WO2024045415A1 - 一种数字孪生模型合并方法 - Google Patents
一种数字孪生模型合并方法 Download PDFInfo
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Definitions
- the invention belongs to the technical field of digital twin modeling, and in particular relates to a digital twin model merging method.
- Digital twin technology is a technical means that integrates multi-physics, multi-scale, and multi-disciplinary attributes, has real-time synchronization, faithful mapping, and high fidelity characteristics, and can realize the interaction and integration of the physical world and the information world.
- digital twins use virtual models of physical entities to simultaneously simulate the behavior of physical entities in the real environment, adding or expanding new capabilities to physical entities.
- the digital twin model is an important part of the digital twin.
- the twin model digitally expresses the physical entity in the virtual world and is the basis for realizing digital twin technology.
- digital twin modeling technology has developed rapidly in recent years, current research or applications on digital twin modeling mainly focus on the construction of geometric models, but there is a lack of simplified methods to avoid duplication of work in the modeling process. Research. In view of this, the present invention proposes a digital twin model merging method to solve the problem of reusability of part of the dimensional models in the process of building the same or similar digital twin models.
- a digital twin model merging method includes the following steps:
- Step 1 Specify the basic application scenarios and basic concepts of digital twin model merging
- Step 2 Distinguish the similarity of models based on the implemented service and its physical entity model
- Step 3 For the two similarities, the model merging method includes two types of merging methods, and based on the two merging methods, a
- Step 4 Merge the specific implementation methods of modeling in different dimensions.
- step 1 the basic application scenarios and basic concepts of digital twin model merging are specified.
- the digital twin model involves multiple independent models, that is, there are no mutual constraints or assembly relationships between these models, and each model can be built independently. If the digital twin models of each model have similarities, then certain modeling dimensions of these digital twin models can be merged and modeled into the same model.
- This process is digital twin model merging modeling.
- the digital twin models modeled independently by these models are called sub-models, and the combined modeling set is called the main model.
- step 2 from the physical entity model of the digital twin and the service implemented by the digital twin model, the similarity differentiation strategy of the model is specified, and the similarity levels are sequentially reduced into three levels: identical, similar, and different.
- the similarity of the digital twin model is distinguished based on the goal of the digital twin service and the physical entity of the digital twin. Since the ultimate goal of building the digital twin model is to achieve oriented to different fields through virtual and real interactive feedback of the digital twin model, data fusion analysis, and decision iteration optimization, etc. , services for different users and different business needs, so use digital twin services and physical models to determine model similarity.
- the digital twin physical model functions and models involved in realizing the corresponding services are the same. Then these sub-models are the same model, and these sub-models can be modeled using the same digital twin model merging method; if the digital twin services to be implemented by each model are the same, the functions of the digital twin physical models involved in implementing the corresponding services are the same, but there are If there are three-dimensional geometric differences such as shape and size, these sub-models are of the same type. These sub-models can be modeled using the same type of digital twin model merging method; the above two model similarity determination methods are used to determine the similarity of the overall model.
- the model can be judged to have a hybrid similarity model, which leads to the merging strategy of the hybrid similarity model.
- the physical properties of the model The entities are partially different, but this part does not involve the digital twin service to be implemented.
- the construction of this non-critical model can be discarded, and the model similarity can be judged by the discarded simplified model.
- the merging of identical digital twin models refers to the completion of merging two or more identical digital twin models into one digital twin master model. Based on the model similarity differentiation strategy, if the models are the same model, the virtual model, service model, and connection model can be merged and modeled during the process of modeling the sub-model as the main model.
- Each sub-model is a unique individual in reality, and the physical entities of the sub-models are different.
- the physical entity of the main model is the union of the physical entities of all sub-models;
- Each sub-model belongs to the same model, so the virtual entities of each sub-model are the same and can be replaced with each other, that is, the virtual entity of the main model is the virtual entity of any sub-model;
- each sub-model belongs to the same model, and the same digital twin service is the prerequisite for the model to belong to the same model. Therefore, the service model of each sub-model is the same, that is, the service model of the main model is the service model of any sub-model;
- Each sub-model belongs to the same model, and the twin data type of each sub-model is the same. However, during the operation of its equipment, the data of each sub-model is determined by the current operating status of the equipment in reality, so the twin data of each sub-model is different. , that is, the twin data is the union of the twin data of all sub-models;
- connection model realizes the interconnection between physical entities, virtual entities, service models, and twin data. Since the sub-models belong to the same model, the data collection method, service implementation mode, iterative optimization algorithm, etc. of each sub-model are the same. That is, the connection model of the main model is the connection model of any sub-model.
- the merging of similar digital twin models refers to the completion of merging two or more similar digital twin models into a digital twin master model.
- the strategy is distinguished by the similarity of the models. If the model is For similar models, the service model and connection model can be merged and modeled in the process of modeling the sub-model as the main model.
- the method of merging similar twin models in the five-dimensional structural model is as follows:
- Each sub-model is a unique individual in reality, and the physical entities of the sub-models are different.
- the physical entity of the main model is the union of the physical entities of all sub-models;
- Each sub-model belongs to the same type but is not the same model, so the virtual model of each sub-model is different.
- the virtual entity of the main model is the union of the virtual entities of all sub-models;
- Each sub-model belongs to the same type of model, and the same digital twin service is the prerequisite for the model to belong to the same type of model. Therefore, the service model of each sub-model is the same, that is, the service model of the main model is the service model of any sub-model;
- Each sub-model belongs to the same type of model, and the twin data type of each sub-model is the same. However, during the operation of its equipment, the data of each sub-model is determined by the current operating status of the equipment in reality, so the twin data of each sub-model is different. , that is, the twin data is the union of the twin data of all sub-models;
- connection model realizes the interconnection between physical entities, virtual entities, service models, and twin data. Since the sub-models are of the same type, the data collection method, service implementation mode, iterative optimization algorithm, etc. of each sub-model are the same. That is, the connection model of the main model is the connection model of any sub-model.
- the hybrid similarity model is specifically reflected in the fact that digital twin modeling involves multiple models composed of multiple modules.
- the overall similarity level of these models is lower than that of the parts that make up the model.
- the similarity level of the module, and these modules with higher similarity are not highly coupled with other modules and are connected in a simple way.
- the hybrid similarity model can be divided into three situations: different digital twin models with the same module, different digital twin models with the same module, and similar digital twin models with the same module.
- step 3 digital twin modeling is performed on multiple different digital twin models that have the same module, and the same module that is not highly coupled with other modules is isolated in the model.
- This independent same module Use the same digital twin model merging method to model separately, and then add the digital twin model of the same module and the digital twin models of different modules to form the main model.
- step 3 perform digital twin modeling on multiple different digital twin models that have similar modules, and separate similar modules that are not highly coupled with other modules in the model.
- This independent similar module Use the merging method of similar digital twin models to build separate models, and then add the digital twin models of the same module and the digital twin models of different modules to form the main model.
- step 3 digital twin modeling is performed on multiple similar digital twin models that have the same module, and the same module that is not highly coupled with other modules is isolated in the model.
- This independent identical module The same digital twin model merging method is used to model separately, other modules are modeled separately using the same digital twin model merging method, and then the digital twin models of the same module and the digital twin models of other modules are added to form the main model.
- Step 4 The merging of digital twin models is implemented for the merging of the same dimensions in twin models.
- the three dimensions of virtual entity, service model and connection model of each sub-model are the same; digital twins of the same type
- the model merging method the two dimensions of service model and connection model of each sub-model are the same.
- the specific implementation method of merging in the above three dimensions is as follows:
- the template includes the three-dimensional model of the sub-model, the multi-dimensional data structure that needs to be collected in the sub-model, the behavioral response interface of the sub-model and Data-driven rules of sub-models, etc.
- the construction of each sub-model is an instantiated copy of this virtual entity template. These instantiated copies run independently of each other.
- Each sub-model is driven by the data collected by the current model;
- each sub-model uses the same sensor, the same data transmission protocol, the same communication protocol, etc. during data transmission.
- This method mainly explores two methods of digital twin merging, basically meets the merging modeling needs of similar or identical digital twin models, and can provide guidance for the digital twin transformation of the production workshop. It is suggested that, based on these two merging methods, the digital twin model merging thinking is abstracted and a digital twin model merging strategy with mixed similarity is formed.
- This method defines the merging method of multiple digital twin models when there is similarity, refines the merging implementation method of digital twin model modeling in different dimensions in the five-dimensional structural model, and reduces the number of problems in the digital twin modeling process. Repeated work reduces efficiency losses or missing model elements caused by unclear modeling methods for similar or identical models.
- Figure 1 is a schematic diagram of the same digital twin model merging method of the present invention.
- Figure 2 is a schematic diagram of the merging method of similar digital twin models of the present invention.
- Figure 3 is a schematic diagram of the merging strategy of different digital twin models with the same module according to the present invention.
- Figure 4 is a schematic diagram of the merging strategy of different digital twin models with similar modules in the present invention.
- Figure 5 is a schematic diagram of the merging strategy of similar digital twin models with the same module according to the present invention.
- Figure 6 is a schematic plan view of the present invention taking a production workshop as an example.
- the present invention mainly explains two digital twin merging methods, and proposes three merging strategies of hybrid similarity models based on the two merging methods. These three merging strategies are a combination of the two merging methods at the digital twin model module level. application. Therefore, in order to deepen the knowledge and understanding of the present invention, two methods mainly described in the present invention will be further introduced below: the merging method of identical digital twin models and the merging method of similar digital twin models.
- Embodiment 1 A digital twin model merging method, the method includes the following steps:
- Step 1 Specify the basic application scenarios and basic concepts of digital twin model merging
- Step 2 Distinguish the similarity of models based on the implemented service and its physical entity model
- Step 3 For the two similarities, the model merging method includes two types of merging methods, and based on the two merging methods, a
- Step 4 Merge the specific implementation methods of modeling in different dimensions.
- the basic application scenario of digital twin model merging is that the digital twin model involves multiple independent models, that is, there are no mutual constraints or assembly relationships between these models. Each model can be independently modeled, and each model has If there is similarity in digital twin models, some modeling dimensions of these digital twin models can be merged and modeled into the same model. This process is digital twin model merge modeling.
- the similarity of the digital twin model is then distinguished based on the goals of the digital twin service and the physical entity of the digital twin.
- the similarity levels are sequentially reduced into three levels: identical, similar, and different. Since the ultimate goal of digital twin model construction is to realize services for different fields, different users, and different business needs through virtual and real interactive feedback, data fusion analysis, and decision-making iterative optimization of the digital twin model, it is necessary to use digital twin services and physical models to determine the model. Whether there is similarity.
- the digital twin services to be implemented by each model are the same, and the functions and models of the digital twin physical models involved in implementing the corresponding services are the same, then these sub-models are the same model, and these sub-models can be modeled using the same digital twin model merging method; In the same way, if the digital twin services to be implemented by each model are the same, and the digital twin physical models involved in implementing the corresponding services have the same functions, but there are three-dimensional geometric differences such as shape and size, then these sub-models are of the same type, and these sub-models can Use similar digital twin model merging methods for modeling; the above two model similarity determination methods are used to determine the similarity of the entire model.
- the model has a mixed similarity model, and the merging strategy of the mixed similarity model is derived.
- the construction of this non-critical model can be discarded, and the model similarity can be judged from the discarded simplified model.
- the model merging method includes two types of merging methods for the two similarities:
- Merging identical digital twin models refers to merging two or more identical digital twin models into one digital twin master model.
- Model similarity differentiation strategy if the models are the same model, the virtual model, service model, and connection model can be merged and modeled during the process of modeling the sub-model into the main model.
- the above merging method is detailed in Figure 1.
- the symbol ⁇ represents the combination operation.
- PE i represents the physical model of the sub-model of the digital twin model before merging.
- VE i is the virtual model of the sub-model of the digital twin model before merging.
- Ss i is the virtual model of the sub-model of the digital twin model before merging.
- the service model of the sub-model before model merging DD i is the twin data model of the sub-model before the digital twin model is merged, and CN i is the connection model of the sub-model before the digital twin model is merged.
- Each sub-model is a unique individual in reality, and the physical entities of the sub-models are different.
- the physical entity of the main model is the union of the physical entities of all sub-models;
- Each sub-model belongs to the same model, so the virtual entities of each sub-model are the same and can be replaced with each other, that is, the virtual entity of the main model is the virtual entity of any sub-model;
- each sub-model belongs to the same model, and the same digital twin service is the prerequisite for the model to belong to the same model. Therefore, the service model of each sub-model is the same, that is, the service model of the main model is the service model of any sub-model;
- Each sub-model belongs to the same model, and the twin data type of each sub-model is the same. However, during the operation of its equipment, the data of each sub-model is determined by the current operating status of the equipment in reality, so the twin data of each sub-model is different. , that is, the twin data is the union of the twin data of all sub-models;
- connection model realizes the interconnection between physical entities, virtual entities, service models, and twin data. Since the sub-models belong to the same model, the data collection method, service implementation mode, iterative optimization algorithm, etc. of each sub-model are the same. That is, the connection model of the main model is the connection model of any sub-model.
- the merging of similar digital twin models refers to the completion of merging two or more similar digital twin models into a digital twin master model.
- Model similarity differentiation strategy if the models are similar models, the service model and connection model can be merged and modeled during the process of modeling the sub-model into the main model.
- the above merging method is detailed in Figure 2.
- the symbol ⁇ represents the combination operation
- Pe i represents the physical model of the sub-model of the digital twin model before merging
- VE i is the virtual model of the sub-model of the digital twin model before merging
- Ss i is the virtual model of the sub-model of the digital twin model before merging.
- the service model of the sub-model before model merging DD i is the twin data model of the sub-model before the digital twin model is merged
- CN i is the connection model of the sub-model before the digital twin model is merged.
- the method of merging similar twin models in the five-dimensional structural model is as follows:
- Each sub-model is a unique individual in reality, and the physical entities of the sub-models are different.
- the physical entity of the main model is the union of the physical entities of all sub-models;
- Each sub-model belongs to the same type but is not the same model, so the virtual model of each sub-model is different.
- the virtual entity of the main model is the union of the virtual entities of all sub-models;
- Each sub-model belongs to the same type of model, and the same digital twin service is the prerequisite for the model to belong to the same type of model. Therefore, the service model of each sub-model is the same, that is, the service model of the main model is the service model of any sub-model;
- Each sub-model belongs to the same type of model, and the twin data type of each sub-model is the same. However, during the operation of its equipment, the data of each sub-model is determined by the current operating status of the equipment in reality, so the twin data of each sub-model is different. , that is, the twin data is the union of the twin data of all sub-models;
- connection model realizes the interconnection between physical entities, virtual entities, service models, and twin data. Since the sub-models are of the same type, the data collection method, service implementation mode, iterative optimization algorithm, etc. of each sub-model are the same. That is, the connection model of the main model is the connection model of any sub-model.
- the digital twin merging method is abstracted into digital twin merging thinking.
- digital twin modeling involves multiple models composed of multiple modules. The overall similarity of these models The level is lower than the similarity level of some modules that make up the model, and these modules with higher similarity are not highly coupled with other modules and can be connected in a simple way.
- a divide-and-conquer modeling strategy is adopted for the digital twin model, and the merging method is abstracted into a merging thinking.
- modules with higher similarity in each sub-model a higher similarity merging method is adopted, and other modules adopt a more similar merging method.
- the merging of hybrid similarity models includes three merging strategies:
- the merging strategy of different digital twin models with the same modules refers to merging two or more different digital twin models with the same modules into one main model. Identical modules that are not highly coupled with other modules are isolated in the model. This independent identical module is modeled separately using the same digital twin model merging method, and then the digital twin model of the same module is combined with the digital twin model of different modules. and form the main model.
- the above merging strategy is detailed in Figure 3.
- the merging strategy of different digital twin models with similar modules refers to merging two or more different digital twin models with similar modules into one main model. Perform digital twin modeling on multiple different digital twin models that have similar modules, and separate similar modules that are not highly coupled with other modules in the model. This independent similar module is modeled separately using the same digital twin model merging method. Then, the digital twin models of the same module and the digital twin models of different modules are added to form the main model.
- the above merging strategy is detailed in Figure 4.
- the merging strategy of similar digital twin models with the same modules refers to merging two or more similar digital twin models with the same modules into one main model. Perform digital twin modeling on multiple similar digital twin models that have the same modules, and separate the same modules that are not highly coupled with other modules in the model. This independent identical module is modeled separately using the same digital twin model merging method. Other modules are modeled separately using the same digital twin model merging method, and then the digital twin models of the same module and the digital twin models of other modules are added to form the main model.
- the above merging strategy is detailed in Figure 5.
- the merging of digital twin models is for the merging of the same dimensions in the twin models.
- each sub-model has three virtual entities, service models, and connection models. The dimensions are the same; in similar digital twin model merging methods, the service model and connection model dimensions of each sub-model are the same.
- the specific implementation methods of merging in the above three dimensions are as follows:
- the template includes the three-dimensional model of the sub-model, the multi-dimensional data structure that needs to be collected in the sub-model, the behavioral response interface of the sub-model and Data-driven rules of sub-models, etc.
- the construction of each sub-model is an instantiated copy of this virtual entity template. These instantiated copies run independently of each other, and each sub-model is driven by the data collected by the current model.
- each sub-model uses the same sensor, the same data transmission protocol, the same communication protocol, etc. during data transmission.
- a certain production workshop consists of multiple processing equipment ⁇ M1, M2...Mi...Mn ⁇ of the same model.
- the processing status of each processing equipment is collected through sensors.
- digital twin modeling with the goal of real-time monitoring of each processing equipment as an example, the detailed steps of the merging method of the same digital twin model are explained:
- each processing equipment operates independently, and there is no mutual constraint or assembly relationship between them. That is, each equipment is an independent model. Therefore, each device can be modeled independently, which is consistent with the application scenarios of the digital twin merging method.
- each processing equipment determines the similarity of each sub-model.
- the goal of each processing equipment is real-time monitoring, and their digital twin services are the same. Because each device has the same model and function, it meets the application scenario of merged modeling of the same digital twin model.
- the physical entities of the main model include the physical entities of all processing equipment, that is, the union of the physical entities of all sub-models;
- the virtual entities of each sub-model are the same, that is, the virtual entity of the main model is the virtual entity of any sub-model.
- the service model of each sub-model is the same, that is, the service model of the main model is the service model of any sub-model.
- twin data dimension the twin data of each sub-model is different, that is, the twin data is the union of the twin data of all sub-models.
- type of twin data collected by each device is the same, but during the operation of the device, each device is independent of each other.
- the twin data model of the device is determined by the current operating status of the device in reality. Therefore, the working data of each device obtained by the deployed sensors is different, so the main model needs to store the twin data collected by the sensors of each device.
- connection dimension the connection of each sub-model is the same, that is, the connection model of the main model is the connection model of any sub-model.
- a reusable virtual entity template of the processing equipment is constructed in the main model.
- the template includes the three-dimensional model of the processing equipment, the multi-dimensional data structure that needs to be collected in the processing equipment, the behavioral response interface of the processing equipment and Data-driven rules for processing equipment, etc.
- the digital twin model of each processing equipment is an instantiated copy of such a virtual entity template.
- the digital twin models of these processing equipment run independently of each other. Each equipment is driven by the data collected by the current model. .
- the twin service to be implemented is to monitor the working status of each equipment in the workshop in real time. It needs to display the real-time operating status of each equipment and detect whether there are abnormalities in the current equipment. Therefore, each equipment needs to The data types transmitted in real time are the same, the algorithm logic and simulation process are the same, the monitoring interface for each device in the application software is the same, and the function implementation code can be reused.
- the sensors set up to achieve real-time data transmission or the data transmission protocols used are the same, and the service implementation mode, iterative optimization algorithm, data transmission method, etc. can be reused in each sub-model.
- a certain production workshop is composed of multiple different types of logistics transportation equipment ⁇ V1, V2...Vi...Vn ⁇ . These logistics transportation have path planning for different types of logistics equipment, so as to plan the path of each logistics equipment. Taking digital twin modeling for a target as an example, the detailed steps of merging similar digital twin models are explained:
- each logistics transportation equipment operates independently, and there is no mutual constraint or assembly relationship between them. That is, each equipment is an independent model, so each equipment can be independently modeled, which is in line with the digital twin merger method.
- each logistics transportation equipment determines the similarity of each sub-model.
- the goal of each logistics transportation equipment is path planning, and their digital twin services are the same. Because the equipment involved in path planning has the same functions, but the equipment has three-dimensional geometric differences in shape, size, etc., it is consistent with the application scenario of merged modeling of similar digital twin models.
- the physical entities of the main model include the physical entities of all logistics and transportation equipment, that is, the union of the physical entities of all sub-models;
- the virtual entities of each sub-model are different, that is, the virtual entity of the main model is the union of the virtual entities of all sub-models.
- the virtual model of each logistics transportation equipment is different, and each different logistics transportation equipment needs to be modeled in the main model.
- the service model of each sub-model is the same, that is, the service model of the main model is the service model of any sub-model.
- twin data dimension the twin data of each sub-model is different, that is, the twin data is the union of the twin data of all sub-models.
- the twin data collected by each device is of the same type, including but not limited to the device's operating speed, acceleration, current absolute coordinates, etc.
- each device is independent of each other.
- the twin data model is determined by the current operating status of the device in reality. Therefore, the working data of each device obtained by the deployed depth camera is different. Therefore, the main model needs to store the twin data collected in real time for each device.
- connection dimension the connection of each sub-model is the same, that is, the connection model of the main model is the connection model of any sub-model.
- connection and service can be combined.
- specific implementation is as follows:
- the twin service to be implemented is the path planning for each logistics transportation equipment in the workshop. It is necessary to display the real-time operating status of each equipment and detect the movement and operation of the current equipment. Therefore, the data type transmitted by each device in real time is the same, the path algorithm logic and simulation process are the same, the monitoring interface for each device in the application software is the same, and the function implementation code can be reused.
- the depth camera or data transmission protocol used to monitor the real-time position and speed of each device is the same.
- the service implementation mode, path optimization algorithm, data transmission method, etc. can be used in each sub-model. Reuse.
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Abstract
本发明涉及数字孪生模型的合并方法，该方法针对数字孪生模型构建的过程中，相同或者相似的孪生模型合并流程和方法缺失的问题，提出了一种数字孪生模型合并方法，基于数字孪生五维概念模型，根据模型相似度的差异，提出两种基础的数字孪生模型合并方法：相同的数字孪生模型合并方法；同类数字孪生模型合并方法。并基于这两种合并方法，提出三种具有混合相似度的数字孪生模型合并策略。该方法定义了多个数字孪生模型在存在相似度的合并方法，细化了数字孪生五维结构模型在不同维度上的合并实施方式，减少了在数字孪生建模过程中重复工作，减少了因相似或者相同模型合并建模方法不明确导致的效率损失或者模型要素缺失。
Description
本发明属于数字孪生建模技术领域，尤其涉及一种数字孪生模型合并方法。
数字孪生技术是一种集成多物理、多尺度、多学科属性，具有实时同步、忠实映射、高保真度特性，能够实现物理世界与信息世界交互与融合的技术手段。具体来说，数字孪生是借助物理实体的虚拟模型，同步模拟物理实体在现实环境中的行为，为物理实体增加或扩展新的能力。数字孪生模型是数字孪生的重要组成部分，孪生模型将物理实体在虚拟世界进行数字化表达，是实现数字孪生技术的基础。尽管，近些年来数字孪生建模技术得到了飞速的发展，当前针对数字孪生建模的研究或应用主要集中在几何模型的构建上，但缺少对建模过程中可避免的重复工作的简化方法的研究。鉴于此，本发明针对相同或者相似的数字孪生模型构建的过程，其部分维度模型可重用的问题，提出了一种数字孪生模型合并方法。
发明内容
针对上述所述合并方法，其应用于数字孪生模型涉及到多个相互独立模型，这些模型间不存在相互间的约束或者装配关系，每个模型都可以独立建模，并且每个模型的数字孪生模型存在相似度，2019年1月公开的《计算机集成制造系统》中，介绍了数字孪生五维模型及十大领域应用，这些数字孪生模型的某些维度模型可以合并建模为相同的模型，该方法定义了每个维度下的合并方法。
为了实现上述目的，本发明的技术方案如下：一种数字孪生模型合并方法，所述方法包括以下步骤：
步骤1：规定数字孪生模型合并的基本应用场景和基本概念；
步骤2：基于所实现的服务及其物理实体模型区分模型的相似度；
步骤3：针对两种相似度，模型合并方法包含两类合并方法，并根据两种合并方法，提
出对存在混合相似度的模型的合并策略：
(1)对多个相同的数字孪生模型实现相同数字孪生模型合并方法；
(2)对多个同类的数字孪生模型实现同类数字孪生模型合并方法；
(3)对存在混合相似度的模型采取分治建模、相似合并的策略，混合相似度模型合并包括三种合并策略：
(31)对多个存在相同模块的不同数字孪生模型合并策略；
(32)对多个存在同类模块的不同数字孪生模型合并策略；
(33)对多个存在相同模块的同类数字孪生模型合并策略；
步骤4：合并建模在不同维度的具体实现方式。
步骤1中，规定数字孪生模型合并的基本应用场景和基本概念，当数字孪生模型涉及到多个相互独立模型，即这些模型间不存在相互间的约束或者装配关系，每个模型都可以独立建模，并且每个模型的数字孪生模型存在相似度，则这些数字孪生模型的某些建模维度可以合并建模为相同的模型，这个过程为数字孪生模型合并建模。这些模型独立建模的数字孪生模型称之为子模型，合并建模后的集合称之为主模型。
步骤2中，从数字孪生的物理实体模型及数字孪生模型实现的服务规定模型的相似度区分策略，将相似度等级依次降低分为：相同、同类、不同三级，在构建数字孪生模型前，根据数字孪生服务的目标及数字孪生物理实体来区分数字孪生模型的相似度，由于数字孪生模型构建最终目标是通过数字孪生模型的虚实交互反馈、数据融合分析、决策迭代优化等手段实现面向不同领域、不同用户、不同业务需求的服务，因此利用数字孪生服务和实体模型判断模型相似度，若每个模型要实现的数字孪生服务相同，实现相应服务涉及到的数字孪生物理模型功能、型号相同，则这些子模型为相同模型，这些子模型可以利用相同的数字孪生模型合并方法建模；若每个模型要实现的数字孪生服务相同，实现相应服务涉及到的数字孪生物理模型功能相同，但存在形状、尺寸等三维几何差异，则这些子模型属同类模型，这些子模型可以利用同类的数字孪生模型合并方法建模；上述两种模型相似度判定方式用于判断模型整体的相似度。此外，对于构成模型的部分模块的相似度等级高于模型整体的相似度等级的模型，可以判定模型为具有混合相似度模型，对此引申出混合相似度模型的合并策略,此外若模型的物理实体存在部分不同，但这部分不涉及到要实现的数字孪生服务，可以舍弃掉这部分不关键模型的构建，由舍弃后的简化模型判断模型相似度。
步骤3中的合并策略(1)中，相同的数字孪生模型合并是指完成两个或两个以上相同的数字孪生模型合并成一个数字孪生主模型。由模型的相似度区分策略，若模型为相同模型，则子模型建模为主模型过程中虚拟模型、服务模型、连接模型可以进行合并建模。
相同的孪生模型在五维结构模型中合并方法下：
1)每个子模型都是现实中独一无二的个体，子模型的物理实体不同，主模型的物理实体是所有子模型的物理实体并集；
2)每个子模型属于相同模型，故每个子模型的虚拟实体相同，可以相互替换，即主模型虚拟实体是任意一个子模型的虚拟实体；
3)每个子模型属于相同模型，数字孪生服务相同是模型属于相同模型的前提，因此每个 子模型的服务模型相同，即主模型的服务模型是任意一个子模型的服务模型；
4)每个子模型属于相同模型，每个子模型孪生数据类型相同，但在其设备运转的过程中，每个子模型的数据都由现实中当前设备的运行状态决定，因此每个子模型的孪生数据不同，即孪生数据是所有子模型的孪生数据的并集；
5)连接模型是实现物理实体、虚拟实体、服务模型、孪生数据之间的互联互通，由于子模型属相同模型，故每个子模型的数据采集方式、服务实现模式、迭代优化算法等都相同，即主模型的连接模型是任意一个子模型的连接模型。
步骤3中的合并策略(2)中，同类的数字孪生模型合并是指完成两个或两个以上同类的数字孪生模型合并成一个数字孪生主模型，由模型的相似度区分策略，若模型为同类模型，则子模型建模为主模型过程中服务模型、连接模型可以进行合并建模。
同类的孪生模型在五维结构模型中合并方法如下：
1)每个子模型都是现实中独一无二的个体，子模型的物理实体不同，主模型的物理实体是所有子模型的物理实体并集；
2)每个子模型属于同类的、但不相同模型，故每个子模型的虚拟模型不同，主模型的虚拟实体是所有子模型的虚拟实体的并集；
3)每个子模型属于同类模型，数字孪生服务相同是模型属于同类模型的前提，因此每个子模型的服务模型相同，即主模型的服务模型是任意一个子模型的服务模型；
4)每个子模型属于同类模型，每个子模型孪生数据类型相同，但在其设备运转的过程中，每个子模型的数据都由现实中当前设备的运行状态决定，因此每个子模型的孪生数据不同，即孪生数据是所有子模型的孪生数据的并集；
5)连接模型是实现物理实体、虚拟实体、服务模型、孪生数据之间的互联互通，由于子模型属同类模型，故每个子模型的数据采集方式、服务实现模式、迭代优化算法等都相同，即主模型的连接模型是任意一个子模型的连接模型。
步骤3中的合并策略(3)中，混合相似度模型其具体表现在，数字孪生建模涉及到的多个由多个模块组成的模型，这些模型整体的相似度等级低于构成模型的部分模块的相似度等级，并且这些相似度更高的模块与其他模块耦合度不高，通过简单的方式连接，此时对数字孪生模型采取一种分而治之的建模策略，将合并方法抽象为一种合并思维，对每个子模型中存在更高相似度的模块采取更高相似度的合并方法、其他模块采用更低相似度的合并建模方法或者一般建模方法，最后再将分治建模的模型合并为主模型，可以将混合相似度模型分为三种情况：存在相同模块的不同数字孪生模型、存在同类模块的不同数字孪生模型、存在相 同模块的同类数字孪生模型。
步骤3中的合并策略(31)中，对多个存在相同模块的不同数字孪生模型进行数字孪生建模，将与其他模块耦合度不高的相同模块在模型中独立出来，此独立的相同模块利用相同数字孪生模型合并方法单独建模，再将此相同模块的数字孪生模型与不同模块的数字孪生模型加和组成主模型。
步骤3中的合并策略(32)中，对多个存在同类模块的不同数字孪生模型进行数字孪生建模，将与其他模块耦合度不高的同类模块在模型中独立出来，此独立的同类模块利用同类数字孪生模型合并方法单独建模，再将此同类模块的数字孪生模型与不同模块的数字孪生模型加和组成主模型。
步骤3中的合并策略(33)中，对多个存在相同模块的同类数字孪生模型进行数字孪生建模，将与其他模块耦合度不高的相同模块在模型中独立出来，此独立的相同模块利用相同数字孪生模型合并方法单独建模，其他模块利用同类数字孪生模型合并方法单独建模，再将相同模块的数字孪生模型与其他模块的数字孪生模型加和组成主模型。
步骤4中：数字孪生模型的合并实施针对孪生模型中相同的维度的合并，相同的数字孪生模型合并方法中，每个子模型的虚拟实体、服务模型、连接模型三个维度相同；同类的数字孪生模型合并方法中，每个子模型的服务模型、连接模型两个维度相同，合并在上述三个维度中具体实施方式如下：
1)对于子模型中相同的虚拟实体，在主模型中构建可复用的虚拟实体模板，模板中包括子模型的三维模型、子模型中需要采集的多维数据结构、子模型的行为响应接口及子模型的数据驱动规则等，每子模型的构建都是对这个虚拟实体模板的实例化副本，这些实例化副本间相互独立运行，每个子模型中的受当前模型采集的数据驱动；
2)对于子模型中相同的服务模型，在主模型中构建模型管理服务、数据处理服务等服务算法及应用软件、移动端APP等在子模型中复用；
3)对于相同的连接模型，每个子模型在数据传输时采用相同的传感器、相同的数据传输协议、相同的通讯协议等。
相对于现有技术，本发明的优点如下，该方法主要探究了数字孪生合并两种方式，基本满足具有相似或者相同的数字孪生模型合并建模需求，能为生产车间的数字孪生转化提出指导性建议，并基于这两种合并方法，抽象出数字孪生模型合并思维，形成具有混合相似度的数字孪生模型合并策略。该方法定义了多个数字孪生模型在存在相似度的合并方法，细化了数字孪生模型建模在五维结构模型中的在不同维度上的合并实施方式，减少了在数字孪生建 模过程中重复工作，减少了因相似或者相同模型建模方法不明确导致的效率损失或者模型要素缺失。
图1为本发明的相同数字孪生模型合并方法示意图；
图2为本发明的同类数字孪生模型合并方法示意图；
图3为本发明的存在相同模块的不同数字孪生模型的合并策略示意图；
图4为本发明的存在同类模块的不同数字孪生模型的合并策略示意图；
图5为本发明的存在相同模块的同类数字孪生模型的合并策略示意图；
图6为本发明以生产车间为例的平面示意图。
本发明主要阐述了两种数字孪生合并方法，并基于两种合并方法提出了三种混合相似度模型的合并策略，这三种合并策略，是对两种合并方法在数字孪生模型模块层次的组合应用。故为了加深对本发明的认识和理解，下面将进一步介绍本发明主要阐述的两种方法：相同数字孪生模型的合并方法和同类数字孪生模型的合并方法。
实施例1：一种数字孪生模型合并方法，所述方法包括以下步骤：
步骤1：规定数字孪生模型合并的基本应用场景和基本概念；
步骤2：基于所实现的服务及其物理实体模型区分模型的相似度；
步骤3：针对两种相似度，模型合并方法包含两类合并方法，并根据两种合并方法，提
出对存在混合相似度的模型的合并策略：
(1)对多个相同的数字孪生模型实现相同数字孪生模型合并方法；
(2)对多个同类的数字孪生模型实现同类数字孪生模型合并方法；
(3)对存在混合相似度的模型采取分治建模、相似合并的策略，混合相似度模型合并包括三种合并策略：
(31)对多个存在相同模块的不同数字孪生模型合并策略；
(32)对多个存在同类模块的不同数字孪生模型合并策略；
(33)对多个存在相同模块的同类数字孪生模型合并策略；
步骤4：合并建模在不同维度的具体实现方式。
详细说明如下：
首先，数字孪生模型合并的基本应用场景为数字孪生模型涉及到多个相互独立模型，即这些模型间不存在相互间的约束或者装配关系，每个模型都可以独立建模，并且每个模型的 数字孪生模型存在相似度，则这些数字孪生模型的某些建模维度可以合并建模为相同的模型，这个过程为数字孪生模型合并建模。
随后根据数字孪生服务的目标及数字孪生物理实体来区分数字孪生模型的相似度。首先将相似度等级依次降低分为：相同、同类、不同三级。由于数字孪生模型构建最终目标是通过数字孪生模型的虚实交互反馈、数据融合分析、决策迭代优化等手段实现面向不同领域、不同用户、不同业务需求的服务，因此利用数字孪生服务和实体模型判断模型是否存在相似度。若每个模型要实现的数字孪生服务相同，实现相应服务涉及到的数字孪生物理模型功能、型号相同，则这些子模型为相同模型，这些子模型可以利用相同的数字孪生模型合并方法建模；同理，若每个模型要实现的数字孪生服务相同，实现相应服务涉及到的数字孪生物理模型功能相同，但存在形状、尺寸等三维几何差异，则这些子模型属同类模型，这些子模型可以利用同类的数字孪生模型合并方法建模；上述两种模型相似度判定方式用于判断模型整体的相似度，此外，对于构成模型的部分模块的相似度等级高于模型整体的相似度等级的模型，可以判定模型为具有混合相似度模型，对此引申出混合相似度模型的合并策略。此外若模型的物理实体存在部分不同，但这部分不涉及到要实现的数字孪生服务，可以舍弃掉这部分不关键模型的构建，由舍弃后的简化模型判断模型相似度。
根据上述方法，若数字孪生模型符合数字孪生模型合并方法中的应用场景和相似度要求，针对两种相似度，模型合并方法包含两类合并方法：
(1)相同的数字孪生模型合并方法
相同的数字孪生模型合并是指完成两个或两个以上相同的数字孪生模型合并成一个数字孪生主模型。模型的相似度区分策略，若模型为相同模型，则子模型建模为主模型过程中虚拟模型、服务模型、连接模型可以进行合并建模。
上述合并方法具体如图1，符号∪表示组合操作，PE
i代表数字孪生模型在合并前子模型的实体模型，VE
i是数字孪生模型在合并前子模型的虚拟模型，Ss
i是在数字孪生模型合并前子模型的服务模型，DD
i是在数字孪生模型合并前子模型的孪生数据模型，CN
i是在数字孪生模型合并前子模型的连接模型。
相同的孪生模型在五维结构模型中合并方法下：
1)每个子模型都是现实中独一无二的个体，子模型的物理实体不同，主模型的物理实体是所有子模型的物理实体并集；
2)每个子模型属于相同模型，故每个子模型的虚拟实体相同，可以相互替换，即主模型虚拟实体是任意一个子模型的虚拟实体；
3)每个子模型属于相同模型，数字孪生服务相同是模型属于相同模型的前提，因此每个子模型的服务模型相同，即主模型的服务模型是任意一个子模型的服务模型；
4)每个子模型属于相同模型，每个子模型孪生数据类型相同，但在其设备运转的过程中，每个子模型的数据都由现实中当前设备的运行状态决定，因此每个子模型的孪生数据不同，即孪生数据是所有子模型的孪生数据的并集；
5)连接模型是实现物理实体、虚拟实体、服务模型、孪生数据之间的互联互通，由于子模型属相同模型，故每个子模型的数据采集方式、服务实现模式、迭代优化算法等都相同，即主模型的连接模型是任意一个子模型的连接模型。
(2)同类的数字孪生模型合并方法
同类的数字孪生模型合并是指完成两个或两个以上同类的数字孪生模型合并成一个数字孪生主模型。模型的相似度区分策略，若模型为同类模型，则子模型建模为主模型过程中服务模型、连接模型可以进行合并建模。
上述合并方法具体如图2，符号∪表示组合操作，Pe
i代表数字孪生模型在合并前子模型的实体模型，VE
i是数字孪生模型在合并前子模型的虚拟模型，Ss
i是在数字孪生模型合并前子模型的服务模型，DD
i是在数字孪生模型合并前子模型的孪生数据模型，CN
i是在数字孪生模型合并前子模型的连接模型。
同类的孪生模型在五维结构模型中合并方法如下：
1)每个子模型都是现实中独一无二的个体，子模型的物理实体不同，主模型的物理实体是所有子模型的物理实体并集；
2)每个子模型属于同类的、但不相同模型，故每个子模型的虚拟模型不同，主模型的虚拟实体是所有子模型的虚拟实体的并集；
3)每个子模型属于同类模型，数字孪生服务相同是模型属于同类模型的前提，因此每个子模型的服务模型相同，即主模型的服务模型是任意一个子模型的服务模型；
4)每个子模型属于同类模型，每个子模型孪生数据类型相同，但在其设备运转的过程中，每个子模型的数据都由现实中当前设备的运行状态决定，因此每个子模型的孪生数据不同，即孪生数据是所有子模型的孪生数据的并集；
5)连接模型是实现物理实体、虚拟实体、服务模型、孪生数据之间的互联互通，由于子模型属同类模型，故每个子模型的数据采集方式、服务实现模式、迭代优化算法等都相同，即主模型的连接模型是任意一个子模型的连接模型。
基于上述两类合并方法，将数字孪生合并方法抽象为数字孪生合并思维，在数字孪生建 模过程中，数字孪生建模涉及到的多个由多个模块组成的模型，这些模型整体的相似度等级低于构成模型的部分模块的相似度等级，并且这些相似度更高的模块与其他模块耦合度不高，可以通过简单的方式连接。此时对数字孪生模型采取一种分而治之的建模策略，将合并方法抽象为一种合并思维，对每个子模型中存在更高相似度的模块采取更高相似度的合并方法、其他模块采用更低相似度的合并建模方法或者一般建模方法，最后再将分治建模的模型合并为主模型。应用上述两种合并方法，混合相似度模型的合并包括三种合并策略：
(1)存在相同模块的不同数字孪生模型合并策略
存在相同模块的不同数字孪生模型合并策略是指完成两个或两个以上存在相同模块的不同数字孪生模型合并为一个主模型。将与其他模块耦合度不高的相同模块在模型中独立出来，此独立的相同模块利用相同数字孪生模型合并方法单独建模，再将此相同模块的数字孪生模型与不同模块的数字孪生模型加和组成主模型，上述合并策略具体如图3。
(2)存在同类模块的不同数字孪生模型合并策略
存在同类模块的不同数字孪生模型合并策略是指完成两个或两个以上存在同类模块的不同数字孪生模型合并为一个主模型。对多个存在同类模块的不同数字孪生模型进行数字孪生建模，将与其他模块耦合度不高的同类模块在模型中独立出来，此独立的同类模块利用同类数字孪生模型合并方法单独建模，再将此同类模块的数字孪生模型与不同模块的数字孪生模型加和组成主模型，上述合并策略具体如图4。
(3)存在相同模块的同类数字孪生模型的合并策略
存在相同模块的同类数字孪生模型的合并策略是指完成两个或两个以上存在相同模块的同类数字孪生模型合并为一个主模型。对多个存在相同模块的同类数字孪生模型进行数字孪生建模，将与其他模块耦合度不高的相同模块在模型中独立出来，此独立的相同模块利用相同数字孪生模型合并方法单独建模，其他模块利用同类数字孪生模型合并方法单独建模，再将相同模块的数字孪生模型与其他模块的数字孪生模型加和组成主模型，上述合并策略具体如图5。
在数字孪生模型合并的具体实施过程中，数字孪生模型的合并是针对孪生模型中相同的维度的合并，相同的数字孪生模型合并方法中，每个子模型的虚拟实体、服务模型、连接模型三个维度相同；同类的数字孪生模型合并方法中，每个子模型的服务模型、连接模型两个维度相同。合并在上述三个维度中具体实现方式如下：
1)对于子模型中相同的虚拟实体，在主模型中构建可复用的虚拟实体模板，模板中包括子模型的三维模型、子模型中需要采集的多维数据结构、子模型的行为响应接口及子模型的 数据驱动规则等，每子模型的构建都是对这个虚拟实体模板的实例化副本，这些实例化副本间相互独立运行，每个子模型中的受当前模型采集的数据驱动。
2)对于子模型中相同的服务模型，在主模型中构建模型管理服务、数据处理服务等服务算法及应用软件、移动端APP等在子模型中复用。
3)对于相同的连接模型，每个子模型在数据传输时采用相同的传感器、相同的数据传输协议、相同的通讯协议等。
实施例2：
如图6，某一生产车间由多台相同型号的加工设备{M1,M2…Mi…Mn}组成，现为实现对每台加工设备的实时监控，通过传感器采集每台加工设备的加工状态，以对每台加工设备进行实时监控为目标进行数字孪生建模为例，阐述相同数字孪生模型的合并方法的详细步骤：
首先，判断此建模过程是否符合数字孪生合并方法的应用场景，在此车间中每台加工设备独立运行，它们之间不存在相互间的约束或者装配关系，即每台设备为相互独立模型，因此每台设备都可以独立建模，符合数字孪生合并方法的应用场景。
其次，判断每个子模型的相似度。每台加工设备要实现的目标都为实时监控，它们的数字孪生服务相同。因为每台设备的型号、功能相同，故符合相同数字孪生模型的合并建模的应用场景。
因此，可以对车间多台设备在物理实体、虚拟实体、连接、孪生数据和服务五个维度进行利用相同数字孪生模型的合并方法，子模型各个维度合并为主模型后结果如下：
1)在物理实体维度上，主模型的物理实体包含所有加工设备的物理实体，即所有子模型的物理实体的并集；
2)在虚拟实体维度上，每个子模型的虚拟实体相同，即主模型虚拟实体是任意一个子模型的虚拟实体。
3)在服务模型维度上，每个子模型的服务模型相同，即主模型的服务模型是任意一个子模型的服务模型。
4)在孪生数据维度上，每个子模型的孪生数据不同，即孪生数据是所有子模型的孪生数据的并集。在数字孪生建模过程中，每台设备采集的孪生数据类型相同，但在其设备运转的过程中，每台设备相互独立，设备的孪生数据模型都由现实中当前设备的运行状态决定的，因此部署的传感器获得的每台设备的工作数据不相同，因此主模型需要储存每台设备的传感器采集来的孪生数据。
5)在连接维度上，每个子模型的连接都相同，即主模型的连接模型是任意一个子模型的连接模型。
对虚拟实体、连接、服务三个维度进行合并，具体实施方式如下：
1)在虚拟实体维度上，在主模型中构建可复用的加工设备的虚拟实体模板，模板中包括加工设备的三维模型、加工设备中需要采集的多维数据结构、加工设备的行为响应接口及加工设备的数据驱动规则等，每个加工设备的数字孪生模型都是这样一个虚拟实体模板的一个实例化副本，这些加工设备的数字孪生模型相互独立运行，每个设备受当前模型采集的数据驱动。
2)在服务模型维度上，要实现的孪生服务为对车间中每个设备的工作情况进行实时监控，需要展示每台设备的实时运行状态，并且检测当前设备是否存在异常，因此每台设备在实时传输的数据类型相同、算法逻辑和仿真过程相同，应用软件中对每台设备的监控界面相同、功能实现代码可以复用。
3)在连接维度上，为实现实时数据传输所设置的传感器、或者采用的数据传输协议相同，服务实现模式、迭代优化算法、数据传输方式等在每个子模型中都可复用。
综上所述，实现该加工车间的多台相同加工设备的数字孪生模型的合并建模。
实施例3：
如图6，某一生产车间由多台不同型号的物流运输设备{V1,V2…Vi…Vn}组成，这些物流运输具有不同型号的物流设备的路径规划，以对每台物流设备的路径规划为目标进行数字孪生建模为例，阐述同类数字孪生模型的合并方法的详细步骤：
首先，判断此建模过程是否符合数字孪生合并方法的应用场景。在此车间中每台物流运输设备独立运行，它们之间不存在相互间的约束或者装配关系，即每台设备为相互独立模型，因此每台设备都可以独立建模，符合数字孪生合并方法的应用场景。
其次，判断每个子模型的相似度。每台物流运输设备要实现的目标都为路径规划，它们的数字孪生服务相同。因为实现路径规划涉及到的设备功能相同，但设备存在形状、尺寸等三维几何差异故符合同类数字孪生模型的合并建模的应用场景。
因此，可以对车间多台设备在物理实体、虚拟实体、连接、孪生数据和服务五个维度进行利用同类数字孪生模型的合并方法，子模型各个维度合并为主模型后结果如下：
1)在物理实体维度上，主模型的物理实体包含所有的物流运输设备的物理实体，即所有子模型的物理实体的并集；
2)在虚拟实体维度上，每个子模型的虚拟实体不同，即主模型虚拟实体是所有子模型 的虚拟实体的并集。在数字孪生建模过程中，每台物流运输设备的虚拟模型不同，在主模型中需要对每台不同的物流运输设备建模。
3)在服务模型维度上，每个子模型的服务模型相同，即主模型的服务模型是任意一个子模型的服务模型。
4)在孪生数据维度上，每个子模型的孪生数据不同，即孪生数据是所有子模型的孪生数据的并集。在数字孪生建模过程中，每台设备采集的孪生数据类型相同，包括但不限于设备运行速度、加速度、当前绝对坐标等，但在其设备运转的过程中，每台设备相互独立，设备的孪生数据模型都由现实中当前设备的运行状态决定的，因此部署的深度摄像头获得的每台设备的工作数据不相同，因此主模型需要储存每台设备的实时采集来的孪生数据。
5)在连接维度上，每个子模型的连接都相同，即主模型的连接模型是任意一个子模型的连接模型。
可以对连接、服务两个维度进行合并，具体实施方式如下：
1)在服务模型维度上，要实现的孪生服务为要实现的孪生服务为对车间中每个物流运输设备的路径规划，需要展示每台设备的实时运行状态，并且检测当前设备的运动和运行数据，因此每台设备在实时传输的数据类型相同、路径算法逻辑和仿真过程相同，应用软件中对每台设备的监控界面相同、功能实现代码可以复用。
2)在连接维度上，为实现对每台设备实时位置和速度监控设置的深度摄像头或者采用的数据传输协议相同，服务实现模式、路径优化算法算法、数据传输方式等在每个子模型中都可复用。
综上所述，实现了该生产车间的运输系统物流设备的数字孪生模型的合并建模。
Claims (10)
- 一种数字孪生模型合并方法，其特征在于，所述方法包括以下步骤：步骤1：规定数字孪生模型合并的应用场景和概念；步骤2：基于所实现的服务及其物理实体模型区分模型的相似度；步骤3：针对相同和同类两种相似度，模型合并方法包含两类合并方法，并根据两种合并方法，提出对存在混合相似度的模型的合并策略：(1)对多个相同的数字孪生模型实现相同数字孪生模型合并方法；(2)对多个同类的数字孪生模型实现同类数字孪生模型合并方法；(3)基于上述合并方法，提出对存在混合相似度的模型采取分治建模、相似合并的策略，混合相似度模型合并包括三种合并策略：(31)对多个存在相同模块的不同数字孪生模型合并策略；(32)对多个存在同类模块的不同数字孪生模型合并策略；(33)对多个存在相同模块的同类数字孪生模型合并策略；步骤4：合并建模在不同维度的具体实现方式。
- 根据权利要求1所述的数字孪生模型合并方法，其特征在于，步骤1中，规定数字孪生模型合并的基本应用场景和基本概念，当数字孪生模型涉及到多个相互独立模型，即这些模型间不存在相互间的约束或者装配关系，每个模型都独立建模，并且每个模型的数字孪生模型存在相似度，则这些数字孪生模型的某些建模维度合并建模为相同的模型，这个过程为数字孪生模型合并建模，这些独立建模的数字孪生模型称之为子模型，合并建模后的集合称之为主模型。
- 根据权利要求1所述的数字孪生模型合并方法，其特征在于，步骤2中，从数字孪生的物理实体模型及数字孪生模型实现的服务规定模型的相似度区分策略，将相似度等级依次降低分为：相同、同类、不同三级，在构建数字孪生模型前，根据数字孪生服务的目标及数字孪生物理实体来区分数字孪生模型的相似度，由于数字孪生模型构建最终目标是通过数字孪生模型的虚实交互反馈、数据融合分析、决策迭代优化手段实现面向不同领域、不同用户、不同业务需求的服务，因此利用数字孪生服务和实体模型判断模型相似度，若每个模型要实现的数字孪生服务相同，实现相应服务涉及到的数字孪生物理模型功能、型号相同，则这些子模型为相同模型，这些子模型利用相同的数字孪生模型合并方法建模；若每个模型要实现的数字孪生服务相同，实现相应服务涉及到的数字孪生物理模型功能相同，但存在形状、尺寸三维几何差异，则这些子模型属同类模型，这些子模型利用同类的数字孪生模型合并方法建模；上述两种模型相似度判定方式用于判断模型整体的相似度。
- 根据权利要求1所述的数字孪生模型合并方法，其特征在于，步骤3中的合并策略(1)中，相同的数字孪生模型合并是指完成两个或两个以上相同的数字孪生模型合并成一个 数字孪生主模型，由模型的相似度区分策略，若模型为相同模型，则子模型建模为主模型过程中虚拟模型、服务模型、连接模型进行合并建模，相同的孪生模型在五维结构模型中合并方法下：1)每个子模型都是现实中独一无二的个体，子模型的物理实体不同，主模型的物理实体是所有子模型的物理实体并集；2)每个子模型属于相同模型，故每个子模型的虚拟实体相同，相互替换，即主模型虚拟实体是任意一个子模型的虚拟实体；3)每个子模型属于相同模型，数字孪生服务相同是模型属于相同模型的前提，因此每个子模型的服务模型相同，即主模型的服务模型是任意一个子模型的服务模型；4)每个子模型属于相同模型，每个子模型孪生数据类型相同，但在其设备运转的过程中，每个子模型的数据都由现实中当前设备的运行状态决定，因此每个子模型的孪生数据不同，即孪生数据是所有子模型的孪生数据的并集；5)连接模型是实现物理实体、虚拟实体、服务模型、孪生数据之间的互联互通，由于子模型属相同模型，故每个子模型的数据采集方式、服务实现模式、迭代优化算法都相同，即主模型的连接模型是任意一个子模型的连接模型。
- 根据权利要求1所述的数字孪生模型合并方法，其特征在于，步骤3中的合并策略(2)中，同类的数字孪生模型合并是指完成两个或两个以上同类的数字孪生模型合并成一个数字孪生主模型，由模型的相似度区分策略，若模型为同类模型，则子模型建模为主模型过程中服务模型、连接模型进行合并建模，同类的孪生模型在五维结构模型中合并方法如下：1)每个子模型都是现实中独一无二的个体，子模型的物理实体不同，主模型的物理实体是所有子模型的物理实体并集；2)每个子模型属于同类的、但不相同模型，故每个子模型的虚拟模型不同，主模型的虚拟实体是所有子模型的虚拟实体的并集；3)每个子模型属于同类模型，数字孪生服务相同是模型属于同类模型的前提，因此每个子模型的服务模型相同，即主模型的服务模型是任意一个子模型的服务模型；4)每个子模型属于同类模型，每个子模型孪生数据类型相同，但在其设备运转的过程中，每个子模型的数据都由现实中当前设备的运行状态决定，因此每个子模型的孪生数据不同，即孪生数据是所有子模型的孪生数据的并集；5)连接模型是实现物理实体、虚拟实体、服务模型、孪生数据之间的互联互通，由于子模型属同类模型，故每个子模型的数据采集方式、服务实现模式、迭代优化算法都相同，即主模型的连接模型是任意一个子模型的连接模型。
- 根据权利要求1所述的数字孪生模型合并方法，其特征在于，步骤3中的合并策略(3)中，混合相似度模型其具体表现在，数字孪生建模涉及到的多个由多个模块组成的模型，这些模型整体的相似度等级低于构成模型的部分模块的相似度等级，并且这些相似度更高的模块与其他模块耦合度不高，通过简单的方式连接，此时对数字孪生模型采取一种分而治之的建模策略，将合并方法抽象为一种合并思维，对每个子模型中存在更高相似度的模块采取更高相似度的合并方法、其他模块采用更低相似度的合并建模方法或者一般建模方法，最后再将分治建模的模型合并为主模型，将混合相似度模型分为三种情况：存在相同模块的不同数字孪生模型、存在同类模块的不同数字孪生模型、存在相同模块的同类数字孪生模型。
- 根据权利要求1所述的数字孪生模型合并方法，其特征在于，步骤3中的合并策略(31)中，对多个存在相同模块的不同数字孪生模型进行数字孪生建模，将与其他模块耦合度不高的相同模块在模型中独立出来，此独立的相同模块利用相同数字孪生模型合并方法单独建模，再将此相同模块的数字孪生模型与不同模块的数字孪生模型加和组成主模型。
- 根据权利要求1所述的数字孪生模型合并方法，其特征在于，步骤3中的合并策略(32)中，对多个存在同类模块的不同数字孪生模型进行数字孪生建模，将与其他模块耦合度不高的同类模块在模型中独立出来，此独立的同类模块利用同类数字孪生模型合并方法单独建模，再将此同类模块的数字孪生模型与不同模块的数字孪生模型加和组成主模型。
- 根据权利要求1所述的数字孪生模型合并方法，其特征在于，步骤3中的合并策略(33)中，对多个存在相同模块的同类数字孪生模型进行数字孪生建模，将与其他模块耦合度不高的相同模块在模型中独立出来，此独立的相同模块利用相同数字孪生模型合并方法单独建模，其他模块利用同类数字孪生模型合并方法单独建模，再将相同模块的数字孪生模型与其他模块的数字孪生模型加和组成主模型。
- 根据权利要求1所述的数字孪生模型合并方法，其特征在于，步骤4中：数字孪生模型的合并实施针对孪生模型中相同的维度的合并，相同的数字孪生模型合并方法中，每个子模型的虚拟实体、服务模型、连接模型三个维度相同；同类的数字孪生模型合并方法中，每个子模型的服务模型、连接模型两个维度相同，合并在上述三个维度中具体实施方式如下：1)对于子模型中相同的虚拟实体，在主模型中构建可复用的虚拟实体模板，模板中包括子模型的三维模型、子模型中需要采集的多维数据结构、子模型的行为响应接口及子模型的数据驱动规则，每子模型的构建都是对这个虚拟实体模板的实例化副本，这些实例化副本间相互独立运行，每个子模型中的受当前模型采集的数据驱动；2)对于子模型中相同的服务模型，在主模型中构建模型管理服务、数据处理服务算法及应用软件、移动端APP在子模型中复用；3)对于相同的连接模型，每个子模型在数据传输时采用相同的传感器、相同的数据传输 协议、相同的通讯协议。
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| CN112100155A (zh) * | 2020-09-09 | 2020-12-18 | 北京航空航天大学 | 一种云边协同的数字孪生模型组装与融合方法 |
| US20220058182A1 (en) * | 2020-08-18 | 2022-02-24 | Electronics And Telecommunications Research Institute | Method and apparatus for configurating digital twin |
| CN114117619A (zh) * | 2021-12-15 | 2022-03-01 | 北京航空航天大学 | 一种数字孪生车间可组态可重构构建方法和系统 |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111538294A (zh) * | 2019-11-15 | 2020-08-14 | 武汉理工大学 | 基于数字孪生的工业机器人制造系统可重构系统与方法 |
| US20220058182A1 (en) * | 2020-08-18 | 2022-02-24 | Electronics And Telecommunications Research Institute | Method and apparatus for configurating digital twin |
| CN112100155A (zh) * | 2020-09-09 | 2020-12-18 | 北京航空航天大学 | 一种云边协同的数字孪生模型组装与融合方法 |
| CN114117619A (zh) * | 2021-12-15 | 2022-03-01 | 北京航空航天大学 | 一种数字孪生车间可组态可重构构建方法和系统 |
| CN114706842A (zh) * | 2022-06-02 | 2022-07-05 | 东南大学 | 一种数字孪生模型组装方法 |
| CN115130333A (zh) * | 2022-09-01 | 2022-09-30 | 东南大学 | 一种数字孪生模型合并方法 |
Non-Patent Citations (2)
| Title |
|---|
| FEI TAO, LIU WEIRAN; ZHANG MENG; HU TIANLIANG; QI QINGLIN: "Five-dimension digital twin model and its ten applications", COMPUTER INTEGRATED MANUFACTURING SYSTEMS, vol. 25, no. 1, 15 January 2019 (2019-01-15), pages 1 - 18, XP093143289 * |
| FEI TAO, ZHANG HE; QI QINGLIN; XU JUN; SUN ZHENG: "Theory of digital twin modeling and its application", COMPUTER INTEGRATED MANUFACTURING SYSTEMS, vol. 27, no. 1, 15 January 2021 (2021-01-15), pages 1 - 15, XP093143285 * |
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