Digital twin and computing engine collaborative working method, device, equipment and medium

Abstract

The application discloses a digital twin and computing engine collaborative operation method and device, equipment and medium, relates to the technical field of digital twin, fault alarm, index calculation and model scheduling, and comprises the following steps: performing virtual-real mapping on a virtual digital twin instance to obtain a digital twin instance; binding to-be-bound data and the digital twin instance to obtain a target digital twin instance; configuring a rule configuration engine rule to obtain a target rule, determining different business operation types, screening a business computing engine for configuration, obtaining a target business computing engine, performing operation calculation based on the target rule and computing parameters and using the target business computing engine to obtain a calculation result; and feeding back the calculation result to the target digital twin instance to realize collaborative operation between the target digital twin instance and the target business computing engine, thereby saving cost, reducing development amount and personnel workload, improving collaborative operation efficiency and standardization, and increasing business scene diversity and universality.

Description

Technical Field

[0001] This invention relates to the fields of digital twins, fault alarms, index calculation, and model scheduling, and particularly to a method, apparatus, device, and medium for collaborative operation of a digital twin and a computing engine. Background Technology

[0002] Existing technologies, especially those of similar nature, often fail to achieve widespread applicability across diverse business scenarios. This leads to significant duplication of development for the same business needs, resulting in unnecessary costs and increased development effort. Other similar technologies typically manage the digital twin independently from business analysis and computation, creating data silos and adding extra processing steps when these two parts are linked, thus reducing data transfer and operational efficiency. Furthermore, current technologies heavily rely on manual data collection and processing, lacking standards and specifications, making business processes difficult to manage and systematize. When data evolution (adding more dimensions of data) is required, further development is needed, resulting in poor scalability and sustainability.

[0003] As can be seen from the above, how to save costs, reduce development workload and personnel workload, improve the efficiency and standardization of collaborative operations, and increase the diversity and versatility of business scenarios are problems that need to be solved in this field. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method, apparatus, device, and medium for collaborative operation between a digital twin and a computing engine, which can save costs, reduce development workload and personnel workload, improve the efficiency and standardization of collaborative operation, and increase the diversity and versatility of business scenarios. The specific solution is as follows:

[0005] In a first aspect, this application discloses a method for collaborative operation between a digital twin and a computing engine, including:

[0006] A virtual digital twin instance is established, and a virtual-to-real mapping is performed on the virtual digital twin instance to obtain a digital twin instance. Data to be bound is obtained, and the data to be bound is bound to the digital twin instance to obtain a target digital twin instance.

[0007] Utilize the local rule configuration engine to configure rules and obtain target rules. Determine different business operation types based on business needs and select the corresponding business computing engines from the local system.

[0008] Configure each of the business computing engines according to the calculation parameters to obtain the target business computing engine. Perform job calculation based on the target rules and the calculation parameters and using the target business computing engine to obtain the calculation result.

[0009] The calculation results are fed back to the target digital twin instance to enable collaborative operation between the target digital twin instance and the target business computing engine.

[0010] Optionally, establishing a virtual digital twin instance includes:

[0011] An initial virtual digital twin instance with a tree structure is created based on the core business objects;

[0012] Obtain structural parameters and configure the initial virtual digital twin instance using the structural parameters to obtain the virtual digital twin instance; the structural parameters include geographic location parameters, time axis parameters, metadata parameters, and master data parameters.

[0013] Optionally, the step of obtaining the data to be bound and binding the data to be bound with the digital twin instance to obtain the target digital twin instance includes:

[0014] Data to be bound is obtained from an IoT platform system connected locally; the data types of the data to be bound include real-time data, online data, and offline data.

[0015] The data points in the data to be bound are bound to the digital twin instance to obtain the target digital twin instance and the bound data information.

[0016] Optionally, after obtaining the target digital twin instance and binding data information, the method further includes:

[0017] Determine the scenario type of the bound data information; the scenario type includes real-time scenario type and historical scenario type;

[0018] If the scenario type is a real-time scenario type, the bound data information is saved to the local real-time repository.

[0019] If the scenario type is a historical scenario type, the binding data information will be saved to the local historical repository.

[0020] Optionally, the step of configuring rules using a local rule configuration engine to obtain target rules includes:

[0021] Get rule configuration parameters;

[0022] According to the preset rule configuration options and in combination with the rule configuration parameters, the local rule configuration engine is used to configure the rules to obtain the target rules; the rule configuration options include application programming interface call configuration options, streaming computing configuration options, and timed computing configuration options.

[0023] Optionally, the step of determining different business operation types based on business needs and selecting business computing engines corresponding to each business operation type from the local database includes:

[0024] Different business operation types are determined based on business needs;

[0025] Select the business computing engine corresponding to the business operation type from all local engines; the types of engines include fault analysis engine, indicator calculation engine, and model scheduling engine.

[0026] Optionally, after feeding the calculation result back to the target digital twin instance, the method further includes:

[0027] The target digital twin instance is labeled to obtain the labeled target digital twin instance;

[0028] The labeled target digital twin instance is used to generate and output target data including standard data structures, formats, and interfaces.

[0029] Secondly, this application discloses a device for collaborative operation of a digital twin and a computing engine, comprising:

[0030] A binding module is created to establish a virtual digital twin instance, perform virtual-real mapping on the virtual digital twin instance to obtain a digital twin instance, obtain data to be bound, and bind the data to be bound to the digital twin instance to obtain a target digital twin instance.

[0031] The engine determination module is used to configure rules using the local rule configuration engine to obtain target rules, determine different business operation types according to business needs, and filter the business computing engines corresponding to each business operation type from the local system.

[0032] The job calculation module is used to configure each of the business calculation engines according to the calculation parameters to obtain the target business calculation engine, and to perform job calculation based on the target rules and the calculation parameters and using the target business calculation engine to obtain the calculation result.

[0033] The collaborative operation module is used to feed back the calculation results to the target digital twin instance, so as to realize collaborative operation between the target digital twin instance and the target business computing engine.

[0034] Thirdly, this application discloses an electronic device, including:

[0035] Memory, used to store computer programs;

[0036] A processor is used to execute the computer program to implement the aforementioned method of collaborative operation between the digital twin and the computing engine.

[0037] Fourthly, this application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed method for collaborative operation of a digital twin and a computing engine.

[0038] As can be seen, this application provides a method for collaborative operation between a digital twin and a computing engine, including: establishing a virtual digital twin instance; performing virtual-real mapping on the virtual digital twin instance to obtain a digital twin instance; acquiring data to be bound and binding the data to be bound with the digital twin instance to obtain a target digital twin instance; configuring rules using a local rule configuration engine to obtain target rules; determining different business operation types according to business needs; selecting business computing engines corresponding to each business operation type from the local system; configuring each business computing engine according to calculation parameters to obtain a target business computing engine; performing job calculations based on the target rules and the calculation parameters using the target business computing engine to obtain calculation results; and feeding back the calculation results to the target digital twin instance to achieve collaborative operation between the target digital twin instance and the target business computing engine. This application utilizes a digital twin instance to bind data to obtain a target digital twin instance. By configuring various business computing engines, a target business computing engine is obtained, thereby increasing the diversity and versatility of business scenarios. It can solve the problems of end-user data application and the generalization of intermediate data interaction processes. The target business computing engine is used to perform job calculations to obtain calculation results, which are then fed back to the target digital twin instance to achieve collaborative operation between the target digital twin instance and the target business computing engine. It has strong scalability, transforming the original reliance on manual operations for various businesses into process operations, making data in each business link manageable and traceable. It also improves business processing efficiency and standardization, saves costs, and reduces development workload and personnel workload. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0040] Figure 1 This application discloses a flowchart of a method for collaborative operation between a digital twin and a computing engine.

[0041] Figure 2 This application discloses a flowchart of a fault analysis engine operation.

[0042] Figure 3 This application discloses a flowchart of an index calculation engine operation.

[0043] Figure 4 This application discloses a flowchart of a model scheduling engine job process.

[0044] Figure 5 This is a flowchart of another method for collaborative operation between a digital twin and a computing engine disclosed in this application;

[0045] Figure 6 This is a schematic diagram of a digital twin structure disclosed in this application;

[0046] Figure 7 This is a flowchart illustrating the collaborative operation of a digital twin and a multi-service type computing engine as disclosed in this application.

[0047] Figure 8 This is a schematic diagram of the structure of a digital twin and computing engine collaborative operation device disclosed in this application;

[0048] Figure 9 This application provides a structural diagram of an electronic device. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Existing technologies, especially those of similar nature, often fail to achieve widespread applicability across diverse business scenarios. This leads to significant duplication of development for the same scenarios, resulting in unnecessary cost and workload increases. Other similar technologies typically manage the digital twin independently from business analysis and computation, creating data silos and adding extra processing steps when these two parts are linked, thus reducing data transfer and operational efficiency. Furthermore, current technologies heavily rely on manual data collection and processing, lacking standards and specifications, making business processes difficult to manage and systematize. When data evolution (adding more dimensions) is required, further development is necessary, resulting in poor scalability and sustainability. Therefore, reducing costs, development workload, and personnel workload, improving the efficiency and standardization of collaborative work, and increasing the diversity and versatility of business scenarios are pressing issues in this field.

[0051] See Figure 1 As shown in the figure, this invention discloses a method for collaborative operation between a digital twin and a computing engine, which may specifically include:

[0052] Step S11: Establish a virtual digital twin instance, perform virtual-real mapping on the virtual digital twin instance to obtain a digital twin instance, obtain the data to be bound, and bind the data to be bound to the digital twin instance to obtain the target digital twin instance.

[0053] In this embodiment, after obtaining the digital twin instance, data to be bound is acquired from the locally connected IoT platform system. The data types of the data to be bound include real-time data, online data, and offline data. Data points in the data to be bound are bound to the digital twin instance to obtain the target digital twin instance and the bound data information. The scenario type of the bound data information is determined; the scenario type includes real-time scenario type and historical scenario type. If the scenario type is a real-time scenario type, the bound data information is saved to a local real-time repository; if the scenario type is a historical scenario type, the bound data information is saved to a local historical repository.

[0054] Specifically, after obtaining the digital twin instance, virtual-physical mapping is required to achieve data binding. This precondition is the acquisition of the data to be bound, which can be achieved through methods including, but not limited to, having a usable data source, theoretically real-time data acquisition from a usable data source, online databases (warehouses), and offline data files. In this application, the data source is device data collected in real-time by an accessed IoT platform system, which serves as the usable data source. This usable data is then used to access the digital twin instance, and virtual-physical mapping is achieved through association configuration, binding the data points in the data to be bound to the digital twin instance. At this point, external entities can access the data interface exposed by the digital twin instance to obtain the data information bound to the instance.

[0055] In this embodiment, after data binding is implemented, the bound data information is stored locally. There are two fixed storage methods: the first is real-time memory storage (for a small amount of data), which can be considered a real-time storage repository; the second is time-series data warehouse storage (for all data), which can be considered a historical storage repository. The purpose of distinguishing between real-time and historical storage repositories is to consider the different data usage scenarios and requirements to provide the best data acquisition method. Specifically, the data in the real-time storage repository is used to provide ultra-low latency data queries, such as providing input for multi-service computing engines and real-time push notifications, while the data in the historical storage repository is used to provide non-ultra-low latency queries, such as querying historical data and data statistical analysis.

[0056] Step S12: Configure rules using the local rule configuration engine to obtain target rules, determine different business operation types according to business needs, and select the business calculation engines corresponding to each business operation type from the local system.

[0057] In this embodiment, after obtaining the target rule, different business operation types are determined according to business requirements, and the business calculation engine corresponding to the business operation type is selected from all local engines; the types of engines include fault analysis engine, indicator calculation engine, and model scheduling engine.

[0058] Specifically, depending on different business needs, different business computing engines of different business operation types are selected, and computing parameters are injected into the business computing engine. The business computing engine will start to execute job calculations according to the rules defined by the rule configuration engine, the injected computing execution parameters, and the standard business processing flow. The calculation results will be integrated and reflected in the data structure of the digital twin instance and made publicly available.

[0059] Step S13: Configure each of the business computing engines according to the calculation parameters to obtain the target business computing engine, and perform job calculation based on the target rules and the calculation parameters using the target business computing engine to obtain the calculation result.

[0060] This application provides three standardized business type engines: a fault analysis engine, an indicator calculation engine, and a model scheduling engine. These three engines form a closed-loop business flow based on the digital twin established in steps 1 and 2, i.e., a "1+3" collaborative operation mode. During implementation, those conforming to the above three standard business specifications can directly use the standardized business flow to complete the business process. Unsupported business scenarios can be expanded to cover more scenarios, thus extending to a "1+N" collaborative operation mode, and its applicability can be gradually improved. The specific job calculation processes for the fault analysis engine, indicator calculation engine, and model scheduling engine are as follows: Figure 2 As shown, the rule configuration engine configures rules to obtain target rules. The fault analysis engine's job calculation is as follows: Based on the target rules, a fault system business model is created; the fault system business model is managed; a fault analysis task is created; the fault analysis task is executed; and thus, the calculation results are obtained. The job calculation process corresponding to the indicator calculation engine is as follows: Figure 3 As shown, the rule configuration engine configures rules to obtain target rules. The metric calculation engine's job calculation is as follows: based on the target rules, a data synchronization task is created, the data table is managed, then a data analysis task is created, the data analysis task is managed, and metric calculation is executed to obtain the calculation results. The job calculation process corresponding to the model scheduling engine is as follows. Figure 4 As shown, the rule configuration engine configures rules to obtain target rules, and the model scheduling engine performs the following job calculations: creating a model based on the target rules, managing the model, creating a model optimization task, managing the model optimization task, executing model optimization, and thus obtaining the calculation results.

[0061] Step S14: Feed back the calculation results to the target digital twin instance to realize collaborative operation between the target digital twin instance and the target business computing engine.

[0062] In this embodiment, the calculation results are fed back to the target digital twin instance, and the target digital twin instance is labeled to obtain the labeled target digital twin instance. The labeled target digital twin instance is used to generate and output target data including standard data structure, format and interface, so as to realize the collaborative operation between the target digital twin instance and the target business computing engine.

[0063] Specifically, the calculation results will be fed back to the target digital twin instance, and the associated target digital twin instance will be reinterpreted or annotated. Finally, the digital twin instance will provide standard data structures, formats, and interfaces for standard and unified output.

[0064] In this embodiment, a virtual digital twin instance is established, and a virtual-to-real mapping is performed on the virtual digital twin instance to obtain a digital twin instance. Data to be bound is obtained and bound to the digital twin instance to obtain a target digital twin instance. A local rule configuration engine is used to configure rules to obtain target rules. Different business operation types are determined according to business needs, and business computing engines corresponding to each business operation type are selected from the local database. Each business computing engine is configured according to calculation parameters to obtain a target business computing engine. Based on the target rules and the calculation parameters, job calculations are performed using the target business computing engine to obtain calculation results. The calculation results are fed back to the target digital twin instance to achieve collaborative operation between the target digital twin instance and the target business computing engine. This application utilizes a digital twin instance to bind data to obtain a target digital twin instance. By configuring various business computing engines, a target business computing engine is obtained, thereby increasing the diversity and versatility of business scenarios. It can solve the problems of end-user data application and the generalization of intermediate data interaction processes. The target business computing engine is used to perform job calculations to obtain calculation results, which are then fed back to the target digital twin instance to achieve collaborative operation between the target digital twin instance and the target business computing engine. It has strong scalability, transforming the original reliance on manual operations for various businesses into process operations, making data in each business link manageable and traceable. It also improves business processing efficiency and standardization, saves costs, and reduces development workload and personnel workload.

[0065] See Figure 5 As shown in the figure, this invention discloses a method for collaborative operation between a digital twin and a computing engine, which may specifically include:

[0066] Step S21: Establish an initial virtual digital twin instance with a tree structure based on the core business object, obtain the structure parameters, and configure the initial virtual digital twin instance using the structure parameters to obtain the virtual digital twin instance; the structure parameters include geographic location parameters, time axis parameters, metadata parameters, and master data parameters.

[0067] In this embodiment, an initial virtual digital twin instance needs to be created for the core business object, and this object instance needs to be described with structured semantics. The semantic content describing this instance is collectively referred to as "structural parameters." These structural parameters are configured to define the basic information of the initial virtual digital twin instance as concretely, clearly, and accurately as possible, such as: geographic location, timeline, metadata, master data, etc. Once the structural parameters are configured, the creation of the digital twin instance is complete. It is important to note that each initial virtual digital twin instance and each digital twin instance is presented in a tree structure. When configuring instances, the core business object needs to be deconstructed, and the business entity needs to be semantically described as a digital twin in a tree structure. The specific structure of the digital twin is as follows: Figure 6 As shown.

[0068] Step S22: Perform virtual-real mapping on the virtual digital twin instance to obtain a digital twin instance, obtain the data to be bound, and bind the data to be bound to the digital twin instance to obtain the target digital twin instance.

[0069] Step S23: Obtain rule configuration parameters, configure rules using the local rule configuration engine according to the preset rule configuration options and in combination with the rule configuration parameters to obtain the target rule; the rule configuration options include application programming interface call configuration options, streaming computing configuration options and timed computing configuration options, determine different business operation types according to business needs, and select the business computing engines corresponding to each business operation type from the local machine.

[0070] In this embodiment, a rule configuration engine is used for rule configuration, such as input source, running mode, running environment, and job execution rules. Here, rule configuration expresses the target computing engine job mode for multiple business types in the next step. Three common rule configuration options are provided, allowing rule configuration according to preset options and combined with rule configuration parameters to obtain the target rules. These rule configuration options are API (Application Programming Interface) call configuration options, streaming computing configuration options, and scheduled computing configuration options. This application allows users to not only choose to use rule configuration options, but also directly use standard configuration content combined with numerical fine-tuning to complete rule configuration, or choose not to use rule configuration options and complete job execution rule configuration through customization. This method is common in the implementation of atypical business requirement scenarios.

[0071] Step S24: Configure each of the business computing engines according to the calculation parameters to obtain the target business computing engine, and perform job calculation based on the target rules and the calculation parameters using the target business computing engine to obtain the calculation result.

[0072] Step S25: Feed back the calculation results to the target digital twin instance to realize collaborative operation between the target digital twin instance and the target business computing engine.

[0073] In this embodiment, after achieving collaborative operation between the target digital twin instance and the target business computing engine, data distribution is performed. Data distribution continuously provides data delivery services to downstream data business applications. Its service targets cover various application systems in the Sass (Software as a Service) layer and other related Pass (Platform-as-a-Service) layer service platforms, such as operation and management systems, big data platform systems, etc. The distributable data content includes: (1) basic data of the digital twin instance; (2) real-time running data of virtual-physical mapping; (3) calculation result data of multi-business computing engines; (4) real-time memory library storage data; (5) time-series data warehouse storage data. It should be noted that (1), (2), and (3) are uniformly exposed to the outside world through the standard interface of the digital twin instance, while (4) and (5) can be exposed to the outside world through independent standard interfaces via library queries.

[0074] The collaborative operation method of the digital twin + multi-service type computing engine in this application is as follows: Figure 7As shown, this application revolves around a digital twin as the main core, employing a collaborative operation method that combines multiple business-type computing engines. By using multi-dimensional data information to enrich the core data content of the digital twin through these multi-business-type computing engines, and then releasing this information in a standardized data format, it addresses issues related to end-user data applications and the generalization of intermediate data interaction processes. The specific steps are as follows: First, establish a virtual digital twin instance; then, perform virtual-real mapping on the virtual digital twin instance to obtain a digital twin instance, acquire the data to be bound, and bind the data to be bound to the digital twin instance to obtain a target digital twin instance; then, configure rules using a local rule configuration engine to obtain target rules; then, select different business operation types according to business needs, and filter the business computing engines corresponding to each business operation type from the local database; then, configure each business computing engine according to the calculation parameters to obtain a target business computing engine; perform job calculations based on the target rules and the calculation parameters using the target business computing engine to obtain calculation results; then, feed the calculation results back to the target digital twin instance to achieve collaborative operation between the target digital twin instance and the target business computing engine; finally, provide standard data structures, formats, and interfaces through the digital twin instance for standard and unified output, and distribute the data. Advantages of this application: (1) Applicable to multiple business scenarios, increasing the diversity of business scenarios: It proposes a collaborative operation mode of a composite multi-business type computing engine to solve the data application and data process service of multiple business scenarios based on digital twins, and proposes a business flow processing and data application applicable to three business scenarios: fault analysis, indicator calculation, and model algorithm; (2) Business process generalization: It standardizes and encapsulates the highly homogeneous business flow of typical scenarios, transforms the original reliance on manual operation of each business into process operation, making the data of each business link manageable and traceable, and also improving the efficiency and standardization of business processing; (3) Improves High efficiency of collaborative operation: Adopting the "1+N" closed-loop business model, three standardized business computing engines have established a business flow closed loop around the digital twin as the core carrier, realizing the elimination of information silos, strengthening the effective integration of digital twin and business, and improving business operation efficiency; (4) Promote the evolution of digital twin data: Through virtual-real mapping and feedback fusion, real-time data and business computing data are continuously fed back and integrated into the digital twin data structure, which can continuously promote the data evolution of digital twin and enrich its data dimensions; (5) Cross-platform and cross-language: Cross-platform data interaction is possible, such as accessing the Internet of Things platform to collect data as a usable data source. It provides a cross-language running environment and can directly run program code in different languages ​​such as Python, Java (JavaScript), and SQL (Structured Query Language).

[0075] In this embodiment, a virtual digital twin instance is established, and a virtual-to-real mapping is performed on the virtual digital twin instance to obtain a digital twin instance. Data to be bound is obtained and bound to the digital twin instance to obtain a target digital twin instance. A local rule configuration engine is used to configure rules to obtain target rules. Different business operation types are determined according to business needs, and business computing engines corresponding to each business operation type are selected from the local database. Each business computing engine is configured according to calculation parameters to obtain a target business computing engine. Based on the target rules and the calculation parameters, job calculations are performed using the target business computing engine to obtain calculation results. The calculation results are fed back to the target digital twin instance to achieve collaborative operation between the target digital twin instance and the target business computing engine. This application utilizes a digital twin instance to bind data to obtain a target digital twin instance. By configuring various business computing engines, a target business computing engine is obtained, thereby increasing the diversity and versatility of business scenarios. It can solve the problems of end-user data application and the generalization of intermediate data interaction processes. The target business computing engine is used to perform job calculations to obtain calculation results, which are then fed back to the target digital twin instance to achieve collaborative operation between the target digital twin instance and the target business computing engine. It has strong scalability, transforming the original reliance on manual operations for various businesses into process operations, making data in each business link manageable and traceable. It also improves business processing efficiency and standardization, saves costs, and reduces development workload and personnel workload.

[0076] See Figure 8 As shown in the figure, an embodiment of the present invention discloses a device for collaborative operation of a digital twin and a computing engine, which may specifically include:

[0077] A binding module 11 is created to establish a virtual digital twin instance, perform virtual-real mapping on the virtual digital twin instance to obtain a digital twin instance, obtain data to be bound, and bind the data to be bound to the digital twin instance to obtain a target digital twin instance.

[0078] Engine determination module 12 is used to configure rules using a local rule configuration engine to obtain target rules, determine different business operation types according to business needs, and filter the business calculation engines corresponding to each business operation type from the local system.

[0079] The job calculation module 13 is used to configure each of the business calculation engines according to the calculation parameters to obtain the target business calculation engine, and to perform job calculation based on the target rules and the calculation parameters and using the target business calculation engine to obtain the calculation result.

[0080] The collaborative operation module 14 is used to feed back the calculation results to the target digital twin instance, so as to realize the collaborative operation between the target digital twin instance and the target business computing engine.

[0081] In this embodiment, a virtual digital twin instance is established, and a virtual-to-real mapping is performed on the virtual digital twin instance to obtain a digital twin instance. Data to be bound is obtained and bound to the digital twin instance to obtain a target digital twin instance. A local rule configuration engine is used to configure rules to obtain target rules. Different business operation types are determined according to business needs, and business computing engines corresponding to each business operation type are selected from the local database. Each business computing engine is configured according to calculation parameters to obtain a target business computing engine. Based on the target rules and the calculation parameters, job calculations are performed using the target business computing engine to obtain calculation results. The calculation results are fed back to the target digital twin instance to achieve collaborative operation between the target digital twin instance and the target business computing engine. This application utilizes a digital twin instance to bind data to obtain a target digital twin instance. By configuring various business computing engines, a target business computing engine is obtained, thereby increasing the diversity and versatility of business scenarios. It can solve the problems of end-user data application and the generalization of intermediate data interaction processes. The target business computing engine is used to perform job calculations to obtain calculation results, which are then fed back to the target digital twin instance to achieve collaborative operation between the target digital twin instance and the target business computing engine. It has strong scalability, transforming the original reliance on manual operations for various businesses into process operations, making data in each business link manageable and traceable. It also improves business processing efficiency and standardization, saves costs, and reduces development workload and personnel workload.

[0082] In some specific embodiments, the creation binding module 11 may specifically include:

[0083] The initial instance creation module is used to create initial virtual digital twin instances with a tree structure based on core business objects;

[0084] An initial instance configuration module is used to obtain structural parameters and configure the initial virtual digital twin instance using the structural parameters to obtain the virtual digital twin instance; the structural parameters include geographic location parameters, time axis parameters, metadata parameters, and master data parameters.

[0085] In some specific embodiments, the creation binding module 11 may specifically include:

[0086] The data acquisition module is used to acquire data to be bound from an IoT platform system connected locally; the data types of the data to be bound include real-time data, online data, and offline data.

[0087] The binding module is used to bind data points in the data to be bound to the digital twin instance to obtain the target digital twin instance and the binding data information.

[0088] In some specific embodiments, the creation binding module 11 may specifically include:

[0089] The scenario type determination module is used to determine the scenario type of the bound data information; the scenario type includes real-time scenario type and historical scenario type;

[0090] The first information storage module is used to save the bound data information to a local real-time storage repository if the scene type is a real-time scene type.

[0091] The second information storage module is used to save the bound data information to a local historical repository if the scene type is a historical scene type.

[0092] In some specific embodiments, the engine determining module 12 may specifically include:

[0093] The parameter acquisition module is used to obtain rule configuration parameters;

[0094] The rule configuration module is used to configure rules using the local rule configuration engine according to preset rule configuration options and in combination with the rule configuration parameters to obtain target rules; the rule configuration options include application programming interface call configuration options, streaming computing configuration options, and timed computing configuration options.

[0095] In some specific embodiments, the engine determining module 12 may specifically include:

[0096] The business operation type determination module is used to determine different business operation types based on business requirements.

[0097] The business computing engine filtering module is used to filter the business computing engine corresponding to the business computing type from all local engines; the types of engines include fault analysis engine, indicator calculation engine, and model scheduling engine.

[0098] In some specific embodiments, the collaborative operation module 14 may specifically include:

[0099] The annotation module is used to annotate the target digital twin instance to obtain the annotated target digital twin instance;

[0100] The target data output module is used to generate and output target data, including standard data structures, formats, and interfaces, using the labeled target digital twin instance.

[0101] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the digital twin and computing engine collaborative operation method disclosed in any of the foregoing embodiments.

[0102] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0103] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored on it include operating system 221, computer program 222 and data 223, etc., and the storage method can be temporary storage or permanent storage.

[0104] The operating system 221 manages and controls the various hardware devices on the electronic device 20 and the computer program 222 to enable the processor 21 to perform calculations and processing on the data 223 in the memory 22. The operating system 221 can be Windows, Unix, Linux, etc. The computer program 222, in addition to including a computer program capable of performing the digital twin and computing engine collaborative operation method disclosed in any of the foregoing embodiments, may further include computer programs capable of performing other specific tasks. The data 223 may include data received by the digital twin and computing engine collaborative operation device from external devices, as well as data collected by its own input / output interface 25.

[0105] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0106] Furthermore, embodiments of this application also disclose a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, it implements the steps of the collaborative operation method between the digital twin and the computing engine disclosed in any of the foregoing embodiments.

[0107] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0108] The above provides a detailed description of a method, apparatus, device, and storage medium for collaborative operation of a digital twin and a computing engine provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for collaborative operation between a digital twin and a computing engine, characterized in that, include: A virtual digital twin instance is established, and a virtual-to-real mapping is performed on the virtual digital twin instance to obtain a digital twin instance. Data to be bound is obtained, and the data to be bound is bound to the digital twin instance to obtain a target digital twin instance. Utilize the local rule configuration engine to configure rules and obtain target rules. Determine different business operation types based on business needs and select the corresponding business computing engines from the local system. Configure each of the business computing engines according to the calculation parameters to obtain the target business computing engine. Perform job calculation based on the target rules and the calculation parameters and using the target business computing engine to obtain the calculation result. The calculation results are fed back to the target digital twin instance to enable collaborative operation between the target digital twin instance and the target business computing engine; The process of feeding back the calculation results to the target digital twin instance further includes: labeling the target digital twin instance to obtain the labeled target digital twin instance; and using the labeled target digital twin instance to generate and output target data including standard data structures, formats, and interfaces.

2. The method for collaborative operation of a digital twin and a computing engine according to claim 1, characterized in that, The establishment of the virtual digital twin instance includes: An initial virtual digital twin instance with a tree structure is created based on the core business objects; Obtain structural parameters and configure the initial virtual digital twin instance using the structural parameters to obtain the virtual digital twin instance; the structural parameters include geographic location parameters, time axis parameters, metadata parameters, and master data parameters.

3. The method for collaborative operation of a digital twin and a computing engine according to claim 1, characterized in that, The step of obtaining the data to be bound and binding the data to be bound with the digital twin instance to obtain the target digital twin instance includes: Data to be bound is obtained from an IoT platform system connected locally; the data types of the data to be bound include real-time data, online data, and offline data. The data points in the data to be bound are bound to the digital twin instance to obtain the target digital twin instance and the bound data information.

4. The method for collaborative operation of a digital twin and a computing engine according to claim 3, characterized in that, After obtaining the target digital twin instance and its binding data information, the process further includes: Determine the scenario type of the bound data information; the scenario type includes real-time scenario type and historical scenario type; If the scenario type is a real-time scenario type, the bound data information is saved to the local real-time repository. If the scenario type is a historical scenario type, the binding data information will be saved to the local historical repository.

5. The method for collaborative operation of a digital twin and a computing engine according to claim 1, characterized in that, The process of configuring rules using a local rule configuration engine to obtain target rules includes: Get rule configuration parameters; According to the preset rule configuration options and in combination with the rule configuration parameters, the local rule configuration engine is used to configure the rules to obtain the target rules; the rule configuration options include application programming interface call configuration options, streaming computing configuration options, and timed computing configuration options.

6. The method for collaborative operation of a digital twin and a computing engine according to claim 1, characterized in that, The step of determining different business operation types based on business needs and selecting business computing engines corresponding to each business operation type from the local database includes: Different business operation types are determined based on business needs; Select the business computing engine corresponding to the business operation type from all local engines; the types of engines include fault analysis engine, indicator calculation engine, and model scheduling engine.

7. A device for collaborative operation of a digital twin and a computing engine, characterized in that, include: A binding module is created to establish a virtual digital twin instance, perform virtual-real mapping on the virtual digital twin instance to obtain a digital twin instance, obtain data to be bound, and bind the data to be bound to the digital twin instance to obtain a target digital twin instance. The engine determination module is used to configure rules using the local rule configuration engine to obtain target rules, determine different business operation types according to business needs, and filter the business computing engines corresponding to each business operation type from the local system. The job calculation module is used to configure each of the business calculation engines according to the calculation parameters to obtain the target business calculation engine, and to perform job calculation based on the target rules and the calculation parameters and using the target business calculation engine to obtain the calculation result. The collaborative operation module is used to feed back the calculation results to the target digital twin instance, so as to realize the collaborative operation between the target digital twin instance and the target business computing engine; The process of feeding back the calculation results to the target digital twin instance further includes: labeling the target digital twin instance to obtain the labeled target digital twin instance; and using the labeled target digital twin instance to generate and output target data including standard data structures, formats, and interfaces.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the method of collaborative operation of a digital twin and a computing engine as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein, when the computer programs are executed by a processor, they implement the method for collaborative operation of a digital twin and a computing engine as described in any one of claims 1 to 6.

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