WO2022148077A1 - 智能挖掘机的结构性能数字孪生体构建方法 - Google Patents

智能挖掘机的结构性能数字孪生体构建方法 Download PDF

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WO2022148077A1
WO2022148077A1 PCT/CN2021/122532 CN2021122532W WO2022148077A1 WO 2022148077 A1 WO2022148077 A1 WO 2022148077A1 CN 2021122532 W CN2021122532 W CN 2021122532W WO 2022148077 A1 WO2022148077 A1 WO 2022148077A1
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real
intelligent excavator
data
display
module
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French (fr)
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宋学官
来孝楠
邹亚男
王鑫
何西旺
张天赐
付涛
孙伟
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大连理工大学
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Priority to US17/633,069 priority Critical patent/US20230115586A1/en
Publication of WO2022148077A1 publication Critical patent/WO2022148077A1/zh

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    • G05D1/0011—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots associated with a remote control arrangement
    • G05D1/0022—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots associated with a remote control arrangement characterised by the communication link
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Definitions

  • the invention belongs to the field of digital twins, in particular to a method for constructing a structural performance digital twin of an intelligent excavator.
  • Intelligent excavators are the key equipment for open-pit mining and occupy an important position in the mining of mineral resources. Due to the harsh working environment, high work intensity and long operation time during mining, there is a potential risk of structural failure. And once the structural failure occurs, it will bring heavy economic losses and even casualties. Therefore, in order to ensure the safe, continuous and stable operation of the intelligent excavator, it is necessary to monitor the structural performance of the intelligent excavator in real time. With the rapid popularization and application of new-generation information and communication technologies such as big data, Internet of Things, and cloud computing, the real application of digital twin technology has been technically guaranteed. Digital twin is a concept of virtual and real combination, which usually includes physical entities, virtual entities and the connection between them.
  • the present invention proposes a method for monitoring the structural performance of intelligent excavators based on digital twins;
  • the construction of a digital twin for structural performance monitoring integrates physical geometry modules, communication modules, algorithm modules and real-time virtual display modules to achieve real-time monitoring and display of the performance of parts and components during the excavation process of the intelligent excavator.
  • a method for constructing a structural performance digital twin of an intelligent excavator is based on a digital twin system combined with four modules: a physical geometry module, a communication module, an algorithm module and a real-time virtual display module.
  • Machine geometry plan each action unit of the excavation action, pay attention to the spatial geometric position and mutual cooperation of the parts, and install industrial sensors on the key monitored parts to extract the input variables to ensure the real-time capture of the excavation action.
  • data processing and fusion are carried out through the decoding system of the communication module, and the real-time motion data is stored and transmitted lightly and accurately.
  • the data is passed into the algorithm module to construct the mathematical model, and the corresponding mathematical relationship between the physical motion information and the structural performance information is constructed.
  • the structural performance information available for rendering is transmitted to the real-time virtual display module, and the internal structural performance and external motion behavior of the virtual twin can be displayed on various terminal platforms.
  • the operation data is saved for continuous correction of the mathematical model in the algorithm module, so as to ensure the high fidelity of the digital twin. Specifically include the following steps:
  • the first step is to construct the physical entity part of the digital twin system with the help of the physical geometry module for the intelligent excavator.
  • the physical geometry module includes a perception unit, a control unit, a drive unit, and an action realization unit. Specifically:
  • the 3D solid modeling of the excavation stockpile is realized by the 3D scanner in the perception unit, which is convenient for observing the progress of the excavation operation in real time.
  • the key components of the intelligent excavator such as bucket, boom and gear
  • the key factors affecting the structural performance of the intelligent excavator components are determined.
  • the operating condition input variables of the excavator excavation process and the performance information of the demand solution Therefore, corresponding industrial sensors are arranged on key components to collect real-time operating condition information.
  • the real-time operating condition information of the key components collected by the industrial sensors in the physical geometry module is input into the communication module, and the industrial sensors are collected in real time through various protocols and data cleaning and classification systems in the communication module. data are classified and distributed.
  • the intelligent excavator is equipped with an upper industrial computer with functions of data storage, data processing and wireless communication, and the sensing unit, control unit and drive unit in the physical geometry module are wired with the upper industrial computer through a USB interface for connecting to the upper industrial computer.
  • Historical operation data and real-time collection data of industrial sensors are stored in the upper industrial computer.
  • the PC terminal can be wirelessly connected with the upper industrial computer to read the above data, process the data through the data cleaning and classification system, and transmit the processed data to different terminals through different communication protocols, so as to realize simplicity and lightness. Quantify and standardize transmission communications.
  • the deep neural network method with the advantages of accurate and fast prediction is selected through the algorithm module to establish the corresponding relationship between the actual operating conditions and the internal structural performance information of the part.
  • the training set and test set required to construct the algorithm are selected, which are respectively used for the construction of the deep neural network model and the inspection of the accuracy of the deep neural network model.
  • the input working condition information determined by the static analysis in the physical geometry module is used as an input variable.
  • the input working condition set that can represent the entire design space is uniformly selected, and the structural mechanics information corresponding to the input working condition set is solved by the finite element method as the output variable.
  • Use the training set to build a deep neural network, and construct the corresponding relationship between the actual operating conditions and the structural mechanical properties of components.
  • the selected test set is used to test the accuracy of the deep neural network model, and the coefficient of determination R2 is selected as the model accuracy test index to ensure the accuracy of the built model.
  • the fourth step is to quickly calculate the internal performance information of the part according to the real-time operating conditions transmitted by the communication module.
  • industrial sensors arranged on key components are used to collect real-time operating condition information of the intelligent excavator. storage.
  • the PC terminal uses wireless connection to communicate with the upper industrial computer. After data cleaning and classification, the processed data is used as input, and the deep neural network model is used for calculation to solve the structural mechanical properties of the intelligent excavator under the current operating conditions. .
  • the fifth step is to perform three-dimensional rendering and display of the performance information through the real-time monitoring and display module.
  • the browser is selected as the monitoring display platform to construct a virtual three-dimensional scene, and realize the intuitive and high-fidelity twin mapping of the structural performance of the intelligent excavator.
  • three.js based on the WebGL standard is used as the scripting language for 3D rendering and display.
  • the advantage is that the underlying graphics hardware is used to accelerate graphics rendering to meet the requirements of real-time display. Specifically:
  • the UI interface planning of the real-time monitoring and display module is realized, and the operating limit positions of the parts are monitored in real time, so as to realize timely early warning and prevent accidents.
  • the excavation trajectory drawing in the intelligent excavator excavation process realizes virtual visualization excavation.
  • the invention realizes that the intelligent excavator can use the deep neural network algorithm and the sensor communication technology to calculate the mechanical properties of the internal structure of the parts in real time under various operating conditions, and evaluate the performance of the intelligent excavator in combination with the actual collected data. , prediction and feedback optimization, etc.
  • the present invention can realize the high-fidelity real-time display of the structural performance information of the intelligent excavator during the entire running action by only using a small amount of sensor information. Real-time monitoring of the performance of each key part of the intelligent excavator to prevent accidents.
  • Fig. 2 is the schematic flow chart of system construction of the present invention
  • Fig. 3 is the schematic diagram of the intelligent excavator of the present invention.
  • FIG. 4 is a schematic diagram of a communication technology of the present invention.
  • Fig. 5 is the schematic diagram of the algorithm module data fusion process of the present invention.
  • FIG. 6 is a schematic diagram of the twin display system of the present invention.
  • FIG. 1 is a frame diagram of a structural performance digital twin system of an intelligent excavator provided by the present invention.
  • a real-time virtual display platform that can reflect the structural performance information of the physical geometry module is built, and driven by data, it can solve various structural safety problems such as structural fatigue, structural wear, structural deformation, and meshing failure.
  • the twin data is a bridge for interactive feedback between multiple modules.
  • the training set is selected through feature extraction, the deep neural network model is trained, and the performance information of the intelligent excavator is calculated in real time combined with the perception data. Visual display of performance changes with the help of a virtual display platform.
  • FIG. 2 is a construction process of a structural performance digital twin system for an intelligent excavator based on a mathematical model and sensor communication technology provided by the present invention.
  • the method needs to gradually build four main modules: physical geometry module, communication module, algorithm module and real-time virtual display module.
  • the main steps include: First, in the physical geometry module, according to the real intelligent excavator geometry, plan each action unit of the excavation action, and pay attention to the spatial geometric position of the parts and the mutual cooperation relationship. Appropriate sensors are installed on the key monitored parts to extract input variables to ensure real-time capture of mining movements. Secondly, data processing and fusion are carried out through the decoding system of the communication module, and the real-time motion data is stored and transmitted lightly and accurately.
  • the data is fed into the algorithm module to construct a mathematical model, and the corresponding mathematical relationship between the physical motion information and the structural performance information is constructed.
  • the structural performance information available for rendering is transmitted to the real-time virtual display module, and the internal structural performance and external motion behavior of the virtual twin can be displayed on various terminal platforms.
  • the operation data is saved for continuous correction of the mathematical model in the algorithm module, so as to ensure the high fidelity of the digital twin.
  • FIG. 3 is an overall schematic diagram of the intelligent excavator.
  • Boom 3, gear 4 and bucket 6 are important components for structural performance testing of intelligent excavators.
  • the three key actions of bucket lifting, bucket pushing and body rotation are mainly realized.
  • a rotary motor and a rotary encoder are installed on the rotary body 1 to collect rotation angle information in real time.
  • the boom 3 and the bucket 6 are connected by a rack and pinion, and a hoisting motor and a rotary encoder are installed to collect the hoisting angle information in real time.
  • a tensile force measuring sensor is installed at the hoisting rope of the bucket 6 to collect the load of the bucket in real time.
  • the bucket push length can be calculated using the Equivalent Cosine Theorem using mathematical relationships.
  • the physical geometry module in the structural performance digital twin of the intelligent excavator is completed.
  • the communication module of the intelligent excavator is completed around the upper industrial computer installed in the excavator.
  • the industrial computer is a micro-server based on the ROS system, with a processor and a memory.
  • the single-chip microcomputer, signal converter, and controller that control the movement of the intelligent excavator are connected to the industrial computer through the USB interface, and are used to control the intelligent excavator to operate according to the specified movement trajectory.
  • the control directly controls the operation of the intelligent excavator.
  • the intelligent excavator supports manual control of excavation, and the control handle can be connected to the upper industrial computer through Bluetooth.
  • the real-time data collected by the sensors installed in the intelligent excavator, such as lidar, tension sensor and torque sensor, are stored by the upper industrial computer.
  • a router is installed in the upper industrial computer, and the PC terminal communicates with the upper industrial computer through WIFI wireless connection. It is convenient to further clean and classify the data collected by the sensor. With the help of WebSocket protocol to transmit relevant performance information, it can be visualized through PC terminal, monitoring display screen, VR equipment, etc. to realize real-time display of intelligent excavator performance.
  • FIG. 5 it is a schematic diagram of the data fusion process of the algorithm module in the digital twin system, and the figure illustrates the data processing and modeling process in the present invention in detail. It mainly includes the analysis process of the numerical model, the construction process of the mathematical model, and the data storage process of the digital twin database.
  • the numerical model based on the entire design space, representative operating states are uniformly selected as the input variables of the training set, and its structural mechanical properties are calculated as the output of the training set. Solve by the finite element method.
  • a deep neural network model is established by using the operating state and structural mechanical performance information of the numerical model to complete the effective and high-precision prediction of the structural performance information of the entire design space variables.
  • the structural performance information of the components can be calculated in real time.
  • the numerical model and the deep neural network model are stored in the twin database for subsequent data analysis, operation action realization, performance calculation and dynamic 3D display.
  • the algorithm module in the structural performance digital twin of the intelligent excavator is completed.
  • FIG. 6 is a schematic diagram of a digital twin performance display platform of the present invention. It involves the resource layer, service layer, interface layer, web layer, and access layer.
  • the resource layer includes simplified 3D model information for constructing a digital twin, such as 3D coordinates of parts and components, and the cooperative motion relationship between parts; and data information of structural performance calculated in real time by the algorithm module. It also has data storage and caching functions.
  • the service layer includes a communication module, a business module, and a management module.
  • a communication module Complete the information exchange between the digital twin display platform and other systems, and realize the business logic of intelligent excavator historical mining data management, performance display human-computer interaction, monitoring and alarming.
  • Real-time rendering and display of the digital twin performance display system on each platform is realized through the interface layer computer graphics card related graphics interface API.
  • the present invention can display the three-dimensional performance of the digital twin system on the PC client, the web terminal and the mobile terminal by accessing the domain name.
  • real-time feedback functions such as key point chart monitoring, limit state early warning, and intelligent excavation trajectory display are realized for the performance information of the intelligent excavator.

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Abstract

一种智能挖掘机的结构性能数字孪生体构建方法，通过对智能挖掘机挖掘过程中关键零部件进行有限元分析，得到相关结构力学性能；采集智能挖掘机关键零部件在挖掘过程中的重要运行状态，通过数据处理计算得等到关键运行数据；将传感器数据与人工智能算法融合，利用预测模型对多种未知工况下智能挖掘机零部件进行结构性能预测；最后利用计算机图形学技术将性能数据信息建模渲染，得到智能挖掘机结构性能显示的数字孪生体，实现智能挖掘机在挖掘过程中关键零部件性能信息的数字孪生映射。上述方法在多种运行工况下，利用传感器以及人工智能算法实时计算智能挖掘机关键零部件结构力学性能，实现性能信息的实时显示监测、挖掘轨迹显示、反馈控制、故障预警等实用性功能。

Description

智能挖掘机的结构性能数字孪生体构建方法 技术领域
本发明属于数字孪生领域，具体为一种智能挖掘机的结构性能数字孪生体构建方法。
 背景技术
智能挖掘机是露天矿开采的关键设备，在矿产资源的开采中占据着重要地位。由于矿产开采时的工作环境恶劣、工作强度大、作业时间长，存在潜在的结构失效风险。且一旦发生结构失效，将带来重大的经济损失甚至是人员伤亡。因此为保障智能挖掘机能够安全连续稳定作业，需要对智能挖掘机的结构性能进行实时监测。随着大数据、物联网、云计算等新一代信息与通信技术的快速普及与应用，数字孪生技术的真实落地应用得到了技术保障。数字孪生是一种虚实结合的概念，通常包含物理实体，虚拟实体以及它们之间的连接。使用数字孪生的理念，可以构建一种能够在多维度、多时间尺度上对物理实体进行高保真度描述的系统。能够实时模拟、监控、诊断物理实体在真实环境中的状态和行为，表征一些无法直接观察得到的信息。为融合真实动态运行数据与虚拟性能分析数据，实现对智能挖掘机运行期间工作状态监测，需要发明一种用于智能挖掘机结构性能信息实时监测的数字孪生系统。
 技术问题
在智能挖掘机结构性能监测需求日益提高的背景下，综合分析现有结构性能实时计算方法的缺陷和不足，本发明提出一种基于数字孪生的智能挖掘机结构性能监测方法；通过对智能挖掘机结构性能监测数字孪生体的构建，集成物理几何模块、通信模块、算法模块与实时虚拟显示模块实现对智能挖掘机挖掘过程中零部件性能的实时监测显示。
 技术解决方案
一种智能挖掘机的结构性能数字孪生体构建方法，该方法基于数字孪生系统结合物理几何模块、通信模块、算法模块以及实时虚拟显示模块四个模块实现：首先，在物理几何模块针对真实智能挖掘机几何体，规划挖掘动作的各个动作单元，关注零件的空间几何位置及相互配合关系，并在重点监测的零件上安装工业传感器，提取输入变量，确保挖掘动作的实时捕捉。其次，通过通信模块的解码系统进行数据处理与融合，将实时运动数据进行轻量准确存储与传输。再次，将数据传入算法模块进行数学模型的搭建，构建物理运动信息与结构性能信息的对应数学关系。最后，将可供渲染的结构性能信息，传入实时虚拟显示模块，可在多种终端平台进行虚拟孪生体内在结构性能与外在运动行为的展现。借助数据存储与管理，将运行数据进行保存，用于对所述算法模块中数学模型的不断修正，保证数字孪生体的高度保真。具体包括以下步骤：
第一步，针对智能挖掘机，首先借助所述物理几何模块构造数字孪生系统中物理实体部分，所述物理几何模块中包含感知单元、控制单元、驱动单元、以及动作实现单元。具体为：
首先，需对智能挖掘机的工作环境进行实时采集。通过感知单元中3D扫描仪实现对挖掘料堆的三维实体建模，便于实时观察挖掘作业进度。通过对智能挖掘机各关键零部件如铲斗、大臂、齿轮，进行静力学分析，确定影响智能挖掘机零部件结构性能的关键因素。以提取挖掘机挖掘过程的运行工况输入变量以及需求解的性能信息。因此在关键零部件上布置相应工业传感器，采集实时运行工况信息。
其次，针对挖掘料堆的具体形状，规划挖掘动作。向控制单元中的单片机输入相应的运动指令，该运动指定规划了驱动单元中的步进电机旋转编码器的行程，可控制所述动作实现单元中相关零部件按照指定的挖掘轨迹进行挖掘作业。使智能挖掘机以较小功率消耗、较大满斗率进行挖掘。
最后，实现智能挖掘机挖掘作业过程中各关键零部件实体模型的三维空间位置与运动配合关系监测。为后续实时监控显示模块中模型的搭建提供数据信息。
第二步，将上述物理几何模块中工业传感器采集的关键零部件的实时运行工况信息输入至所述通信模块，通过通信模块中的各类协议与数据清洗、分类系统，将工业传感器实时采集的数据进行分类与分发。智能挖掘机中装有具备数据存储、数据处理与无线通信功能的上位工控机，所述物理几何模块中的感知单元、控制单元与驱动单元通过USB接口与上位工控机进行有线连接，用于将历史运行数据与工业传感器实时采集数据存储在上位工控机中。通过PC端可与上位工控机无线连接，对上述数据进行读取，经所述数据清洗与分类系统对数据进行处理，并通过不同通信协议将处理后的数据传输至不同终端，实现简洁、轻量化、标准化传输通信。
第三步，通过算法模块选择具有精确、快速预测优势的深度神经网络方法建立实际运行工况与零件内部结构性能信息的对应关系。首先选取构建算法所需的训练集与测试集，分别用于深度神经网络模型的搭建与深度神经网络模型精度的检验。将所述物理几何模块中静力学分析所确定的输入工况信息作为输入变量。均匀选取能够代表整个设计空间的输入工况集，利用有限元法方法求解输入工况集对应的结构力学信息，作为输出变量。使用训练集搭建深度神经网络，构建实际运行工况与零部件结构力学性能的对应关系。使用所选取的测试集进行深度神经网络模型精度检验，选择决定系数R2作为模型精度检验指标，确保所建模型的准确性。
第四步，根据所述通信模块传递的实时运行工况情况快速计算零件内部性能信息。在第三步深度神经网络模型的基础上，使用布置在关键零部件上的工业传感器，实时采集智能挖掘机运行工况信息，经所述通信模块中布置在智能挖掘机中的上位工控机进行存储。在PC端使用无线连接的方式与上位工控机进行通信，经数据清洗与分类，将处理好的数据作为输入，经深度神经网络模型进行计算，求解智能挖掘机当前运行工况下的结构力学性能。使用WebSocket通信协议将数据与所述实时监控显示模块进行连接。
第五步，通过所述实时监控显示模块对性能信息进行三维渲染显示。选择浏览器作为监控显示平台，构建虚拟三维场景，实现智能挖掘机结构性能的直观，高保真孪生映射。通过浏览器渲染引擎，采用基于WebGL标准的three.js为脚本语言进行三维渲染显示，优点在于使用底层图形硬件加速图形渲染，达到实时显示的要求。具体为：
首先，将零部件的三维模型以GLTF格式导入构建的虚拟三维场景中，使用所述物理几何模块中各零部件的三维空间位置以及各零部件间的配合运动关系信息，构建初始化三维显示，实现虚拟三维模型与真实物理模型的运动同步。
其次，显示关键零部件的结构性能信息，将关键零部件模型以四面体形式进行导入，在四面体节点上通过所述算法模块的深度神经网络模型计算零部件实时性能信息，以三维云图形式显示结构性能的变化。
最后，实现实时监控显示模块的UI界面规划，并且实时监测零部件运行极限位置，实现及时预警，防止事故发生。并且在智能挖掘机挖掘过程中的挖掘轨迹绘制，实现虚拟可视化挖掘。
 有益效果
本发明的有益效果在于：本发明实现了智能挖掘机在多种运行工况下，利用深度神经网络算法和传感器通信技术实时计算零件内部结构力学性能，结合实际采集数据对智能挖掘机性能进行评估、预测和反馈优化等。本发明仅利用少量的传感器信息，便可实现智能挖掘机在整个运行动作期间的结构性能信息高保真实时显示。实现对智能挖掘机各个关键零件的性能实时监测，防止事故发生。
 附图说明
图1为本发明的系统框架图；
图2为本发明的系统搭建流程示意图；
图3为本发明的智能挖掘机示意图；
图4为本发明的通信技术示意图；
图5为本发明的算法模块数据融合过程示意图；
图6为本发明的孪生显示系统示意图。
图中：1回转车身、2 A字架、3大臂、4齿轮、5天轮、6铲斗。
 本发明的实施方式
下面结合附图和具体实例对本发明技术方案作进一步详细描述，所描述的具体实例仅对本发明进行解释说明，并不用以限制本发明。
本发明搭建了一种智能挖掘机的结构性能数字孪生体。参考图1，图1 是本发明提供的一种智能挖掘机的结构性能数字孪生系统框架图。针对物理几何模块搭建能够反映其结构性能信息的实时虚拟显示平台，以数据作为驱动，解决结构疲劳、结构磨损、结构变形、啮合失效等多种结构安全问题。其中孪生数据是多个模块间交互反馈的桥梁，通过特征提取进行训练集选取，训练深度神经网络模型，结合感知数据实时计算智能挖掘机的性能信息。借助虚拟显示平台实现性能变化可视化显示。
参考图2 ，图2是本发明提供的一种基于数学模型、传感器通信技术的面向智能挖掘机的结构性能数字孪生系统搭建流程。该方法需逐步构建四个主要模块分别为：物理几何模块，通信模块，算法模块以及实时虚拟显示模块。主要步骤包括：首先，在物理几何模块针对真实智能挖掘机几何体，规划挖掘动作的各个动作单元，关注零件的空间几何位置及相互配合关系。并在重点监测的零件上安装合适的传感器，提取输入变量，确保挖掘动作的实时捕捉。其次，通过所述通信模块的解码系统进行数据处理与融合，将实时运动数据进行轻量准确存储与传输。将数据传入所述算法模块进行数学模型的搭建，构建物理运动信息与结构性能信息的对应数学关系。将可供渲染的结构性能信息，传入所述实时虚拟显示模块，可在多种终端平台进行虚拟孪生体内在结构性能与外在运动行为的展现。借助数据存储与管理，将运行数据进行保存，用于对所述算法模块中数学模型的不断修正，保证数字孪生体的高度保真。
下面通过实施案例对本发明的具体实施方式做进一步说明。
具体以建立智能挖掘机的数字孪生体为例进行说明。
以智能挖掘机为实例对象，参考图3，图3为智能挖掘机整体示意图。大臂3、齿轮4、铲斗6为智能挖掘机结构性能检测的重要零部件。在智能挖掘机运动期间主要实现铲斗提升，铲斗推压以及车身回转这三个关键性动作。经静力学分析，铲斗挖掘负载、铲斗提升角度、铲斗推压长度是能够体现挖掘工况的输入变量。因此在回转车身1安装回转电机及旋转编码器，用于实时采集旋转角度信息。在大臂3与铲斗6之间采用齿轮齿条配合连接，安装提升电机及旋转编码器，用于实时采集提升角度信息。在铲斗6的提升绳索处安装拉力测力传感器，用于实时采集铲斗负载情况。利用数学关系使用等效余弦定理可计算铲斗推压长度。综上智能挖掘机的结构性能数字孪生体中物理几何模块搭建完毕。
智能挖掘机的通信模块围绕安装在挖掘机中的上位工控机完成。参考图4，工控机为基于ROS系统的微服务器，具有处理器及内存。其中控制智能挖掘机运动的单片机、信号转换器、控制器通过USB接口与工控机进行连接，用于控制智能挖掘机按照指定的运动轨迹进行作业，其中控制器通过对步进电机及旋转编码器的控制直接控制智能挖掘机的运行。另外，智能挖掘机支持手动控制挖掘，控制手柄可通过蓝牙连接至上位工控机。安装在智能挖掘机中的传感器，如激光雷达、拉力传感器与扭矩传感器实时采集的数据通过上位工控机进行存储。在上位工控机中安装路由器，PC端通过WIFI无线连接与上位工控机进行通信。便于对传感器采集数据进行进一步清洗与分类。借助WebSocket协议传输相关性能信息，可通过PC端、监控显示屏、VR设备等进行可视化输出，实现智能挖掘机性能实时显示。
参考图5是数字孪生系统中算法模块数据融合过程示意图，该图详细的阐述了本发明中的数据处理与建模过程。主要包含数值模型的分析过程，数学模型的搭建过程，以及数字孪生数据库的数据存储过程。在数值模型的建立过程中，基于整个设计空间，均匀选取具有代表性的运行状态作为训练集的输入变量，计算其结构力学性能作为训练集的输出，通过定义几何体的单元类型、材料、边界条件进行有限单元法求解。利用数值模型的运行状态和结构力学性能信息建立深度神经网络模型，完成对整个设计空间变量的结构性能信息的有效高精度预测。当有运行数据传输时，便可实时计算出零部件的结构性能信息。将数值模型以及深度神经网络模型在孪生数据库中，用于后续对进行数据分析、运行动作实现、性能计算以及动态三维显示。综上智能挖掘机的结构性能数字孪生体中算法模块搭建完毕。
综合上述物理几何模块与算法模块的相关计算信息，借助通信模块中的数据通信传输，进行数字孪生体的实时虚拟显示模块搭建。为直观显示智能挖掘机性能信息，借助计算机图形学技术，搭建数字孪生性能显示平台。参考图6，图6为本发明的数字孪生性能显示平台示意图。共涉及资源层、服务层、接口层、web层、访问层。其中资源层包含构建数字孪生体的简化三维模型信息，如零部件三维坐标、部件间配合运动关系；以及通过所述算法模块实时计算的结构性能的数据信息。同时具备数据存储与缓存功能。服务层包括通信模块、业务模块、及管理模块。完成数字孪生显示平台与其他系统的信息交流，实现智能挖掘机历史挖掘数据管理、性能显示人机交互、监控报警等业务逻辑。通过接口层计算机显卡相关图形接口API实现数字孪生性能显示系统在各个平台实时渲染显示。本发明可在PC客户端、web端及移动端通过访问域名进行数字孪生系统的三维性能显示。同时针对智能挖掘机性能信息实现关键点图表监测、极限状态预警、智能挖掘轨迹显示等实时反馈功能。
本发明虽然已以较佳实施例公开如上，但其并不是用来限定本发明，任何本领域技术人员在不脱离本发明的精神和范围内，都可以利用上述揭示的方法和技术内容对本发明技术方案做出可能的变动和修改，因此，凡是未脱离本发明技术方案的内容，依据本发明的技术实质对以上实施例所作的任何简单修改、等同变化及修饰，均属于本发明技术方案的保护范围。
本说明书仅仅是对发明构思的实现形式的举例，本发明的保护范围不应该局限于实施例所述的具体形式，还应涉及本领域技术人员根据本发明构思所能想到的同等技术手段。

Claims (1)

  1. 一种智能挖掘机的结构性能数字孪生体构建方法，其特征在于，所述方法基于数字孪生系统结合物理几何模块、通信模块、算法模块以及实时虚拟显示模块四个模块实现，包括以下步骤：
    第一步，针对智能挖掘机，通过所述物理几何模块构造数字孪生系统中物理实体部分，所述物理几何模块中包含感知单元、控制单元、驱动单元、以及动作实现单元；具体为：
    首先，需对智能挖掘机的工作环境进行实时采集；通过感知单元中3D扫描仪实现对挖掘料堆的三维实体建模，用于实时观察挖掘作业进度；对智能挖掘机各关键零部件进行静力学分析，确定影响智能挖掘机零部件结构性能的关键因素；以提取挖掘机挖掘过程的运行工况输入变量以及需求解的性能信息；关键零部件上布置相应工业传感器，采集实时运行工况信息；
    其次，针对挖掘料堆的具体形状，规划挖掘动作；向控制单元中的单片机输入相应的运动指令，该运动指定规划驱动单元中的步进电机旋转编码器的行程，可控制所述动作实现单元中相关零部件按照指定的挖掘轨迹进行挖掘作业；
    最后，实现智能挖掘机挖掘作业过程中各关键零部件实体模型的三维空间位置与运动配合关系监测；为后续实时监控显示模块中模型的搭建提供数据信息；
    第二步，将上述物理几何模块中工业传感器采集的关键零部件的实时运行工况信息输入通信模块，通过通信模块将工业传感器实时采集的数据进行分类与分发；智能挖掘机中装有具备数据存储、数据处理与无线通信功能的上位工控机；所述物理几何模块中的感知单元、控制单元与驱动单元通过USB接口与上位工控机进行有线连接，用于将历史运行数据与工业传感器实时采集数据存储在上位工控机中；通过PC端与上位工控机无线连接，读取上述数据后进行处理，并将处理后的数据传输至不同终端；
    第三步，通过深度神经网络方法建立实际运行工况与零件内部结构性能信息的对应关系；首先选取构建算法所需的训练集与测试集，分别用于深度神经网络模型的搭建与深度神经网络模型精度的检验；将物理几何模块中静力学分析所确定的输入工况信息作为输入变量；均匀选取能够代表整个设计空间的输入工况集，利用有限元法方法求解输入工况集对应的结构力学信息，作为输出变量；使用训练集搭建深度神经网络，构建实际运行工况与零部件结构力学性能的对应关系；使用所选取的测试集进行深度神经网络模型精度检验，选择决定系数R2作为模型精度检验指标，保证所建模型的准确性；
    第四步，根据通信模块传递的实时运行工况情况快速计算零件内部性能信息；在第三步深度神经网络模型的基础上，使用布置在关键零部件上的工业传感器，实时采集智能挖掘机运行工况信息，经所述通信模块中布置在智能挖掘机中的上位工控机进行存储；在PC端使用无线连接的方式与上位工控机进行通信，经数据清洗与分类，将处理好的数据作为输入，经深度神经网络模型进行计算，求解智能挖掘机当前运行工况下的结构力学性能；使用WebSocket通信协议将数据与实时监控显示模块进行连接；
    第五步，通过实时监控显示模块对性能信息进行三维渲染显示；选择浏览器作为监控显示平台，构建虚拟三维场景，实现智能挖掘机结构性能的直观，高保真孪生映射；通过浏览器渲染引擎，进行三维渲染显示，具体为：
    首先，将零部件的三维模型以GLTF格式导入构建的虚拟三维场景中，使用物理几何模块中各零部件的三维空间位置以及各零部件间的配合运动关系信息，构建初始化三维显示，实现虚拟三维模型与真实物理模型的运动同步；
    其次，显示关键零部件的结构性能信息，将关键零部件模型以四面体形式进行导入，在四面体节点上通过算法模块的深度神经网络模型计算零部件实时性能信息，以三维云图形式显示结构性能的变化；
    最后，实现实时监控显示模块的UI界面规划，并且实时监测零部件运行极限位置，实现及时预警，防止事故发生；并且在智能挖掘机挖掘过程中的挖掘轨迹绘制，实现虚拟可视化挖掘。
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