Method and system for manufacturing digital twin model and electronic equipment
By generating identification data and data width filtering matching data sets, we can determine whether the data to be replaced can be replaced, and efficient update of the digital twin model is achieved, solving the problem of repeated modeling and improving monitoring efficiency.
Patent Information
- Application Number
- CN202510455205.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-11
- Publication Date
- 2025-07-22
AI Technical Summary
The update process of digital twin models in the prior art requires re-acquisition of data, resulting in duplicate modeling, occupancy of resources, and reducing monitoring efficiency.
By obtaining the data to be replaced, generating identification data and data width, filtering the matching identification data set, determining whether the data to be replaced can replace the matching data, and data updates in the matching identification data set to avoid remodeling.
It improves the monitoring efficiency of digital twin models, reduces modeling consumption, and can quickly complete model updates.
Smart Images
Figure CN120354604A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of digital twins, and particularly to a method, a system, and an electronic device for creating a digital twin model. Background Art
[0002] Digital twin is a technical system that constructs a virtual mapping model of a physical entity through digital means to achieve real-time data interaction, closed-loop optimization, and full-life cycle management. By constructing digital twin models of physical devices, buildings, cities, or even entire ecosystems in the real world, we can monitor and analyze various types of information in the real world at any time, so as to make more accurate predictions and decisions that are more beneficial to production or life.
[0003] In related technologies, through image analysis of the terrain image of its corresponding target scene, contour information, height information, etc. of each building in the target scene are obtained, and then based on the contour information and height information, the digital twin model is detected and optimized to find problems such as data missing and data errors in the digital twin model and process them, adding missing data and correcting incorrect data, so as to obtain a higher-quality digital twin model.
[0004] However, the existing models in reality will be correspondingly trimmed according to different requirements, which will cause the constructed digital twin model to be different from the actual one, and it is necessary to re-collect data and then update the digital twin model according to the collected data, which will lead to repeated modeling, occupying modeling resources and resulting in low digital twin monitoring efficiency. Summary of the Invention
[0005] In order to reduce modeling consumption and improve digital twin monitoring efficiency, the present application provides a method, a system, and an electronic device for creating a digital twin model.
[0006] In a first aspect, the present application provides a method for creating a digital twin model, adopting the following technical solution: A method for creating a digital twin model includes the following steps: Obtain data to be replaced, and generate identification data and corresponding data width based on the data to be replaced; Screen out a corresponding set of matching identification data from the original data corresponding to the model to be updated with the identification data; Obtain corresponding matching data from the set of matching identification data according to the data width, and determine whether the data to be replaced can replace the matching data; If the data to be replaced can replace the matching data, generate a data update signal based on the data to be replaced, and delete the matching data from the set of matching identification data according to the data update signal; Store the data to be replaced in the matching identification data set, and update the model to be updated according to the updated matching identification data set.
[0007] By adopting the above technical solution, identification data and the corresponding data width are generated according to the data to be replaced, matching data is obtained based on the identification data and the data width, and it is judged whether the data to be replaced can replace the matching data. If the data to be replaced can replace the matching data, a data update signal is generated based on the data to be replaced, and the matching data is deleted from the matching identification data set according to the data update signal; the data to be replaced is stored in the matching identification data set, and the model to be updated is updated according to the updated matching identification data set, so that it is possible to avoid re-modeling according to the updated actual model, reduce the modeling consumption, and quickly complete the modeling according to the data to be replaced, thereby improving the monitoring efficiency of the digital twin for the actual model.
[0008] In some of the embodiments, the judging whether the data to be replaced can replace the matching data includes the following steps: Obtain the model replacement type according to the data to be replaced, and judge whether the model replacement type belongs to the building maintenance state; If the model replacement type belongs to the building maintenance state, obtain the data similarity according to the data to be replaced and the matching data, and judge whether the data to be replaced can replace the matching data according to the data similarity; If the model replacement type does not belong to the building maintenance state, judge whether the data to be replaced can replace the matching data according to the data width.
[0009] By adopting the above technical solution, the model replacement type is obtained according to the data to be replaced, and it is judged whether the model replacement type belongs to the building maintenance state, so that it is possible to more accurately judge whether the current data to be replaced can directly replace the matching data, reduce the update of the original data, improve the model update efficiency, and further improve the monitoring efficiency of the digital twin for the actual model.
[0010] In some of the embodiments, judging whether the data to be replaced can replace the matching data according to the data similarity includes the following steps: Compare the data similarity with a preset similarity, and judge whether the data similarity exceeds the preset similarity; If the data similarity exceeds the preset similarity, it is determined that the data to be replaced can replace the matching data; If the data similarity does not exceed the preset similarity, judge whether the data to be replaced can replace the matching data according to the data width.
[0011] By adopting the above technical solution, when the model replacement type belongs to the building maintenance status, by judging the comparison between the data similarity and the preset similarity, it is further judged whether the current data to be replaced can directly replace the matching data. If the data similarity exceeds the preset similarity, the replacement can be directly carried out. If the data similarity does not exceed the preset similarity, further judgment is made according to the data width of the data to be replaced, so that updated data can be obtained according to different situations, and then accurate updated data can be obtained, reducing the modeling consumption, and being able to complete the modeling quickly based on the data to be replaced, thereby improving the monitoring efficiency of the digital twin for the actual model.
[0012] In some of these embodiments, judging whether the data to be replaced can replace the matching data according to the data width includes the following steps: Obtain a set of overlapping data based on the data to be replaced and the matching data, and the set of overlapping data includes several groups of the overlapping data; Judge whether the number of data in the overlapping data is less than a preset number; If the number of data in the overlapping data is less than the preset number, it is determined that the data to be replaced can replace the matching data; If the number of data in the overlapping data is not less than the preset number, it is determined that the data to be replaced cannot replace the matching data.
[0013] By adopting the above technical solution, judge whether the number of data in the overlapping data is less than the preset number. If the number of data in the overlapping data is less than the preset number, the replacement can be directly carried out. If the number of data in the overlapping data is not less than the preset number, it is determined that the data to be replaced cannot replace the matching data, so that the data that needs to be updated can be accurately replaced, and the number of data replacements is reduced, improving the efficiency of data replacement, and thus being able to improve the monitoring efficiency of the digital twin for the actual model.
[0014] In some of these embodiments, after determining that the data to be replaced cannot replace the matching data, the following steps are further included: Remove the overlapping data of the data to be replaced and the matching data according to the set of overlapping data to obtain the first data to be replaced and the first matching data; Delete the first matching data from the set of matching identification data, and store the first data to be replaced in the set of matching identification data.
[0015] By adopting the above technical solution, the overlapping data of the data to be replaced and the matching data is removed according to the overlapping data set, so as to obtain the first data to be replaced and the first matching data; the first matching data is deleted from the matching identification data set, and the first data to be replaced is stored in the matching identification data set, which can accurately replace the data that needs to be updated, reduce the number of data replacements, improve the efficiency of data replacement, and further improve the monitoring efficiency of the digital twin for the actual model.
[0016] In some embodiments, the identification data is used to screen out the corresponding matching identification data set from the original data corresponding to the model to be updated, wherein the construction of the model to be updated includes the following steps: The original data is divided to obtain several groups of data sets to be identified; The data sets to be identified are screened to obtain the corresponding matching identification data; The data sets to be identified are marked according to the matching identification data to obtain a marked data set; The model to be updated is generated based on the marked data set by using model generation technology.
[0017] In some embodiments, the screening of the data sets to be identified to obtain the corresponding matching identification data includes the following steps: The data in the data sets to be identified is screened to obtain the central data of the data sets to be identified, and the central data is used as the matching identification data.
[0018] In some embodiments, before screening out the corresponding matching identification data set from the original data corresponding to the model to be updated by using the identification data, the following steps are further included: Predictive update data is obtained according to the model to be updated and the estimated historical data, and corresponding predictive identification data is obtained according to the predictive update data; The predictive identification data is compared with the matching identification data, and it is judged whether data corresponding to the predictive identification data can be screened out from the matching identification data; If it is determined that data corresponding to the predictive identification data can be screened out from the matching identification data, the predictive identification data is used as the matching data.
[0019] In a second aspect, the present application provides a production system for a digital twin model, adopting the following technical solution: A production system for a digital twin model, which executes the method for producing a digital twin model described in the first aspect, includes: A data acquisition module, which is used to acquire data to be replaced and generate identification data and corresponding data widths based on the data to be replaced; A matching and screening module, which is used to screen out a corresponding set of matching identification data from the original data corresponding to the model to be updated based on the identification data; A data judgment module, which is used to obtain corresponding matching data from the set of matching identification data based on the data width and judge whether the data to be replaced can replace the matching data; A data processing module, if the data to be replaced can replace the matching data, the data processing module is used to generate a data update signal based on the data to be replaced and delete the matching data from the set of matching identification data based on the data update signal; The data processing module is further used to store the data to be replaced in the set of matching identification data and update the model to be updated based on the updated set of matching identification data.
[0020] In a third aspect, the present application provides an electronic device, adopting the following technical solution: An electronic device, the electronic device includes a processor and a memory coupled to each other, and a computer program capable of running on the processor is stored on the memory; When the computer program is executed by the processor, it implements the method for making a digital twin model as described in the first aspect.
[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. Generate identification data and corresponding data widths according to the data to be replaced, obtain matching data based on the identification data and data width, judge whether the data to be replaced can replace the matching data, if the data to be replaced can replace the matching data, then generate a data update signal based on the data to be replaced, and delete the matching data from the set of matching identification data based on the data update signal; store the data to be replaced in the set of matching identification data, and update the model to be updated based on the updated set of matching identification data, so as to avoid re-modeling according to the updated actual model, reduce the modeling consumption, and be able to quickly complete the modeling according to the data to be replaced, thereby improving the monitoring efficiency of the digital twin for the actual model; 2. Obtain the model replacement type according to the data to be replaced and judge whether the model replacement type belongs to the building maintenance state, so as to more accurately judge whether the current data to be replaced can directly replace the matching data, reduce the update of the original data, improve the model update efficiency, and further improve the monitoring efficiency of the digital twin for the actual model; 3. When the model replacement type belongs to the building maintenance status, by judging the comparison between the data similarity and the preset similarity, it is further judged whether the current data to be replaced can directly replace the matching data. If the data similarity exceeds the preset similarity, it can be directly replaced. If the data similarity does not exceed the preset similarity, further judgment is made according to the data width of the data to be replaced, so as to obtain updated data according to different situations, and then accurate updated data can be obtained, reducing the modeling consumption, and being able to quickly complete the modeling based on the data to be replaced, thereby improving the monitoring efficiency of the digital twin for the actual model. Description of the Drawings
[0022] Figure 1 is a block diagram of a method for making a digital twin model provided by an embodiment of the present application; Figure 2 is a block diagram of a method for judging whether data to be replaced can replace matching data provided by an embodiment of the present application; Figure 3 is a block diagram of a method for judging based on the data width provided by an embodiment of the present application; Figure 4 is a structural diagram of a system for making a digital twin model provided by an embodiment of the present application; Figure 5 is a block diagram of the structure of an electronic device provided by this embodiment.
[0023] Description of the reference numerals: 10, data acquisition module; 20, matching and screening module; 30, data judgment module; 40, data processing module; 51, processor; 52, memory; 53, computer program. Detailed Embodiments
[0024] To more clearly understand the purpose, technical solution and advantages of the present application, the present application will be described and illustrated below with reference to the drawings and embodiments. However, those of ordinary skill in the art should understand that the present application can be implemented without these details. In some cases, in order to avoid unnecessary description from making aspects of the present application obscure, well-known methods, processes, systems, components and / or circuits that have been described at a higher level will not be described in too much detail. For those of ordinary skill in the art, it is obvious that various changes can be made to the embodiments disclosed in the present application, and without departing from the principles and scope of the present application, the general principles defined in the present application can be applied to other embodiments and application scenarios. Therefore, the present application is not limited to the illustrated embodiments, but conforms to the broadest scope consistent with the scope claimed in the present application.
[0025] An embodiment of the present application discloses a method for making a digital twin model.
[0026] As Figure 1As shown, a method for creating a digital twin model includes the following steps: S100. Obtain the data to be replaced, and generate identification data and the corresponding data width based on the data to be replaced.
[0027] Among them, the data to be replaced represents the data for updating the model. The specific way to obtain this data is that the processor receives the data collected by the data acquisition module. In order to ensure that when the data is replaced, it can match the data of the original model, after the processor receives the collected data, it is necessary to uniformly process the data so that the data can be consistent with the data of the original model to be built. The specific data processing process is not described in detail, as long as it is ensured that the obtained data to be replaced can generate an updated model based on the model building technology.
[0028] The identification data is screened based on the data to be replaced. Along the connection direction between the first central position of the data to be replaced and the second central position of the model to be updated, a position at a preset distance from the second central position of the model to be updated is obtained as the identification data. The preset distance can be set as the distance between the first central position of the data to be replaced and the second central position of the model to be updated. The preset distance can also be the farthest or nearest distance from the second central position of the model to be updated in the data to be replaced. The position at the preset distance in the data to be replaced is used as the identification data.
[0029] It should be noted here that the data width can be the interval between the farthest-separated data in the data to be replaced. The so-called farthest separation here is along the direction from the first central position of the data to be replaced towards the second central position of the model to be updated. The data width can also be the horizontal width of the model generated based on the data to be replaced from the central position of the original model, or the vertical width of the model generated based on the data to be replaced from the central position of the original model. In this embodiment, only the interval between the farthest-separated data in the data to be replaced is used as the data width for explanation.
[0030] S200. Screen out the corresponding set of matching identification data from the original data corresponding to the model to be updated using the identification data.
[0031] Among them, the original data is used to build the data of the model to be updated, while the matching identification data set represents the data set existing according to the corresponding matching identification obtained from the identification data. Here, it is necessary to first obtain the matching identification corresponding to the identification data from the original data of the model to be updated, and then obtain the corresponding matching identification data set according to the matching identification. The matching identification is based on the position at a preset distance from the second central position of the model to be updated. In order to ensure that the obtained data is consistent, the acquisition method of the matching identification is in the same specification as the identification data, and both are along the direction of the line connecting the first central position of the data to be replaced and the second central position of the model to be updated. The data around the position at a preset distance from the matching identification is used as the matching identification data. Here, the data with a radius equal to the data width centered on the matching identification position is used as the matching identification data.
[0032] Since the model to be updated may be a model for updating the corners or a model for updating the middle position, the identification data of the data to be replaced is not unique. The identification data can be a set of data or two sets of data at the same time. Specifically, when the model to be updated is a model corner, the central position of the data set can be used as a set of data, or the position closest to or farthest from the center of the original model can be used as a set of data. When the model to be updated is in the middle part of the model, it is necessary to use both the position farthest from and closest to the center of the original model in the data set as the identification data, that is, two sets of data are obtained.
[0033] It should be noted here that if there are a large number of data to be replaced, directly processing according to the data to be replaced will cause data loss. Therefore, it is necessary to divide the data to be replaced to obtain several data sets, and obtain the identification data for each data set. When obtaining the identification data, it is necessary to obtain it according to the same benchmark for each data set, either from the first central position to the second central position, or from the farthest or closest distance from the second central position of the model to be updated in the data to be replaced.
[0034] Select the corresponding matching identification data set from the original data of the model to be updated for the data to be replaced, and judge the number of data sets obtained by dividing the data to be replaced. If the number of data sets obtained by division is unique, directly obtain the corresponding matching identification data set from the original data of the model to be updated according to the identification data. If the divided data sets are not unique, directly obtain two sets of identification data with the farthest distance as the comparison matching data, and obtain the corresponding matching identification data set from the original data of the model to be updated according to the comparison matching data. The obtained matching identification data set is the set of data corresponding to the two sets of identification data with the farthest distance.
[0035] S300, obtaining corresponding matching data in a matching identification data set according to the data width, and determining whether the data to be replaced can replace the matching data.
[0036] S400: If the data to be replaced can replace the matching data, a data update signal is generated according to the data to be replaced, and the matching data is deleted from the matching identification data set according to the data update signal.
[0037] S500: storing the data to be replaced in a matching identification data set, and updating the model to be updated according to the updated matching identification data set.
[0038] The matching data represents the data with the same data width as the data to be replaced, and the matching data here is based on the identification data to obtain the data with the same width. The data update signal represents the signal used to delete the matching data in the matching identification data set and automatically store the data to be replaced in the matching identification data set.
[0039] Specifically, step S300 is to select data of corresponding width from the matching identification data set according to the data width as matching data, and then determine whether the data to be replaced can directly replace the matching data. If the data to be replaced can replace the matching data, a data update signal is generated according to the data to be replaced, and the matching data is deleted from the matching identification data set according to the data update signal. The data to be replaced is stored in the matching identification data set, and the model to be updated is updated according to the updated matching identification data set.
[0040] It should be noted here that, since the identification data is uncertain, when the identification data is located at the first center position, the first center position is taken as the center of the circle, and half of the data width is taken as the center of the circle, and the data included in the sphere is obtained as the matching data. When the identification data is the farthest distance or the shortest distance from the second center position of the model to be updated in the data to be replaced, as long as the width of the obtained matching data is the same as that of the data to be replaced, and the data to be replaced and the matching data are both located on the same side of the identification data.
[0041] In one embodiment, after determining that the data to be replaced cannot replace the matching data, the method further includes the following steps: S600: Eliminate overlapping data of the data to be replaced and the matching data according to the overlapping data set to obtain first data to be replaced and first matching data.
[0042] S700: Delete the first matching data in the matching identification data set, and store the first data to be replaced in the matching identification data set.
[0043] Among them, the overlapping data set includes several groups of overlapping data, and the overlapping data set is obtained based on the data to be replaced and the matching data. Specifically, the overlapping data between the data to be replaced and the matching data is obtained. The first matching data in the matching identification data set is directly deleted, and the first data to be replaced is directly replaced to prevent data overlap and reduce the model construction efficiency.
[0044] Refer to Figure 2 , in one of the embodiments, determining whether the data to be replaced can replace the matching data includes the following steps: S310, obtain the model replacement type based on the data to be replaced, and determine whether the model replacement type belongs to the building maintenance state.
[0045] S320, if the model replacement type belongs to the building maintenance state, obtain the data similarity based on the data to be replaced and the matching data, and determine whether the data to be replaced can replace the matching data based on the data similarity.
[0046] S330, if the model replacement type does not belong to the building maintenance state, determine whether the data to be replaced can replace the matching data based on the data width.
[0047] Among them, the model replacement type includes the building maintenance state and the building reconstruction state. The building maintenance state represents the trimming of some areas of the physical object corresponding to the model, while the building reconstruction state represents the re-modification of the physical object corresponding to the model. The data similarity represents the coincidence degree of the corresponding positions of the data to be replaced and the matching data, and directly determines whether the data to be replaced can replace the matching data based on the data similarity. If the model replacement type does not belong to the building maintenance state, determine whether the data to be replaced can replace the matching data based on the data width.
[0048] It should be noted here that how to determine whether the model replacement type belongs to the building maintenance state can be specifically based on the comparison between the model replacement type and the original type. If the model replacement type is similar to the original type of the model, it is determined that the model replacement type belongs to the building maintenance state. If the model replacement type is not similar to the original type of the model, it is determined that the model replacement type does not belong to the building maintenance state. If the model replacement type is 80% similar to the original type of the model, it can be determined that the two models are similar.
[0049] In one of the embodiments, determining whether the data to be replaced can replace the matching data based on the data similarity includes the following steps: S321, compare the data similarity with the preset similarity, and determine whether the data similarity exceeds the preset similarity.
[0050] S322, if the data similarity exceeds the preset similarity, determine that the data to be replaced can replace the matching data.
[0051] S323. If the data similarity does not exceed the preset similarity, then determine whether the data to be replaced can replace the matching data based on the data width.
[0052] Among them, the preset similarity represents the lowest coincidence degree at which the data to be replaced can directly replace the matching data. In this embodiment, the preset similarity is set to 70%, but it is not limited thereto. If the data similarity exceeds the preset similarity, it is determined that the data to be replaced can replace the matching data. If the data similarity does not exceed the preset similarity, then determine whether the data to be replaced can replace the matching data based on the data width.
[0053] Refer to Figure 3 , in one of the embodiments, determining whether the data to be replaced can replace the matching data based on the data width includes the following steps: S331. Obtain a set of overlapping data based on the data to be replaced and the matching data. The set of overlapping data includes several groups of overlapping data.
[0054] S332. Determine whether the number of data in the overlapping data is less than the preset number.
[0055] S333. If the number of data in the overlapping data is less than the preset number, then determine that the data to be replaced can replace the matching data.
[0056] S334. If the number of data in the overlapping data is not less than the preset number, then determine that the data to be replaced cannot replace the matching data.
[0057] Among them, the preset number represents the lowest number standard for determining that the overlapping data is a lot. For the number of data in the overlapping data less than the preset number, replacement can be directly performed. For more overlapping data, it is necessary to eliminate the overlapping data and then perform replacement.
[0058] It should be noted here that how to perform the replacement of the overlapping data can be carried out according to steps S600 to S700.
[0059] In one of the embodiments, screen out the corresponding set of matching identification data from the original data corresponding to the model to be updated for the identification data. Among them, the construction of the model to be updated includes the following steps: S210. Divide according to the original data to obtain several sets of data to be identified.
[0060] S220. Screen the sets of data to be identified to obtain the corresponding matching identification data.
[0061] S230. Identify the sets of data to be identified based on the matching identification data to obtain a set of marked data.
[0062] S240. Generate the model to be updated based on the set of marked data using the model generation technology.
[0063] Among them, the data set to be identified includes a set of data to be identified. Specifically here, the original data is divided. It can be divided into equal parts or unequal parts. The specific division criteria can be based on the size of the original data. If the original data is too large, it can be divided according to the physical modules of the original model. If the original data is small, the original data is directly divided into equal parts. The matching identification data is specifically the same as the above-mentioned method for obtaining the identification data, so as to facilitate matching the corresponding matching identification data set according to the identification data.
[0064] The marked data set represents the set of marked data in the data set to be identified. When matching is required, the identification data is directly matched with the marked data corresponding to each data set to be identified, and the corresponding matching identification data set can be obtained.
[0065] It should be noted here that since there is a three-dimensional model of the physical object, for the same marked data, it includes several data sets. Therefore, the data at the same marked data position is used as a marked data set. When obtaining the matching identification data set according to the identification data, only the identification data needs to be matched with the marked data corresponding to each marked data set, and then the marked data set is used as the matching identification, and the data around the matching identification position is used as the matching identification data.
[0066] In one of the embodiments, screening the data set to be identified to obtain the corresponding matching identification data includes the following steps: S221, screening the data in the data set to be identified to obtain the central data of the data set to be identified, and using the central data as the matching identification data.
[0067] Specifically, screening the data in the data set to be identified to obtain the central data of the data set to be identified, and using the central data as the matching identification data.
[0068] In one of the embodiments, before screening out the corresponding matching identification data set from the original data corresponding to the model to be updated by the identification data, the following steps are further included: S250, obtaining prediction update data according to the model to be updated and the estimated historical data, and obtaining the corresponding prediction identification data according to the prediction update data.
[0069] S260, comparing the prediction identification data with the matching identification data, and determining whether data corresponding to the prediction identification data is screened out from the matching identification data.
[0070] S270, if it is determined that data corresponding to the prediction identification data is screened out from the matching identification data, then using the prediction identification data as the matching data.
[0071] Among them, the estimated historical data represents the existing historical data, and the predicted update data is the predicted data obtained based on the model to be updated and the estimated historical data. The specific acquisition method can be that the processor obtains the area where there may be an update in the previous estimated historical data of the model to be updated, and obtains the predicted update data of the poison based on this area. The predicted identification data represents the identification data corresponding to the predicted update data. Here, the predicted identification data and the above-mentioned identification data are obtained in the same way according to the matching identification. Determine the data corresponding to the predicted identification data in the matching identification data, then use the predicted identification data as the matching data, skip steps S200 to S300, and execute steps 400 to S500.
[0072] The embodiment of the present application also discloses a manufacturing system for a digital twin model, which executes a manufacturing method for a digital twin model disclosed in the above embodiment.
[0073] As Figure 4 shown, the manufacturing system for the digital twin model includes a data acquisition module 10, a matching and screening module 20, a data judgment module 30, and a data processing module 40. The data acquisition module 10 is used to acquire the data to be replaced, and generate identification data and the corresponding data width based on the data to be replaced. The matching and screening module 20 is connected to the data acquisition module 10 through a network to receive the identification data, and screens out the corresponding set of matching identification data in the original data corresponding to the model to be updated. The data judgment module 30 is connected to the matching and screening module 20 through a network to receive the data width, obtains the corresponding matching data in the set of matching identification data according to the data width, and judges whether the data to be replaced can replace the matching data. If the data to be replaced can replace the matching data, the data processing module 40 is used to generate a data update signal based on the data to be replaced, and delete the matching data in the set of matching identification data according to the data update signal. The data processing module 40 is also used to store the data to be replaced in the set of matching identification data, and update the model to be updated according to the updated set of matching identification data.
[0074] The other functions executed by the above data acquisition module 10, matching and screening module 20, data judgment module 30, and data processing module 40, as well as the technical details of each function, are the same as or similar to the corresponding features in the manufacturing method of the digital twin model described above, so they will not be repeated here.
[0075] The embodiment of the present application also discloses an electronic device.
[0076] Refer to Figure 5, the electronic device includes a processor 51 and a memory 52 which are coupled to each other. A computer program 53 that can run on the processor 51 is stored on the memory 52. When the computer program 53 is executed by the processor 51, it implements the method for creating a digital twin model disclosed in the above embodiments.
[0077] The processor 51 can be a central processing unit 51, a general-purpose processor 51, a digital signal processor 51, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It is used to run the program code stored in the memory 52 or process data.
[0078] The memory 52 can be a ROM or other types of static storage devices that can store static information and instructions, a random access memory 52, or other types of dynamic storage devices that can store information and instructions. It can also be an electrically erasable programmable read-only memory 52, a compact disc read-only memory, or other optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 52 can be an internal storage unit in some embodiments.
[0079] The processor 51 and the memory 52 are connected by a bus. The bus can include a path for transmitting information between the above components. The bus can be a peripheral component interconnect standard bus or an extended industry standard architecture bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 5 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.
[0080] Figure 5 Only the electronic device with the memory 52, the processor 51, and the bus is shown. Those skilled in the art can understand that Figure 5 the structure shown does not constitute a limitation on the electronic device. It can be a bus-type structure or a star structure. The electronic device can also include more or fewer components than Figure 5 those shown, or combine certain components, or deploy different components. Other existing or future possible electronic devices, if applicable, should also be included within the protection scope and are hereby incorporated by reference.
[0081] The implementation principle is as follows: First, the data acquisition module 10 in the processor 51 is used to acquire the data to be replaced collected by the data acquisition module, and generate identification data and the corresponding data width based on the data to be replaced.
[0082] The matching and screening module 20 is connected to the data acquisition module 10 through a network to receive identification data, and screens out the corresponding set of matching identification data from the original data corresponding to the model to be updated based on the identification data.
[0083] The data judgment module 30 is connected to the matching and screening module 20 through a network to receive the data width, obtains the corresponding matching data from the set of matching identification data according to the data width, and judges whether the data to be replaced can replace the matching data.
[0084] If the data to be replaced can replace the matching data, the data processing module 40 is used to generate a data update signal based on the data to be replaced, and delete the matching data from the set of matching identification data according to the data update signal.
[0085] The data processing module 40 is also used to store the data to be replaced in the set of matching identification data, and update the model to be updated according to the updated set of matching identification data. It should be understood that although the steps in the flowchart of the accompanying drawings are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit and can be executed in other orders.
[0086] The above are all the preferred embodiments of this application. The protection scope of this application is not limited by this. Therefore, all equivalent changes made according to the structure, shape, and principle of this application should be covered within the protection scope of this application.
Claims
1. A method for creating a digital twin model, characterized in that Including the following steps: Obtain the data to be replaced, and generate identification data and corresponding data width based on the data to be replaced; Filter out the corresponding set of matching identification data from the original data corresponding to the model to be updated using the identification data; Obtain the corresponding matching data from the set of matching identification data according to the data width, and determine whether the data to be replaced can replace the matching data; If the data to be replaced can replace the matching data, generate a data update signal based on the data to be replaced, and delete the matching data from the set of matching identification data according to the data update signal; Store the data to be replaced in the set of matching identification data, and update the model to be updated according to the updated set of matching identification data.
2. The method for manufacturing a digital twin model according to claim 1, wherein The determination of whether the data to be replaced can replace the matching data includes the following steps: Obtain the model replacement type according to the data to be replaced, and determine whether the model replacement type belongs to the building maintenance status; If the model replacement type belongs to the building maintenance status, obtain the data similarity according to the data to be replaced and the matching data, and determine whether the data to be replaced can replace the matching data according to the data similarity; If the model replacement type does not belong to the building maintenance status, determine whether the data to be replaced can replace the matching data according to the data width.
3. The method for manufacturing a digital twin model according to claim 2, characterized in that, The determination of whether the data to be replaced can replace the matching data according to the data similarity includes the following steps: Compare the data similarity with a preset similarity, and determine whether the data similarity exceeds the preset similarity; If the data similarity exceeds the preset similarity, determine that the data to be replaced can replace the matching data; If the data similarity does not exceed the preset similarity, determine whether the data to be replaced can replace the matching data according to the data width.
4. The method for fabricating a digital twin model according to claim 2 or 3, wherein The determination of whether the data to be replaced can replace the matching data according to the data width includes the following steps: Obtain a set of overlapping data according to the data to be replaced and the matching data, and the set of overlapping data includes several groups of the overlapping data; Determine whether the number of data in the overlapping data is less than a preset number; If the number of data in the overlapping data is less than the preset number, determine that the data to be replaced can replace the matching data; If the number of data in the overlapping data is not less than the preset number, determine that the data to be replaced cannot replace the matching data.
5. The method for manufacturing a digital twin model according to claim 4, characterized in that, After determining that the data to be replaced cannot replace the matching data, the following steps are further included: Remove the overlapping data of the data to be replaced and the matching data according to the set of overlapping data to obtain the first data to be replaced and the first matching data; Delete the first matching data from the set of matching identification data, and store the first data to be replaced in the set of matching identification data.
6. The method for manufacturing a digital twin model according to claim 1, characterized in that, Filter out the corresponding set of matching identification data from the original data corresponding to the model to be updated using the identification data, wherein the construction of the model to be updated includes the following steps: Partition according to the original data to obtain several sets of data sets to be identified; Screen the data sets to be identified to obtain corresponding matching identification data; Mark the data sets to be identified according to the matching identification data to obtain marked data sets; Generate the model to be updated based on the marked data sets using model generation techniques.
7. The method for fabricating a digital twin model according to claim 6, wherein The screening of the data sets to be identified to obtain corresponding matching identification data includes the following steps: Screen the data in the data sets to be identified to obtain the central data of the data sets to be identified, and use the central data as the matching identification data.
8. The method for creating a digital twin model according to claim 6, wherein Before screening the identification data to obtain the corresponding matching identification data set in the original data corresponding to the model to be updated, the following steps are also included: Obtain predicted update data according to the model to be updated and estimated historical data, and obtain corresponding predicted identification data according to the predicted update data; Compare the predicted identification data with the matching identification data, and determine whether data corresponding to the predicted identification data can be screened out from the matching identification data; If it is determined that data corresponding to the predicted identification data can be screened out from the matching identification data, then use the predicted identification data as the matching data.
9. A manufacturing system for a digital twin model, characterized in that, Implementing the method for making a digital twin model according to any one of claims 1-8 includes: A data acquisition module (10), the data acquisition module (10) is used to acquire data to be replaced, and generate identification data and corresponding data widths according to the data to be replaced; A matching and screening module (20), the matching and screening module (20) is used to screen the identification data in the original data corresponding to the model to be updated to obtain a corresponding set of matching identification data; A data judgment module (30), the data judgment module (30) is used to obtain corresponding matching data from the set of matching identification data according to the data width, and judge whether the data to be replaced can replace the matching data; A data processing module (40), if the data to be replaced can replace the matching data, the data processing module (40) is used to generate a data update signal according to the data to be replaced, and delete the matching data from the set of matching identification data according to the data update signal; The data processing module (40) is also used to store the data to be replaced in the set of matching identification data, and update the model to be updated according to the updated set of matching identification data.
10. An electronic device, characterized in that, The electronic device includes a processor (51) and a memory (52) that are coupled to each other, and a computer program (53) that can run on the processor (51) is stored on the memory (52); when the computer program (53) is executed by the processor (51), it implements the method for making a digital twin model according to any one of claims 1-8.