Event digital twinning method, system and equipment

Abstract

The invention discloses an event digital twinning method, system and device, and relates to the technical field of digital twinning. The method comprises the following steps: building a digital twinborn model of an event; creating a non-relational database supporting horizontal expansion; acquiring a plurality of pieces of information of each object contained in the event in real time, and storing the information in the non-relational database in an array form; taking each object in the non-relational database and each piece of information of each object as an entity, extracting an entity relationship, and constructing a knowledge graph of the event; mapping a plurality of pieces of information of each object obtained in real time to the digital twin model; and according to the knowledge graph of the event, optimizing the mapped digital twinborn model to obtain a real-time digital twinborn model of the event. According to the method, the digital twinborn model can be optimized according to the change relation between the objects, and the universality and accuracy of digital twinborn model mapping are continuously improved.

Description

Technical Field

[0001] The present application relates to the field of digital twin technology, and in particular to an event digital twin method, system and device. Background Art

[0002] Digital twins make full use of data such as physical models, sensor updates, and operation history, integrate multi-disciplinary, multi-physical, multi-scale, and multi-probability simulation processes, and complete mapping in virtual space. Digital twins establish a combination, association, and unified query platform for existing data.

[0003] At present, the digital twin method can provide real-time feedback on the status of an event, but it cannot reflect the full change relationship between objects during the development of the event. When the development and changes exceed the boundaries set by the original system, it will be difficult or impossible to implement the description system under the original system, affecting the versatility and accuracy of the digital twin model. Summary of the invention

[0004] The purpose of this application is to provide an event digital twin method, system and device, which can optimize the digital twin model according to the change relationship between the objects themselves and the objects, and continuously improve the versatility and accuracy of the digital twin model mapping.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In the first aspect, the present application provides an event digital twin method, including: building a digital twin model of an event; creating a non-relational database that supports horizontal expansion; obtaining multiple pieces of information for each object contained in the event in real time, and storing them in the form of an array in the non-relational database; treating each object in the non-relational database and each piece of information of each object as an entity, extracting entity relationships, and constructing a knowledge graph of the event; mapping the multiple pieces of information of each object obtained in real time to the digital twin model; optimizing the mapped digital twin model according to the knowledge graph of the event to obtain a real-time digital twin model of the event.

[0007] In the second aspect, the present application provides an event digital twin system, including: a non-relational database, a processing unit and a human-computer interaction unit; the human-computer interaction unit and the processing unit are both connected to the non-relational database; the human-computer interaction unit is used to record multiple pieces of information of each object contained in the event in real time, and store them in the non-relational database; the processing unit is used to build a digital twin model of the event, and take each object in the non-relational database and each piece of information of each object as an entity, extract the relationship between entities, and construct a knowledge graph of the event; based on the knowledge graph of the event, the multiple pieces of information of each object obtained in real time are mapped to the digital twin model to obtain a real-time digital twin model of the event; the human-computer interaction unit is also used to display the real-time digital twin model of the event.

[0008] In a third aspect, the present application provides a computer device comprising: a memory, a processing unit, and a computer program stored in the memory and executable on the processing unit, wherein the processing unit executes the computer program to implement any of the event digital twin methods described above.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects:

[0010] The present application provides an event digital twin method, system and device, creates a non-relational database that supports horizontal expansion, and stores objects and information in the form of arrays in the non-relational database. Since the non-relational database can be dynamically expanded horizontally according to actual needs, it can adapt to various application scenarios and data requirements. By treating each object in the non-relational database and each piece of information of each object as an entity, extracting entity relationships, and constructing a knowledge graph of events, the knowledge graph connects the associations between entities in the form of edges, thereby reflecting the development and change process of objects and information in the event through entity nodes and edges, adjusting and optimizing the mapped digital twin model, and continuously improving the versatility and accuracy of the digital twin model mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 This is an application environment diagram of an event digital twin method in one embodiment of the present application;

[0013] Figure 2 A schematic diagram of a flow chart of an event digital twin method provided in one embodiment of the present application;

[0014] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0016] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0017] The event digital twin method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send multiple pieces of information of each object contained in the event to the server 104. After the server 104 receives the multiple pieces of information of each object contained in the event, for the multiple pieces of information of each object contained in the event, the server 104 builds a digital twin model of the event; creates a non-relational database that supports horizontal expansion; obtains multiple pieces of information of each object contained in the event in real time, and stores them in the form of an array to the non-relational database; treats each object in the non-relational database and each piece of information of each object as an entity, extracts the entity relationship, and constructs a knowledge graph of the event; according to the knowledge graph of the event, the multiple pieces of information of each object obtained in real time are mapped to the digital twin model to obtain a real-time digital twin model of the event. The server 104 can feedback the obtained real-time digital twin model of the event to the terminal 102. In addition, in some embodiments, the event digital twin method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform event digital twins on multiple pieces of information for each object included in the event, or the server 104 can obtain multiple pieces of information for each object included in the event from the data storage system and perform event digital twins on the multiple pieces of information for each object included in the event.

[0018] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0019] In an exemplary embodiment, Figure 2 As shown, an event digital twin method is provided, which is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps 201 to 206. Among them:

[0020] Step 201: Build a digital twin model of the event.

[0021] Step 202: Create a non-relational database that supports horizontal expansion.

[0022] Step 203: Acquire multiple pieces of information of each object included in the event in real time, and store them in the non-relational database in the form of an array.

[0023] Step 204: Take each object in the non-relational database and each piece of information of each object as an entity, extract the entity relationship, and construct a knowledge graph of the event.

[0024] Step 205: Map multiple pieces of information of each object acquired in real time to the digital twin model.

[0025] Step 206: According to the knowledge graph of the event, optimize the mapped digital twin model to obtain a real-time digital twin model of the event.

[0026] By implementing the above steps 201 to 206, a non-relational database that supports horizontal expansion is created. Objects and information are stored in the non-relational database in the form of arrays. Since the non-relational database can be dynamically expanded horizontally according to actual needs, it can adapt to various application scenarios and data requirements. By treating each object and each piece of information of each object in the non-relational database as an entity, extracting entity relationships, and constructing a knowledge graph of events, the knowledge graph connects the associations between entities in the form of edges, thereby reflecting the development and change process of objects and information in the event through entity nodes and edges, adjusting and optimizing the mapped digital twin model, and continuously improving the versatility and accuracy of the digital twin model mapping.

[0027] In another exemplary embodiment of the present application, the non-relational database supporting horizontal expansion constructed in the above step 202 does not require a pre-defined data structure and can adapt to various application scenarios and data requirements.

[0028] In another exemplary embodiment of the present application, the event in the above step 201 is the most basic component form of nature and human society. The most basic component form of nature and human society can be people, land, things, objects and organizations.

[0029] In another exemplary embodiment of the present application, in the above step 202, multiple pieces of information of each object included in the event are obtained in real time, and the multiple pieces of information may include: associated object number, relationship description, relationship description, relationship start and end time, entry time, entry person, remarks, etc.

[0030] In another exemplary embodiment of the present application, each piece of information of each object contained in the event obtained in real time is classified and described in a non-relational database in a scene language (non-standard or metadata). For example: when the object is a person, if the words spoken by a person contain dialects, they are still stored in the non-relational database in the dialect. Then when constructing the knowledge graph, different languages ​​expressing the same meaning need to be classified into the same category.

[0031] In another exemplary embodiment of the present application, in order to clarify the object relationship and development process, the integrity and relevance of the event can be revealed by constructing a knowledge graph. Then the above step 204 can be replaced by the following steps 301 to 303:

[0032] Step 301: each object in the non-relational database and each piece of information of each object are regarded as entities, and two are grouped into an entity pair.

[0033] Step 302: Extract entity pair relationships.

[0034] Step 303: Map entities to nodes and map extracted entity pair relationships to edges to obtain a knowledge graph of the event.

[0035] Knowledge graph is a technical method that uses graph models to describe knowledge and model the relationship between all things in the world. Knowledge graph consists of nodes and edges. Nodes can be entities, such as a person, a book, etc., or abstract concepts, such as artificial intelligence. Edges can be attributes of entities, such as names, book titles, or relationships between entities, such as friends.

[0036] By using the boundless storage of non-relational databases and mapping the stored data to a knowledge graph, the full range of change relationships between objects during the development of events can be found in the knowledge graph.

[0037] In another exemplary embodiment of the present application, the knowledge graph can be dynamically updated, and after the above step 204, the method can also include the following steps 401 to 403. Among them:

[0038] Step 401: Convert the latest entered object or information into structured data.

[0039] Step 402: extract and segment the structured data.

[0040] Step 403: Mine the relationship between the entity after word segmentation and the entity in the current knowledge graph, generate edges, and obtain the latest knowledge graph of the event.

[0041] The knowledge graph is constantly updated as the data is updated.

[0042] In another exemplary embodiment of the present application, the knowledge graph describes concepts, entities and their relationships in the objective world in a structured form, which can be applied to search, recommendation, intelligent question and answer and other fields. Search based on the knowledge graph includes semantic search, relationship search and structured display. After the above step 204, the method can also query nodes and paths in the knowledge graph of the event according to the retrieval request.

[0043] In another exemplary embodiment of the present application, the knowledge graph can be logically divided into two levels: the model layer and the data layer. The data layer is mainly composed of a series of facts, and knowledge will be stored in units of facts. If the facts are expressed by triples such as (entity 1, relationship, entity 2), (entity, attribute, attribute value), a graph database can be selected as the storage medium, such as the open source Neo4j, Twitter's FlockDB, sones' GraphDB, etc. After the above step 204, the method can also extract triple data from a non-relational database and store the triple data in a graph database. Exemplarily, the Neo4j graph database is used to store data in the form of a directed graph.

[0044] According to the knowledge graph of the event, the mapped digital twin model is optimized to restore the change process of each object contained in the event. By integrating the digital twin with the knowledge graph, real-time monitoring and analysis of events can be achieved. The system where the digital twin model of this application is located theoretically has a borderless normalized digital twin management architecture. When the development and changes exceed the boundaries set by the original system, it can still be implemented under the original system.

[0045] Based on the same inventive concept, the embodiment of the present application also provides an event digital twin system for implementing the event digital twin method involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more event digital twin system embodiments provided below can refer to the limitations of the event digital twin method above, and will not be repeated here.

[0046] In an exemplary embodiment, an event digital twin system is provided, including: a non-relational database, a processing unit, and a human-computer interaction unit. The human-computer interaction unit and the processing unit are both connected to the non-relational database; the human-computer interaction unit is used to record multiple pieces of information of each object contained in the event in real time, and store them in the non-relational database; the processing unit is used to build a digital twin model of the event, and each object and each piece of information of each object in the non-relational database are taken as entities, the relationship between entities is extracted, and the knowledge graph of the event is constructed; the multiple pieces of information of each object obtained in real time are mapped to the digital twin model; according to the knowledge graph of the event, the mapped digital twin model is optimized to obtain a real-time digital twin model of the event; the human-computer interaction unit is also used to display the real-time digital twin model of the event.

[0047] As an optional implementation, the event digital twin system further includes: a graph database. The graph database is used to store the knowledge graph of the event.

[0048] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processing unit, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processing unit, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processing unit of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store a real-time digital twin model of the event. The input / output interface of the computer device is used to exchange information between the processing unit and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processing unit, a method for realizing event digital twins is implemented.

[0049] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processing unit, wherein a computer program is stored in the memory, and the processing unit implements the steps in the above-mentioned method embodiments when executing the computer program.

[0050] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0051] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. An event digital twin method, characterized in that: include: Build a digital twin model of the event; Create a non-relational database that supports horizontal scalability; Acquire multiple pieces of information of each object included in the event in real time, and store them in the non-relational database in the form of an array; Treat each object and each piece of information of each object in the non-relational database as an entity, extract entity relationships, and build a knowledge graph of events; Mapping multiple pieces of information of each object acquired in real time to the digital twin model; According to the knowledge graph of the event, the mapped digital twin model is optimized to obtain a real-time digital twin model of the event.

2. The event digital twin method according to claim 1, characterized in that: The events described include: the most basic components of nature and human society.

3. The event digital twin method according to claim 1, characterized in that: Each piece of information of each object contained in the event acquired in real time is classified and described in the non-relational database in the form of scenario language.

4. The event digital twin method according to claim 1, characterized in that: Take each object and each piece of information of each object in the non-relational database as an entity, extract the entity relationship, and build the knowledge graph of the event, including: Each object and each piece of information of each object in the non-relational database is regarded as an entity, and two of them form an entity pair; Extract entity pair relationships; Map entities into nodes and the extracted entity pair relationships into edges to obtain the knowledge graph of the event.

5. The event digital twin method according to claim 1, characterized in that: Take each object and each piece of information of each object in the non-relational database as an entity, extract the entity relationship, and build the knowledge graph of the event, which also includes: Convert the latest entered objects or information into structured data; Extract and segment structured data; Mine the relationship between the entities after word segmentation and the entities in the current knowledge graph, generate edges, and obtain the latest knowledge graph of the event.

6. The event digital twin method according to claim 1, characterized in that: Take each object and each piece of information of each object in the non-relational database as an entity, extract the entity relationship, and build the knowledge graph of the event, which also includes: According to the search request, nodes and paths are queried in the knowledge graph of the event.

7. The event digital twin method according to claim 1, characterized in that: Take each object and each piece of information of each object in the non-relational database as an entity, extract the entity relationship, and build the knowledge graph of the event, which also includes: Triple data is extracted from the non-relational database, and the triple data is stored in a graph non-relational database.

8. An event digital twin system, characterized in that: The event digital twin system includes: a non-relational database, a processing unit and a human-computer interaction unit; Both the human-computer interaction unit and the processing unit are connected to a non-relational database; The human-computer interaction unit is used to record multiple pieces of information of each object included in the event in real time and store them in a non-relational database; The processing unit is used to build a digital twin model of the event, and take each object and each piece of information of each object in the non-relational database as an entity, extract the relationship between the entities, and build a knowledge graph of the event; map multiple pieces of information of each object obtained in real time to the digital twin model; optimize the mapped digital twin model according to the knowledge graph of the event to obtain a real-time digital twin model of the event; The human-computer interaction unit is also used to display a real-time digital twin model of an event.

9. The event digital twin system according to claim 8, characterized in that: The event digital twin system also includes: a graph database; The graph database is used to store the knowledge graph of events.

10. A computer device comprising: A memory, a processing unit, and a computer program stored in the memory and executable on the processing unit, wherein the processing unit executes the computer program to implement the event digital twin method according to any one of claims 1 to 7.

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