技术领域Technical Field
本发明涉及前端开发技术领域，具体涉及用于前端页面的虚拟数据标签生成系统及方法。The present invention relates to the technical field of front-end development, and in particular to a system and method for generating virtual data tags for front-end pages.
背景技术Background Art
随着工业自动化和信息化的不断发展，生产企业往往部署了大量的计量设备，如电表、水表等。这些设备之间存在复杂的上下级关系，有时还需要经常调整，如继电箱的上下级关系。此外，图形化的设备监控界面中通常包含大量的判断条件，随着系统复杂度的提升，判断条件也越来越多。现有技术中，图形界面一般只能关联直接的数据点。对于需要二次计算的数据点，需要在后台配置计算公式，生成新的数据点才能在前端使用。前端页面在处理绑点数据时，主要通过直接调用后台接口，由后台根据各项变量和表达式进行计算，再将结果返回前端展示。然而，这种处理方式存在以下不足：当遇到复杂计算时，前端需要频繁向后台发起请求，增加了系统的通信开销和延迟。同时，由于缺乏前端的临时变量作为载体，导致绑定了数据点的图形难以复用，不同图形之间的表达式也难以嵌套使用。这些问题限制了前端页面的灵活性和效率，影响了用户的操作体验。With the continuous development of industrial automation and informatization, manufacturing enterprises often deploy a large number of metering equipment, such as electricity meters and water meters. There are complex hierarchical relationships between these devices, and sometimes they need to be adjusted frequently, such as the hierarchical relationship of relay boxes. In addition, the graphical equipment monitoring interface usually contains a large number of judgment conditions. As the complexity of the system increases, the judgment conditions are also increasing. In the prior art, the graphical interface can generally only associate direct data points. For data points that require secondary calculations, it is necessary to configure the calculation formula in the background and generate new data points before they can be used in the front end. When the front-end page processes the binding point data, it mainly directly calls the background interface, and the background calculates according to various variables and expressions, and then returns the results to the front end for display. However, this processing method has the following shortcomings: when encountering complex calculations, the front end needs to frequently initiate requests to the background, which increases the communication overhead and delay of the system. At the same time, due to the lack of temporary variables in the front end as a carrier, it is difficult to reuse the graphics bound to the data points, and it is also difficult to nest expressions between different graphics. These problems limit the flexibility and efficiency of the front-end page and affect the user's operating experience.
发明内容Summary of the invention
本申请通过提供了用于前端页面的虚拟数据标签生成系统及方法，旨在解决现有技术中前端页面处理绑点数据时，需要频繁调用后台进行计算，且缺乏临时变量作为载体导致绑点图形难以复用和表达式难以嵌套使用的技术问题。The present application provides a virtual data label generation system and method for front-end pages, aiming to solve the technical problems in the prior art that when the front-end page processes binding point data, it is necessary to frequently call the background for calculation, and the lack of temporary variables as carriers makes it difficult to reuse binding point graphics and difficult to nest expressions.
鉴于上述问题，本申请提供了用于前端页面的虚拟数据标签生成系统及方法。In view of the above problems, the present application provides a virtual data tag generation system and method for front-end pages.
本申请公开的第一个方面，提供了用于前端页面的虚拟数据标签生成系统，该系统包括：获取模块，用于获取用户的输入信息，输入信息包括设备的图片信息、基础信息；构建模块，用于构建抽象子图，抽象子图通过抽象网络构建而成，抽象网络为进行基础图元拆解重构的智能处理网络，且抽象网络设置有自适应更新的区别数据库，区别数据库用于存储已构建的抽象子图，将图片信息输入至抽象网络，生成抽象子图；第一解析模块，用于解析基础信息，建立设备的状态标识，并通过状态标识更新抽象子图；第二解析模块，用于解析基础信息，建立设备配置信息、现场工况数据、指标配置信息、指标统计数据，根据设备配置信息、现场工况数据、指标配置信息、指标统计数据建立虚拟数据标签；搭建模块，用于将更新后的抽象子图进行虚拟数据标签的动态表达式嵌入，完成抽象子图的搭建；管理模块，用于当引用搭建完成的抽象子图时，读取抽象子图的虚拟数据标签，并对虚拟数据标签赋值，根据赋值结果更新抽象子图状态，完成基于虚拟数据标签的设备管理。The first aspect disclosed in the present application provides a virtual data label generation system for a front-end page, the system comprising: an acquisition module for acquiring user input information, the input information including image information and basic information of the device; a construction module for constructing an abstract subgraph, the abstract subgraph is constructed by an abstract network, the abstract network is an intelligent processing network for disassembling and reconstructing basic graphic elements, and the abstract network is provided with an adaptively updated distinction database, the distinction database is used to store the constructed abstract subgraph, the image information is input into the abstract network, and the abstract subgraph is generated; a first parsing module is used to parse the basic information, establish the status identification of the device, and The status identifier updates the abstract subgraph; the second parsing module is used to parse the basic information, establish equipment configuration information, on-site operating data, indicator configuration information, and indicator statistical data, and establish virtual data tags based on the equipment configuration information, on-site operating data, indicator configuration information, and indicator statistical data; the construction module is used to embed the dynamic expression of the virtual data tag of the updated abstract subgraph to complete the construction of the abstract subgraph; the management module is used to read the virtual data tag of the abstract subgraph when referencing the completed abstract subgraph, and assign a value to the virtual data tag, update the abstract subgraph state according to the assignment result, and complete the equipment management based on the virtual data tag.
本申请公开的另一个方面，提供了用于前端页面的虚拟数据标签生成方法，该方法包括：获取用户的输入信息，输入信息包括设备的图片信息、基础信息；构建抽象子图，抽象子图通过抽象网络构建而成，抽象网络为进行基础图元拆解重构的智能处理网络，且抽象网络设置有自适应更新的区别数据库，区别数据库用于存储已构建的抽象子图，将图片信息输入抽象网络，生成抽象子图；解析基础信息，建立设备的状态标识，并通过状态标识更新抽象子图；解析基础信息，建立设备配置信息、现场工况数据、指标配置信息、指标统计数据，根据设备配置信息、现场工况数据、指标配置信息、指标统计数据建立虚拟数据标签；将更新后的抽象子图进行虚拟数据标签的动态表达式嵌入，完成抽象子图的搭建；当引用搭建完成的抽象子图时，读取抽象子图的虚拟数据标签，并对虚拟数据标签赋值，根据赋值结果更新抽象子图状态，完成基于虚拟数据标签的设备管理。Another aspect disclosed in the present application provides a method for generating virtual data tags for front-end pages, the method comprising: obtaining user input information, the input information including image information and basic information of the device; constructing an abstract subgraph, the abstract subgraph being constructed by an abstract network, the abstract network being an intelligent processing network for disassembling and reconstructing basic graphic elements, and the abstract network being provided with an adaptively updated distinction database, the distinction database being used to store the constructed abstract subgraph, the image information being input into the abstract network to generate the abstract subgraph; parsing the basic information, establishing a status identifier of the device, and updating the abstract subgraph through the status identifier; parsing the basic information, establishing device configuration information, on-site operating data, indicator configuration information, and indicator statistical data, and establishing a virtual data tag based on the device configuration information, on-site operating data, indicator configuration information, and indicator statistical data; embedding the dynamic expression of the virtual data tag into the updated abstract subgraph to complete the construction of the abstract subgraph; when referencing the constructed abstract subgraph, reading the virtual data tag of the abstract subgraph, and assigning a value to the virtual data tag, updating the abstract subgraph state based on the assignment result, and completing device management based on the virtual data tag.
本申请中提供的一个或多个技术方案，至少具有如下技术效果或优点：One or more technical solutions provided in this application have at least the following technical effects or advantages:
由于采用了通过获取模块获取用户输入的设备图片信息和基础信息，其中，图片信息用于生成设备的抽象子图，基础信息用于建立设备状态标识和虚拟数据标签；构建模块基于图片信息，利用抽象网络构建抽象子图，抽象网络通过智能处理对基础图元进行拆解和重构，并设有自适应更新的区别数据库，用于存储已构建的抽象子图，提高了图形构建效率；第一解析模块解析基础信息，建立设备状态标识，并据此更新抽象子图；第二解析模块进一步解析基础信息，建立设备配置、工况、指标等信息，并据此建立虚拟数据标签，引入虚拟数据标签，充当前端的临时变量，方便表达式嵌套使用和图形复用；搭建模块将虚拟数据标签的动态表达式嵌入更新后的抽象子图，完成抽象子图的搭建，通过将数据与图形解耦，动态表达式嵌入图形，使得图形可灵活适配不同数据，增强了图形的可复用性；管理模块在引用抽象子图时，读取其虚拟数据标签并赋值，根据赋值结果更新子图状态，实现基于虚拟标签的设备管理，将设备状态与虚拟标签映射，通过管理虚拟标签来管理设备，简化了管理流程的技术方案，解决了现有技术中前端页面处理绑点数据时，需要频繁调用后台进行计算，且缺乏临时变量作为载体导致绑点图形难以复用和表达式难以嵌套使用的技术问题，达到了优化绑点数据表达式的嵌套使用，增强绑点图形的可复用性，简化计算逻辑、节约内存资源，使网页展示更加简洁高效的技术效果。The acquisition module is used to obtain the device image information and basic information input by the user, wherein the image information is used to generate the abstract subgraph of the device, and the basic information is used to establish the device status identification and virtual data labels; the construction module is based on the image information and uses the abstract network to construct the abstract subgraph. The abstract network disassembles and reconstructs the basic graphics elements through intelligent processing, and is provided with an adaptively updated difference database for storing the constructed abstract subgraphs, thereby improving the efficiency of graphics construction; the first parsing module parses the basic information, establishes the device status identification, and updates the abstract subgraph accordingly; the second parsing module further parses the basic information, establishes the device configuration, working conditions, indicators and other information, and establishes the virtual data labels accordingly, and introduces the virtual data labels to act as temporary variables of the front end, which is convenient for expression nesting and graphics reuse; the construction module embeds the dynamic expression of the virtual data label into the update The abstract subgraph is constructed after the abstract subgraph is built. By decoupling data from graphics and embedding dynamic expressions into graphics, the graphics can be flexibly adapted to different data, thereby enhancing the reusability of graphics. When referencing the abstract subgraph, the management module reads its virtual data label and assigns a value, updates the subgraph status according to the assignment result, implements device management based on virtual labels, maps device status to virtual labels, manages devices by managing virtual labels, simplifies the technical solution of the management process, and solves the technical problems in the prior art that when the front-end page processes the binding point data, it needs to frequently call the background for calculation, and the lack of temporary variables as a carrier makes it difficult to reuse the binding point graphics and difficult to nest the expressions. The technical effect of optimizing the nested use of binding point data expressions, enhancing the reusability of binding point graphics, simplifying the calculation logic, saving memory resources, and making the web page display more concise and efficient is achieved.
上述说明仅是本申请技术方案的概述，为了能够更清楚了解本申请的技术手段，而可依照说明书的内容予以实施，并且为了让本申请的上述和其它目的、特征和优点能够更明显易懂，以下特举本申请的具体实施方式。The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本申请实施例提供了用于前端页面的虚拟数据标签生成系统的一种结构示意图；FIG1 is a schematic diagram of a structure of a virtual data tag generation system for a front-end page provided in an embodiment of the present application;
图2为本申请实施例提供了用于前端页面的虚拟数据标签生成方法的一种流程示意图。FIG2 is a flow chart of a method for generating virtual data tags for a front-end page provided in an embodiment of the present application.
附图标记说明：获取模块11，构建模块12，第一解析模块13，第二解析模块14，搭建模块15，管理模块16。Explanation of the reference numerals: acquisition module 11 , construction module 12 , first analysis module 13 , second analysis module 14 , construction module 15 , management module 16 .
具体实施方式DETAILED DESCRIPTION
本申请提供的技术方案总体思路如下：The overall idea of the technical solution provided by this application is as follows:
本申请实施例提供了用于前端页面的虚拟数据标签生成系统及方法，通过自动构建抽象子图、引入虚拟数据标签、数据与图形解耦以及基于虚拟标签的设备管理等环节，构建高效灵活的前端绑点数据处理方案。The embodiments of the present application provide a system and method for generating virtual data tags for front-end pages, which constructs an efficient and flexible front-end binding point data processing solution by automatically constructing abstract subgraphs, introducing virtual data tags, decoupling data and graphics, and managing devices based on virtual tags.
具体来说，首先，获取用户输入的设备信息，利用抽象网络智能生成设备抽象子图，避免了手动绘制的繁琐。然后，解析设备的基础信息，建立设备状态标识和虚拟数据标签，引入前端临时变量，增强了图形复用性和表达式适配性。接着，将虚拟标签的动态表达式嵌入抽象子图，实现数据与图形的解耦，使图形可灵活适配不同数据。之后，通过管理虚拟数据标签来映射设备状态，简化设备管理流程。Specifically, first, the device information input by the user is obtained, and the abstract network is used to intelligently generate the device abstract subgraph, avoiding the tedious manual drawing. Then, the basic information of the device is parsed, the device status identification and virtual data tags are established, and the front-end temporary variables are introduced to enhance the graphics reusability and expression adaptability. Next, the dynamic expression of the virtual tag is embedded in the abstract subgraph to achieve the decoupling of data and graphics, so that the graphics can be flexibly adapted to different data. After that, the device status is mapped by managing the virtual data tags to simplify the device management process.
综上所述，本申请克服了现有前端绑点数据处理技术的不足，实现了图形构建自动化、数据绑定灵活化、设备管理简易化，从而大幅提升前端页面开发和运行效率，优化了人机交互体验。To sum up, this application overcomes the shortcomings of the existing front-end binding point data processing technology, realizes the automation of graphics construction, flexibility of data binding, and simplification of device management, thereby greatly improving the front-end page development and operation efficiency and optimizing the human-computer interaction experience.
在介绍了本申请基本原理后，下面将结合说明书附图来具体介绍本申请的各种非限制性的实施方式。After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.
实施例一，如图1所示，本申请实施例提供了用于前端页面的虚拟数据标签生成系统，该系统包括：Embodiment 1, as shown in FIG1 , the embodiment of the present application provides a virtual data tag generation system for a front-end page, the system comprising:
获取模块11，用于获取用户的输入信息，所述输入信息包括设备的图片信息、基础信息。The acquisition module 11 is used to acquire the user's input information, and the input information includes the image information and basic information of the device.
具体而言，设备的图片信息是用户通过上传图片文件、扫描识别设备图纸、拍摄设备照片等方式提供的与设备外观相关的图像数据。获取模块11支持多种图片格式，如JPG、PNG、BMP等。通过获取设备的图片信息，系统可以直观地了解设备的外观特征，为后续的图形化处理提供数据基础。Specifically, the image information of the device is image data related to the appearance of the device provided by the user by uploading an image file, scanning and identifying a device drawing, taking a photo of the device, etc. The acquisition module 11 supports a variety of image formats, such as JPG, PNG, BMP, etc. By acquiring the image information of the device, the system can intuitively understand the appearance characteristics of the device and provide a data basis for subsequent graphical processing.
设备的基础信息则涵盖了设备的各种属性数据，例如设备型号、规格参数、安装位置、测点配置等。用户通过手动录入、导入电子表格、读取数据库等方式将设备基础信息提供给获取模块11。系统支持用户自定义设备属性字段，以满足不同行业和应用场景的需求。通过全面获取设备的基础信息，为后续的数据绑定、逻辑控制等功能奠定了数据基础。The basic information of the equipment covers various attribute data of the equipment, such as equipment model, specification parameters, installation location, measurement point configuration, etc. The user provides the basic information of the equipment to the acquisition module 11 by manual entry, importing into a spreadsheet, reading a database, etc. The system supports users to customize the equipment attribute fields to meet the needs of different industries and application scenarios. By comprehensively acquiring the basic information of the equipment, a data foundation is laid for subsequent data binding, logic control and other functions.
获取用户的输入信息后，获取模块11对用户输入的图片信息和基础信息进行格式检查和数据校验，以确保信息的完整性和准确性。同时，获取模块11提供错误提示和纠正引导，帮助用户高效、准确地完成信息录入过程。After obtaining the user's input information, the acquisition module 11 performs format check and data verification on the image information and basic information input by the user to ensure the integrity and accuracy of the information. At the same time, the acquisition module 11 provides error prompts and correction guidance to help users complete the information entry process efficiently and accurately.
总之，获取模块11全面获取用户提供的设备图片和基础信息，为虚拟数据标签生成系统提供了高质量、可靠的数据输入，是实现后续图形化展示和数据驱动功能的基础。In short, the acquisition module 11 comprehensively acquires the device images and basic information provided by the user, provides high-quality and reliable data input for the virtual data label generation system, and is the basis for realizing subsequent graphical display and data-driven functions.
构建模块12，用于构建抽象子图，所述抽象子图通过抽象网络构建而成，所述抽象网络为进行基础图元拆解重构的智能处理网络，且所述抽象网络设置有自适应更新的区别数据库，所述区别数据库用于存储已构建的抽象子图，将所述图片信息输入至所述抽象网络，生成抽象子图。The construction module 12 is used to construct an abstract subgraph, which is constructed by an abstract network. The abstract network is an intelligent processing network for disassembling and reconstructing basic graphic elements, and the abstract network is provided with an adaptively updated distinction database, which is used to store the constructed abstract subgraph. The image information is input into the abstract network to generate an abstract subgraph.
具体而言，构建模块12负责根据获取到的设备图片信息构建抽象子图。其中，构建模块12采用抽象网来自动生成抽象子图，提高了图形构建的效率和智能化水平。Specifically, the construction module 12 is responsible for constructing an abstract subgraph according to the acquired device image information. The construction module 12 uses an abstract network to automatically generate an abstract subgraph, thereby improving the efficiency and intelligence level of graph construction.
抽象网络是专门用于图形拆解和重构的智能处理网络，以设备图片为输入，通过对图片中的基础图形元素进行识别、提取和组合，生成表征设备结构和特征的抽象子图。与传统的人工绘图方法相比，抽象网络可以快速、准确地完成图形抽象，降低了对绘图技能的要求。The abstract network is an intelligent processing network specifically used for graphic disassembly and reconstruction. It takes a device image as input and generates an abstract subgraph that represents the device structure and features by identifying, extracting and combining the basic graphic elements in the image. Compared with traditional manual drawing methods, the abstract network can quickly and accurately complete graphic abstraction, reducing the requirements for drawing skills.
为了进一步提高抽象网络的适应性和精确度，引入了自适应更新的区别数据库。区别数据库用于存储历史生成的抽象子图样本，通过对样本的分析和学习，不断优化抽象网络的参数和策略，使其能够适应不同设备和场景的图形抽象需求。In order to further improve the adaptability and accuracy of the abstract network, an adaptively updated difference database is introduced. The difference database is used to store historically generated abstract subgraph samples. By analyzing and learning the samples, the parameters and strategies of the abstract network are continuously optimized to enable it to adapt to the graph abstraction requirements of different devices and scenarios.
具体地，当图片信息输入到抽象网络后，抽象网络首先对图片进行预处理，如噪声去除、边缘检测等，提高图像质量。然后，抽象网络利用图形识别算法对图片中的基础图元进行提取，例如识别出设备的外轮廓、关键部件、连接线等。接着，抽象网络通过图元分析和语义理解，将提取出的图元进行分类和组合，生成高级别的设备组件和结构表示。最后，抽象网络结合区别数据库中的历史样本，对生成的抽象子图进行优化和调整，以更好地符合设备的实际特征。生成的抽象子图以矢量图形的形式表示设备的结构和组成，包含了设备的关键组件、布局方式、连接关系等信息。抽象子图可以方便地与其他模块进行数据交互和视觉呈现，为后续的状态标识、虚拟数据绑定等功能提供了基础。Specifically, when the image information is input into the abstract network, the abstract network first preprocesses the image, such as noise removal and edge detection, to improve the image quality. Then, the abstract network uses the graphic recognition algorithm to extract the basic primitives in the image, such as identifying the outer contour, key components, and connecting lines of the device. Then, the abstract network classifies and combines the extracted primitives through primitive analysis and semantic understanding to generate high-level device components and structural representations. Finally, the abstract network optimizes and adjusts the generated abstract subgraphs in combination with historical samples in the difference database to better meet the actual characteristics of the device. The generated abstract subgraph represents the structure and composition of the device in the form of vector graphics, which contains information such as the key components, layout methods, and connection relationships of the device. The abstract subgraph can easily interact with other modules for data and visual presentation, providing a basis for subsequent functions such as status identification and virtual data binding.
构建模块12通过抽象网络实现了设备图形的自动抽象和智能构建，结合自适应更新的区别数据库不断优化生成效果，提升了图形构建的效率和质量，为虚拟数据标签生成系统的应用提供了支撑。The construction module 12 realizes the automatic abstraction and intelligent construction of device graphics through the abstract network, and continuously optimizes the generation effect by combining the adaptively updated difference database, thereby improving the efficiency and quality of graphics construction and providing support for the application of the virtual data label generation system.
第一解析模块13，用于解析所述基础信息，建立设备的状态标识，并通过所述状态标识更新所述抽象子图。The first parsing module 13 is used to parse the basic information, establish a status identifier of the device, and update the abstract subgraph through the status identifier.
具体而言，第一解析模块13用于解析获取模块11获取的设备基础信息，建立设备的状态标识，并根据状态标识对构建模块12生成的抽象子图进行更新。Specifically, the first parsing module 13 is used to parse the basic information of the device acquired by the acquiring module 11, establish a state identifier of the device, and update the abstract subgraph generated by the constructing module 12 according to the state identifier.
首先，第一解析模块13对设备基础信息进行语义分析和数据提取。基础信息中包含了设备的各种状态数据，如开关状态、运行模式、故障标志等。第一解析模块13通过自然语言处理和正则表达式匹配等技术，准确识别和提取这些状态数据，为后续的状态标识建立提供了数据基础。然后，第一解析模块13根据提取的状态数据，建立与之对应的状态标识。状态标识以形象直观的图形符号表示设备的不同状态，如用红色表示故障状态，绿色表示正常运行状态等。第一解析模块13维护了一套状态标识库，预定义了常见设备状态的标识符号。同时，系统也允许用户自定义状态标识，以满足特定设备和场景的需求。First, the first parsing module 13 performs semantic analysis and data extraction on the basic information of the equipment. The basic information contains various status data of the equipment, such as switch status, operation mode, fault sign, etc. The first parsing module 13 accurately identifies and extracts these status data through natural language processing and regular expression matching technologies, providing a data basis for the subsequent establishment of status identification. Then, the first parsing module 13 establishes a corresponding status identification based on the extracted status data. The status identification uses vivid and intuitive graphic symbols to represent the different states of the equipment, such as red for fault status and green for normal operation status. The first parsing module 13 maintains a set of status identification libraries, which predefines identification symbols for common equipment states. At the same time, the system also allows users to customize status identification to meet the needs of specific equipment and scenarios.
建立状态标识后，第一解析模块13将状态标识与抽象子图进行关联，实现抽象子图的状态更新。具体地，第一解析模块13分析抽象子图的结构和组成，识别出与状态标识对应的图形元素，如设备的关键部件、连接线路等。然后，根据状态标识的语义信息，第一解析模块13对相应的图形元素进行属性修改，如更改颜色、添加文字说明、显示动画效果等，直观地反映设备的实时状态。通过状态标识与抽象子图的动态关联，用户可以直观地了解设备的运行状况，快速定位故障点，提高可视化效果和交互性能。同时，状态标识的引入也为后续的监控预警、智能诊断等功能奠定了基础。After the status identifier is established, the first parsing module 13 associates the status identifier with the abstract subgraph to update the status of the abstract subgraph. Specifically, the first parsing module 13 analyzes the structure and composition of the abstract subgraph and identifies the graphic elements corresponding to the status identifier, such as key components of the equipment, connecting lines, etc. Then, according to the semantic information of the status identifier, the first parsing module 13 modifies the attributes of the corresponding graphic elements, such as changing the color, adding text descriptions, displaying animation effects, etc., to intuitively reflect the real-time status of the equipment. Through the dynamic association of the status identifier and the abstract subgraph, the user can intuitively understand the operating status of the equipment, quickly locate the fault point, and improve the visualization effect and interactive performance. At the same time, the introduction of the status identifier also lays the foundation for subsequent monitoring and early warning, intelligent diagnosis and other functions.
第一解析模块13通过解析设备基础信息，建立与设备状态对应的标识符号，并将其与抽象子图进行关联更新，实现了设备状态的可视化表达和实时映射，提升前端页面的监控效率和用户体验。The first parsing module 13 parses the basic information of the device, establishes an identification symbol corresponding to the device status, and associates and updates it with the abstract subgraph, thereby realizing the visual expression and real-time mapping of the device status and improving the monitoring efficiency and user experience of the front-end page.
第二解析模块14，用于解析所述基础信息，建立设备配置信息、现场工况数据、指标配置信息、指标统计数据，根据设备配置信息、现场工况数据、指标配置信息、指标统计数据建立虚拟数据标签。The second parsing module 14 is used to parse the basic information, establish equipment configuration information, field working condition data, indicator configuration information, and indicator statistical data, and establish virtual data tags based on the equipment configuration information, field working condition data, indicator configuration information, and indicator statistical data.
具体而言，首先，第二解析模块14对设备基础信息进行全面解析，提取设备配置信息、现场工况数据、指标配置信息、指标统计数据等多方面的数据。其中，设备配置信息包括设备的型号、规格、安装位置等静态属性数据；现场工况数据反映了设备的实时运行状态，如温度、压力、电流等测点数据；指标配置信息定义了设备的关键性能指标及其计算方法；指标统计数据则记录了设备指标的历史趋势和统计规律。第二解析模块14采用如正则表达式、关键词提取、数据映射等多种数据解析技术，从基础信息中准确无误地提取出所需的数据要素。Specifically, first, the second analysis module 14 comprehensively analyzes the basic information of the equipment and extracts various data such as equipment configuration information, field operating data, indicator configuration information, and indicator statistical data. Among them, the equipment configuration information includes static attribute data such as the model, specification, and installation location of the equipment; the field operating data reflects the real-time operating status of the equipment, such as temperature, pressure, current and other measurement point data; the indicator configuration information defines the key performance indicators of the equipment and their calculation methods; and the indicator statistical data records the historical trends and statistical laws of the equipment indicators. The second analysis module 14 uses a variety of data analysis technologies such as regular expressions, keyword extraction, and data mapping to accurately extract the required data elements from the basic information.
在提取数据的基础上，第二解析模块14按照预定义的数据模型，将提取出的数据进行结构化组织和语义关联，建立起完整的设备配置信息、现场工况数据、指标配置信息和指标统计数据的多维度数据模型。该数据模型以对象属性图的形式表示设备的静态特征和动态行为，并建立起数据要素之间的逻辑关系和计算依赖，为后续的数据分析和智能决策奠定了坚实的数据基础。On the basis of the extracted data, the second parsing module 14 organizes the extracted data into structured and semantic associations according to the predefined data model, and establishes a multi-dimensional data model of complete equipment configuration information, field working condition data, indicator configuration information and indicator statistical data. The data model represents the static characteristics and dynamic behaviors of the equipment in the form of an object attribute graph, and establishes the logical relationship and calculation dependency between the data elements, laying a solid data foundation for subsequent data analysis and intelligent decision-making.
基于建立的多维度数据模型，第二解析模块14进一步根据预定义的映射规则，生成与数据要素对应的虚拟数据标签。虚拟数据标签以自定义的标识符形式，对设备的关键数据点进行抽象表示，如“$deviceName$”表示设备名称，“$deviceCode$”表示设备编码，“$pointName$”表示测点名称，“$pointCode$”表示测点编码，“$pointValue$”表示测点值等。虚拟数据标签的生成遵循统一的命名约定和语义规则，确保标签的唯一性和一致性。Based on the established multi-dimensional data model, the second parsing module 14 further generates virtual data tags corresponding to the data elements according to predefined mapping rules. Virtual data tags abstractly represent key data points of devices in the form of custom identifiers, such as "$deviceName$" for device name, "$deviceCode$" for device code, "$pointName$" for measuring point name, "$pointCode$" for measuring point code, "$pointValue$" for measuring point value, etc. The generation of virtual data tags follows unified naming conventions and semantic rules to ensure the uniqueness and consistency of tags.
通过虚拟数据标签，抽象子图可以方便地与实际设备数据进行绑定和交互。在图形化展示和交互操作时，只需通过虚拟标签引用对应的数据要素，无需关注具体的数据采集和计算过程，提高了系统的灵活性和可扩展性。同时，虚拟数据标签的引入也为图形模板的复用和共享提供了便利，不同设备和场景可以共享相同的图形模板和标签定义，减少了重复开发的工作量。Through virtual data tags, abstract subgraphs can be easily bound and interacted with actual device data. When displaying and interacting graphically, you only need to reference the corresponding data elements through virtual tags, without having to pay attention to the specific data collection and calculation process, which improves the flexibility and scalability of the system. At the same time, the introduction of virtual data tags also facilitates the reuse and sharing of graphic templates. Different devices and scenarios can share the same graphic templates and label definitions, reducing the workload of repeated development.
通过第二解析模块14对设备基础信息的深度解析和数据建模，建立起完整的设备数据全景，并生成与数据要素对应的虚拟标签，为抽象子图的数据驱动和智能分析提供了数据支撑。Through the in-depth analysis and data modeling of the basic information of the equipment by the second analysis module 14, a complete equipment data panorama is established, and virtual labels corresponding to the data elements are generated, providing data support for the data-driven and intelligent analysis of the abstract subgraph.
搭建模块15，用于将更新后的抽象子图进行所述虚拟数据标签的动态表达式嵌入，完成抽象子图的搭建。The building module 15 is used to embed the dynamic expression of the virtual data label into the updated abstract subgraph to complete the building of the abstract subgraph.
具体而言，搭建模块15负责将第一解析模块13更新后的抽象子图与第二解析模块14生成的虚拟数据标签进行动态关联，通过在抽象子图中嵌入虚拟标签的动态表达式，实现图形与数据的深度融合，完成最终的抽象子图搭建。Specifically, the construction module 15 is responsible for dynamically associating the abstract subgraph updated by the first parsing module 13 with the virtual data labels generated by the second parsing module 14, and realizing the deep integration of graphics and data by embedding the dynamic expression of the virtual labels in the abstract subgraph, thereby completing the final construction of the abstract subgraph.
首先，搭建模块15分析更新后的抽象子图，识别出其中的关键图形元素，如设备部件、连接线路、状态标识等。同时，搭建模块15也读取第二解析模块14生成的虚拟数据标签列表，提取标签的语义信息和数据类型。然后，搭建模块15根据预定义的绑定规则，将虚拟数据标签与抽象子图的图形元素进行匹配和关联。绑定规则定义了图形元素与数据标签的对应关系，例如设备部件与其测点标签的绑定、状态标识与其判断条件标签的绑定等。搭建模块15采用图形语义分析和模式匹配等技术，自动识别和推荐最优的绑定方案，同时也允许用户手动调整和优化绑定关系。First, the construction module 15 analyzes the updated abstract subgraph and identifies key graphic elements therein, such as equipment components, connection lines, status identifiers, etc. At the same time, the construction module 15 also reads the virtual data label list generated by the second parsing module 14 to extract the semantic information and data type of the label. Then, the construction module 15 matches and associates the virtual data labels with the graphic elements of the abstract subgraph according to the predefined binding rules. The binding rules define the correspondence between graphic elements and data labels, such as the binding of equipment components to their measurement point labels, the binding of status identifiers to their judgment condition labels, etc. The construction module 15 uses technologies such as graphic semantic analysis and pattern matching to automatically identify and recommend the optimal binding solution, while also allowing users to manually adjust and optimize the binding relationship.
在完成图形元素与数据标签的绑定后，搭建模块15进一步将虚拟数据标签的动态表达式嵌入到抽象子图中。动态表达式以占位符的形式嵌入到图形元素的属性定义中，将标签的值动态填充到图形的文本属性中。通过虚拟标签动态表达式的嵌入，抽象子图实现了与实时数据的动态绑定。当设备数据发生变化时，绑定的虚拟标签会自动更新，动态表达式根据更新后的标签值重新计算图形元素的属性，并触发图形的重绘和刷新。通过动态绑定机制使得抽象子图能够实时反映设备的状态和数据变化，提供了高度交互和响应的用户体验。之后，搭建模块15对嵌入了动态表达式的抽象子图进行最终的优化和调整，如调整图形布局、优化绘制性能等，生成完整的可视化界面。搭建完成的抽象子图以组件化的形式封装，灵活地嵌入到不同的应用界面和场景中，实现图形复用和模块化开发。After completing the binding of the graphic element and the data label, the construction module 15 further embeds the dynamic expression of the virtual data label into the abstract subgraph. The dynamic expression is embedded in the attribute definition of the graphic element in the form of a placeholder, and the value of the label is dynamically filled into the text attribute of the graphic. Through the embedding of the dynamic expression of the virtual label, the abstract subgraph realizes dynamic binding with real-time data. When the device data changes, the bound virtual label is automatically updated, the dynamic expression recalculates the attributes of the graphic element according to the updated label value, and triggers the redrawing and refreshing of the graphic. The dynamic binding mechanism enables the abstract subgraph to reflect the status and data changes of the device in real time, providing a highly interactive and responsive user experience. Afterwards, the construction module 15 performs the final optimization and adjustment of the abstract subgraph embedded with the dynamic expression, such as adjusting the graphic layout, optimizing the drawing performance, etc., to generate a complete visual interface. The constructed abstract subgraph is encapsulated in a componentized form and flexibly embedded in different application interfaces and scenarios to achieve graphic reuse and modular development.
搭建模块15通过将虚拟数据标签与抽象子图进行动态绑定，并在图形中嵌入标签的动态表达式，实现了图形与数据的深度融合和实时交互，极大地提升了系统的可视化效果和用户体验。Building module 15 achieves deep integration and real-time interaction between graphics and data by dynamically binding virtual data labels with abstract subgraphs and embedding dynamic expressions of labels in graphics, greatly improving the system's visualization effect and user experience.
管理模块16，用于当引用搭建完成的抽象子图时，读取抽象子图的虚拟数据标签，并对所述虚拟数据标签赋值，根据赋值结果更新抽象子图状态，完成基于虚拟数据标签的设备管理。The management module 16 is used to read the virtual data tag of the abstract subgraph when referencing the constructed abstract subgraph, assign a value to the virtual data tag, update the abstract subgraph state according to the assignment result, and complete the device management based on the virtual data tag.
具体而言，管理模块16负责引用和管理搭建完成的抽象子图，通过对虚拟数据标签的读取和赋值，实现抽象子图状态的动态更新，完成基于虚拟标签的设备管理功能。Specifically, the management module 16 is responsible for referencing and managing the constructed abstract subgraph, and realizes the dynamic update of the abstract subgraph state by reading and assigning values to virtual data tags, thereby completing the device management function based on virtual tags.
在具体应用中，当需要引用抽象子图时，管理模块16首先读取抽象子图的定义信息，包括图形元素、绑定关系、虚拟标签等。通过解析抽象子图的元数据，管理模块16识别出其中的虚拟数据标签，并根据标签的语义信息和数据类型，生成与之对应的数据访问接口和管理控件。然后，管理模块16通过数据访问接口，从实时数据库或历史数据库中读取与虚拟标签对应的设备数据。数据访问过程遵循预定义的数据安全和权限控制策略，确保数据的机密性和完整性。管理模块16支持多种数据源的集成，包括关系型数据库、时序数据库、消息队列等，灵活地适配不同的数据环境。In specific applications, when it is necessary to reference an abstract subgraph, the management module 16 first reads the definition information of the abstract subgraph, including graphic elements, binding relationships, virtual tags, etc. By parsing the metadata of the abstract subgraph, the management module 16 identifies the virtual data tags therein, and generates corresponding data access interfaces and management controls based on the semantic information and data types of the tags. Then, the management module 16 reads the device data corresponding to the virtual tag from the real-time database or historical database through the data access interface. The data access process follows predefined data security and authority control policies to ensure the confidentiality and integrity of the data. The management module 16 supports the integration of multiple data sources, including relational databases, time series databases, message queues, etc., and flexibly adapts to different data environments.
在获取到虚拟标签的实际数据后，管理模块16根据数据类型和范围，对虚拟标签进行赋值。对于数值类型的标签，管理模块16直接将获取到的数据赋给标签；对于枚举类型的标签，管理模块16根据预定义的映射关系，将数据转换为对应的枚举值；对于布尔类型的标签，管理模块16根据数据的逻辑条件，将其转换为真或假。标签赋值过程自动进行数据类型转换和范围检查，确保数据的正确性和一致性。After obtaining the actual data of the virtual tag, the management module 16 assigns the virtual tag according to the data type and range. For numeric tags, the management module 16 directly assigns the acquired data to the tag; for enumeration tags, the management module 16 converts the data into the corresponding enumeration value according to the predefined mapping relationship; for Boolean tags, the management module 16 converts the data into true or false according to the logical condition of the data. The tag assignment process automatically performs data type conversion and range checking to ensure the correctness and consistency of the data.
虚拟标签赋值完成后，管理模块16触发抽象子图的状态更新。绑定了虚拟标签的图形元素根据标签的新值，重新计算动态表达式，并更新图形的属性和样式。例如，当温度标签的值超过预设阈值时，温度计图形的指针会自动转动到相应位置，并触发报警颜色的变化。管理模块16负责协调标签赋值与图形更新之间的时序和一致性，确保图形状态与设备数据保持同步。After the virtual label is assigned, the management module 16 triggers the state update of the abstract subgraph. The graphic element bound to the virtual label recalculates the dynamic expression according to the new value of the label and updates the properties and style of the graphic. For example, when the value of the temperature label exceeds the preset threshold, the pointer of the thermometer graphic will automatically move to the corresponding position and trigger the change of the alarm color. The management module 16 is responsible for coordinating the timing and consistency between label assignment and graphic update to ensure that the graphic state is synchronized with the device data.
通过虚拟标签的动态赋值和图形状态的自动更新，管理模块16实现了基于虚拟标签的设备管理。用户可以通过交互界面，直接操作和监视虚拟标签，而无需关注具体的设备通信和数据处理细节。管理模块16通过引用抽象子图，读取和赋值虚拟数据标签，并自动更新图形状态，实现了基于虚拟标签的设备管理。这种基于数据驱动的图形化管理方式，极大地提高了系统的易用性和可维护性，为用户提供了直观高效的设备管理手段。Through the dynamic assignment of virtual tags and the automatic update of graphic states, the management module 16 implements device management based on virtual tags. Users can directly operate and monitor virtual tags through the interactive interface without paying attention to the specific device communication and data processing details. The management module 16 implements device management based on virtual tags by referencing abstract subgraphs, reading and assigning virtual data tags, and automatically updating graphic states. This data-driven graphical management method greatly improves the ease of use and maintainability of the system, and provides users with an intuitive and efficient device management method.
进一步的，构建模块还用于：Furthermore, the building blocks are also used to:
通过预处理子网络执行图片信息的解析，根据解析结果生成图像复杂度；将所述图像复杂度同步至分解子网络，配置分解子网络的第一梯度的基础图元数量、第二梯度的基础图元数量、第三梯度的基础图元数量；根据配置完成的分解子网络进行图片信息分解，建立第一梯度基础图元、第二梯度基础图元、第三梯度基础图元；调用所述区别数据库，执行所述第二梯度基础图元和第三梯度基础图元的适配评价，根据适配评价结果和第一梯度基础图元输出抽象子图。The image information is parsed through the preprocessing subnetwork, and the image complexity is generated according to the parsing result; the image complexity is synchronized to the decomposition subnetwork, and the number of basic primitives of the first gradient, the number of basic primitives of the second gradient, and the number of basic primitives of the third gradient of the decomposition subnetwork are configured; the image information is decomposed according to the configured decomposition subnetwork, and the first gradient basic primitives, the second gradient basic primitives, and the third gradient basic primitives are established; the distinction database is called to perform adaptation evaluation of the second gradient basic primitives and the third gradient basic primitives, and an abstract sub-image is output according to the adaptation evaluation result and the first gradient basic primitives.
在一种可行的实施方式中，首先，预处理子网络首先对输入的图片信息进行解析和预处理。预处理过程包括图像去噪、边缘检测、颜色空间转换等操作，旨在提高图像质量，突出关键特征。其中，预处理子网络采用卷积神经网络等深度学习模型，通过对大量设备图片的训练，学习到鲁棒高效的图像预处理策略。在图片预处理的基础上，进一步分析图片的复杂程度，生成反映图像复杂度的定量指标。其中，图像复杂度根据图片的细节丰富程度、结构复杂性、色彩多样性等因素，通过信息熵、边缘密度、颜色直方图等特征的加权组合计算得出。然后，分解子网络负责将预处理后的图片分解为多个基础图元。为了适应不同复杂度的图片，分解子网络采用了多梯度的分解策略。第一梯度处理简单图形，提取最基本的几何元素；第二梯度处理中等复杂图形，提取组合的图形单元；第三梯度处理复杂图形，提取抽象的语义单元。分解子网络根据预处理子网络生成的图像复杂度，自适应地配置各梯度的基础图元数量，以平衡分解精度和计算效率。通过逐梯度的图元分解，分解子网络从图片中提取出三个层次的基础图元：第一梯度基础图元、第二梯度基础图元和第三梯度基础图元。这些图元以矢量图形的形式表示，包含了位置、大小、方向等属性信息，为后续的图元组合提供了基础单元。In a feasible implementation, first, the preprocessing subnetwork first parses and preprocesses the input image information. The preprocessing process includes operations such as image denoising, edge detection, and color space conversion, which aims to improve image quality and highlight key features. Among them, the preprocessing subnetwork adopts deep learning models such as convolutional neural networks, and learns robust and efficient image preprocessing strategies through training on a large number of device images. On the basis of image preprocessing, the complexity of the image is further analyzed to generate a quantitative index reflecting the complexity of the image. Among them, the image complexity is calculated by a weighted combination of features such as information entropy, edge density, and color histogram based on factors such as the richness of details, structural complexity, and color diversity of the image. Then, the decomposition subnetwork is responsible for decomposing the preprocessed image into multiple basic primitives. In order to adapt to images of different complexities, the decomposition subnetwork adopts a multi-gradient decomposition strategy. The first gradient processes simple graphics and extracts the most basic geometric elements; the second gradient processes medium-complex graphics and extracts combined graphic units; the third gradient processes complex graphics and extracts abstract semantic units. The decomposition subnetwork adaptively configures the number of basic primitives of each gradient according to the image complexity generated by the preprocessing subnetwork to balance the decomposition accuracy and computational efficiency. Through gradient-by-gradient primitive decomposition, the decomposition subnetwork extracts three levels of basic primitives from the image: first gradient basic primitives, second gradient basic primitives, and third gradient basic primitives. These primitives are represented in the form of vector graphics, containing attribute information such as position, size, and direction, providing basic units for subsequent primitive combinations.
在图元分解完成后，从区别数据库中读取已有的图元组合模板，然后将提取出的第二梯度基础图元和第三梯度基础图元与模板进行匹配，计算图元与模板之间的相似度。其中，相似度根据图元的几何属性、拓扑关系、语义标签等多种因素，采用图形匹配算法进行计算。根据适配评价结果，从第二梯度基础图元和第三梯度基础图元中选取相似度最高的部分，并将其与第一梯度基础图元进行组合，生成最终的抽象子图。抽象子图以矢量图形的形式表示设备的关键组成部分，保留了设备的基本结构和语义信息，同时也剔除了冗余的细节，实现了对设备图形的高度抽象。After the primitive decomposition is completed, the existing primitive combination template is read from the difference database, and then the extracted second gradient basic primitive and third gradient basic primitive are matched with the template to calculate the similarity between the primitive and the template. The similarity is calculated using a graph matching algorithm based on multiple factors such as the geometric properties, topological relationships, and semantic labels of the primitives. According to the adaptation evaluation results, the parts with the highest similarity are selected from the second gradient basic primitive and the third gradient basic primitive, and combined with the first gradient basic primitive to generate the final abstract subgraph. The abstract subgraph represents the key components of the device in the form of vector graphics, retaining the basic structure and semantic information of the device, while also eliminating redundant details, achieving a high degree of abstraction of the device graphics.
构建模块12通过引入预处理子网络、分解子网络和适配评价，实现了自适应的多梯度图元分解和优化组合，极大地提高了抽象子图的生成质量和效率。通过渐进式的图形抽象方法，充分利用了数据库中的历史样本，同时也兼顾了图形分解的灵活性和多样性，为后续的虚拟标签绑定奠定了图形基础。By introducing the preprocessing sub-network, decomposition sub-network and adaptation evaluation, the construction module 12 realizes the adaptive multi-gradient primitive decomposition and optimization combination, which greatly improves the generation quality and efficiency of the abstract sub-graph. Through the progressive graph abstraction method, the historical samples in the database are fully utilized, while taking into account the flexibility and diversity of graph decomposition, laying a graph foundation for the subsequent virtual label binding.
进一步的，本申请实施例还包括：Furthermore, the embodiment of the present application also includes:
顺序排序惩罚系数构建模块，用于获取第二梯度基础图元和第三梯度基础图元内的顺序排序，建立顺序排序惩罚系数；梯度信任值构建模块，用于获取第二梯度基础图元和第三梯度基础图元的梯度信任值，通过所述顺序排序惩罚系数对梯度信任值惩罚调整，并根据适配评价结果进行惩罚调整结果加权计算，根据加权计算结果进行顺序筛选，基于顺序筛选结果和第一梯度基础图元构建抽象子图。A sequence sorting penalty coefficient construction module is used to obtain the sequence sorting within the second gradient basic primitive and the third gradient basic primitive, and establish a sequence sorting penalty coefficient; a gradient trust value construction module is used to obtain the gradient trust value of the second gradient basic primitive and the third gradient basic primitive, adjust the gradient trust value penalty by the sequence sorting penalty coefficient, and perform weighted calculation of the penalty adjustment result according to the adaptation evaluation result, perform sequence screening according to the weighted calculation result, and construct an abstract subgraph based on the sequence screening result and the first gradient basic primitive.
在一种优选的实施方式中，引入顺序排序惩罚系数构建模块和梯度信任值构建模块，用于进一步优化第二梯度基础图元和第三梯度基础图元的筛选和组合，提高抽象子图的构建质量。In a preferred embodiment, a sequential sorting penalty coefficient construction module and a gradient trust value construction module are introduced to further optimize the screening and combination of the second gradient basic primitives and the third gradient basic primitives, thereby improving the construction quality of the abstract subgraph.
其中，顺序排序惩罚系数构建模块负责分析第二梯度基础图元和第三梯度基础图元的顺序关系，并据此建立顺序排序惩罚系数。具体地，顺序排序惩罚系数构建模块首先提取每个图元的位置、方向、连接关系等几何属性，然后根据属性的相似度和邻接关系，计算图元之间的顺序距离。顺序距离越大，表示图元在原始图片中的位置越远，同时出现在抽象子图中的可能性越小。根据计算得到的顺序距离，顺序排序惩罚系数构建模块生成顺序排序惩罚系数矩阵。惩罚系数矩阵的每个元素表示两个图元的顺序距离，距离越大，惩罚系数越高。顺序排序惩罚系数矩阵反映了图元之间的相对位置关系，为后续的图元筛选提供重要依据。Among them, the sequential sorting penalty coefficient construction module is responsible for analyzing the sequential relationship between the second gradient basic primitive and the third gradient basic primitive, and establishing the sequential sorting penalty coefficient accordingly. Specifically, the sequential sorting penalty coefficient construction module first extracts the geometric attributes of each primitive, such as position, direction, and connection relationship, and then calculates the sequential distance between the primitives based on the similarity and adjacency of the attributes. The larger the sequential distance, the farther the primitive is in the original image, and the less likely it is to appear in the abstract sub-image at the same time. According to the calculated sequential distance, the sequential sorting penalty coefficient construction module generates a sequential sorting penalty coefficient matrix. Each element of the penalty coefficient matrix represents the sequential distance between two primitives. The larger the distance, the higher the penalty coefficient. The sequential sorting penalty coefficient matrix reflects the relative position relationship between the primitives, providing an important basis for subsequent primitive screening.
梯度信任值构建模块则用于评估第二梯度基础图元和第三梯度基础图元的可信程度，生成梯度信任值。初始时，梯度信任值构建模块根据图元的分解层级、匹配度等因素，赋予每个图元一个基础信任值。层级越高、匹配度越高的图元，其基础信任值越大，表示它们更有可能成为抽象子图的组成部分。然后，梯度信任值构建模块利用顺序排序惩罚系数对基础信任值进行惩罚调整。具体地，对于每个图元，梯度信任值构建模块计算其与其他图元的顺序排序惩罚系数之和，作为惩罚项。惩罚项越大，表示该图元与其他图元的顺序关系越不协调，其基础信任值受到的惩罚越大。梯度信任值构建模块通过减去惩罚项，得到图元的调整后信任值。The gradient trust value construction module is used to evaluate the trustworthiness of the second gradient basic primitive and the third gradient basic primitive and generate a gradient trust value. Initially, the gradient trust value construction module assigns a basic trust value to each primitive based on factors such as the decomposition level and matching degree of the primitive. The higher the level and the higher the matching degree of the primitive, the larger its basic trust value, indicating that they are more likely to become part of the abstract subgraph. Then, the gradient trust value construction module uses the order sorting penalty coefficient to penalize the basic trust value. Specifically, for each primitive, the gradient trust value construction module calculates the sum of its order sorting penalty coefficients with other primitives as a penalty term. The larger the penalty term, the more inconsistent the order relationship between the primitive and other primitives, and the greater the penalty on its basic trust value. The gradient trust value construction module obtains the adjusted trust value of the primitive by subtracting the penalty term.
在此基础上，梯度信任值构建模块进一步结合适配评价结果，对调整后的信任值进行加权计算。适配评价结果反映了图元与数据库中历史样本的匹配程度，匹配度越高，图元的权重系数越大。梯度信任值构建模块将调整后的信任值与对应的权重系数相乘，得到图元的最终梯度信任值。之后，梯度信任值构建模块根据梯度信任值对第二梯度基础图元和第三梯度基础图元进行顺序筛选。信任值越高的图元，越优先被选中。筛选过程遵循预设的组合规则和约束条件，如图元之间的连接兼容性、语义一致性等。梯度信任值构建模块将筛选出的最优图元与第一梯度基础图元进行组合，生成最终的抽象子图。On this basis, the gradient trust value construction module further combines the adaptation evaluation results to perform weighted calculation on the adjusted trust value. The adaptation evaluation results reflect the degree of match between the primitive and the historical samples in the database. The higher the match, the greater the weight coefficient of the primitive. The gradient trust value construction module multiplies the adjusted trust value with the corresponding weight coefficient to obtain the final gradient trust value of the primitive. Afterwards, the gradient trust value construction module sequentially screens the second gradient basic primitive and the third gradient basic primitive according to the gradient trust value. The higher the trust value of the primitive, the higher the priority it is selected. The screening process follows the preset combination rules and constraints, such as connection compatibility and semantic consistency between primitives. The gradient trust value construction module combines the selected optimal primitive with the first gradient basic primitive to generate the final abstract subgraph.
通过顺序排序惩罚和梯度信任值调整，在图元筛选和组合过程中，充分考虑了图元的顺序关系和可信程度，有效提高了抽象子图的准确性和合理性，同时也降低了计算复杂度，使得抽象子图的构建更加高效和可靠。Through sequential sorting penalty and gradient trust value adjustment, the order relationship and credibility of the primitives are fully considered in the process of primitive screening and combination, which effectively improves the accuracy and rationality of the abstract subgraph, while also reducing the computational complexity, making the construction of the abstract subgraph more efficient and reliable.
进一步的，梯度信任值构建模块还用于：Furthermore, the gradient trust value building module is also used to:
判断区别数据库的预设大小是否满足预设阈值；若所述区别数据库的预设大小满足预设阈值，则激活所述抽象网络的附加生成子网络；在根据顺序筛选结果和第一梯度基础图元构建抽象子图之前，通过所述附加生成子网络基于图片信息进行设备匹配，根据设备匹配结果建立附加特征；根据所述附加特征、顺序筛选结果和第一梯度基础图元完成抽象子图构建。Determine whether a preset size of a distinction database meets a preset threshold; if the preset size of the distinction database meets the preset threshold, activate an additional generation subnetwork of the abstract network; before constructing an abstract subgraph according to a sequential screening result and a first gradient basic primitive, perform device matching based on image information through the additional generation subnetwork, and establish additional features according to the device matching results; complete the construction of the abstract subgraph according to the additional features, the sequential screening result and the first gradient basic primitive.
在一种优选的实施方式中，梯度信任值构建模块还包括阈值判断和附加生成子网络，用于在区别数据库达到一定规模时，引入附加的设备匹配信息，进一步增强抽象子图的构建能力。In a preferred embodiment, the gradient trust value construction module also includes a threshold judgment and an additional generation sub-network, which is used to introduce additional device matching information when the distinction database reaches a certain scale, so as to further enhance the construction capability of the abstract sub-graph.
首先，获取区别数据库的当前大小，即数据库中已存储的抽象子图样本数量。然后，将当前大小与预设阈值进行比较。预设阈值是经验值，表示区别数据库达到该规模时，已经积累了足够多的历史样本，可以支持较为完善和准确的图形抽象任务。如果区别数据库的大小小于或等于预设阈值，则触发附加生成子网络，将其激活。这种情况下，区别数据库中的历史样本数量相对有限，仅依靠现有样本进行图形理解和匹配，难以覆盖所有的设备类型和图形特征，导致抽象子图的构建质量下降。First, the current size of the difference database is obtained, that is, the number of abstract subgraph samples stored in the database. Then, the current size is compared with the preset threshold. The preset threshold is an empirical value, which means that when the difference database reaches this size, enough historical samples have been accumulated to support a relatively complete and accurate graph abstraction task. If the size of the difference database is less than or equal to the preset threshold, the additional generative subnetwork is triggered and activated. In this case, the number of historical samples in the difference database is relatively limited. It is difficult to cover all device types and graph features by relying solely on existing samples for graph understanding and matching, resulting in a decrease in the construction quality of the abstract subgraph.
为了弥补样本不足的问题，附加生成子网络被引入作为一种补充手段。附加生成子网络是基于深度学习的图像理解模型，通过在大规模通用图像数据集上进行预训练，学习到了丰富的视觉特征和语义概念。当面对新的设备图片时，即使在区别数据库样本有限的情况下，附加生成子网络利用其通用的图像理解能力，捕捉到图片中的关键信息和抽象特征。在抽象子图构建的阶段，即在根据顺序筛选结果和第一梯度基础图元生成抽象子图之前，附加生成子网络对输入的原始设备图片进行分析和理解。通过卷积神经网络提取图片的深层特征，并结合预训练的语义知识，输出图片所蕴含的设备类型、功能属性等高层信息，生成附加特征。In order to make up for the problem of insufficient samples, an additional generative subnetwork is introduced as a supplementary means. The additional generative subnetwork is an image understanding model based on deep learning. It has learned rich visual features and semantic concepts through pre-training on a large-scale general image dataset. When faced with new device images, the additional generative subnetwork uses its general image understanding ability to capture key information and abstract features in the image even when the samples in the difference database are limited. In the stage of abstract subgraph construction, that is, before generating an abstract subgraph based on the sequential screening results and the first gradient basic primitives, the additional generative subnetwork analyzes and understands the input original device image. The deep features of the image are extracted through the convolutional neural network, and combined with the pre-trained semantic knowledge, the high-level information such as the device type and functional attributes contained in the image is output to generate additional features.
之后，梯度信任值构建模块将附加特征与顺序筛选结果和第一梯度基础图元相结合，共同指导抽象子图的生成。附加特征弥补了区别数据库样本不足可能导致的图形理解盲区，为抽象子图的构建提供了更全面、更准确的参考依据，有效提升了生成质量。Afterwards, the gradient trust value construction module combines the additional features with the sequential screening results and the first gradient basic primitives to jointly guide the generation of abstract subgraphs. The additional features make up for the blind spots in graph understanding that may be caused by insufficient samples in the distinction database, providing a more comprehensive and accurate reference for the construction of abstract subgraphs, effectively improving the generation quality.
通过阈值判断和附加生成子网络，根据区别数据库的实际规模，自适应地调整图形理解和匹配策略。在样本充足时，直接利用区别数据库进行精准匹配；在样本不足时，则通过附加生成子网络补充通用特征，维持抽象子图构建的可靠性。通过动态适配，使其能够在不同的数据条件下，稳定地生成高质量的抽象子图，为后续的虚拟数据标签绑定提供坚实基础。Through threshold judgment and additional generative subnetworks, the graph understanding and matching strategies are adaptively adjusted according to the actual size of the difference database. When there are sufficient samples, the difference database is directly used for accurate matching; when there are insufficient samples, general features are supplemented by additional generative subnetworks to maintain the reliability of abstract subgraph construction. Through dynamic adaptation, it can stably generate high-quality abstract subgraphs under different data conditions, providing a solid foundation for subsequent virtual data label binding.
进一步的，管理模块还用于：Furthermore, the management module is also used to:
配置字符串替换策略和数值替换策略，根据所述字符串替换策略和/或数值替换策略对所述虚拟数据标签赋值。A character string replacement strategy and a numerical value replacement strategy are configured, and a value is assigned to the virtual data label according to the character string replacement strategy and/or the numerical value replacement strategy.
在一种可行的实施方式中，字符串替换策略定义了如何将设备的属性信息、状态描述等字符串类型的数据映射到虚拟标签中。例如，将设备的型号名称映射到虚拟标签“\$设备型号\$”中，将设备的位置描述映射到“\$设备位置\$”中。通过字符串替换，抽象子图中的文本标签动态地显示设备的实际属性信息。数值替换策略则定义了如何将设备的测点数据、统计指标等数值类型的数据映射到虚拟标签中。例如，将设备的温度测点映射到虚拟标签“\$温度值\$”中，将设备的效率指标映射到“\$效率值\$”中。通过数值替换，抽象子图中的数值标签实时反映设备的运行状态和性能指标。提供可视化的配置界面，允许用户自定义字符串和数值的替换规则。用户可通过简单的拖拽操作，将设备属性、测点等数据源与虚拟标签进行映射，并设置相应的格式化选项，如小数位数、单位等，生成相应的映射脚本和转换函数，以便在运行时执行数据替换。In a feasible implementation, the string replacement strategy defines how to map string-type data such as device attribute information and status description to virtual labels. For example, the model name of the device is mapped to the virtual label "\$device model\$", and the location description of the device is mapped to "\$device location\$". Through string replacement, the text label in the abstract subgraph dynamically displays the actual attribute information of the device. The value replacement strategy defines how to map the numerical data such as the device's measurement point data and statistical indicators to the virtual label. For example, the temperature measurement point of the device is mapped to the virtual label "\$temperature value\$", and the efficiency index of the device is mapped to "\$efficiency value\$". Through numerical replacement, the numerical label in the abstract subgraph reflects the operating status and performance indicators of the device in real time. Provide a visual configuration interface to allow users to customize the replacement rules of strings and values. Users can map data sources such as device attributes and measurement points to virtual labels through simple drag and drop operations, and set corresponding formatting options such as decimal places, units, etc., to generate corresponding mapping scripts and conversion functions to perform data replacement at runtime.
随后，在系统运行过程中，根据定义的替换策略，对虚拟数据标签进行实际的赋值操作。具体地，通过数据访问接口，实时获取设备的属性信息和运行数据，并根据替换策略中定义的映射关系，将获取到的数据动态填充到对应的虚拟标签中。对于字符串类型的虚拟标签，执行字符串替换，将设备属性信息替换到标签的占位符位置。替换过程自动进行字符串格式化和截断，以适应标签的显示要求；对于数值类型的虚拟标签，执行数值替换，将设备数据转换为指定格式的数值，并填充到标签中，数值替换过程会进行必要的数据类型转换、精度控制和范围检查，确保赋值的正确性和合理性。Subsequently, during the operation of the system, the virtual data tags are actually assigned according to the defined replacement strategy. Specifically, the attribute information and operation data of the device are obtained in real time through the data access interface, and the acquired data is dynamically filled into the corresponding virtual tag according to the mapping relationship defined in the replacement strategy. For virtual tags of string type, string replacement is performed to replace the device attribute information with the placeholder position of the tag. The replacement process automatically formats and truncates the string to meet the display requirements of the label; for virtual tags of numeric type, numeric replacement is performed to convert the device data into a numeric value of the specified format and fill it into the label. The numeric replacement process will perform necessary data type conversion, precision control and range checking to ensure the correctness and rationality of the assignment.
在虚拟标签赋值完成后，触发抽象子图的重绘和更新，使得图形界面能够实时反映设备的最新状态和数据。通过定期的数据获取和标签赋值，管理模块16实现了虚拟数据标签与实际设备数据的动态同步，确保了系统呈现的实时性和准确性。After the virtual tag assignment is completed, the abstract sub-graph is redrawn and updated, so that the graphical interface can reflect the latest status and data of the device in real time. Through regular data acquisition and tag assignment, the management module 16 realizes the dynamic synchronization of virtual data tags and actual device data, ensuring the real-time and accuracy of the system presentation.
综上所述，管理模块16通过引入替换策略配置和标签赋值机制，增强了虚拟数据标签的灵活性和适用性。用户可根据具体的应用场景和数据特点，定制个性化的替换策略，实现虚拟标签与设备数据的无缝对接。同时，标签赋值的自动化和实时性，也提高了系统的运行效率和响应速度。基于数据驱动的动态标签更新，使得抽象子图能够随设备状态的变化而自适应调整，为用户提供了实时、准确、直观的设备监控和管理体验。In summary, the management module 16 enhances the flexibility and applicability of virtual data tags by introducing replacement strategy configuration and label assignment mechanism. Users can customize personalized replacement strategies according to specific application scenarios and data characteristics to achieve seamless connection between virtual tags and device data. At the same time, the automation and real-time nature of label assignment also improves the operating efficiency and response speed of the system. Based on data-driven dynamic label updates, the abstract subgraph can be adaptively adjusted as the device status changes, providing users with real-time, accurate, and intuitive device monitoring and management experience.
进一步的，本申请实施例还包括优化模块，用于：Furthermore, the embodiment of the present application also includes an optimization module, which is used to:
获取前端页面内的抽象子图信息，对所述抽象子图信息进行图形元素识别，提取图形分布特征；根据所述图形分布特征进行图形元素间距、对齐度和分布分析，根据分布分析结果进行布局优化。The abstract sub-graph information in the front-end page is obtained, graphic elements are identified for the abstract sub-graph information, and graphic distribution characteristics are extracted; the graphic element spacing, alignment and distribution analysis are performed according to the graphic distribution characteristics, and the layout is optimized according to the distribution analysis results.
在一种可行的实施方式中，虚拟数据标签生成系统引入了优化模块，用于对生成的抽象子图进行布局优化，提高图形界面的美观性和可读性。In a feasible implementation, the virtual data label generation system introduces an optimization module for optimizing the layout of the generated abstract subgraph to improve the aesthetics and readability of the graphical interface.
优化模块首先获取前端页面内嵌入的所有抽象子图的信息，包括子图的位置、大小、内部组成等。然后，优化模块对每个抽象子图进行深入分析，识别出其中包含的图形元素，如几何图形、文本标签、连接线等。通过图形语义分割和模式匹配等技术，优化模块准确提取出子图的图形组成和结构信息。在完成图形元素识别后，优化模块进一步提取图形分布特征。图形分布特征描述了图形元素在子图中的空间排布规律，包括元素间距、对齐度、对称性等几何属性。优化模块通过计算元素之间的欧氏距离、夹角、重心位置等指标，构建出反映图形分布的特征向量。The optimization module first obtains the information of all abstract subgraphs embedded in the front-end page, including the position, size, internal composition, etc. of the subgraph. Then, the optimization module conducts an in-depth analysis of each abstract subgraph to identify the graphic elements contained therein, such as geometric figures, text labels, connecting lines, etc. Through techniques such as graphic semantic segmentation and pattern matching, the optimization module accurately extracts the graphic composition and structural information of the subgraph. After completing the identification of graphic elements, the optimization module further extracts the graphic distribution features. The graphic distribution features describe the spatial arrangement of graphic elements in the subgraph, including geometric properties such as element spacing, alignment, and symmetry. The optimization module constructs a feature vector reflecting the distribution of graphics by calculating indicators such as the Euclidean distance, angle, and center of gravity between elements.
基于提取的图形分布特征，优化模块对子图的布局进行全面评估和优化。首先，优化模块分析图形元素之间的间距是否合理，过大或过小的间距都会影响图形的美观和清晰度。优化模块通过计算元素间距的均值和方差，识别出间距异常的区域，并通过调整元素的大小和位置，使得间距分布更加均匀合理。其次，优化模块评估图形元素的对齐度。良好的对齐可以提高图形的整体美感和协调性。优化模块通过分析元素的边界和中心线，检测出存在错位、偏斜等对齐问题的元素。针对这些元素，优化模块通过平移、旋转等几何变换，使其与相邻元素对齐，提升整体的视觉效果。此外，优化模块还分析图形元素的分布均衡性。均衡的布局可以使得图形重心稳定，信息分布合理。优化模块通过计算元素的空间密度和分布熵，评估布局的均衡程度。对于分布不均的区域，优化模块通过调整元素的间距和位置，使得布局更加平衡协调。在完成间距、对齐度和分布均衡性分析后，得到分布分析结果，优化模块综合考虑分布分析结果，生成布局优化方案，在满足布局约束条件的前提下，找到布局评估指标的全局最优解。优化模块再根据布局优化方案，调整抽象子图中图形元素的位置和属性，生成优化后的布局。Based on the extracted graphic distribution features, the optimization module comprehensively evaluates and optimizes the layout of the sub-graph. First, the optimization module analyzes whether the spacing between graphic elements is reasonable. Too large or too small spacing will affect the beauty and clarity of the graphics. The optimization module identifies the areas with abnormal spacing by calculating the mean and variance of the element spacing, and adjusts the size and position of the elements to make the spacing distribution more uniform and reasonable. Secondly, the optimization module evaluates the alignment of graphic elements. Good alignment can improve the overall beauty and coordination of the graphics. The optimization module detects elements with alignment problems such as misalignment and skewness by analyzing the boundaries and center lines of the elements. For these elements, the optimization module aligns them with adjacent elements through geometric transformations such as translation and rotation to improve the overall visual effect. In addition, the optimization module also analyzes the distribution balance of graphic elements. A balanced layout can make the center of gravity of the graphics stable and the information distribution reasonable. The optimization module evaluates the balance of the layout by calculating the spatial density and distribution entropy of the elements. For unevenly distributed areas, the optimization module adjusts the spacing and position of the elements to make the layout more balanced and coordinated. After completing the spacing, alignment and distribution balance analysis, the distribution analysis results are obtained. The optimization module comprehensively considers the distribution analysis results and generates a layout optimization plan. Under the premise of meeting the layout constraints, the global optimal solution of the layout evaluation index is found. The optimization module then adjusts the position and attributes of the graphic elements in the abstract subgraph according to the layout optimization plan to generate an optimized layout.
通过优化模块的布局优化，能够智能调整抽象子图的视觉呈现，使其更加美观、清晰、合理。优化后的布局能够充分利用空间，合理组织图形元素，突出关键信息，提高用户的视觉体验。By optimizing the layout of the optimization module, the visual presentation of the abstract sub-graph can be intelligently adjusted to make it more beautiful, clear and reasonable. The optimized layout can make full use of the space, reasonably organize the graphic elements, highlight the key information and improve the user's visual experience.
进一步的，根据分布分析结果进行布局优化，还包括：Furthermore, layout optimization is performed according to the distribution analysis results, and further includes:
根据所述抽象子图信息进行关键抽象子图识别，建立关键抽象子图标识；通过所述关键抽象子图标识进行位置优化，生成第一优化结果；基于所述第一优化结果对分布分析结果调整，完成布局优化。Key abstract subgraphs are identified according to the abstract subgraph information to establish key abstract subgraph identifiers; position optimization is performed using the key abstract subgraph identifiers to generate a first optimization result; and distribution analysis results are adjusted based on the first optimization result to complete layout optimization.
在一种优选的实施方式中，优化模块在根据图形分布特征进行布局优化时，引入关键抽象子图识别和优先布局机制，以进一步提高布局优化的针对性和有效性。In a preferred embodiment, when performing layout optimization according to the graph distribution characteristics, the optimization module introduces key abstract subgraph identification and priority layout mechanism to further improve the pertinence and effectiveness of layout optimization.
优化模块首先对前端页面内的所有抽象子图进行分析，识别出其中的关键抽象子图。关键抽象子图是指在页面布局和信息传递中起核心作用的抽象子图，通常具有较大的尺寸、复杂的结构、丰富的信息量等特点。优化模块通过综合考虑抽象子图的视觉属性、交互行为、业务重要性等因素，建立关键抽象子图的评分模型。评分模型中包含多个权重参数，用于量化不同因素对关键程度的影响。通过模型计算，优化模块得到每个抽象子图的关键度评分，并选取得分最高的前N个抽象子图作为关键抽象子图，建立关键抽象子图标识。在识别出关键抽象子图后，优化模块优先对关键抽象子图的布局进行优化。通过关键抽象子图标识，优化模块在布局优化过程中给予关键抽象子图更高的权重和优先级，确保它们的布局质量最优。具体地，优化模块重新计算关键抽象子图的图形分布特征，并将其作为优化的主要参考依据。在间距、对齐度和分布均衡性优化时，优化模块优先满足关键抽象子图的布局要求，并以此为基础，调整其他抽象子图的布局。通过以关键抽象子图为中心的布局优化策略，优化模块生成第一优化结果，实现了关键内容的突出展示和重点优化。The optimization module first analyzes all abstract subgraphs in the front-end page and identifies the key abstract subgraphs. Key abstract subgraphs refer to abstract subgraphs that play a core role in page layout and information transmission, and usually have characteristics such as large size, complex structure, and rich information. The optimization module establishes a scoring model for key abstract subgraphs by comprehensively considering factors such as the visual attributes, interactive behavior, and business importance of abstract subgraphs. The scoring model contains multiple weight parameters to quantify the impact of different factors on the criticality. Through model calculation, the optimization module obtains the criticality score of each abstract subgraph, selects the top N abstract subgraphs with the highest scores as key abstract subgraphs, and establishes key abstract subgraph identifiers. After identifying the key abstract subgraphs, the optimization module prioritizes the layout of the key abstract subgraphs. Through the key abstract subgraph identifier, the optimization module gives higher weights and priorities to the key abstract subgraphs during the layout optimization process to ensure that their layout quality is optimal. Specifically, the optimization module recalculates the graphic distribution characteristics of the key abstract subgraphs and uses them as the main reference for optimization. When optimizing spacing, alignment, and distribution balance, the optimization module prioritizes the layout requirements of key abstract subgraphs, and based on this, adjusts the layout of other abstract subgraphs. Through the layout optimization strategy centered on key abstract subgraphs, the optimization module generates the first optimization result, achieving prominent display and key optimization of key content.
然后，优化模块以第一优化结果为基础，对原有的图形分布分析结果进行调整和修正。第一优化结果反映了关键抽象子图的最优布局方案，其图形分布特征代表了页面布局的核心骨架。优化模块将第一优化结果的图形分布特征作为新的参考标准，对其他抽象子图的布局进行二次优化。在间距、对齐度和分布均衡性分析时，优化模块将非关键抽象子图的布局与第一优化结果进行对比，识别出布局差异较大的区域，并通过调整非关键抽象子图的位置和属性，使其与第一优化结果更加协调一致。通过基于关键抽象子图优化结果的布局调整，优化模块最终得到全局最优的页面布局方案。Then, the optimization module adjusts and corrects the original graphic distribution analysis results based on the first optimization result. The first optimization result reflects the optimal layout plan of the key abstract subgraph, and its graphic distribution characteristics represent the core skeleton of the page layout. The optimization module uses the graphic distribution characteristics of the first optimization result as a new reference standard to perform secondary optimization on the layout of other abstract subgraphs. When analyzing spacing, alignment, and distribution balance, the optimization module compares the layout of non-key abstract subgraphs with the first optimization result, identifies areas with large layout differences, and adjusts the position and attributes of non-key abstract subgraphs to make them more consistent with the first optimization result. Through layout adjustments based on the optimization results of key abstract subgraphs, the optimization module ultimately obtains the globally optimal page layout plan.
通过引入关键抽象子图识别和优先布局机制，实现了更加智能、更加精准的布局优化。关键抽象子图代表了页面中最重要、最关键的信息载体，对用户的视觉感知和交互操作有决定性影响。优化模块通过优先保证关键抽象子图的布局质量，并以此为基准优化其他抽象子图的布局，使得页面整体的信息层次更加清晰，关键内容更加突出，布局更加合理。同时，关键抽象子图的引入也为布局优化提供了更加明确的方向和目标，减少了优化过程中的盲目搜索和无效计算，提高了优化效率。By introducing the key abstract subgraph identification and priority layout mechanism, a more intelligent and accurate layout optimization is achieved. The key abstract subgraph represents the most important and critical information carrier in the page, and has a decisive influence on the user's visual perception and interactive operations. The optimization module prioritizes the layout quality of the key abstract subgraph and uses it as a benchmark to optimize the layout of other abstract subgraphs, making the overall information hierarchy of the page clearer, the key content more prominent, and the layout more reasonable. At the same time, the introduction of key abstract subgraphs also provides a clearer direction and goal for layout optimization, reduces blind searches and invalid calculations during the optimization process, and improves optimization efficiency.
进一步的，本申请实施例还包括预警模块，用于：Furthermore, the embodiment of the present application also includes an early warning module, which is used to:
根据所述抽象子图信息进行设备的全局异常分析，建立异常标识；根据所述异常标识进行前端页面的显示预警。A global abnormality analysis of the device is performed based on the abstract sub-graph information, and an abnormality mark is established; and a display warning of the front-end page is performed based on the abnormality mark.
在一种可行的实施方式中，虚拟数据标签生成系统引入预警模块，用于根据抽象子图信息对设备的运行状态进行全局分析，及时发现和预警异常情况，提高安全性和可靠性。In a feasible implementation, the virtual data tag generation system introduces an early warning module for globally analyzing the operating status of the equipment based on the abstract subgraph information, timely discovering and warning of abnormal situations, and improving safety and reliability.
预警模块实时监测前端页面内的所有抽象子图，获取其中包含的设备运行信息。通过解析虚拟数据标签与真实设备数据的绑定关系，预警模块从抽象子图中提取出设备的状态参数、性能指标、告警信号等关键数据。基于获取的设备运行数据，预警模块对设备的整体运行状态进行全局异常分析。其中，全局异常分析采用多维度、多指标的综合评估方法，从不同角度评估设备的健康水平和风险程度。预警模块通过建立设备运行的正常模型，并实时将采集的数据与正常模型进行比对，识别出偏离正常工作状态的异常点。异常分析涉及多个维度，包括单个设备的异常检测、设备间的关联异常分析、全局的趋势和模式挖掘等。预警模块采用统计学方法、机器学习算法等技术手段，构建异常检测模型，自动发现设备运行中的各类异常问题，并生成异常标识。异常标识包含了异常的类型、严重程度、发生时间、关联设备等多种属性信息。The early warning module monitors all abstract subgraphs in the front-end page in real time and obtains the equipment operation information contained therein. By parsing the binding relationship between virtual data tags and real equipment data, the early warning module extracts key data such as equipment status parameters, performance indicators, and alarm signals from the abstract subgraph. Based on the acquired equipment operation data, the early warning module performs global abnormality analysis on the overall operation status of the equipment. Among them, the global abnormality analysis adopts a comprehensive evaluation method with multiple dimensions and multiple indicators to evaluate the health level and risk level of the equipment from different angles. The early warning module establishes a normal model for equipment operation and compares the collected data with the normal model in real time to identify abnormal points that deviate from the normal working state. Abnormal analysis involves multiple dimensions, including abnormal detection of a single device, analysis of associated abnormalities between devices, and global trend and pattern mining. The early warning module uses technical means such as statistical methods and machine learning algorithms to build an abnormality detection model, automatically discover various abnormal problems in equipment operation, and generate abnormal identification. The abnormal identification contains a variety of attribute information such as the type, severity, occurrence time, and associated equipment of the abnormality.
针对不同类型和不同严重程度的异常，预警模块定义了相应的预警策略和处理流程。当识别出异常标识后，预警模块根据预警策略，在前端页面上触发相应的显示预警。前端显示预警的方式多种多样，包括弹窗提示、声音告警、图标闪烁、颜色变化等。预警模块根据异常的严重程度和用户的设置偏好，选择合适的预警方式，以最直观、最有效的方式提醒用户注意异常情况。例如，对于严重的故障异常，预警模块弹出警示对话框，并伴有刺耳的告警音；对于一般的偏离异常，预警模块在相应的抽象子图上显示黄色的警告图标，并在悬浮提示中显示具体的异常信息。For abnormalities of different types and severity, the early warning module defines corresponding early warning strategies and processing procedures. When the abnormal identification is identified, the early warning module triggers the corresponding display warning on the front-end page according to the early warning strategy. There are various ways to display early warnings on the front end, including pop-up prompts, sound alarms, icon flashing, color changes, etc. The early warning module selects the appropriate early warning method according to the severity of the abnormality and the user's setting preferences, and reminds the user of the abnormal situation in the most intuitive and effective way. For example, for serious fault abnormalities, the early warning module pops up a warning dialog box accompanied by a harsh alarm sound; for general deviation abnormalities, the early warning module displays a yellow warning icon on the corresponding abstract sub-graph, and displays specific abnormal information in the floating prompt.
通过预警模块的异常分析和预警显示，实现了设备运行状态的实时监控和智能告警，通过自动识别设备的异常情况，并通过前端页面的可视化方式，及时、准确地向用户传递异常信息，引起用户的关注和重视。基于抽象子图的全局异常分析和预警机制，可以有效提高设备管理的效率和准确性，避免异常问题的恶化和扩大化，保障设备的安全稳定运行。Through the abnormal analysis and early warning display of the early warning module, real-time monitoring and intelligent alarm of the equipment operation status are realized. By automatically identifying the abnormal situation of the equipment and visualizing it on the front-end page, abnormal information is delivered to the user in a timely and accurate manner, attracting the user's attention and attention. The global abnormal analysis and early warning mechanism based on the abstract subgraph can effectively improve the efficiency and accuracy of equipment management, avoid the deterioration and expansion of abnormal problems, and ensure the safe and stable operation of the equipment.
综上所述，本申请实施例所提供的用于前端页面的虚拟数据标签生成系统具有如下技术效果：In summary, the virtual data tag generation system for front-end pages provided by the embodiment of the present application has the following technical effects:
获取模块，用于获取用户的输入信息，输入信息包括设备的图片信息、基础信息，为后续的图形构建和数据绑定提供原始素材。构建模块，用于构建抽象子图，抽象子图通过抽象网络构建而成，抽象网络为进行基础图元拆解重构的智能处理网络，且抽象网络设置有自适应更新的区别数据库，区别数据库用于存储已构建的抽象子图，将图片信息输入至抽象网络，生成抽象子图，提高了图形构建效率。第一解析模块，用于解析基础信息，建立设备的状态标识，并通过状态标识更新抽象子图，实现了设备状态与图形的关联，为后续的状态管理奠定基础。第二解析模块，用于解析基础信息，建立设备配置信息、现场工况数据、指标配置信息、指标统计数据，根据设备配置信息、现场工况数据、指标配置信息、指标统计数据建立虚拟数据标签，引入虚拟标签作为前端的临时变量，方便了表达式的嵌套使用和图形的复用。搭建模块，用于将更新后的抽象子图进行虚拟数据标签的动态表达式嵌入，完成抽象子图的搭建，通过动态表达式将数据灵活注入图形，实现了数据与图形的解耦，使得图形可适配不同的数据源。管理模块，用于当引用搭建完成的抽象子图时，读取抽象子图的虚拟数据标签，并对虚拟数据标签赋值，根据赋值结果更新抽象子图状态，完成基于虚拟数据标签的设备管理，将设备状态映射到虚拟标签，通过管理虚拟标签来管理设备，实现了优化绑点数据表达式的嵌套使用，增强绑点图形的可复用性，简化计算逻辑、节约内存资源，使网页展示更加简洁高效。The acquisition module is used to obtain the user's input information, which includes the image information and basic information of the equipment, and provides the original material for the subsequent graphics construction and data binding. The construction module is used to construct the abstract subgraph. The abstract subgraph is constructed through the abstract network. The abstract network is an intelligent processing network for disassembling and reconstructing the basic graphics elements, and the abstract network is provided with an adaptively updated difference database. The difference database is used to store the constructed abstract subgraphs, input the image information into the abstract network, generate the abstract subgraph, and improve the efficiency of graphics construction. The first parsing module is used to parse the basic information, establish the status identification of the equipment, and update the abstract subgraph through the status identification, so as to realize the association between the equipment status and the graphics, and lay the foundation for the subsequent status management. The second parsing module is used to parse the basic information, establish the equipment configuration information, the on-site working condition data, the indicator configuration information, and the indicator statistical data, and establish the virtual data label according to the equipment configuration information, the on-site working condition data, the indicator configuration information, and the indicator statistical data, and introduce the virtual label as the temporary variable of the front end, which is convenient for the nested use of expressions and the reuse of graphics. The construction module is used to embed the dynamic expression of the virtual data label of the updated abstract subgraph to complete the construction of the abstract subgraph. The data can be flexibly injected into the graph through dynamic expressions, which realizes the decoupling of data and graph, making the graph adaptable to different data sources. The management module is used to read the virtual data label of the abstract subgraph when referencing the constructed abstract subgraph, assign values to the virtual data label, update the abstract subgraph state according to the assignment result, complete the device management based on the virtual data label, map the device state to the virtual label, manage the device by managing the virtual label, realize the nested use of optimized binding point data expression, enhance the reusability of binding point graphics, simplify the calculation logic, save memory resources, and make the web page display more concise and efficient.
实施例二，基于与前述实施例中用于前端页面的虚拟数据标签生成系统相同的发明构思，如图2所示，本申请实施例提供了用于前端页面的虚拟数据标签生成方法，该方法包括：Embodiment 2, based on the same inventive concept as the virtual data tag generation system for the front-end page in the aforementioned embodiment, as shown in FIG2 , the embodiment of the present application provides a method for generating a virtual data tag for the front-end page, the method comprising:
获取用户的输入信息，所述输入信息包括设备的图片信息、基础信息；Obtaining user input information, including device image information and basic information;
构建抽象子图，所述抽象子图通过抽象网络构建而成，所述抽象网络为进行基础图元拆解重构的智能处理网络，且所述抽象网络设置有自适应更新的区别数据库，所述区别数据库用于存储已构建的抽象子图，将所述图片信息输入至所述抽象网络，生成抽象子图；Constructing an abstract subgraph, wherein the abstract subgraph is constructed by an abstract network, wherein the abstract network is an intelligent processing network for disassembling and reconstructing basic graphic elements, and the abstract network is provided with an adaptively updated difference database, wherein the difference database is used to store the constructed abstract subgraph, and the image information is input into the abstract network to generate an abstract subgraph;
解析所述基础信息，建立设备的状态标识，并通过所述状态标识更新所述抽象子图；Parsing the basic information, establishing a status identifier of the device, and updating the abstract subgraph through the status identifier;
解析所述基础信息，建立设备配置信息、现场工况数据、指标配置信息、指标统计数据，根据设备配置信息、现场工况数据、指标配置信息、指标统计数据建立虚拟数据标签；Parse the basic information, establish equipment configuration information, on-site working condition data, indicator configuration information, and indicator statistical data, and establish virtual data tags according to the equipment configuration information, on-site working condition data, indicator configuration information, and indicator statistical data;
将更新后的抽象子图进行所述虚拟数据标签的动态表达式嵌入，完成抽象子图的搭建；Embed the dynamic expression of the virtual data label into the updated abstract subgraph to complete the construction of the abstract subgraph;
当引用搭建完成的抽象子图时，读取抽象子图的虚拟数据标签，并对所述虚拟数据标签赋值，根据赋值结果更新抽象子图状态，完成基于虚拟数据标签的设备管理。When referencing the constructed abstract subgraph, the virtual data tag of the abstract subgraph is read, and the virtual data tag is assigned a value, and the state of the abstract subgraph is updated according to the assignment result, thereby completing the device management based on the virtual data tag.
进一步的，本申请实施例还包括：Furthermore, the embodiment of the present application also includes:
通过预处理子网络执行图片信息的解析，根据解析结果生成图像复杂度；The preprocessing sub-network is used to parse the image information and generate image complexity based on the parsing results.
将所述图像复杂度同步至分解子网络，配置分解子网络的第一梯度的基础图元数量、第二梯度的基础图元数量、第三梯度的基础图元数量；Synchronize the image complexity to the decomposition sub-network, and configure the number of basic image elements of the first gradient, the number of basic image elements of the second gradient, and the number of basic image elements of the third gradient of the decomposition sub-network;
根据配置完成的分解子网络进行图片信息分解，建立第一梯度基础图元、第二梯度基础图元、第三梯度基础图元；Decomposing the image information according to the configured decomposition sub-network, establishing a first gradient basic primitive, a second gradient basic primitive, and a third gradient basic primitive;
调用所述区别数据库，执行所述第二梯度基础图元和第三梯度基础图元的适配评价，根据适配评价结果和第一梯度基础图元输出抽象子图。The difference database is called to perform adaptation evaluation of the second gradient basic primitive and the third gradient basic primitive, and an abstract sub-image is output according to the adaptation evaluation result and the first gradient basic primitive.
进一步的，本申请实施例还包括：Furthermore, the embodiment of the present application also includes:
获取第二梯度基础图元和第三梯度基础图元内的顺序排序，建立顺序排序惩罚系数；Obtain the order of the second gradient basic primitive and the third gradient basic primitive, and establish the order penalty coefficient;
获取第二梯度基础图元和第三梯度基础图元的梯度信任值，通过所述顺序排序惩罚系数对梯度信任值惩罚调整，并根据适配评价结果进行惩罚调整结果加权计算，根据加权计算结果进行顺序筛选，基于顺序筛选结果和第一梯度基础图元构建抽象子图。Obtain the gradient trust values of the second gradient basic primitive and the third gradient basic primitive, adjust the gradient trust value penalty by the sequential sorting penalty coefficient, perform weighted calculation on the penalty adjustment results according to the adaptation evaluation results, perform sequential screening according to the weighted calculation results, and construct an abstract subgraph based on the sequential screening results and the first gradient basic primitive.
进一步的，本申请实施例还包括：Furthermore, the embodiment of the present application also includes:
判断区别数据库的预设大小是否满足预设阈值；Determining whether a preset size of the difference database meets a preset threshold;
若所述区别数据库的预设大小满足预设阈值，则激活所述抽象网络的附加生成子网络；If the preset size of the difference database meets a preset threshold, activating an additional generation sub-network of the abstract network;
在根据顺序筛选结果和第一梯度基础图元构建抽象子图之前，通过所述附加生成子网络基于图片信息进行设备匹配，根据设备匹配结果建立附加特征；Before constructing the abstract subgraph according to the sequential screening result and the first gradient basic primitive, performing device matching based on the image information through the additional generation subnetwork, and establishing additional features according to the device matching result;
根据所述附加特征、顺序筛选结果和第一梯度基础图元完成抽象子图构建。The abstract sub-graph is constructed according to the additional features, the sequential screening results and the first gradient basic primitive.
进一步的，本申请实施例还包括：Furthermore, the embodiment of the present application also includes:
配置字符串替换策略和数值替换策略，根据所述字符串替换策略和/或数值替换策略对所述虚拟数据标签赋值。A character string replacement strategy and a numerical value replacement strategy are configured, and a value is assigned to the virtual data label according to the character string replacement strategy and/or the numerical value replacement strategy.
进一步的，本申请实施例还包括：Furthermore, the embodiment of the present application also includes:
获取前端页面内的抽象子图信息，对所述抽象子图信息进行图形元素识别，提取图形分布特征；Obtaining abstract sub-graph information in the front-end page, performing graphic element recognition on the abstract sub-graph information, and extracting graphic distribution features;
根据所述图形分布特征进行图形元素间距、对齐度和分布分析，根据分布分析结果进行布局优化。The spacing, alignment and distribution of graphic elements are analyzed according to the graphic distribution characteristics, and the layout is optimized according to the distribution analysis results.
进一步的，本申请实施例还包括：Furthermore, the embodiment of the present application also includes:
根据所述抽象子图信息进行关键抽象子图识别，建立关键抽象子图标识；Perform key abstract subgraph identification according to the abstract subgraph information, and establish a key abstract subgraph identifier;
通过所述关键抽象子图标识进行位置优化，生成第一优化结果；Performing position optimization through the key abstract subgraph identifier to generate a first optimization result;
基于所述第一优化结果对分布分析结果调整，完成布局优化。The distribution analysis result is adjusted based on the first optimization result to complete the layout optimization.
进一步的，本申请实施例还包括：Furthermore, the embodiment of the present application also includes:
根据所述抽象子图信息进行设备的全局异常分析，建立异常标识；Perform global abnormality analysis of the device according to the abstract subgraph information and establish an abnormality mark;
根据所述异常标识进行前端页面的显示预警。A front-end page display warning is performed according to the abnormal identification.
综上所述的方法的任意步骤都可作为计算机指令或者程序存储在不设限制的计算机存储器中，并可以被不设限制的计算机处理器调用识别用以实现本申请实施例中的任一项方法，在此不做多余限制。Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.
进一步的，综上所述的第一或第二可能不止代表次序关系，也可能代表某项特指概念，和/或指的是多个元素之间可单独或全部选择。显然，本领域的技术人员可以对本申请进行各种改动和变型而不脱离本申请的范围。这样，倘若本申请的这些修改和变型属于本申请及其等同技术的范围之内，则本申请意图包括这些改动和变型在内。Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and/or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.
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