装使用说明书、备案证明等相关信息，并为施工机械制作机械设备二维码，通过扫描二维码便可立即获取机械的相关信息。机械的检查和维护定期工作要有工作提醒，及时传送到具体负责人，并记录责任人的回复信息和工作完成情况，实现施工机械定期工作的闭环管理。
(2)基建工程成本管控
1)投资概算管控：同步投资数据、概算数据，包括初设概算、管理概算、已完成投资、预计最终投资、已签订合同额、已签合同已完的投资、已签合同未完的投资、合同税金、合同进度资金支付等情况进行统计和分类，辅助管理者评估项目概算执行情况、投资和工程进度偏差及影响。通过对年度、月度下达概算与进度数据的采集和对比，实现项目概算执行分析、审核，以及偏差预警。
2)采购成本管控：同步每个工程项目的设备采购合同、建筑安装合同、设计\监理\调试合同、其他合同、工程量的合同交付及付款情况。查看项目实施状态、合同签约额、结算情况签约金额-已结算金额、比对设计概算，计算设计概算、管理概算与合同预估差额等。
3)施工成本管控：同步厂内设备费，安装工程费、建筑工程费、其他费用的执行情况，进行统计和分类。
(3)工程质量辅助管理
1)设计变更管控：同步每册图纸设计变更记录，详细记录卷册号、卷册名称、提出变更单位、设计单位、变更原因、变更部位、变更提出日期，变更费用。并对卷册变更数据进行统计分析。
2)碰撞管理：在建立三维信息模型、执行综合碰撞检查后，制定碰撞问题的处理、审批流程，以三维方式查看碰撞点，直观了解碰撞情况，反馈碰撞处理方式。
3)电缆敷设数据管理：可视化的展示电缆敷设路径，生成三维敷设长度。
(4)基建工程资料管理
1)模型中心：部署发布二维图纸和三维数字化模型，通过二三维关联，三维可视化技术，还原建筑全专业设备设施，并根据选择的三维对象，查看相关工程资料。
2)资料交付校验：对生成资料移交数据完整性校验统计表，通过对资料管控模块发布的数据进行统计。
3)补充和完善全厂标识系统编码：基于统一的编码原则，对设计院、厂家的编码进行审核，并负责新增设备的kks编码。
4)文档管理：对电厂所有文档进行批量导入，包括但不限于设备使用说明书、系统布置图、设备厂家图纸、历史维修记录、与设备设施相关的工单等文档，文档应支持版本控制。同时文档与文档、文档与三维对象建立关联，图纸文档中的二三维联动。
(5)智慧仓储管控
1)物资管理：为基建项目物资管理提供计划、监控数据信息，在为项目的设备到货需求计划和设备监造催交、到货、领用等一系列的相互关联业务提供基础数据信息。
2)交付计划预警管理：提供设备实际交付进度与计划差异的信息，在出现问题之前自动判断识别，及时预警。
3)仓位管理：根据货物特点和存储需求，自动为货物分配最合适的仓位，实时监控仓位状态，包括货物数量、种类等；记录货物在各仓位的存储情况，便于库存盘点与管理，通过分析仓位数据，为仓储运营提供决策支持。
数据展示层，通过门户界面完成报表数据查看、工作流审批以及消息推送多种服务完成，并进行数据展示。
在一种实施例中，数据展示层可部署移动应用和基建协同工作应用，通过门户界面，报表数据查看，工作流审批以及消息推送等服务完成数据展示，展示可集成视频监控、施工作业区域划分、施工人员管理、特殊工种管理、施工任务信息、施工进度、道路占用情况、现场射线作业管理、安全风险管理等内容，实现对基建现场施工信息的全局可视化管理，具体的，建立3D可视化管理平台，将各类监控信息、环境参数、设备状态等以直观方式展现，便于管理人员进行实时监控和远程运维。
(一)移动应用通过移动门户、移动数据查看、消息推送和工作流审批等服务完成数据展示。
(1)移动门户：定制开发移动端门户，支持用户、权限和内容管理。移动门户的展示内容包括：人员档案、设备档案、安全措施过程档案、视频监控画面、消息推送、考勤管理、基建工程成本管控、现场人员分布等模块的业务数据。
(2)移动数据查看：移动端应用支持系统内数据的检索和查看，例如各应用中心的数据、图表、曲线、三维模型等。移动端定制相关数据监控画面，满足不同维度数据展示的需求，包括进度看板、安全统计、人员统计等内容。
(3)消息推送：通过对移动端订阅集成后的信息和数据，包括施工滞后预警数据、设备供货滞后预警、违章消息、参建单位自主申报消息，不同的用户拥有不同的订阅权限，订阅后，系统根据订阅情况推送相关信息。
(4)工作流审批：对于协同工作等系统的待办流程，在移动端统一提醒，并且执行审批操作。移动端能进行的审批包括：参建单位自主申报审批、设备进场审批、培训申请审批，培训考核结果审批。
(二)智慧基建协同工作应用通过门户界面，综合报表，个人工作台和认证授权等服务完成数据展示。
(1)门户界面：门户综合展示基建项目的进度、成本、安全管理、现场作业情况、人员管理情况等相关智能分析结果，通过合理划分现场显示区域，通过门禁和监控视频、工作任务计划，人员分布、现场作业情况等数据智能联动、实时远程访问全面数据和现场视频摄像头、查看现场情况。
(2)综合报表：开发部署可视化表单、灵活报表设计模块，具备可自定义设计表单、配置报表和报表可视化展示等功能。
(3)个人工作台：个人工作台包括待办事项和已办事项，事项为自动识别的违章相关处理流程，包含超速、未佩带安全帽、吸烟、人员倒地、人数异常等违章工作流转，对违章事件进行审核，审核通过后自动生成安全考核通知单。
(4)认证授权：授权中心定时或实时地将系统中的用户名同步到子系统，保证子系统自带的权限管理正常运行。
需要说明的是，基于本申请提供的云边协同1+6+N智慧火电厂智慧基建系统是电力行业数字化、智能化转型背景下的一种符合时代需求的新型发电理念和模式，基于超融合一体化边缘云平台，建设涵盖电厂基建、安全、运行、检修、燃料及管理的6大应用场景，可良好支撑各种传感、分析、建模、计算、控制、优化、决策新技术，全方位、立体化满足电厂建设与生产的各种需求。在云边协同1+6+N智慧火电厂智慧基建系统体系架构下，电厂针对基建过程建立全新的管理理念，助力实现基建全局管控。
本申请还提供了一种云边协同1+6+N智慧火电厂智慧基建系统部署方法，基于上述方法实施例中任意一种的云边协同1+6+N智慧火电厂智慧基建系统，如图3所示，该方法包括：
S1：在轻量化IaaS层自行部署和运行任意软件或应用程序，实现智慧火电厂智慧基建系统基本的计算、存储和调用网络资源；
S2：在轻量化PaaS层，为用户部署的任意软件或应用程序设置应用程序开发和部署的环境以及智慧火电厂智慧基建系统的终端感知设备和控制设备的数据的接入；
S3：在轻量化SaaS层，对智慧火电厂智慧基建系统的应用和服务采用微服务架构的形式进行部署，满足智慧火电厂智慧基建系统的各种业务需求。
需要说明的是通过本申请提供的云边协同1+6+N智慧火电厂智慧基建系统部署方法可设置出入上述云边协同1+6+N智慧火电厂智慧基建系统完全一样的系统，此处不再赘述。
此外，本申请提供的云边协同1+6+N智慧火电厂智慧基建系统降低了数据管理以及业务决策的难度，采用多种管理终端互补协同管控，可以实现对基建期的全局协同管理，有效的将人员、位置、考勤、作业、物资等信息进行整合，为管理层进行人员调度、现场作业、设备和物资监管以及项目整体进度、质量、安全、成本管理提供决策依据，并且具备业务并发小、可靠性高、实时性高、扩展灵活、维护难度低、信息安全等级高等特点。

Claims (10)

  1. 一种云边协同1+6+N智慧火电厂智慧基建系统，其特征在于，包括：轻量化IaaS层、轻量化PaaS层和轻量化SaaS层；
    其中所述轻量化IaaS层，用于用户自行部署和运行任意软件或应用程序，实现智慧火电厂智慧基建系统基本的计算、存储和调用网络资源；
    所述轻量化PaaS层，用于为所述用户部署的任意软件或应用程序设置应用程序开发和部署的环境以及所述智慧火电厂智慧基建系统的终端感知设备和控制设备的数据的接入；
    轻量化SaaS层，用于所述智慧火电厂智慧基建系统的应用和服务采用微服务架构的形式进行部署，满足所述智慧火电厂智慧基建系统的各种业务需求。
  2. 如权利要求1所述的云边协同1+6+N智慧火电厂智慧基建系统，其特征在于，所述轻量化IaaS层主要包含基础设施平台，所述基础设施平台由容器、虚拟机、软件管理平台、硬件管理平台构成。
  3. 如权利要求1所述的云边协同1+6+N智慧火电厂智慧基建系统，其特征在于，所述轻量化PaaS层主要包括：数据基础层、数据接入层、数据处理层、数据应用层和数据展示层。
  4. 如权利要求1所述的云边协同1+6+N智慧火电厂智慧基建系统，其特征在于，
    所述数据基础层，用于通过设备终端实现所述智慧火电厂智慧基建系统的数据采集和汇总；
    所述数据接入层，用于通过5G网络将所述数据基础成采集和汇总的数据传输至所述数据处理层；
    所述数据处理层，用于相关数据的传输共享、计算分析与交互融合，并支持所述轻量化IaaS层定制化应用的开发与集成。
  5. 如权利要求4所述的云边协同1+6+N智慧火电厂智慧基建系统，其特征在于，所述数据接入层的数据传输方式包含远程访问、实时同步、增量同步。
  6. 如权利要求1所述的云边协同1+6+N智慧火电厂智慧基建系统，其特征在于，所述数据处理层抽取模块从门禁终端系统、车辆道闸终端系统、违章行为识别信息、进度数据、成本数据、设计图纸、交互文档、渐进式三维模型、风险管控数据、工程管理数据多种业务数据库抽取原始数据作为基础数据的输入，然后通过转换和加载模块将所述业务数据加载到数据仓库或数据集市中，生成OLAP数据库。
  7. 如权利要求1所述的云边协同1+6+N智慧火电厂智慧基建系统，其特征在于，所述数据处理层通过关键字提取功能，利用数据管理标准自动形成关联体，完成基建过程中多源异构数据的融合分析。
  8. 如权利要求1所述的云边协同1+6+N智慧火电厂智慧基建系统，其特征在于，所述数据处理层还结合智能视频分析算法模型对采集的视频数据进行分析，当发现人员违章时，自动创建违章事件，并联动违章现场语音播报违章情况，及时发出阻止信号；根据门禁终端采集的人员基础数据，实现参建单位管理。
  9. 如权利要求1所述的云边协同1+6+N智慧火电厂智慧基建系统，其特征在于，所述轻量化SaaS层包括：数据应用层和数据展示层；
    所述数据应用层，用于通过完善KKS编码标识系统，建立先于建设进度的渐进式高精度云渲染三维信息化模型，实现施工数据管控、质量管理、成本管控、工程资料管理应用；基于三维模型部署图纸校验分析、三维碰撞检查、三维电缆敷设管理、三维进度分析、三维模拟施工、文档管理实现所述应用的可视化；以所述三维模型为核心载体的数据关联体，实现数据、知识的连续积累；
    所述数据展示层，通过门户界面完成报表数据查看、工作流审批以及消息推送多种服务完成，并进行数据展示。
  10. 一种云边协同1+6+N智慧火电厂智慧基建系统部署方法，基于权利要求1-9任意权利要求所述的云边协同1+6+N智慧火电厂智慧基建系统，其特征在于，该方法包括：
    在轻量化IaaS层自行部署和运行任意软件或应用程序，实现智慧火电厂智慧基建系统基本的计算、存储和调用网络资源；
    在轻量化PaaS层，为所述用户部署的任意软件或应用程序设置应用程序开发和部署的环境以及所述智慧火电厂智慧基建系统的终端感知设备和控制设备的数据的接入；
    在轻量化SaaS层，对所述智慧火电厂智慧基建系统的应用和服务采用微服务架构的形式进行部署，满足所述智慧火电厂智慧基建系统的各种业务需求。
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CN118819041A (zh) * 2024-07-01 2024-10-22 西安热工研究院有限公司 一种云边协同1+6+n智慧火电厂智慧基建系统及部署方法
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