Attribute determination method and device of grid model, storage medium and processor
By traversing the single mesh of the target mesh model, determining the single mesh that intersects with the known mesh model, calculating the intersection volume, and determining the attributes of the target mesh based on the grid properties and intersection volume, the accuracy and universality of attribute conversion between structured mesh and unstructured mesh is solved, and high-precision property data conversion is achieved.
Patent Information
- Application Number
- CN202311640247.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-01
- Publication Date
- 2025-06-03
AI Technical Summary
The prior art lacks an attribute conversion method suitable for structured mesh and unstructured mesh, resulting in low accuracy of attribute data conversion and not universal.
By traversing the single mesh of the target mesh model, determine the single mesh that intersects with the known mesh model, calculate the intersection volume, and determine the properties of the target mesh based on the mesh properties and the intersection volume.
It realizes high-precision conversion of grid attribute data, which is suitable for different data types and grid types, solving the shortcomings of traditional methods in accuracy and universality.
Smart Images
Figure CN120087102A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of numerical simulation, and particularly to a method, device, storage medium and processor for determining attributes of a grid model. Background Art
[0002] In the technical field of numerical simulation, with the progress of characterization technology, characterization models have become increasingly refined and complex, and grid technology has also evolved from structured grids to unstructured grids. Usually, after a grid model is generated, it is necessary to perform attribute modeling on it, that is, to assign various required attributes to the grid for subsequent numerical simulation work. Due to factors such as efficiency and feasibility, people usually do not directly perform the attribute modeling step on an unstructured grid system. Therefore, the attribute modeling of unstructured grids involves the problem of attribute conversion between structured grids and unstructured grids. To balance simulation accuracy and efficiency, sometimes it is necessary to coarsen or refine the grid system, which also involves the problem of attribute conversion between grids of the same type.
[0003] Currently, traditional methods for grid attribute conversion are mainly proposed for structured grids such as rectangular grids and corner point grids, such as the harmonic average method, arithmetic average method, threshold function method, etc., but there is still a lack of methods for attribute conversion between unstructured grids and between structured and unstructured grids. In addition, the numerical grid attribute data types in various fields are also different, and traditional single data conversion methods are no longer applicable to certain types of attributes, resulting in poor conversion accuracy.
[0004] In summary, it is necessary to develop a set of attribute conversion methods applicable to structured grids and unstructured grids, which can ensure high-precision conversion of attribute data while having strong universality for attribute data types and grid types, which is a key issue in the field of numerical simulation. Summary of the Invention
[0005] An object of an embodiment of the present invention is to provide a method for determining attributes of a grid model, which is universal for attribute data of different data types and different grid types in the field of numerical simulation.
[0006] To achieve the above object, an embodiment of the present invention provides a method for determining attributes of a grid model, which is used to determine the grid attributes of a target grid model according to the grid attributes of a known grid model, where the known grid model and the target grid model are grid models constructed for the same object, and the method includes:
[0007] Traverse the first single grid of the target grid model to determine at least one second single grid in the known grid model that intersects with the first single grid;
[0008] Calculate the intersection volume of each second single grid in at least one second single grid and the first single grid; and
[0009] Determine the grid attributes of the first single grid according to the grid attributes and intersection volume of the second single grid.
[0010] Preferably, the types of grid attributes include at least one of nominal attributes and numerical attributes.
[0011] Furthermore, for the nominal attributes and numerical attributes of the second single grid, different methods are used to determine the grid attributes of the first single grid.
[0012] Optionally, if the grid attribute of the second single grid is a nominal attribute, the grid attribute of the first single grid is determined by the following formula:
[0013]
[0014] where x I is the grid attribute of the first single grid; is the grid attribute of the one with the largest intersection volume with the first single grid among at least one second single grid.
[0015] Optionally, if the grid attribute of the second single grid is a numerical attribute, the grid attribute of the first single grid is determined by the following formula:
[0016]
[0017] where x I is the grid attribute of the first single grid; x i is the grid attribute of the i-th second single grid among at least one second single grid, and V i is the intersection volume of the i-th second single grid among at least one second single grid and the first single grid.
[0018] Preferably, the target grid model is a set of grid models, each of which includes single grids of different specifications;
[0019] The method further includes: determining the degree of coincidence of each of a set of grid models with a known grid model, and taking the one with the highest degree of coincidence as the determined target grid model.
[0020] On the other hand, the present invention provides a device for determining the attributes of a grid model, which is used to determine the grid attributes of a target grid model according to the grid attributes of a known grid model, where the known grid model and the target grid model are grid models constructed for the same object, and is characterized in that it includes:
[0021] A grid determination module, which traverses the first single grid of the target grid model and determines at least one second single grid in the known grid model that intersects with the first single grid;
[0022] A volume calculation module that calculates the intersection volume of each second single grid in at least one second single grid with the first single grid; and
[0023] A grid attribute determination module that determines the grid attribute of the first single grid according to the grid attribute and the intersection volume of the second single grid.
[0024] Optionally, the type of grid attribute includes at least one of a nominal attribute and a numerical attribute.
[0025] Further, in the grid attribute determination module, different methods are used to determine the grid attribute of the first single grid for the second single grid with nominal attributes and numerical attributes.
[0026] Optionally, if the grid attribute of the second single grid is a nominal attribute, the grid attribute of the first single grid is determined by the following formula:
[0027]
[0028] where x I is the grid attribute of the first single grid; is the grid attribute of the one with the largest intersection volume of at least one second single grid with the first single grid.
[0029] Optionally, if the grid attribute of the second single grid is a numerical attribute, the grid attribute of the first single grid is determined by the following formula:
[0030]
[0031] where x I is the grid attribute of the first single grid; x i is the grid attribute of the i-th second single grid in at least one second single grid, and V i is the intersection volume of the i-th second single grid in at least one second single grid with the first single grid.
[0032] Optionally, the target grid model is a set of grid models, each of which includes single grids of different specifications; and
[0033] The grid attribute determination module is further configured to determine the degree of coincidence of each of the set of grid models with the known grid model, and use the one with the highest degree of coincidence as the determined target grid model.
[0034] On the other hand, the present invention provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause a machine to execute the method for determining the attributes of the grid model of the present application.
[0035] On the other hand, the present invention provides a processor for running a program, wherein when the program is run, it is used to execute the method for determining the attributes of the grid model of the present application.
[0036] Through the above technical solution, first traverse the first single grid of the target grid model, determine at least one second single grid in the known grid model that intersects with the first single grid, then calculate the intersection volume of each second single grid in the at least one second single grid and the first single grid, and finally determine the grid attributes of the first single grid according to the grid attributes and intersection volume of the second single grid. This method realizes the high-precision conversion of grid attribute data through the attribute classification processing conversion and volume weighted discrimination mechanism, and has universality for attribute data of different data types and different grid types in the field of numerical simulation.
[0037] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation part. Brief Description of the Drawings
[0038] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings:
[0039] Figure 1 is a flowchart of an embodiment of the method for determining the attributes of the grid model of the present invention;
[0040] Figure 2 is a flowchart of another embodiment of the method for determining the attributes of the grid model of the present invention;
[0041] Figure 3 is the grid model to be converted in still another embodiment of the method for determining the attributes of the grid model of the present invention;
[0042] Figure 4 is Figure 2 the target grid model in the embodiment;
[0043] Figure 5 is Figure 2 the comparison chart of the nominal attributes of the reserve partition before and after conversion in the embodiment;
[0044] Figure 6 is Figure 2 the comparison chart of the permeability numerical attributes before and after conversion in the embodiment;
[0045] Figure 7 is Figure 2 the comparison curve graph of the grid simulation production dynamics before and after conversion in the embodiment; and
[0046] Figure 8It is a component structure diagram of an embodiment of the attribute determination device for the mesh model of the present invention. Detailed implementation manners
[0047] The following will describe in detail the specific implementation manners of the embodiments of the present invention with reference to the accompanying drawings. It should be understood that the specific implementation manners described herein are only used to illustrate and explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention.
[0048] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.
[0049] The embodiments of the present invention provide a method for determining the attributes of a mesh model. Through this method, the attribute conversion from the mesh to be converted to the target mesh can be realized, where both the mesh to be converted and the target mesh can be any type of structured mesh or unstructured mesh. The flow of this embodiment is as Figure 1 shown, and includes the following steps:
[0050] Step 1: Traverse the first single mesh of the target mesh model, and determine at least one second single mesh in the known mesh model that intersects with the first single mesh;
[0051] Step 2: Calculate the intersection volume of each second single mesh in the at least one second single mesh and the first single mesh;
[0052] Step 3: Determine the mesh attribute of the first single mesh according to the mesh attribute and the intersection volume of the second single mesh.
[0053] It should be noted that the above terms "first", "second", etc. are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order different from those illustrated or described here.
[0054] In this embodiment, the meshes in the known mesh model are the meshes to be converted, that is, the "second single meshes" in the above steps. The purpose of this embodiment is to select and extract the attribute values or the calculated values of the attribute values of at least one second single mesh from the meshes to be converted, and assign them to the corresponding first single meshes of the target mesh model.
[0055] It should be noted that grid attributes can include different types of attributes, such as numerical attributes and nominal attributes. Numerical attributes are usually measurable quantities represented by real numbers; nominal attributes, also known as categorical attributes, are usually represented by positive integers, and each value represents a certain category, code, or status, etc. For example, when the grid model is used to represent numerical simulation of oil and gas reservoirs, grid attributes can include numerical attributes such as porosity, permeability, net-to-gross ratio, daily oil production, average reservoir pressure, water production, gas production, permeability, and saturation, and can also simultaneously include nominal attributes such as medium type, reserve type, PVT physical property type, pore structure, fluid property, and producible property. When the grid model is used in other technical fields, technicians can select one of the known grid models that includes appropriate attributes as the extraction object according to actual needs.
[0056] In specific implementations, sometimes coarsening of the grid system is required. At this time, a single grid in the target grid model may intersect with multiple single grids in the known grid model; sometimes refinement of the grid system is required. At this time, multiple single grids in the target grid model may intersect with the same single grid in the known grid model. Therefore, in step 1, at least one second single grid that intersects with the first single grid in the target grid model is matched from the known grid model. In the most extreme case, a single grid in the known grid model completely coincides with a single grid in the target grid model, and then the single grid in the target grid model should completely inherit the grid attributes of the corresponding single grid in the known grid model. However, usually, the single grids in the known grid model and the target grid model cannot be matched one by one. Therefore, the purpose of step 1 is to match the single grids in the known grid model and the target grid model, and determine the grid attributes of each single grid in the target grid model according to the process of steps 2-3, that is, determine the grid attributes of each single grid in the target grid model according to the intersection situation with one or more single grids in the known grid model and the corresponding grid attribute values.
[0057] In step 3, usually different methods are also selected according to different types of grid attribute values to calculate the grid attributes of each single grid in the target grid model. For example, for nominal attributes, the single grid in the target grid model can inherit the grid attributes of the single grid with the largest intersection volume among multiple second single grids that intersect with it. Another example is that for numerical attributes, the single grid in the target grid model can be the weighted sum of the attribute values of multiple second single grids that intersect with it, that is, the grid attributes of the single grid in the target grid model can be different from the attribute values of any one of the multiple second single grids that intersect with it.
[0058] In some embodiments, the target mesh model is a set of mesh models, each of which includes single meshes of different specifications. In this case, the single meshes of each of the set of target mesh models can be assigned values according to the known mesh model first, and then, according to the degree of coincidence between each of the set of mesh models after assignment and the known mesh model, the one with the highest degree of coincidence is determined as the final target mesh model.
[0059] Compared with the prior art, the technical advantages of this embodiment include:
[0060] (1) It can convert any type of attribute data. For the attribute differences between nominal attributes and numerical attributes, different methods are used for processing and conversion respectively, effectively improving the conversion accuracy of various types of attributes and being more universal for attribute data in different fields and of different types.
[0061] (2) It can achieve attribute conversion between unstructured meshes and between structured and unstructured meshes. Through the volume-weighted discrimination mechanism, it can effectively achieve attribute conversion for different mesh types and has both efficiency and stability.
[0062] The present invention also provides a preferred embodiment of the above embodiment, as Figure 2 shown, including the following steps:
[0063] S1: Obtain the mesh attributes of the network model to be converted, classify the attributes, and obtain the nominal attribute set X 1 and the numerical attribute set X 2 .
[0064] S2: Traverse each target mesh of the target network model in turn, obtain all the meshes to be converted i that intersect with the target mesh I, and obtain the attribute x i of the corresponding mesh to be converted i, and calculate the intersection volume V i between the target mesh I and each mesh to be converted i.
[0065] S3: For the nominal attribute set X 1 , the attribute conversion is performed by the following formula:
[0066]
[0067] where x I is the attribute value of the target mesh I; is the attribute value of the mesh to be converted corresponding to the maximum intersection volume between the target mesh I and the mesh to be converted.
[0068] S4: For the numerical attribute set X 2 , the following formula is used for attribute conversion, and the formula is:
[0069]
[0070] Among them, x I is the grid attribute of the target grid I; x i is the grid attribute of the i-th to-be-converted grid among at least one to-be-converted grid intersecting with the target grid I, and V i is the intersection volume of the above-mentioned i-th to-be-converted grid and the target grid I.
[0071] S5: Merge the nominal attribute set T 1 and the numerical attribute set T 2 , and complete the attribute conversion from the to-be-converted grid to the target grid. Among them, both the to-be-converted grid and the target grid described in the present invention can be any type of structured grid or unstructured grid.
[0072] In order to make the purpose, technical solution and advantages of the present invention clearer, the following further details the present invention in combination with an embodiment in the field of reservoir numerical simulation and the attached Figures 3 - 7 .
[0073] In this example, it is necessary to perform attribute conversion on 1 set of structured grids obtained from geological modeling to 3 sets of corresponding unstructured grids with different sizes. Figure 3 The to-be-converted structured grid model of this embodiment is shown as Figure 4 . The 3 sets of target unstructured grids of this embodiment are shown as , where the grid sizes increase in sequence from (a) to (c). The steps of this embodiment include:
[0074] The first step is to obtain the attributes of the to-be-converted unstructured grids in the geological model, classify the attributes contained therein, and obtain the nominal attribute set X 1 and the numerical attribute set X 2 .
[0075] In this embodiment, the structured grid attributes obtained from geological modeling include two types of attributes. One is numerical attribute data such as porosity, permeability, and net-to-gross ratio, all of which are measurable quantities represented by real numbers; the other is partition attribute data such as medium type, reserve type, and PVT physical property type, also known as nominal attributes, all of which are represented by positive integers, and each value represents a certain category, code or state.
[0076] The second step is to perform the following steps on each set of target unstructured grids in Figure 4 from (a) to (c) respectively, that is, sequentially traverse each target unstructured grid included, obtain all the to-be-converted structured grids i intersecting with the target unstructured grid I, and obtain the attribute x of the corresponding to-be-converted structured grid i i, and calculate the intersection volume V between the target unstructured grid I and each structured grid i to be converted i .
[0077] This step includes three cases: ① The target grid completely falls into a certain grid to be converted. At this time, the intersection volume is equal to the volume of the target grid; ② The target grid falls into multiple grids to be converted. At this time, it is necessary to accurately calculate the intersection volume between the target grid and each grid to be converted respectively; ③ The target grid does not intersect with the grid to be converted. At this time, the intersection volume is zero.
[0078] The third step is for the nominal class attribute set X 1 , and the target grid attribute is calculated using the above formula (1).
[0079] It can be understood that for nominal class attributes, the intersection volume calculated in the second step is used to determine which grid to be converted the target grid I mainly falls into, and the attribute of this grid to be converted can be directly assigned to the target grid I. Figure 5 Shows the attribute comparison diagram before and after the conversion of the reserve zoning nominal attributes to be converted in this embodiment using the method. Among them, Figure 5 (a) is the nominal attribute effect of the grid model to be converted, Figure 5 (b), 5(c), 5(d) are the nominal attribute conversion effects of 3 sets of target grid models with different sizes implemented using formula (1).
[0080] The fourth step is for the numerical class attribute set X 2 , and the target grid attribute is calculated using the following formula:
[0081]
[0082] It can be understood that this step introduces the method of geometric volume weighting. According to the volume of the target grid falling into each grid to be converted accurately obtained in the second step, the attribute weight of each grid to be converted is determined, and the attribute of the final target grid is calculated. Usually, this process requires a very large amount of computing power. Further, optionally, if the size difference between the target grid and the grid to be converted is not large, formula (1) can be used for calculation. That is, determine which grid to be converted i the target grid I mainly falls into, and directly assign the attribute of i to I. Figure 6 Shows the attribute comparison diagram before and after the conversion of the permeability numerical attributes to be converted in this embodiment using the method. Among them Figure 6 (a) is the numerical attribute effect of the grid model to be converted, Figure 6 (b), 6(c) respectively show the numerical attribute conversion effects of 2 sets of target network models with different sizes from the grid model to be converted implemented using formula (2). Figure 6(d) shows the numerical property conversion effect implemented by Equation (1) for the target grid equivalent to the size of the grid model to be converted.
[0083] Step 5, merge the nominal property set T 1 obtained through conversion and the numerical property set T 2 to complete the property conversion from the structured grid to be converted to 3 sets of target unstructured grids with different sizes. Figure 7 It shows the production performance comparison curves obtained by simulating the 3 sets of unstructured grids converted by the method of the present invention and the structured grid before conversion with the same parameter values. From the good fitting effect of the daily production curve and the pressure curve, it can be seen that this embodiment has high reliability and realizes the effective conversion of different property types between different grid types.
[0084] The embodiment of the present invention provides a device for determining the properties of a grid model, and its composition structure is as Figure 8 shown, including a grid determination module, a volume calculation module, and a grid property determination module. Among them, the grid determination module traverses the first single grid of the target grid model to determine at least one second single grid that intersects the first single grid in the known grid model; the volume calculation module calculates the intersection volume of each second single grid in at least one second single grid with the first single grid; the grid property determination module determines the grid property of the first single grid according to the grid property and the intersection volume of the second single grid.
[0085] In some embodiments, the type of grid property includes at least one of nominal property and numerical property.
[0086] In some embodiments, in the grid property determination module, different methods are used to determine the grid property of the first single grid for the second single grids of nominal property and numerical property.
[0087] In some embodiments, if the grid property of the second single grid is a nominal property, the grid property of the first single grid is determined by the following formula:
[0088]
[0089] where x I is the grid property of the first single grid; is the grid property of the one with the largest intersection volume between the first single grid and at least one second single grid.
[0090] In some embodiments, if the grid property of the second single grid is a numerical property, the grid property of the first single grid is determined by the following formula:
[0091]
[0092] where x I is the grid property of the first single grid; x i is the grid property of the i-th of at least one second single grid, and V i is the intersection volume between the i-th of at least one second single grid and the first single grid.
[0093] In some embodiments, the target grid model is a set of grid models, each of which includes single grids of different specifications; the grid property determination module is further configured to determine the degree of coincidence between each of the set of grid models and a known grid model, and use the one with the highest degree of coincidence as the determined target grid model.
[0094] An embodiment of the present invention provides an apparatus for determining the properties of a grid model. The apparatus for determining the properties of the grid model includes a processor and a memory. The above grid determination module, volume calculation module, grid property determination module, etc. are all stored in the memory as program units, and the corresponding functions are implemented by the processor executing the above program units stored in the memory.
[0095] The processor contains a kernel, and the kernel retrieves the corresponding program units from the memory. One or more kernels can be set, and the grid properties of each single grid in the target grid model are determined by adjusting the kernel parameters.
[0096] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
[0097] An embodiment of the present invention provides a storage medium, on which a program is stored, and when the program is executed by a processor, it implements the method for determining the properties of the grid model of the present application.
[0098] An embodiment of the present invention provides a processor, and the processor is used to run a program. When the program runs, it executes the method for determining the properties of the grid model of the present application.
[0099] An embodiment of the present invention provides a device, which includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of the method for determining the properties of the grid model of the present application. The device herein can be a server, a PC, a PAD, a mobile phone, etc.
[0100] The present application also provides a computer program product, which is suitable for executing a program initialized with the steps of the method for determining the properties of the grid model of the present application when executed on a data processing device.
[0101] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.
[0102] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks
[0103] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks
[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks
[0105] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.
[0106] The memory may include non-permanent memory in the computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
[0107] A computer-readable medium includes permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information accessible by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.
[0108] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.
[0109] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.
Claims
1. A method for determining the attributes of a mesh model, which is used to determine the mesh attributes of a target mesh model based on the mesh attributes of a known mesh model, where the known mesh model and the target mesh model are mesh models constructed for the same object. Characterized in that: It includes: Traverse the first single mesh of the target mesh model to determine at least one second single mesh in the known mesh model that intersects with the first single mesh; Calculate the intersection volume of each of the at least one second single mesh and the first single mesh; And Determine the mesh attributes of the first single mesh according to the mesh attributes of the second single mesh and the intersection volume.
2. The method according to claim 1, Characterized in that: The type of the mesh attributes includes at least one of nominal attributes and numerical attributes.
3. The method according to claim 2, Characterized in that: For the nominal attributes and the numerical attributes of the second single mesh, different methods are used to determine the mesh attributes of the first single mesh.
4. The method according to claim 3, Characterized in that: If the mesh attribute of the second single mesh is a nominal attribute, the mesh attribute of the first single mesh is determined by the following formula: where x I is the grid property of the first single grid; is the grid property of the one with the largest intersection volume with the first single grid among the at least one second single grid.
5. The method according to claim 3, Characterized in that: If the mesh attribute of the second single mesh is a numerical attribute, the mesh attribute of the first single mesh is determined by the following formula: where x I is the grid property of the first single grid; x i is the grid property of the i-th second single grid among the at least one second single grid, and V i is the intersection volume between the i-th second single grid among the at least one second single grid and the first single grid.
6. The method according to claim 1, Characterized in that: The target mesh model is a set of mesh models, each of which includes single meshes of different specifications; The method further includes: determining the degree of coincidence of each of the set of mesh models with the known mesh model, and taking the one with the highest degree of coincidence as the determined target mesh model.
7. A device for determining the attributes of a mesh model, which is used to determine the mesh attributes of a target mesh model based on the mesh attributes of a known mesh model, where the known mesh model and the target mesh model are mesh models constructed for the same object. Characterized in that: It includes: A mesh determination module that traverses the first single mesh of the target mesh model to determine at least one second single mesh in the known mesh model that intersects with the first single mesh; A volume calculation module that calculates the intersection volume of each of the at least one second single mesh and the first single mesh; And A mesh attribute determination module that determines the mesh attributes of the first single mesh according to the mesh attributes of the second single mesh and the intersection volume.
8. The device according to claim 7, Characterized in that: The type of the mesh attributes includes at least one of nominal attributes and numerical attributes.
9. The device according to claim 8, Characterized in that: In the mesh attribute determination module, different methods are used to determine the mesh attributes of the first single mesh for the second single meshes of the nominal attributes and the numerical attributes.
10. The device according to claim 9, Characterized in that: If the mesh attribute of the second single mesh is a nominal attribute, the mesh attribute of the first single mesh is determined by the following formula: where x I is the grid property of the first single grid; is the grid property of the one with the largest intersection volume with the first single grid among the at least one second single grid.
11. The device according to claim 9, wherein, if the grid attribute of the second single grid is a numerical attribute, the grid attribute of the first single grid is determined by the following formula: where x I is the grid property of the first single grid; x i is the grid property of the i-th second single grid among the at least one second single grid, and V i is the intersection volume between the i-th second single grid among the at least one second single grid and the first single grid.
12. The device according to claim 7, wherein, the target grid model is a set of grid models, each of which includes single grids of different specifications; and the grid attribute determination module is further configured to determine the degree of coincidence between each of the set of grid models and the known grid model, and use the one with the highest degree of coincidence as the determined target grid model.
13. A machine-readable storage medium having instructions stored thereon for causing a machine to execute: the method for determining the attributes of a grid model according to any one of claims 1-6.
14. A processor, wherein, for running a program, wherein when the program is run, it is configured to execute: the method for determining the attributes of a grid model according to any one of claims 1-6.