Flow field display method, device, equipment, storage medium and program product

Abstract

The present disclosure provides a flow field display method, device, equipment, storage medium and program product. It relates to the technical field of computational fluid dynamics post-processing and in-situ visualization. The method comprises: obtaining a plurality of sets of flow field data for a target fluid, wherein the plurality of sets of flow field data represent flow field simulation data calculated by using different data grid formats, different calculation methods or different simulation programs respectively; converting the plurality of sets of flow field data into a plurality of sets of point cloud data respectively, each set of point cloud data comprising at least one point cloud data, and the point cloud data comprising geometric data and physical quantity data; segmenting the plurality of sets of point cloud data according to data characteristics in the plurality of sets of point cloud data to obtain data sets of different categories, wherein the data sets represent different spaces or attributes of the flow field of the target fluid; and rendering and displaying the point cloud data in the data sets.

Description

Technical Field

[0001] This disclosure relates to the fields of computational fluid dynamics post-processing and in-situ visualization technology, and more specifically, to a flow field display method, apparatus, device, storage medium, and program product. Background Technology

[0002] In recent years, with the development of technologies such as digital twins and digital prototypes, there has been a greater demand for rapid post-processing and in-situ visualization of flow field data in computational fluid dynamics.

[0003] In the process of realizing the present invention, the inventors discovered at least the following problems in the related technologies: the current flow field visualization methods are based on computational grids. When it comes to the integrated display of flow field data from different grids, different computational methods, and different simulation programs, the grid format needs to be normalized due to the large differences in the data grid format, which makes it impossible to achieve fast and in-situ visualization. Summary of the Invention

[0004] In view of this, the present disclosure provides a flow field display method, apparatus, device, storage medium, and program product.

[0005] One aspect of this disclosure provides a flow field display method, comprising: acquiring multiple sets of flow field data for a target fluid, wherein the multiple sets of flow field data represent flow field simulation data calculated using different data grid formats, different calculation methods, or different simulation programs; converting the multiple sets of flow field data into multiple sets of point cloud data, each set of point cloud data including at least one point cloud data, the point cloud data including geometric data and physical quantity data; segmenting the multiple sets of point cloud data according to the data characteristics in the multiple sets of point cloud data to obtain datasets of different categories, wherein the datasets represent different spaces or properties of the flow field of the target fluid; and rendering and displaying the point cloud data in the datasets.

[0006] According to embodiments of this disclosure, multiple sets of flow field data are converted into multiple sets of point cloud data, including: deleting surface information and mesh information from the multiple sets of flow field data.

[0007] According to embodiments of this disclosure, converting multiple sets of flow field data into multiple sets of point cloud data further includes storing multiple point cloud data in an array with a preset format.

[0008] According to embodiments of this disclosure, multiple sets of point cloud data are segmented based on data features to obtain datasets of different categories, including: acquiring target data features of the point cloud data, the target data features including geometric screening conditions and / or physical quantity screening conditions; and performing vectorized calculations on the arrays based on the target data features to obtain datasets corresponding to the target data features.

[0009] According to embodiments of this disclosure, when the number of point cloud data in the dataset is less than a threshold, the method further includes: copying and moving multiple point cloud data in the dataset to obtain an encrypted dataset; and rendering and displaying the encrypted dataset based on the point cloud data in the encrypted dataset.

[0010] According to an embodiment of this disclosure, when the point cloud data in the dataset represents flow field cross-sectional data, the method further includes: processing multiple point cloud data in the dataset using a preset point-surface generation algorithm to obtain polygons corresponding to each point cloud data; and rendering and displaying the flow field cross-section based on the node information of the multiple polygons.

[0011] Another aspect of this disclosure provides a flow field display device, comprising: an acquisition module for acquiring multiple sets of flow field data for a target fluid, wherein the multiple sets of flow field data represent flow field simulation data calculated using different data grid formats, different calculation methods, or different simulation programs; a conversion module for converting the multiple sets of flow field data into multiple sets of point cloud data, each set of point cloud data including at least one point cloud data, the point cloud data including geometric data and physical quantity data; a processing module for segmenting the multiple sets of point cloud data according to the data characteristics in the multiple sets of point cloud data to obtain datasets of different categories, wherein the datasets represent different spaces or properties of the flow field of the target fluid; and a display module for rendering and displaying the point cloud data in the datasets.

[0012] Another aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the methods described above.

[0013] Another aspect of this disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed, are used to implement the methods described above.

[0014] Another aspect of this disclosure provides a computer program product including computer-executable instructions that, when executed, are used to implement the methods described above.

[0015] According to the embodiments of this disclosure, because a technique of cloudifying flow field information points is adopted, the technical problem of being unable to achieve fast and in-situ visualization when integrating and displaying flow field data from different grids, different calculation methods, and different simulation programs is at least partially overcome. This is because the data grid format is significantly different and requires normalization processing, thus achieving the technical effect of rapidly integrating flow field data from different grids, different calculation methods, and different simulation programs. Attached Figure Description

[0016] The above and other objects, features and advantages of this disclosure will become clearer from the following description of embodiments of this disclosure with reference to the accompanying drawings, in which:

[0017] Figure 1 The illustration schematically shows an exemplary system architecture for the methods and apparatus for flow field display to which this disclosure can be applied;

[0018] Figure 2 A flowchart illustrating a flow field display method according to an embodiment of the present disclosure is shown schematically;

[0019] Figure 3 A schematic diagram illustrating point cloud data stored in an array according to an embodiment of the present disclosure is shown.

[0020] Figure 4 A flowchart illustrating point cloud data segmentation according to an embodiment of the present disclosure is shown schematically;

[0021] Figure 5 (a) schematically illustrates a point cloud data segmentation diagram according to an embodiment of the present disclosure;

[0022] Figure 5 (b) schematically illustrates the display, hiding, rendering, and segmentation of point cloud data according to embodiments of the present disclosure;

[0023] Figure 6A A flowchart illustrating a point cloud data encryption method according to an embodiment of the present disclosure is shown schematically.

[0024] Figure 6B A schematic diagram illustrating a point cloud data encryption method according to an embodiment of the present disclosure is shown.

[0025] Figure 7A A flowchart illustrating a method for generating flow field sections from point cloud data according to an embodiment of the present disclosure is shown schematically.

[0026] Figure 7B A schematic diagram illustrating the generation of a flow field cross section from point cloud data according to an embodiment of the present disclosure is shown.

[0027] Figure 8 A schematic diagram illustrating the structure of a flow field display device according to an embodiment of the present disclosure is shown; and

[0028] Figure 9 A block diagram of an electronic device suitable for implementing a flow field display method according to an embodiment of the present disclosure is shown schematically. Detailed Implementation

[0029] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0030] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0031] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0032] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0033] Embodiments of this disclosure provide a method for flow field display, comprising: acquiring multiple sets of flow field data for a target fluid, wherein the multiple sets of flow field data represent flow field simulation data calculated using different data grid formats, different calculation methods, or different simulation programs; converting the multiple sets of flow field data into multiple sets of point cloud data, each set of point cloud data including at least one point cloud data, the point cloud data including geometric data and physical quantity data; segmenting the multiple sets of point cloud data according to the data characteristics in the multiple sets of point cloud data to obtain different categories of datasets, wherein the datasets represent different spaces or attributes of the flow field of the target fluid; and rendering and displaying the point cloud data in the datasets. By converting the flow field information into point clouds, the point cloud data can be quickly segmented, rendered, displayed, and hidden, thereby achieving rapid display of flow field data with different grids, different calculation methods, and different simulation programs.

[0034] Figure 1 The illustration schematically depicts an exemplary system architecture to which the methods and apparatus for flow field display disclosed herein can be applied. It should be noted that... Figure 1The examples shown are merely examples of system architectures that can be applied to the embodiments of this disclosure, in order to help those skilled in the art understand the technical content of this disclosure, but do not mean that the embodiments of this disclosure cannot be used in other devices, systems, environments or scenarios.

[0035] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0036] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social media platform software, etc. (for example only).

[0037] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0038] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0039] It should be noted that the flow field display method provided in this embodiment can generally be executed by server 105. Correspondingly, the flow field display device provided in this embodiment can generally be located in server 105. The flow field display method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the flow field display device provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Alternatively, the flow field display method provided in this embodiment can also be executed by the first terminal device 101, the second terminal device 102, or the third terminal device 103, or by other terminal devices different from the first terminal device 101, the second terminal device 102, or the third terminal device 103. Accordingly, the flow field display device provided in this embodiment may also be disposed in the first terminal device 101, the second terminal device 102 or the third terminal device 103, or disposed in other terminal devices different from the first terminal device 101, the second terminal device 102 or the third terminal device 103.

[0040] For example, the flow field data to be processed can be originally stored in any one of the first terminal device 101, the second terminal device 102, or the third terminal device 103 (e.g., the first terminal device 101, but not limited thereto), or it can be stored on an external storage device and imported into the first terminal device 101. Then, the first terminal device 101 can execute the flow field display method provided in the embodiments of this disclosure locally, or send the image to be processed to other terminal devices, servers, or server clusters, and have the image processing method provided in the embodiments of this disclosure executed by the other terminal devices, servers, or server clusters that receive the image to be processed.

[0041] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0042] Figure 2 A flowchart illustrating a flow field display method according to an embodiment of the present disclosure is shown schematically.

[0043] like Figure 2 As shown, the method includes operations S210~S240.

[0044] In operation S210, multiple sets of flow field data for the target fluid are acquired. These multiple sets of flow field data represent flow field simulation data calculated using different data grid formats, different calculation methods, or different simulation programs.

[0045] It should be understood that flow field data obtained using different data grid formats refers to the grid type and format used when discretizing fluid motion according to different numerical methods or experimental setups during flow field simulation or experiments. These grids can be structured (such as orthogonal or non-orthogonal grids) or unstructured (such as triangular or tetrahedral grids). Different calculation methods may include, but are not limited to, the finite volume method, the finite element method, and the finite difference method.

[0046] For example, multiple sets of flow field data can be flow field simulation data for different parts of the same target fluid calculated using different data grid formats, different calculation methods, or different simulation programs.

[0047] In operation S220, multiple sets of flow field data are converted into multiple sets of point cloud data. Each set of point cloud data includes at least one point cloud data, which includes geometric data and physical quantity data.

[0048] For example, geometric data includes coordinate information and time information of point clouds; physical quantity data includes velocity, pressure, temperature, gradient, curl, etc. of point clouds.

[0049] In operation S230, based on the data characteristics in multiple sets of point cloud data, the multiple sets of point cloud data are segmented to obtain datasets of different categories. The datasets represent different spatial or property-related aspects of the flow field of the target fluid.

[0050] Among them, the data features characterize the geometric data and physical quantity data of the point cloud data to meet the preset conditions.

[0051] For example, the collection of data for all points in physical quantity data where the pressure is greater than 1 MPa can be a single-category dataset.

[0052] The S240 is used to render and display the point cloud data in the dataset.

[0053] It is understandable that by converting flow field information into point clouds, and then rapidly segmenting, rendering, displaying, and hiding the point cloud data, it is possible to quickly display flow field data from different grids, different calculation methods, and different simulation programs.

[0054] The following is for reference. Figures 3-7B In conjunction with specific embodiments, Figure 2 The method shown will be further explained.

[0055] Figure 3A schematic diagram illustrating point cloud data stored in an array according to an embodiment of the present disclosure is shown.

[0056] like Figure 3 As shown, in some embodiments, multiple sets of flow field data are converted into multiple sets of point cloud data, including: storing multiple point cloud data in an array with a preset format.

[0057] Specifically, multiple point cloud data (point 1, point 2... point n) obtained from different calculation results (i.e., flow field simulation data obtained by using different data grid formats, different calculation methods or different simulation programs) are imported into an array with a unified format for storage.

[0058] It's understandable that storing point cloud data in an array, with a one-to-one correspondence between array positions and spatial positions, allows for direct vector-based computation when retrieving computational fluid dynamics results from the same mesh under different operating conditions and calculation parameters, eliminating the need for complex data structures. This enables rapid access to any element in the array during computation. Furthermore, by using arrays, data for each point can be directly accessed via index, without requiring complex data structures or additional pointer dereferencing operations, facilitating the extraction and display of data from different locations and attributes of the flow field.

[0059] In some embodiments, converting multiple sets of flow field data into multiple sets of point cloud data includes deleting surface information and mesh information from the multiple sets of flow field data.

[0060] Specifically, before importing the flow field data into the array in point cloud format, the flow field data needs to be processed. This involves removing surface and mesh information from each flow field dataset. This ensures that the geometric positions of the flow field data under different operating conditions and at different times are correlated with the array positions, facilitating rapid vectorization calculations.

[0061] Understandably, by removing surface and grid information from the flow field data, the flow field data retains only the flow field variables themselves, allowing data from different times or operating conditions to be interpreted and processed consistently in the same coordinate system, avoiding confusion and errors in positional correspondence caused by changes in grid structure.

[0062] Figure 4 A flowchart illustrating point cloud data segmentation according to an embodiment of the present disclosure is shown schematically; Figure 5 (a) schematically illustrates a point cloud data segmentation diagram according to an embodiment of the present disclosure; Figure 5 (b) schematically illustrates the display, hiding, rendering, and segmentation of point cloud data according to an embodiment of the present disclosure.

[0063] like Figure 4As shown, based on the data characteristics in multiple sets of point cloud data, the multiple sets of point cloud data are segmented to obtain datasets of different categories, including operations S310~S320.

[0064] In operation S310, target data features of point cloud data are acquired, including geometric filtering conditions and / or physical quantity filtering conditions.

[0065] In operation S320, vectorized calculations are performed on the arrays based on the target data features to obtain the dataset corresponding to the target data features.

[0066] For example, refer to Figure 5 In (a), users can filter point cloud data (datasets) from multiple point cloud datasets based on geometric data filtering conditions and physical quantity filtering conditions (target features) according to the needs of actual applications. For example, physical quantity data filtering condition: all points with pressure greater than 1 MPa. Geometric data filtering condition: all points on the flow field interface corresponding to X=1. The dataset is obtained by extracting point cloud data corresponding to the target data features through vectorized calculations between arrays, and then processing and displaying the data in the dataset. Therefore, the rapid segmentation, rendering, display, and hiding of point clouds is achieved through vectorized calculations between arrays, enabling the rapid display of flow field data for different flow field locations, different working conditions, different times, and different cross-sections. Figure 5 (b) in the middle.

[0067] To achieve rapid display of flow field data under different operating conditions, at different times, and at different cross sections, intermediate parts can be supplemented using various methods. For example, the flow field between operating conditions, between time points, and between cross sections can be supplemented using methods such as linear interpolation and neural network algorithm interpolation.

[0068] It is understandable that the flow field can be rapidly segmented, rendered, and displayed in three dimensions and two dimensions by performing vectorized calculations on an array containing spatial and physical quantity information of data points.

[0069] It should be noted that filtering can be performed based on either geometric data filtering criteria or physical quantity filtering criteria alone. Alternatively, filtering can be performed based on both geometric data filtering criteria and physical quantity filtering criteria simultaneously. This disclosure does not impose specific limitations on these methods; users can select filtering criteria according to their actual application needs.

[0070] Figure 6A A flowchart illustrating a point cloud data encryption method according to an embodiment of the present disclosure is shown schematically. Figure 6B A schematic diagram of a point cloud data encryption method according to an embodiment of the present disclosure is shown.

[0071] like Figure 6AAs shown, when the number of point cloud data in the dataset is less than the threshold, the method also includes operations S410~S420.

[0072] By operating the S410, multiple point cloud data in the dataset are copied and moved to obtain an encrypted dataset.

[0073] The S420 is used to render and display point cloud data from an encrypted dataset.

[0074] Specifically, when the point cloud data in the display space is sparse, the point cloud data in the space is copied and translated, and at the same time, the physical quantities are encrypted using linear interpolation.

[0075] For example, such as Figure 6B As shown, encrypted display of point cloud cross-sections is achieved by copying and moving point cloud data at specific cross-sections. During encrypted display, the location and physical magnitude of the encrypted points can be determined using different methods.

[0076] For example, point cloud encryption includes methods such as linear encryption, replication plus micro-movement encryption, and encryption based on physical quantity gradients; the determination of physical quantities of encrypted points after encryption includes methods such as linear interpolation and neural network algorithm flow field encryption.

[0077] Figure 7A A flowchart illustrating a method for generating flow field sections from point cloud data according to an embodiment of the present disclosure is shown schematically. Figure 7B A schematic diagram illustrating the generation of flow field cross sections from point cloud data according to an embodiment of the present disclosure is shown.

[0078] like Figure 7A As shown, when the point cloud data in the dataset represents flow field cross-sectional data, the method also includes operations S510~S520.

[0079] When operating S510, multiple point cloud data in the dataset are processed by a preset point-surface generation algorithm to obtain polygons corresponding to each point cloud data.

[0080] When operating the S520, the flow field cross-section is rendered and displayed based on the node information of multiple polygons.

[0081] For example, such as Figure 7B As shown, when a more refined display of flow field cross-sectional data is required, a point-to-surface generation algorithm can be used to further render and display the surface data. To achieve point-to-surface generation, different methods can be used to generate surface information from the point cloud data. For example, triangles and polygons can be generated using methods such as Delaunay triangles and Thiessen polygons, and the node information of each polygon can be imported into the fragment shader for surface rendering.

[0082] Understandably, in related technologies, displaying data for a specified two-dimensional cross-section requires linear mesh interpolation and regeneration, making rapid display impossible. In this embodiment, if detailed information is required, a two-dimensional surface rendering cloud map can be generated using point-surface generation technology, reducing the computational load of the point-surface generation algorithm (changing the three-dimensional flow field to a two-dimensional cross-section) and improving display efficiency.

[0083] Figure 8 A schematic diagram illustrating the structure of a flow field display device according to an embodiment of the present disclosure is shown.

[0084] like Figure 8 As shown, the flow field display device 800 includes an acquisition module 810, a conversion module 820, a processing module 830, and a display module 840.

[0085] The acquisition module 810 is used to acquire multiple sets of flow field data for the target fluid. These multiple sets of flow field data represent flow field simulation data calculated using different data grid formats, different calculation methods, or different simulation programs. In one embodiment, the acquisition module 810 can be used to perform the operation S210 described above, which will not be repeated here.

[0086] The conversion module 820 is used to convert multiple sets of flow field data into multiple sets of point cloud data, each set of point cloud data including at least one point cloud data, and the point cloud data including geometric data and physical quantity data. In one embodiment, the conversion module 820 can be used to perform the operation S220 described above, which will not be repeated here.

[0087] The processing module 830 is used to segment multiple sets of point cloud data according to the data characteristics in the multiple sets of point cloud data to obtain datasets of different categories, wherein the datasets represent different spatial or property characteristics of the flow field of the target fluid. In one embodiment, the processing module 830 can be used to perform the operation S230 described above, which will not be repeated here.

[0088] The display module 840 is used to render and display the point cloud data in the dataset. In one embodiment, the display module 840 can be used to perform the operation S240 described above, which will not be repeated here.

[0089] Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure, or at least part of the functions of any one or more of them, can be implemented in one module. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be implemented by dividing them into multiple modules. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be at least partially implemented as hardware circuitry, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a System-on-Chip, a System-on-a-Substrate, a System-on-Package, an Application-Specific Integrated Circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, one or more of the modules, submodules, units, and subunits according to embodiments of the present disclosure can be at least partially implemented as computer program modules, which, when run, can perform corresponding functions.

[0090] For example, any plurality of the acquisition module 810, conversion module 820, processing module 830, and display module 840 may be combined into one module / unit / subunit, or any one of these modules / units / subunits may be split into multiple modules / units / subunits. Alternatively, at least part of the functionality of one or more of these modules / units / subunits may be combined with at least part of the functionality of other modules / units / subunits and implemented in one module / unit / subunit. According to embodiments of the present disclosure, at least one of the acquisition module 810, conversion module 820, processing module 830, and display module 840 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the acquisition module 810, conversion module 820, processing module 830 and display module 840 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0091] It should be noted that the data processing system part in the embodiments of this disclosure corresponds to the data processing method part in the embodiments of this disclosure. The specific description of the data processing system part is referred to in the data processing method part, and will not be repeated here.

[0092] Figure 9A block diagram of an electronic device suitable for implementing a flow field display method according to an embodiment of the present disclosure is shown schematically. Figure 9 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0093] like Figure 9 As shown, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0094] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0095] According to embodiments of this disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0096] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0097] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903 described above.

[0098] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the flow field display method provided in the embodiments of this disclosure.

[0099] When the computer program is executed by the processor 901, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0100] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 909, and / or installed from a removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0101] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0102] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0103] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0104] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0105] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A flow field display method, comprising: Acquire multiple sets of flow field data for the target fluid, wherein the multiple sets of flow field data represent flow field simulation data calculated using different data grid formats, different calculation methods, or different simulation programs; The multiple sets of flow field data are converted into multiple sets of point cloud data, each set of point cloud data including at least one point cloud data, and the point cloud data including geometric data and physical quantity data; Based on the data characteristics in the multiple sets of point cloud data, the multiple sets of point cloud data are segmented to obtain datasets of different categories, wherein the datasets represent different spatial or property characteristics of the flow field of the target fluid. The point cloud data in the dataset is rendered and displayed.

2. The method according to claim 1, wherein converting the multiple sets of flow field data into multiple sets of point cloud data respectively includes: Delete the surface information and mesh information from the multiple sets of flow field data.

3. The method according to claim 1 or 2, wherein converting the multiple sets of flow field data into multiple sets of point cloud data further includes: The multiple point cloud data are stored in an array with a preset format.

4. The method according to claim 3, wherein segmenting the multiple sets of point cloud data according to data features to obtain datasets of different categories includes: Obtain the target data features of the point cloud data, wherein the target data features include geometric filtering conditions and / or physical quantity filtering conditions; Based on the target data features, vectorized calculations are performed on the arrays to obtain a dataset corresponding to the target data features.

5. The method according to claim 1, wherein, If the amount of point cloud data in the dataset is less than a threshold, the method further includes: Multiple point cloud data points in the dataset are copied and moved to obtain an encrypted dataset. The data is rendered and displayed based on the point cloud data in the encrypted dataset.

6. The method according to claim 1, wherein, When the point cloud data in the dataset represents the flow field cross-sectional data, the method further includes: The multiple point cloud data in the dataset are processed by a preset point-surface generation algorithm to obtain a polygon corresponding to each point cloud data. The flow field cross-section is rendered and displayed based on the node information of multiple polygons.

7. A flow field display device, comprising: The acquisition module is used to acquire multiple sets of flow field data for the target fluid, wherein the multiple sets of flow field data represent flow field simulation data calculated using different data grid formats, different calculation methods, or different simulation programs; The conversion module is used to convert the multiple sets of flow field data into multiple sets of point cloud data, each set of point cloud data including at least one point cloud data, and the point cloud data including geometric data and physical quantity data. The processing module is configured to segment the multiple sets of point cloud data according to data features to obtain datasets of different categories, wherein the datasets represent different spatial or property-specific flow fields of the target fluid; and The display module is used to render and display the point cloud data in the dataset.

8. An electronic device, comprising: One or more processors; Memory, used to store one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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