Electric energy meter modeling method and device, computer equipment and readable storage medium

Abstract

The invention relates to an electric energy meter modeling method and device, computer equipment and a readable storage medium. The method comprises the steps of obtaining attribute data of an electric energy meter, determining position data of a preset electric energy meter component virtual body in an electric energy meter model according to position data of a component in the electric energy meter in the component attribute data, and based on the position data of the preset electric energy meter component virtual body in the electric energy meter model, assembling the preset electric energy meter component virtual body to obtain the electric energy meter model. By adopting the method, the electric energy meter modeling efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of electric energy meters, and in particular to an electric energy meter modeling method, device, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] With the rapid development of power systems, the requirements for the accuracy of energy measurement are getting higher and higher. The energy meter has also become an important measurement tool. Modeling the energy meter can help engineers gain a deeper understanding of the structure, circuit and function of the energy meter.

[0003] In traditional solutions, professionals often spend a lot of time modeling the electricity meter. During the modeling process, complex geometric shapes and textures need to be processed before the electricity meter model can be finally obtained.

[0004] However, the above modeling scheme requires a lot of manpower and time, and requires a lot of repeated operations, so the efficiency of modeling the electric energy meter is greatly reduced. Summary of the invention

[0005] Based on this, it is necessary to provide an electric energy meter modeling method, device, computer equipment, computer-readable storage medium and computer program product that can improve modeling efficiency in response to the above technical problems.

[0006] In a first aspect, the present application provides an electric energy meter modeling method, comprising:

[0007] Acquire attribute data of the electric energy meter, wherein the attribute data includes component attribute data;

[0008] Determine the position data of the preset electric energy meter component virtual body in the electric energy meter model according to the position data of the component in the electric energy meter in the component attribute data;

[0009] Based on the position data of the preset virtual bodies of electric energy meter components in the electric energy meter model, the preset virtual bodies of electric energy meter components are assembled to obtain the electric energy meter model.

[0010] In a second aspect, the present application also provides an electric energy meter modeling device, comprising:

[0011] A data acquisition module, used to acquire attribute data of the electric energy meter, wherein the attribute data includes component attribute data;

[0012] A position determination module, used to determine the position data of a preset electric energy meter component virtual body in the electric energy meter model according to the position data of the component in the electric energy meter in the component attribute data;

[0013] The model building module is used to assemble the preset virtual bodies of electric energy meter components based on the position data of the preset virtual bodies of electric energy meter components in the electric energy meter model to obtain the electric energy meter model.

[0014] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned electric energy meter modeling method when executing the computer program.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the above-mentioned electric energy meter modeling method are implemented.

[0016] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps in the above-mentioned electricity meter modeling method when executed by a processor.

[0017] The above-mentioned electric energy meter modeling method, device, computer equipment, computer-readable storage medium and computer program product are different from traditional modeling schemes. By obtaining the attribute data of the electric energy meter, accurate data support is provided for the subsequent electric energy meter modeling. Then, according to the position data of the components in the electric energy meter in the component attribute data, the position data of the preset electric energy meter component virtual body in the electric energy meter model is determined, wherein the preset electric energy meter component virtual body is the basic unit of the electric energy meter model. Combined with the position data of the components in the electric energy meter, the position data of the preset electric energy meter component virtual body can be quickly determined. Finally, the preset electric energy meter component virtual body can be assembled according to the electric energy meter component virtual body and its position data, so as to quickly construct the electric energy meter model, reduce the repeated attempts and adjustments of the component positions in the traditional modeling scheme, greatly shorten the modeling time, and thus improve the efficiency of electric energy meter modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 An application environment diagram of an electric energy meter modeling method in one embodiment;

[0020] Figure 2 A schematic diagram of a flow chart of a method for modeling an electric energy meter in one embodiment;

[0021] Figure 3A schematic flow chart of an electric energy meter modeling method in another embodiment;

[0022] Figure 4 A schematic diagram of a process for determining position data of a virtual body of an electric energy meter component in an electric energy meter model in one embodiment;

[0023] Figure 5 A schematic diagram of a flow chart of a method for modeling an electric energy meter in another embodiment;

[0024] Figure 6 A schematic diagram of a flow chart of a method for modeling an electric energy meter in a detailed embodiment;

[0025] Figure 7 It is a structural block diagram of an electric energy meter modeling device in one embodiment;

[0026] Figure 8 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0028] The electric energy meter modeling method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed on the cloud or other network servers.

[0029] Specifically, an operator may upload the attribute data of the electric energy meter to the server 104 through the terminal 102, wherein the attribute data includes component attribute data. Then, the server 104 determines the position data of the preset electric energy meter component virtual bodies in the electric energy meter model based on the position data of the components in the electric energy meter in the component attribute data. Finally, the server 104 assembles the preset electric energy meter component virtual bodies based on the position data of the preset electric energy meter component virtual bodies in the electric energy meter model to obtain the electric energy meter model.

[0030] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices may be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 may be an independent physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0031] In an exemplary embodiment, Figure 2 As shown, a method for modeling an electric energy meter is provided. Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0032] S100, acquiring attribute data of the electric energy meter, where the attribute data includes component attribute data.

[0033] Among them, the electric energy meter includes components of various types and functions, including but not limited to active crystal oscillators, clock chips, thermistors, transformers, memories, power inductors, relays, metering chips, etc. The attribute data of the electric energy meter includes but is not limited to the electric energy meter model, the internal circuit diagram of the electric energy meter, and the working parameters of the electric energy meter, such as rated voltage, rated current, accuracy level, etc. The component attribute data includes the specific attributes of each component, such as the component name, component number, and the location data of the component in the electric energy meter. For a specific component, such as a resistor, the component attribute data includes but is not limited to the resistance value, rated power, error range, packaging form, etc.

[0034] Specifically, the operator can read the attribute data of the electricity meter by referring to the product documents or instructions of the electricity meter, or disassemble the electricity meter through reverse engineering, record the model, appearance and other information of its internal components, and thus obtain the attribute data of the electricity meter.

[0035] S200, determining the position data of a preset electric energy meter component virtual body in the electric energy meter model according to the position data of the component in the electric energy meter in the component attribute data.

[0036] Among them, the preset electric energy meter component virtual body refers to a virtual body pre-stored in a preset modeling engine. The modeling engine can be an Unreal Engine. The pre-created electric energy meter component virtual body can be directly imported into the Unreal Engine to represent the components in the electric energy meter.

[0037] Specifically, before constructing the electric energy meter model, it is necessary to determine the one-to-one correspondence between the components in the real electric energy meter and the preset virtual bodies, for example, to correspond the components in the real electric energy meter with the virtual bodies of the electric energy meter components in the modeling engine based on the model, functional attributes, identification data, etc. of the components. Exemplarily, taking the metering chip in the real electric energy meter as an example, it is necessary to first find the virtual body representing the metering chip in the preset electric energy meter component virtual body. If the model of the metering chip is a specific model, the corresponding electric energy meter component virtual body can also use the specific model as identification data, so as to correspond the real metering chip with the electric energy meter component virtual body, and then determine the position data of the preset electric energy meter component virtual body in the electric energy meter model according to the position of the component in the electric energy meter in the component attribute data.

[0038] S300, assembling preset virtual bodies of electric energy meter components based on position data of preset virtual bodies of electric energy meter components in the electric energy meter model to obtain the electric energy meter model.

[0039] Specifically, in a preset modeling engine, such as Unreal Engine, after determining the position data of each virtual body of the electric energy meter component in the electric energy meter model, the user can move the virtual body of the electric energy meter component to the corresponding position by dragging. In the electric energy meter, the components usually have electrical or physical connections. When assembling the virtual body of the electric energy meter component, the virtual bodies of the electric energy meter component can also be connected by line tools to finally form the electric energy meter model.

[0040] After generating the electric energy meter model, you can also store the coordinates of the electric energy meter model, the location data of each electric energy meter component, the connection relationship, the status information, etc. in the project file for easy viewing later.

[0041] The above-mentioned electric energy meter modeling method is different from the traditional modeling scheme. By obtaining the attribute data of the electric energy meter, it provides accurate data support for the subsequent electric energy meter modeling, and then determines the position data of the preset electric energy meter component virtual body in the electric energy meter model according to the position data of the components in the electric energy meter in the component attribute data, wherein the preset electric energy meter component virtual body is the basic unit of the electric energy meter model. Combined with the position data of the components in the electric energy meter, the position data of the preset electric energy meter component virtual body can be quickly determined. Finally, the preset electric energy meter component virtual body can be assembled according to the electric energy meter component virtual body and its position data, so as to quickly construct the electric energy meter model, reduce the repeated attempts and adjustments of the component positions in the traditional modeling scheme, greatly shorten the modeling time, and thus improve the efficiency of electric energy meter modeling.

[0042] In one embodiment, Figure 3 As shown, S200 includes:

[0043] S210, performing format conversion and unit conversion on the position data of the component in the electric energy meter in the component attribute data to obtain target position data that can be recognized by a preset modeling engine.

[0044] S220, determining the position data of the preset electric energy meter component virtual body in the electric energy meter model according to the target position data.

[0045] Continuing with the above embodiments, the position data of components in the electric energy meter obtained through different channels may have different formats. For example, some position data are stored in text form, and other position data may be stored in list form. It is necessary to first unify the format, and after the format is unified, the format of the position data is converted into a unified format that can be recognized by the modeling engine. In addition, in addition to the inconsistent format, there may also be inconsistent units. For example, the unit of the position data of components in the electric energy meter may be millimeters, while the unit of the position data in the modeling engine may be centimeters, so unit conversion is also required. After completing the format conversion and unit conversion, the position of the corresponding electric energy meter component virtual body can be set in the modeling engine.

[0046] In this embodiment, unit conversion and format conversion are performed on the acquired position data, which can reduce the position error caused by unit error and format error. The modeling engine usually has its default unit system. Converting the position data into the unit used by the modeling engine can make the position of the virtual body of the component in the model space accurate, thereby improving the accuracy of the constructed electric energy meter model.

[0047] In one embodiment, Figure 4 As shown, S220 includes:

[0048] S221, adding identification data to the preset virtual bodies of electric energy meter components, and classifying and grouping the preset virtual bodies of electric energy meter components after the identification data are added, to obtain classification and grouping results.

[0049] S222, establishing a mapping relationship between the identification data of the virtual body of the electric energy meter component preset in the classification and collection result and the component attribute data.

[0050] S223, based on the mapping relationship and the target position data, determine the position data of the preset electric energy meter component virtual body in the electric energy meter model.

[0051] Specifically, the identification data is information used to uniquely identify each virtual body of the electric energy meter component, and may be the name, model, number, etc. of the virtual body of the electric energy meter component. For example, for a virtual body of a resistor, the identification data may be "R100". Then, the virtual bodies of the electric energy meter components to which the identification data is added may be classified and grouped according to component functions, component characteristics, etc. For example, all virtual bodies of resistors may be classified as "resistors", all virtual bodies of capacitors may be classified as "capacitors", and virtual bodies of chips may be classified as "chips", etc. Through classification and grouping, different types of virtual bodies of electric energy meter components may be more conveniently managed or visualized in a unified manner, and the classification and grouping result may be a data structure, such as a dictionary or list containing virtual bodies of electric energy meter components of different categories.

[0052] Furthermore, the component attribute data includes detailed information about the component, such as electrical characteristics, physical dimensions, and location data of the component in the electric energy meter. By establishing a mapping relationship between the identification data and the component attribute data, the virtual body of the component can be associated with the detailed attributes of the real component. For example, the virtual body of the resistor with the identification data "R100" can be matched with the resistor identified as "R100" in the actual electric energy meter. Accordingly, the attribute data of the "R100" resistor in the actual electric energy meter is assigned to the virtual body of the resistor with the identification data "R100". Since the target location data is included in the attribute data, the location data of the virtual body of the electric energy meter component in the electric energy meter model can be determined.

[0053] In this embodiment, by adding identification data to the preset virtual bodies of electric energy meter components, in a complex electric energy meter model, especially when it contains many different types of components, these identification data can help users quickly and accurately identify the actual components represented by each component virtual body, and classify and group the virtual bodies with added identifications. Users can manage and understand the structure of the electric energy meter model from a macro perspective, and thus can combine the component virtual bodies according to certain rules and sequences, thereby improving the efficiency of building the electric energy meter model.

[0054] In one embodiment, Figure 5 As shown, S300 includes:

[0055] S310, based on the position data of the preset electric energy meter component virtual bodies in the electric energy meter model, assemble the preset electric energy meter component virtual bodies, and load the geometric information, material information and texture information of the preset electric energy meter component virtual bodies into the preset virtual modeling scene to determine the initial electric energy meter model.

[0056] S320, adjusting the attribute data of the components in the initial electric energy meter model based on the component attribute data to obtain an adjusted initial electric energy meter model.

[0057] S330, performing position calibration and scale calibration on the virtual bodies of the electric energy meter components in the adjusted initial electric energy meter model and the preset virtual modeling scene to obtain the electric energy meter model.

[0058] Specifically, the process of assembling the virtual bodies of electricity meter components can be completed in a modeling engine, such as the Unreal Engine. Users can combine the virtual bodies according to corresponding positions by dragging components and adjusting the coordinate axes of the virtual bodies of components based on the preset position data of the virtual bodies of electricity meter components in the electricity meter model. For example, the virtual body of the chip generation can be placed in a specific area of ​​the virtual body of the circuit board.

[0059] While assembling the virtual bodies of the energy meter components, the geometric information (such as shape, size, etc.), material information (such as material properties of metal, plastic, etc.) and texture information (such as surface pattern, color, etc.) of each virtual body of the energy meter components can also be loaded into the preset virtual modeling scene. By loading the above geometric information, material information, texture information, etc., the virtual body of the energy meter components can be made more realistic and specific. For example, the texture information of the metal material can be applied to the virtual body of the transformer to make it look like a real metal transformer. In this way, the energy meter model is initially constructed through the assembly and information loading of the virtual bodies of the energy meter components.

[0060] Furthermore, the properties of the component virtual bodies can be adjusted, and corresponding property data can be added to each component virtual body. For example, based on the component property data obtained from the actual electric energy meter, including but not limited to electrical performance (such as resistance value, capacitance value, voltage, etc.), physical characteristics, etc., taking a resistor as an example, if the resistance value of a resistor in the actual electric energy meter is 100 ohms, then its resistance value in the initial electric energy meter model should also be adjusted to 100 ohms accordingly. In the Unreal Engine, the user can adjust the property data of the component virtual body by right-clicking on the component virtual body to obtain the adjusted initial electric energy meter model.

[0061] Finally, the virtual bodies of the electric energy meter components in the adjusted initial electric energy meter model are calibrated in position and proportion with the preset virtual modeling scene. Specifically, the relative positions between the virtual bodies of different components, the relative positions of the virtual bodies of components relative to the preset virtual modeling scene, and the proportional relationship between the virtual bodies of the electric energy meter components and the virtual modeling scene are adjusted. For example, the relative position between the virtual body of the chip and the virtual body of the circuit board is adjusted to be consistent with the relative position in the real electric energy meter. If the overall size of the electric energy meter model is relatively large, but the size of some virtual bodies of the electric energy meter components is relatively small, it may affect the visual effect of the electric energy meter model. Through proportional calibration, the proportions of the virtual bodies of the components of each electric energy meter and the modeling scene are coordinated to meet the actual situation, and finally the electric energy meter model is obtained.

[0062] In this embodiment, by gradually assembling the virtual bodies of the electric energy meter components, loading the geometry, material and texture information, and adjusting and calibrating the electric energy meter model based on the actual attribute data, the electric energy meter model finally constructed can be highly close to the real electric energy meter in appearance and attributes, and can accurately reflect the electrical performance and physical characteristics of the electric energy meter.

[0063] In one embodiment, S100 includes: obtaining the initial attribute data of the electric energy meter through the return reverse retrieval device, formatting and cleaning the initial attribute data of the electric energy meter in sequence, and obtaining the attribute data of the electric energy meter. The attribute data of the electric energy meter includes the electric energy meter model, component name, component number and location data of the component in the electric energy meter.

[0064] Among them, the return reverse analysis device is a device used for reverse analysis of electric energy meters, which can obtain the initial attribute data from the returned electric energy meters, including but not limited to the electric energy meter model, component name, component number, component location data in the electric energy meter, etc. Since the initial attribute data may have problems such as inconsistent format and low data quality, it is necessary to format and clean the initial attribute data.

[0065] Specifically, the initial attribute data is formatted and unified into a standard format. After formatting, data cleaning is required to remove noise, errors and duplicate information in the data. For example, when acquiring the initial attribute data, some duplicate data, invalid data, abnormal values ​​or missing values ​​may be collected. The abnormal values ​​and invalid data can be removed by setting a threshold range, and the duplicate data can be removed by deduplication, and the missing values ​​can be filled. After formatting and data cleaning, the data quality of the initial attribute data is improved, and finally the attribute data of the electric energy meter is obtained, including but not limited to the electric energy meter model, component name, component number and the location data of the component in the electric energy meter.

[0066] In this embodiment, by formatting and cleaning the initial attribute data, the data quality of the obtained attribute data is significantly improved, providing a high-quality data basis for subsequent analysis, modeling and other operations.

[0067] In one embodiment, the initial attribute data of the electric energy meter includes the initial position data of the components in the electric energy meter, and the initial position data of the components in the electric energy meter are obtained by identifying the components through a return inverse process, including: acquiring image data of the electric energy meter, taking the image data as input, calling a trained image analysis model in the return inverse process, and determining the initial position data of the components in the electric energy meter, wherein the trained image analysis model is trained based on historical image data.

[0068] Specifically, various image acquisition devices, such as high-definition cameras and industrial cameras, can be used to shoot the returned electric energy meters from multiple angles to obtain their image data. In the return reverse retrieval device, a trained image analysis model is preset. The image analysis model can be a target recognition model. During the training process, a large amount of historical image data with annotation information is used. The annotation information represents the location information of each component in the historical image data.

[0069] Furthermore, with the image data as input, the trained image analysis model is called, and the image analysis model can identify the initial position data of the components in the electric energy meter. For example, with the upper left corner of the image data as the origin, the width of the image data as the maximum value of the X-axis, and the height of the image data as the maximum value of the Y-axis, the image analysis model can use a bounding box to represent the components, and use the upper left corner coordinates and lower right corner coordinates of the bounding box as the position data of the components in the image data, wherein the upper left corner coordinates are the width and height of the upper left corner of the bounding box from the upper left corner of the image data, and the lower right corner coordinates are the width and height of the lower right corner of the bounding box from the upper left corner of the image data. Then, according to the position data of the components in the image data, the initial position data of the components in the electric energy meter are analyzed and determined.

[0070] In this embodiment, image analysis is performed on the image data of the electric energy meter in combination with the image analysis model to obtain the initial position data. This non-contact measurement method combined with deep learning technology can efficiently and accurately obtain the initial position data of components in the electric energy meter, thereby improving the accuracy and efficiency of the electric energy meter modeling.

[0071] In order to make a clearer description of the electric energy meter modeling method provided in this application, Figure 6 and one A detailed embodiment is explained, and the detailed embodiment includes the following steps:

[0072] S601, obtaining initial attribute data of the electric energy meter through the return inverse device, formatting and cleaning the initial attribute data of the electric energy meter in sequence, and obtaining attribute data of the electric energy meter, wherein the attribute data of the electric energy meter includes component attribute data.

[0073] S602, performing format conversion and unit conversion on the position data of the components in the electric energy meter in the component attribute data to obtain target position data recognizable by the preset modeling engine, adding identification data to the preset electric energy meter component virtual body, and classifying and grouping the preset electric energy meter component virtual body after adding the identification data to obtain the classification and grouping result.

[0074] S603, establishing a mapping relationship between the identification data of the preset electric energy meter component virtual body in the classification and collection result and the component attribute data, and determining the position data of the preset electric energy meter component virtual body in the electric energy meter model based on the mapping relationship and the target position data.

[0075] S604, assembling the preset virtual bodies of electric energy meter components based on the position data of the preset virtual bodies of electric energy meter components in the electric energy meter model, and loading the geometric information, material information and texture information of the preset virtual bodies of electric energy meter components into the preset virtual modeling scene to determine the initial electric energy meter model;

[0076] S605, based on the component attribute data, adjust the attribute data of the components in the initial electric energy meter model to obtain the adjusted initial electric energy meter model, perform position calibration and scale calibration on the virtual bodies of the electric energy meter components in the adjusted initial electric energy meter model and the preset virtual modeling scene to obtain the electric energy meter model.

[0077] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0078] Based on the same inventive concept, the embodiment of the present application also provides an electric energy meter modeling device for implementing the electric energy meter modeling method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more electric energy meter modeling device embodiments provided below can refer to the limitations of the electric energy meter modeling method above, and will not be repeated here.

[0079] In an exemplary embodiment, Figure 7 As shown, an electric energy meter modeling device 700 is provided, comprising: a data acquisition module 710, a position determination module 720 and a model construction module 730, wherein:

[0080] The data acquisition module 710 is used to acquire attribute data of the electric energy meter, and the attribute data includes component attribute data.

[0081] The position determination module 720 is used to determine the position data of the preset electric energy meter component virtual body in the electric energy meter model according to the position data of the component in the electric energy meter in the component attribute data.

[0082] The model building module 730 is used to assemble the preset virtual bodies of electric energy meter components based on the position data of the preset virtual bodies of electric energy meter components in the electric energy meter model to obtain the electric energy meter model.

[0083] In one embodiment, the position determination module 720 is also used to perform format conversion and unit conversion on the position data of components in the electric energy meter in the component attribute data to obtain target position data recognizable by a preset modeling engine, and determine the position data of the preset electric energy meter component virtual body in the electric energy meter model based on the target position data.

[0084] In one embodiment, the position determination module 720 is also used to add identification data to the preset electric energy meter component virtual body, and to classify and group the preset electric energy meter component virtual body after adding the identification data, to obtain the classification and grouping result, to establish a mapping relationship between the identification data of the preset electric energy meter component virtual body and the component attribute data in the classification and grouping result, and to determine the position data of the preset electric energy meter component virtual body in the electric energy meter model based on the mapping relationship and the target position data.

[0085] In one embodiment, the model building module 730 is also used to assemble preset electric energy meter component virtual bodies based on the position data of the preset electric energy meter component virtual bodies in the electric energy meter model, and load the geometric information, material information and texture information of the preset electric energy meter component virtual bodies into the preset virtual modeling scene, determine the initial electric energy meter model, adjust the attribute data of the components in the initial electric energy meter model based on the component attribute data, obtain the adjusted initial electric energy meter model, and perform position calibration and scale calibration on the electric energy meter component virtual bodies in the adjusted initial electric energy meter model and the preset virtual modeling scene to obtain the electric energy meter model.

[0086] In one embodiment, the data acquisition module 710 is also used to obtain the initial attribute data of the electric energy meter through the return reverse retrieval device, and format and clean the initial attribute data of the electric energy meter in turn to obtain the attribute data of the electric energy meter. The attribute data of the electric energy meter includes the electric energy meter model, component name, component number and location data of the component in the electric energy meter.

[0087] In one embodiment, the data acquisition module 710 is also used to acquire image data of the electric energy meter, and use the image data as input to call the trained image analysis model in the return inverse device to determine the initial position data of the components in the electric energy meter, wherein the trained image analysis model is trained based on historical image data.

[0088] Each module in the above-mentioned electric energy meter modeling device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0089] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as attribute data of the electric energy meter. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for modeling an electric energy meter is implemented.

[0090] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0091] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps in the above-mentioned electric energy meter modeling method embodiment are implemented.

[0092] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned electric energy meter modeling method embodiment are implemented.

[0093] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above-mentioned electric energy meter modeling method embodiment when executed by a processor.

[0094] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0095] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0096] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0097] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for modeling an electric energy meter, characterized in that: The method comprises: Acquire attribute data of the electric energy meter, wherein the attribute data includes component attribute data; Determine the position data of the preset electric energy meter component virtual body in the electric energy meter model according to the position data of the component in the electric energy meter in the component attribute data; Based on the position data of the preset virtual bodies of electric energy meter components in the electric energy meter model, the preset virtual bodies of electric energy meter components are assembled to obtain the electric energy meter model.

2. The method according to claim 1, characterized in that: The step of determining the position data of a preset electric energy meter component virtual body in the electric energy meter model according to the position data of the component in the electric energy meter in the component attribute data includes: Performing format conversion and unit conversion on the position data of the components in the electric energy meter in the component attribute data to obtain target position data that can be recognized by a preset modeling engine; According to the target position data, the position data of the preset virtual body of the electric energy meter component in the electric energy meter model is determined.

3. The method according to claim 2, characterized in that The step of determining the position data of the preset electric energy meter component virtual body in the electric energy meter model according to the target position data includes: Adding identification data to the preset virtual bodies of electric energy meter components, and classifying and grouping the preset virtual bodies of electric energy meter components after adding the identification data, to obtain classification and grouping results; Establishing a mapping relationship between the identification data of the virtual body of the electric energy meter component preset in the classification and collection result and the attribute data of the component; Based on the mapping relationship and the target position data, the position data of the preset electric energy meter component virtual body in the electric energy meter model is determined.

4. The method according to any one of claims 1 to 3, characterized in that: The step of assembling the preset virtual bodies of electric energy meter components based on the position data of the preset virtual bodies of electric energy meter components in the electric energy meter model to obtain the electric energy meter model includes: Based on the position data of the preset virtual bodies of electric energy meter components in the electric energy meter model, the preset virtual bodies of electric energy meter components are assembled, and the geometric information, material information and texture information of the preset virtual bodies of electric energy meter components are loaded into a preset virtual modeling scene to determine an initial electric energy meter model; Based on the component attribute data, adjusting the attribute data of the components in the initial electric energy meter model to obtain an adjusted initial electric energy meter model; Position calibration and scale calibration are performed on the virtual bodies of the electric energy meter components in the adjusted initial electric energy meter model and the preset virtual modeling scene to obtain the electric energy meter model.

5. The method according to any one of claims 1 to 3, characterized in that: The obtaining of attribute data of the electric energy meter includes: The initial attribute data of the electric energy meter is obtained by identifying the return reverse device; The initial attribute data of the electric energy meter is formatted and cleaned in sequence to obtain the attribute data of the electric energy meter, wherein the attribute data of the electric energy meter includes the electric energy meter model, component name, component number and location data of the component in the electric energy meter.

6. The method according to claim 5, characterized in that The initial attribute data of the electric energy meter includes initial position data of components in the electric energy meter, and the initial position data of components in the electric energy meter is obtained by identifying the return inverse device, including: Acquire image data of the electric energy meter; Taking the image data as input, calling the trained image analysis model in the return inverse device to determine the initial position data of the components in the electric energy meter; Wherein, the trained image analysis model is trained based on historical image data.

7. An electric energy meter modeling device, characterized in that: The device comprises: A data acquisition module, used to acquire attribute data of the electric energy meter, wherein the attribute data includes component attribute data; A position determination module, used to determine the position data of a preset electric energy meter component virtual body in the electric energy meter model according to the position data of the component in the electric energy meter in the component attribute data; The model building module is used to assemble the preset virtual bodies of electric energy meter components based on the position data of the preset virtual bodies of electric energy meter components in the electric energy meter model to obtain the electric energy meter model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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