Digital twin model updating method and device, equipment and medium

Abstract

The invention discloses a digital twin model updating method and device, equipment and a medium, and the method comprises the steps: responding to a received equipment drawing, and extracting equipment information; matching the equipment information with a digital twin model through a preset mapping relation; and after matching succeeds, updating the digital twin model according to a matching result. According to the method, the equipment points in the CAD drawing can be automatically read and distributed to the corresponding positions of the digital twin model.

Description

Technical Field

[0001] The present application belongs to the technical field of digital twin models, and in particular, relates to methods, devices, equipment and media for updating digital twin models. Background Art

[0002] Digital twin technology is an important tool to connect the physical world with the digital world. Digital twin models can reflect the status, performance and location of equipment in real time, helping companies to monitor, optimize and predict. However, the process of building and maintaining these models usually relies on traditional CAD drawings, which describe the design and layout of the equipment in detail.

[0003] In modern industrial environments, equipment layouts change frequently, and updating data synchronization between CAD drawings and digital twin models becomes a challenge. Failure to update the model in a timely and accurate manner can lead to poor decision-making, inefficiency, and waste of resources. Traditional methods update digital twin models by manually inputting or arranging equipment information and locations in CAD drawings into digital twin models, which is not only time-consuming but also prone to errors, affecting the accuracy and real-time nature of the data. Summary of the invention

[0004] The purpose of this application is to overcome the shortcomings of the prior art and provide a digital twin model update method, device, equipment and medium, aiming to improve the efficiency of digital twin model update through automated processing.

[0005] The purpose of this application is achieved through the following technical solutions:

[0006] A digital twin model updating method, the method comprising:

[0007] Responsive to the received equipment drawing, extracting equipment information;

[0008] Matching the device information with the digital twin model through a preset mapping relationship;

[0009] When the match is successful, the digital twin model is updated with the matching result.

[0010] Further, the device information includes text information, and the method includes:

[0011] Optical character recognition technology is used to extract text information from the device drawings.

[0012] Further, the device information includes device symbol information, and the method includes:

[0013] Computer vision technology is used to analyze the equipment drawings and identify equipment symbols.

[0014] Furthermore, the method also includes classifying device symbols using a pre-trained deep learning model.

[0015] Furthermore, the method comprises:

[0016] The device information is matched with the digital twin model through Euclidean distance calculation to determine whether the distance between the location of the device and the corresponding device location in the digital twin model is within a preset distance threshold range. If so, the match is successful, otherwise the match fails.

[0017] Furthermore, the method also includes building a user interface with a modern front-end framework to display the real-time device layout of the digital twin model.

[0018] Furthermore, the method also includes interacting with the platform of the digital twin model through a RESTful API to batch update device information.

[0019] On the other hand, the present invention also provides a digital twin model updating device, the device comprising:

[0020] A drawing parsing module, wherein the drawing parsing module extracts device information in response to a received device drawing;

[0021] A data mapping module, which matches the device information with the digital twin model through a preset mapping relationship;

[0022] The model updating module updates the digital twin model with the matching result after the matching is successful.

[0023] On the other hand, the present invention also provides a computer device, comprising a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded and executed by the processor to implement any one of the above-mentioned digital twin model updating methods.

[0024] On the other hand, the present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program is loaded and executed by a processor to implement any one of the above-mentioned digital twin model updating methods.

[0025] The beneficial effects of this application are:

[0026] (1) The digital twin model updating method, device, equipment and medium provided by the present invention can significantly reduce the time and labor intensity of manual input, improve work efficiency, reduce the incidence of human errors, ensure the data accuracy and consistency of the digital twin model, thereby reducing resource waste and ultimately reducing overall operating costs.

[0027] (2) The present invention can achieve real-time data synchronization between CAD drawings and digital twin models, ensuring that the model always reflects the actual status of the equipment. In a rapidly changing industrial environment, it can quickly adapt to changes in equipment layout, update the model in a timely manner, and support flexible adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flowchart of a method for updating a digital twin model according to an embodiment of the present invention;

[0029] Figure 2 It is an architecture diagram of a digital twin model updating device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0031] Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of this application.

[0032] The traditional method is to update the digital twin model by manually inputting or arranging the equipment information and location in the CAD drawing into the digital twin model. This is not only time-consuming but also prone to errors, affecting the accuracy and real-time nature of the data.

[0033] In order to solve the above-mentioned technical problems, the following embodiments of the digital twin model updating method, device, equipment and medium of the present application are proposed.

[0034] Example 1

[0035] This embodiment takes the input drawing as a CAD drawing as an example. Figure 1 ,like Figure 1 The figure is a flowchart of a method for updating a digital twin model according to an embodiment of the present application, and the method specifically comprises the following steps:

[0036] Step 1. CAD drawing analysis

[0037] (1) Input drawing preparation:

[0038] a. Users upload CAD drawing files through the system interface. Supported formats include DWG, DXF and other common CAD formats.

[0039] b. The system first checks the validity of the file to ensure that the drawing can be parsed.

[0040] (2) Drawing analysis:

[0041] <1> OCR Technology Applications:

[0042] a. Use optical character recognition (OCR) technology to extract text information (such as equipment name and number) from drawings.

[0043] b. Combined with template matching technology, it can enhance recognition accuracy, especially the recognition ability of complex characters.

[0044] <2> Computer Vision:

[0045] a. Use computer vision technology (such as edge detection and contour extraction) to analyze drawings and identify various equipment symbols.

[0046] b. Use pre-trained deep learning models (such as convolutional neural networks) to classify device symbols to ensure the recognition accuracy of various types of devices.

[0047] <3> Feature extraction: Extract key information about each device, including:

[0048] a. Coordinate information: the specific location of the equipment in the drawing (X, Y coordinates).

[0049] b. Equipment Type: The identified equipment category (e.g. pump, valve, sensor, etc.).

[0050] c. Dimensional information: size and shape characteristics of the device.

[0051] d. Other attributes: related installation requirements, connection information, etc.

[0052] (3) Data storage: The parsed structured data is stored in a temporary database to facilitate subsequent operations.

[0053] Step 2. Data Mapping

[0054] (1) Device information matching:

[0055] <1> Establishing mapping relationship: Establishing mapping relationship between device information and digital twin model in the database. Templates of standard devices can be defined for quick matching.

[0056] <2> Algorithmic matching: Use distance calculation and similarity matching algorithms to ensure that each extracted equipment symbol is consistent with its counterpart in the digital twin model.

[0057] example:

[0058] a. Euclidean distance: used to evaluate the similarity between the device location extracted from the CAD drawing and the corresponding device location in the digital twin model. By calculating the Euclidean distance between the device locations, the system can determine whether the device is within a reasonable distance range.

[0059] Usually used in two-dimensional or three-dimensional space. Its formula is:

[0060]

[0061] For three-dimensional space, the formula is:

[0062]

[0063] b. Threshold judgment: Set the minimum similarity threshold for matching to ensure that only high-confidence matching results are accepted, which means that the device is considered to be a match within this distance.

[0064] Judgment logic:

[0065] 1 Calculate the Euclidean distance between the extracted device locations and the corresponding device locations in the model.

[0066] 2 Compare the calculated distance with the set threshold:

[0067] 3 If the distance is less than or equal to the threshold, the device is considered matched successfully.

[0068] 4 If the distance is greater than the threshold, the device matching is considered to have failed and further verification or adjustment is required.

[0069] (2) Data Verification: The accuracy of the matching results is checked through the verification mechanism. If any inconsistency is found, the system automatically prompts the user to make manual corrections.

[0070] Step 3. Model update

[0071] (1) Real-time update:

[0072] a. The system automatically updates the equipment layout in the digital twin model based on the results of data mapping.

[0073] b. API interface: Interact with the digital twin platform through the RESTful API, supporting batch updates and single device update operations.

[0074] (2) Status monitoring: Record the timestamp and operation log of each update to facilitate subsequent auditing and problem tracking. The optional version control function allows users to trace back to the previous model status.

[0075] Step 4. User Interface Module

[0076] (1) Interface design:

[0077] a. Use a modern front-end framework (Vue) to build the user interface to ensure that the operation is intuitive and responsive.

[0078] b. The interface should support adaptive design so that it can be displayed well on different devices (such as PC and tablet).

[0079] (2) Functional implementation:

[0080] a. Equipment viewing: Users can view the equipment layout in the digital twin model in real time, including the status and real-time data of the equipment (model, status, fault, etc.).

[0081] b. Manual adjustment:

[0082] <1> Provides manual adjustment function for device position. Users can change the position of the device in the model by dragging.

[0083] <2> Ensure that the system can update the device position in real time and record the adjustment history.

[0084] c. Confirmation and feedback:

[0085] <1> After the user has completed the adjustment, they can choose to confirm the changes or return to modify.

[0086] <2> The system provides real-time feedback on operation results, informing users whether the update is successful and any potential problems.

[0087] Step 5. System integration and testing

[0088] (1) Integration testing:

[0089] a. After the entire system is developed, each module is integrated and tested to ensure smooth data flow and seamless connection between modules.

[0090] b. Verify whether the interfaces and interactions between different modules are normal to ensure the accuracy of data transmission.

[0091] (2) Performance test:

[0092] a. Conduct system performance testing to evaluate response time and processing capacity under different loads.

[0093] b. Test the efficiency of CAD drawing parsing and equipment update operations of different scales to ensure that the system can withstand the load in actual applications.

[0094] This embodiment.

[0095] Example 2

[0096] Reference Figure 2 ,like Figure 2The figure shows the architecture diagram of the digital twin model updating device of the embodiment of the present application, which includes:

[0097] (1)CAD drawing parsing module:

[0098] Function: Automatically parse CAD drawings in various formats and extract equipment symbols and their location information.

[0099] Technology: Uses OCR (optical character recognition), computer vision and deep learning algorithms to improve recognition accuracy.

[0100] Input: CAD drawing files (such as DWG, DXF).

[0101] Output: equipment symbols, icons and location information, including coordinates, type, size, etc.

[0102] (2) Data mapping module:

[0103] Function: Accurately match the parsed device information with the digital twin model to ensure data consistency.

[0104] Technology: Use a database to store device information and adopt mapping algorithms and rules for matching.

[0105] Input: Equipment symbol and location information.

[0106] Output: Matching data with the digital twin model (e.g. coordinate transformation, device properties).

[0107] (3) Model update module:

[0108] Function: Automatically update the equipment layout in the digital twin model based on the matching results to ensure the real-time performance of the model.

[0109] Technology: Use API interfaces to interact with digital twin models and perform batch updates.

[0110] Input: Matching data.

[0111] Output: Updated digital twin model state.

[0112] (4) User interface module:

[0113] Function: Provides a friendly user interface that allows users to view and manage device layouts and supports manual adjustment and confirmation.

[0114] Technology: Front-end technology (Vue) builds the interactive interface, and back-end services handle user requests.

[0115] Input: User actions (viewing, adjusting device position).

[0116] Output: Operation result (confirmation of update, error prompt).

[0117] Example 3

[0118] This preferred embodiment provides a computer device, which can implement the steps in any embodiment of the digital twin model update method provided in the embodiments of the present application. Therefore, the beneficial effects of the digital twin model update method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0119] Example 4

[0120] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling related hardware through instructions, and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. To this end, an embodiment of the present invention provides a storage medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps of any embodiment of the digital twin model update method provided in the embodiment of the present invention.

[0121] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0122] Since the instructions stored in the storage medium can execute the steps in any digital twin model update method embodiment provided in the embodiments of the present invention, the beneficial effects that can be achieved by any digital twin model update method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0123] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A digital twin model updating method, characterized in that: The method comprises: Responsive to the received equipment drawing, extracting equipment information; Matching the device information with the digital twin model through a preset mapping relationship; When the match is successful, the digital twin model is updated with the matching result.

2. The digital twin model updating method according to claim 1, characterized in that: The device information includes text information, and the method includes: Optical character recognition technology is used to extract text information from the device drawings.

3. The digital twin model updating method according to claim 1, characterized in that: The device information includes device symbol information, and the method includes: Computer vision technology is used to analyze the equipment drawings and identify equipment symbols.

4. The digital twin model updating method according to claim 3, characterized in that: The method also includes classifying device symbols using a pre-trained deep learning model.

5. The digital twin model updating method according to claim 1, characterized in that: The method comprises: The device information is matched with the digital twin model through Euclidean distance calculation to determine whether the distance between the location of the device and the corresponding device location in the digital twin model is within a preset distance threshold range. If so, the match is successful, otherwise the match fails.

6. The digital twin model updating method according to claim 1, characterized in that: The method also includes building a user interface with a modern front-end framework to display the real-time equipment layout of the digital twin model.

7. The digital twin model updating method according to claim 1, characterized in that: The method also includes interacting with the platform of the digital twin model through a RESTful API to batch update device information.

8. A digital twin model updating device, characterized in that: The device comprises: A drawing parsing module, wherein the drawing parsing module extracts device information in response to a received device drawing; A data mapping module, which matches the device information with the digital twin model through a preset mapping relationship; The model updating module updates the digital twin model with the matching result after the matching is successful.

9. A computer device, characterized in that: The computer device includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the digital twin model updating method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which is loaded and executed by a processor to implement the digital twin model updating method as described in any one of claims 1 to 7.

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