CN116629015A - Digital twin task processing method, device, equipment and storage medium - Google Patents
Digital twin task processing method, device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses a processing method, a device, equipment and a storage medium for a digital twin task, relates to the technical field of digital twin, and can solve the defects of long digital twin construction period and high cost consumption in different application scenes. The specific scheme comprises the following steps: acquiring a function of a target digital twin body for processing the digital twin task; traversing from a root node of a preset twin model library according to a function, and matching a target twin model corresponding to the function, wherein the target twin model comprises at least one sub-twin model, and each sub-twin model comprises a corresponding model function, a model identifier, a model input parameter, a model output parameter, model performance, a model twin level and a dependency relationship with other sub-twin models; and calling each sub-twin model, assembling each sub-twin model into a target digital twin body according to the dependency relationship among the sub-twin models, and processing a digital twin task by utilizing the assembled target digital twin body.
Description
Technical Field
The present application relates to the field of digital twin technologies, and in particular, to a method, an apparatus, a device, and a storage medium for processing a digital twin task.
Background
The digital twin is to fully utilize data such as a physical model, sensor update, operation history and the like, integrate simulation processes of multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities, and complete mapping in a virtual space, thereby reflecting the full life cycle process of corresponding entity equipment. Digital twinning is a universally adapted theoretical technology system, can be applied in a plurality of fields, and has more application in the fields of product design, product manufacturing, medical analysis, engineering construction and the like.
At present, when a digital twin body of a new application scene is constructed, the digital twin body needs to be reconstructed according to the characteristics of the application scene, a great amount of repetitive work exists in the construction process, and the defects of long construction period, high cost consumption and the like exist.
Disclosure of Invention
The application provides a processing method, a device, equipment and a storage medium for a digital twin task, which can solve the defects of long digital twin construction period and high cost consumption in different application scenes.
In order to achieve the above purpose, the application adopts the following technical scheme:
in a first aspect of the embodiment of the present application, a method for processing a digital twin task is provided, where the method includes:
acquiring a function of a target digital twin body for processing the digital twin task;
traversing from a root node of a preset twin model library according to a function, and matching a target twin model corresponding to the function, wherein the target twin model comprises at least one sub-twin model, and each sub-twin model comprises a corresponding model function, a model identifier, a model input parameter, a model output parameter, model performance, a model twin level and a dependency relationship with other sub-twin models;
and calling each sub-twin model, assembling each sub-twin model into a target digital twin body according to the dependency relationship among the sub-twin models, and processing a digital twin task by utilizing the assembled target digital twin body.
In one embodiment, before acquiring the function of the target digital twin corresponding to the digital twin task, the method further includes:
and constructing a twin model library according to the functions of each twin model and the dependency relationship between each twin model and other twin models, wherein the twin model library comprises at least one root node model, the at least one root node model comprises at least one child node model, and each root node model and each child node model comprise corresponding model functions, model identifications, model input parameters, model output parameters, model performances, the dependency relationship between other models and twin levels of the models.
In one embodiment, before constructing a twinning model library from the functions of each twinning model and the dependencies between each twinning model and other models, the method comprises:
acquiring digital twin bodies corresponding to a plurality of different digital twin tasks, and splitting each digital twin body into a plurality of sub-twin models;
obtaining model information corresponding to each sub-twin model after splitting, wherein the model information comprises: model functions, model identification, model input parameters, model output parameters, model performance, dependencies between other models, and twin level of the model;
and after associating each sub-twin model with the corresponding model information, storing each sub-twin model into a twin model library.
In one embodiment, the dependency includes taking the output data of the current model as the input data of another model;
assembling each sub-twin model into a target digital twin according to the dependency relationship between the sub-twin models, comprising:
and acquiring whether the output data format of each sub-twin model is consistent with the input data formats of other sub-twin models with the dependency relationship of the sub-twin models, if so, directly assembling the sub-twin models into a target twin body according to the dependency relationship, and if not, adding a data conversion plug-in between the sub-twin models and the other sub-twin models, and then assembling each sub-twin model into the target twin body according to the dependency relationship, wherein the data conversion plug-in is used for converting the output data format into the input data format.
In one embodiment, model performance includes the twinning accuracy of the model, the response time of the model, and the resources consumed by the model operation;
the twin precision of the model is divided into a plurality of precision levels from high to low, the response time of the model is divided into a plurality of response levels from high to low, and the resources consumed by the running of the model are divided into a plurality of resource consumption levels from more to less.
In one embodiment, after traversing from a root node of a preset twin model library according to the function and matching out a target twin model corresponding to the function, the method further comprises:
if a plurality of target twin models are matched, or the target twin models comprise a plurality of sub-twin models with the same functions, determining one target twin model from the plurality of target twin models according to the model performance of the target twin models, or determining one sub-twin model from the plurality of sub-twin models with the same functions according to the model performance of the sub-twin models.
In one embodiment, prior to processing the digital twinning task with the assembled target digital twinning entity, comprising:
and acquiring resources consumed by model operation of each twin sub-model in the assembled target digital twin, determining the consumed resources of the digital twin according to the sum of the resources consumed by the model operation, and configuring corresponding processing resources from the current processing equipment according to the consumed resources.
In a second aspect of the embodiment of the present application, there is provided a processing apparatus for a digital twin task, including:
the acquisition module is used for acquiring the function of a target digital twin body for processing the digital twin task;
the matching module is used for traversing from a root node of a preset twin model library according to the function and matching out a target twin model corresponding to the function, wherein the target twin model comprises at least one sub-twin model, and each sub-twin model comprises a corresponding model function, a model identifier, a model input parameter, a model output parameter, model performance, a model twin level and a dependency relationship with other sub-twin models;
the processing module is used for calling each sub-twin model, assembling each sub-twin model into a target digital twin body according to the dependency relationship among the sub-twin models, and processing the digital twin task by utilizing the assembled target digital twin body.
In a third aspect of the embodiment of the present application, there is provided an electronic device, including a memory and a processor, where the memory stores a computer program, and the computer program implements the processing method of the digital twin task in the first aspect of the embodiment of the present application when executed by the processor.
In a fourth aspect of the embodiment of the present application, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the processing method of the digital twin task in the first aspect of the embodiment of the present application.
The technical scheme provided by the embodiment of the application has the beneficial effects that at least:
according to the processing method of the digital twin task, provided by the embodiment of the application, the function of a target digital twin body for processing the digital twin task is obtained; traversing from a root node of a preset twin model library according to a function, and matching a target twin model corresponding to the function, wherein the target twin model comprises at least one sub-twin model, and each sub-twin model comprises a corresponding model function, a model identifier, a model input parameter, a model output parameter, model performance, a model twin level and a dependency relationship with other sub-twin models; and calling each sub-twin model, assembling each sub-twin model into a target digital twin body according to the dependency relationship among the sub-twin models, and processing a digital twin task by utilizing the assembled target digital twin body. Therefore, the digital twin body under different application scenes does not need to be reconstructed, the construction period of the model and the cost consumption of construction can be reduced, the reuse rate of the model is improved, and the processing efficiency of the digital twin task can be improved.
Drawings
FIG. 1 is a flowchart of a method for processing a digital twin task according to an embodiment of the present application;
FIG. 2 is a block diagram of a digital twin task processing device according to an embodiment of the present application;
fig. 3 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first" and "second" are used below for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the embodiments of the present disclosure, unless otherwise indicated, the meaning of "a plurality" is two or more.
In addition, the use of "based on" or "according to" is meant to be open and inclusive, as a process, step, calculation, or other action that is "based on" or "according to" one or more conditions or values may in practice be based on additional conditions or exceeded values.
The embodiment of the application provides a processing method of a digital twin task, as shown in fig. 1, comprising the following steps:
step 101, acquiring a function of a target digital twin body for processing the digital twin task.
Wherein the digital twin task is a digital twin technology related task, the digital twin task may include: predicting the endurance time of the unmanned aerial vehicle, performing fault detection on the target device, and the like, the digital twin task may further include: the tasks of product design, engineering construction, or intelligent manufacturing, etc., are not particularly limited in this embodiment of the present application.
The target digital twin is a digital twin model for processing a digital twin task, for example, if the digital twin task is predicting the endurance time of the unmanned aerial vehicle, the digital twin corresponding to the digital twin task is: and predicting the endurance time of the unmanned aerial vehicle.
Step 102, traversing from a root node of a preset twin model library according to the function, and matching a target twin model corresponding to the function.
The target twin model comprises at least one sub-twin model, and each sub-twin model comprises a corresponding model function, a model identification, a model input parameter, a model output parameter, a model performance, a model twin level and a dependency relationship with other sub-twin models.
It should be noted that, before proceeding to step 101, a corresponding twin model library needs to be created, and the creation process of the twin model library may be:
acquiring digital twin bodies corresponding to a plurality of different digital twin tasks, and splitting each digital twin body into a plurality of sub-twin models; obtaining model information corresponding to each sub-twin model after splitting, wherein the model information comprises: model functions, model identification, model input parameters, model output parameters, model performance, dependencies between other models, and twin level of the model; and after associating each sub-twin model with the corresponding model information, storing each sub-twin model into a twin model library. And then constructing a twin model library according to the functions of each twin model and the dependency relationship between each twin model and other twin models, wherein the twin model library comprises at least one root node model, the at least one root node model comprises at least one child node model, and each root node model and each child node model comprise corresponding model functions, model identifications, model input parameters, model output parameters, model performances, the dependency relationship between other models and twin levels of the models.
In the actual implementation process, in order to reduce the difficulty of the digital twin abstract description and generate more simplified digital twin model resources, the digital twin model needs to be split into a plurality of sub-twin models. The target object digital twin is split mainly from the following three angles:
(1) Hierarchy. Since a digital twin is generally complex and contains multiple levels of objects, objects of different levels cannot represent objects of all levels at the same time due to different expressed and ignored elements, and thus the digital twin can be divided into different levels of twin by levels. The twin level is expressed as a unit level, a service level, a system level.
The unit level means that the target object digital twin model indicates that the model can independently complete the function of the current model without depending on other models. The service-level model means that the current model cannot independently complete the current function and needs to rely on other models. The system-level model refers to a digital twin model capable of comprehensively reflecting the overall condition of the target object.
(2) Function. For digital twin models of the same twin level, there are also functional differences between the different models. Therefore, in order to distinguish this difference, after the twin is hierarchically divided, the functional division should be continued, and the functions that can be completed by the different models are determined. Meanwhile, before division, models of different functions may have functional dependency relationships, and thus such functional dependency relationships should be clearly expressed in the division process. After functional partitioning, the complex digital twin model can be further simplified.
(3) Performance. Even the digital twin model of the target object with the same level and function has the difference in performance, and the performance of the model is reflected in indexes such as twin precision, response time, resource consumption and the like. Therefore, in order to further simplify the digital twin, the model should be further divided in performance, and finally a basic simplified digital twin model is formed.
In order to reuse and combine the digital twin model subsequently and reduce the repetitive work in the development process of the digital twin, the construction period is shortened, the construction cost is reduced, and standardized abstract description generation components are required to be carried out on model resources. The formalized description of the components is as follows:
Component＝{Id,Function,Input,Output,Performance,Relations,Level}
component in the above represents the function of the post-split sub-twin model.
Id, the identification information of the representation model, is the global unique identity of the component and is used for distinguishing different digital twin models, and detailed information of the corresponding digital twin models can be obtained according to Id. Functions, representing the functions of the model, are used to describe the functions that the digital twin model of the target object can accomplish. Input, representing the Input parameters of the model. Output, representing the Output parameters of the model. Performance, which represents model Performance. Relationships, which represent dependencies. Level, the twin Level of the representation model, is divided into a unit Level, a service Level and a system Level.
The model function is a function which can be completed by the digital twin model, and is determined according to the work to be processed by the model. For example, the twinning model is: predicting the running state of the equipment, the twin model has the following functions: and predicting the running state of the equipment.
The model identification refers to ID identity information of the model, is a global unique identity identification of the model and is used for distinguishing different target object digital twin model components, and detailed information of a corresponding digital twin model can be obtained according to the ID.
Model input parameters refer to a set of all input parameters of the model, including the number, name, and type of input parameters. Model output parameters refer to a set of model output parameters, including the number, name, and type of output parameters.
Model performance includes the model's twinning accuracy, the model's response time and the resources consumed by the model's operation.
The dependency relationship with other models refers to the set of all dependency models of the current model. Before the complex target object digital twin model is divided, a plurality of simplified target object digital twin models are needed to mutually depend and cooperate to complete complex functions, so that the dependency relationship between the models is needed to be represented during description.
The twinning level of the model is divided into: the system comprises a unit level, a service level and a system level, wherein the unit level refers to the target object digital twin model, which means that the model can independently complete the function of the current model without depending on other models. The service-level model means that the current model cannot independently complete the current function and needs to rely on other models. The system-level model refers to a digital twin model capable of comprehensively reflecting the overall condition of the target object.
Optionally, the model performance includes the twinning accuracy of the model, the response time of the model, and the resources consumed by the model operation;
the twin precision of the model is divided into a plurality of precision levels from high to low, the response time of the model is divided into a plurality of response levels from high to low, and the resources consumed by the running of the model are divided into a plurality of resource consumption levels from more to less.
The twin precision is the level of detail of the target digital twin body for the physical entity, the higher the depiction precision is, the more accurately the physical entity can be reflected, and the qualitative level is divided into A level, B level and C level from highest to lowest. The response time is used to describe the response time of the digital twin model of the target object, i.e. the time required to accomplish the function, qualitatively from shortest to longest, in the order of milliseconds, seconds, minutes, hours. The resources consumed by the model running represent the resources consumed by the running of the target object digital twin body model in the process of completing a certain function, including computing resources, storage resources and the like, and qualitatively divide the consumed resources into three resource consumption levels of low, medium and high from a small to a large.
And 103, calling each sub-twin model, assembling each sub-twin model into a target digital twin body according to the dependency relationship among the sub-twin models, and processing a digital twin task by using the assembled target digital twin body.
Optionally, the dependency comprises taking the output data of the current model as the input data of another model. In step 103, the process of assembling each sub-twin model into the target digital twin according to the dependency relationship between each sub-twin model may be:
and acquiring whether the output data format of each sub-twin model is consistent with the input data formats of other sub-twin models with the dependency relationship of the sub-twin models, if so, directly assembling the sub-twin models into a target twin body according to the dependency relationship, and if not, adding a data conversion plug-in between the sub-twin models and the other sub-twin models, and then assembling each sub-twin model into the target twin body according to the dependency relationship, wherein the data conversion plug-in is used for converting the output data format into the input data format.
In the actual implementation process, complex functions are required to be completed between digital twin models by continuously interacting data, and the data usually has the problem of mismatch of format and type. The data processing plug-in is provided for converting the data format and type so as to meet the interaction requirement between the components. The formalized description of the data processing plug-in is shown below.
DataPlugin＝{Id,InComponent,Process,OutComponent}
Wherein dataplug represents a data processing plug-in. Id represents the globally unique identity of the data processing plug-in. InComponent represents the identity of the input model to which the plug-in is connected. The Process represents defining data processing logic to perform data format and type conversions. OutComponent represents the identity of the output model of the plug-in connection.
After the simplified target object model is abstracted and described into components, component resources which can be selected and combined are formed, and researchers can reuse the combined model component resources according to the function and scene requirements to generate a complex target object digital twin body. Thus, a formal description of the final generated digital twin model of the target object is required.
DT＝{Components,DataPlugins,Relations}
Where DT represents the complex digital twin model that is ultimately generated. Components are used to represent all model sets that make up a complex digital twin, and are the fundamental elements that make up a complex digital twin. Dataplug is used to represent all sets of data processing plug-ins that are used to transform data between components, thereby meeting the data interaction requirements between components.
The relationships are used to describe dependencies between the sub-twin models and to represent the co-operative relationships between the sub-twin models and the sub-twin models, the sub-twin models and the data processing plug-ins.
Optionally, after traversing from a root node of a preset twin model library according to the function and matching out a target twin model corresponding to the function, the method further comprises:
if a plurality of target twin models are matched, or the target twin models comprise a plurality of sub-twin models with the same functions, determining one target twin model from the plurality of target twin models according to the model performance of the target twin models, or determining one sub-twin model from the plurality of sub-twin models with the same functions according to the model performance of the sub-twin models, so that the processing precision and the processing efficiency of the digital twin task can be improved, and the resource consumption can be reduced.
Furthermore, prior to processing the digital twinning task with the assembled target digital twinning entity, the method further comprises:
and acquiring resources consumed by model operation of each twin sub-model in the assembled target digital twin, determining the consumed resources of the digital twin according to the sum of the resources consumed by the model operation, and configuring corresponding processing resources from the current processing equipment according to the consumed resources. Therefore, resource allocation optimization can be performed, and resource insufficiency or resource waste is avoided.
According to the processing method of the digital twin task, provided by the embodiment of the application, the function of a target digital twin body for processing the digital twin task is obtained; traversing from a root node of a preset twin model library according to a function, and matching a target twin model corresponding to the function, wherein the target twin model comprises at least one sub-twin model, and each sub-twin model comprises a corresponding model function, a model identifier, a model input parameter, a model output parameter, model performance, a model twin level and a dependency relationship with other sub-twin models; and calling each sub-twin model, assembling each sub-twin model into a target digital twin body according to the dependency relationship among the sub-twin models, and processing a digital twin task by utilizing the assembled target digital twin body. Therefore, the digital twin body under different application scenes does not need to be reconstructed, the construction period of the model and the cost consumption of construction can be reduced, the reuse rate of the model is improved, and the processing efficiency of the digital twin task can be improved.
As shown in fig. 2, the embodiment of the present application further provides a device for processing a digital twin task, where the device includes:
an acquisition module 11 for acquiring a function of a target digital twin body for processing a digital twin task;
the matching module 12 is configured to traverse from a root node of a preset twin model library according to a function, and match a target twin model corresponding to the function, where the target twin model includes at least one sub-twin model, and each sub-twin model includes a corresponding model function, a model identifier, a model input parameter, a model output parameter, a model performance, a model twin level, and a dependency relationship with other sub-twin models;
and the processing module 13 is used for calling each sub-twin model, assembling each sub-twin model into a target digital twin body according to the dependency relationship among the sub-twin models, and processing the digital twin task by utilizing the assembled target digital twin body.
In one embodiment, the apparatus further comprises: configuration module 14, configuration module 14 is used for:
and constructing a twin model library according to the functions of each twin model and the dependency relationship between each twin model and other twin models, wherein the twin model library comprises at least one root node model, the at least one root node model comprises at least one child node model, and each root node model and each child node model comprise corresponding model functions, model identifications, model input parameters, model output parameters, model performances, the dependency relationship between other models and twin levels of the models.
In one embodiment, configuration module 14 is further configured to:
acquiring digital twin bodies corresponding to a plurality of different digital twin tasks, and splitting each digital twin body into a plurality of sub-twin models;
obtaining model information corresponding to each sub-twin model after splitting, wherein the model information comprises: model functions, model identification, model input parameters, model output parameters, model performance, dependencies between other models, and twin level of the model;
and after associating each sub-twin model with the corresponding model information, storing each sub-twin model into a twin model library.
In one embodiment, the dependency includes taking the output data of the current model as the input data of another model;
the processing module 13 is specifically configured to: and acquiring whether the output data format of each sub-twin model is consistent with the input data formats of other sub-twin models with the dependency relationship of the sub-twin models, if so, directly assembling the sub-twin models into a target twin body according to the dependency relationship, and if not, adding a data conversion plug-in between the sub-twin models and the other sub-twin models, and then assembling each sub-twin model into the target twin body according to the dependency relationship, wherein the data conversion plug-in is used for converting the output data format into the input data format.
In one embodiment, model performance includes the twinning accuracy of the model, the response time of the model, and the resources consumed by the model operation;
the twin precision of the model is divided into a plurality of precision levels from high to low, the response time of the model is divided into a plurality of response levels from high to low, and the resources consumed by the running of the model are divided into a plurality of resource consumption levels from more to less.
In one embodiment, the apparatus further comprises a determining module 15, where the determining module 15 is specifically configured to: if a plurality of target twin models are matched, or the target twin models comprise a plurality of sub-twin models with the same functions, determining one target twin model from the plurality of target twin models according to the model performance of the target twin models, or determining one sub-twin model from the plurality of sub-twin models with the same functions according to the model performance of the sub-twin models.
In one embodiment, the determining module 15 is further configured to:
and acquiring resources consumed by model operation of each twin sub-model in the assembled target digital twin, determining the consumed resources of the digital twin according to the sum of the resources consumed by the model operation, and configuring corresponding processing resources from the current processing equipment according to the consumed resources.
The processing method of the digital twin task provided in the embodiment may execute the above method embodiment, and its implementation principle and technical effects are similar, and will not be repeated here.
For specific limitations on the processing apparatus of the digital twin task, reference may be made to the above limitation on the processing method of the digital twin task, and no further description is given here. The various modules in the digital twin task processing device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or independent of a processor in the electronic device, or may be stored in software in a memory in the electronic device, so that the processor may call and execute operations corresponding to the above modules.
The execution main body of the processing method of the digital twin task provided by the embodiment of the application can be electronic equipment, and the electronic equipment can be computer equipment, a server, mobile terminal equipment, a processor or a processing chip and the like. The embodiment of the present application is not particularly limited thereto.
Fig. 3 is a schematic diagram of an internal structure of an electronic device according to an embodiment of the present application. As shown in fig. 3, the electronic device includes a processor and a memory connected by a system bus. Wherein the processor is configured to provide computing and control capabilities. The memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program is executable by a processor for implementing the steps of a method of processing a digital twin task provided in the above embodiments. The internal memory provides a cached operating environment for the operating system and computer programs in the non-volatile storage medium.
It will be appreciated by those skilled in the art that the structure shown in fig. 3 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the electronic device to which the present inventive arrangements are applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In another embodiment of the present application, there is also provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the processing method of a digital twin task according to an embodiment of the present application.
In another embodiment of the present application, there is also provided a computer program product including computer instructions which, when executed on a processing device of a digital twin task, cause the processing device of a digital twin task to perform the steps of the processing method of a digital twin task in the method flow shown in the method embodiment.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using a software program, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer-executable instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are fully or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, a website, computer, server, or data center via a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices including one or more servers, data centers, etc. that can be integrated with the media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the claims. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A method of processing a digital twin task, the method comprising:
acquiring a function of a target digital twin body for processing the digital twin task;
traversing from a root node of a preset twin model library according to the function, and matching a target twin model corresponding to the function, wherein the target twin model comprises at least one sub-twin model, and each sub-twin model comprises a corresponding model function, a model identifier, a model input parameter, a model output parameter, model performance, a model twin level and a dependency relationship with other sub-twin models;
and calling each sub-twin model, assembling each sub-twin model into the target digital twin according to the dependency relationship between the sub-twin models, and processing the digital twin task by using the assembled target digital twin.
2. The method of claim 1, wherein prior to the acquiring the function of the target digital twin for processing the digital twin task, the method further comprises:
the method comprises the steps of constructing a twin model library according to functions of each twin model and dependency relations between each twin model and other twin models, wherein the twin model library comprises at least one root node model, at least one root node model comprises at least one sub-node model, and each root node model and each sub-node model comprise corresponding model functions, model identifications, model input parameters, model output parameters, model performances, dependency relations among other models and twin levels of the models.
3. The method according to claim 2, wherein before the constructing the twinning model library from the functions of each twinning model and the dependencies between each twinning model and other twinning models, the method comprises:
acquiring digital twin bodies corresponding to a plurality of different digital twin tasks, and splitting each digital twin body into a plurality of sub-twin models;
obtaining model information corresponding to each sub-twin model after splitting, wherein the model information comprises: model functions, model identification, model input parameters, model output parameters, model performance, dependencies between other models, and twin level of the model;
and after associating each sub-twin model with corresponding model information, storing each sub-twin model into the twin model library.
4. The method according to claim 2, wherein the dependency comprises taking output data of a current model as input data of another model;
assembling each sub-twin model into the target digital twin according to the dependency relationship between the sub-twin models, comprising:
and acquiring whether the output data format of each sub-twin model is consistent with the input data formats of other sub-twin models with the dependency relationship of the sub-twin models, if so, directly assembling the sub-twin models into the target twin according to the dependency relationship, and if not, adding a data conversion plug-in between the sub-twin models and the other sub-twin models, and then assembling each sub-twin model into the target twin according to the dependency relationship, wherein the data conversion plug-in is used for converting the output data format into the input data format.
5. The method of claim 1, wherein the model performance includes model twinning accuracy, model response time, and resources consumed by model operation;
the twin precision of the model is divided into a plurality of precision levels from high to low, the response time of the model is divided into a plurality of response levels from high to low, and the resources consumed by the running of the model are divided into a plurality of resource consumption levels from high to low.
6. The method of claim 4, wherein after traversing from a root node of a preset twin model library according to the function and matching a target twin model corresponding to the function, the method further comprises:
if a plurality of target twin models are matched, or the target twin models comprise a plurality of sub-twin models with the same functions, determining one target twin model from the plurality of target twin models according to the model performance of the target twin models, or determining one sub-twin model from the plurality of sub-twin models with the same functions according to the model performance of the sub-twin models.
7. The method of claim 5, wherein prior to said processing said digital twinning task with said assembled target digital twinning body, comprising:
obtaining resources consumed by model operation of each twin sub-model in the assembled target digital twin, determining the consumed resources of the digital twin according to the sum of the resources consumed by the model operation, and configuring corresponding processing resources from current processing equipment according to the consumed resources.
8. A processing apparatus for a digital twin task, the apparatus comprising:
the acquisition module is used for acquiring the function of a target digital twin body for processing the digital twin task;
the matching module is used for traversing from a root node of a preset twin model library according to the function and matching a target twin model corresponding to the function, the target twin model comprises at least one sub-twin model, and each sub-twin model comprises a corresponding model function, a model identifier, a model input parameter, a model output parameter, model performance, a model twin level and a dependency relationship with other sub-twin models;
the processing module is used for calling each sub-twin model, assembling each sub-twin model into the target digital twin according to the dependency relationship among the sub-twin models, and processing the digital twin task by utilizing the assembled target digital twin.
9. An electronic device comprising a memory and a processor, the memory storing a computer program that when executed by the processor implements the method of processing a digital twin task as claimed in any of claims 1-7.
10. A computer-readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the method of processing a digital twin task according to any of claims 1-7.
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