Data post-processing method, apparatus and device
By using automated data post-processing methods to generate local cloud maps using view vectors and feature dimensions, the problem of complex distribution of high stress points in complex-shaped parts is solved, thus improving the efficiency and accuracy of part design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA FAW CO LTD
- Filing Date
- 2022-09-15
- Publication Date
- 2026-07-24
AI Technical Summary
In existing finite element simulation analysis, the distribution of high stress points in complex-shaped parts is complicated, and automated post-processing methods cannot accurately capture all the points of interest, resulting in low efficiency in the design process.
By acquiring cloud map data, a set of target nodes is established. Based on the filtering information, multiple target nodes and their associated nodes are determined. Local cloud maps are generated using view vectors and feature dimensions to achieve automated data post-processing.
It improves the efficiency of data post-processing, generates more accurate local cloud maps, meets the post-processing needs of complex-shaped parts, and avoids interference from related nodes on the screening results.
Smart Images
Figure CN115470586B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and more specifically, to a data post-processing method, apparatus, and device. Background Technology
[0002] In finite element simulation analysis, post-processing of the calculation results is a time-consuming step. Initially, data post-processing was mainly accomplished by manually observing contour plots and performing manual processing, but this method was extremely inefficient. Subsequently, automated post-processing methods were proposed, which can automatically extract the nodes with the highest stress in the calculation results, making them more efficient than manual processing.
[0003] However, for complex-shaped parts, such as the subframe, steering knuckle, and trapezoidal arm of a passenger car chassis, there are usually multiple high-stress points distributed in different locations (e.g., upper surface, lower surface, side, etc.). If the part is a hollow structure, the high-stress points may also be distributed in different locations on the inner surface of the cavity. During the part design process, it is necessary to pay attention to all high-stress locations, assessing and improving each one. Post-processing methods in related technologies, which automatically extract the highest stress, often only identify one location, which cannot meet the post-processing requirements of such parts. Even if more nodes are extracted besides the highest stress point, it may still be impossible to accurately capture the location of interest.
[0004] There is currently no effective solution to the above problems. Summary of the Invention
[0005] This application provides a data post-processing method, apparatus, and device to at least solve the technical problem that the post-processing results of cloud map data in the calculation results are difficult to meet the requirements during finite element simulation analysis.
[0006] According to one aspect of the embodiments of this application, a data post-processing method is provided, comprising: acquiring cloud map data of a target part, and establishing a target node set based on the cloud map data; acquiring filtering information set by the target object, determining multiple target nodes from the target node set based on the filtering information, and determining associated nodes of each target node; determining the view vector and feature size corresponding to each target node based on the associated nodes of each target node, wherein the view vector is used to determine the view direction of the local cloud map corresponding to the target node, and the feature size is used to determine the scaling ratio of the local cloud map corresponding to the target node; generating a local cloud map corresponding to each target node based on the view vector and feature size, saving the local cloud map, and saving the information of the target node and associated nodes.
[0007] Optionally, in response to a selection command for a target object, the finite element simulation file corresponding to the selection command is read to obtain the contour data of the target part. The finite element simulation file includes at least a finite element model file and a finite element result file. A target node set is established based on the contour data. The target node set includes a first node set and a second node set. When the target part is a hollow structure, the first node set includes an outer surface node set consisting of all outer surface nodes of the target part and an inner surface node set consisting of all inner surface nodes of the target part. When the target part is a solid structure, the first node set includes an outer surface node set. The second node set includes nodes in the target part that do not require attention.
[0008] Optionally, the following steps are taken: receiving filtering information input from the target object, wherein the filtering information includes at least: target data type and target threshold; determining the difference between the first node set and the second node set as the third node set; repeatedly executing the following process to determine multiple target nodes in the target node set and the associated nodes of each target node: sorting each node in the third node set from largest to smallest based on the value of the target data type; ending the process when the value of the first-ranked node is not greater than the target threshold; and when the value of the first-ranked node is greater than the target threshold, taking the first-ranked node as the target node, and dividing the other nodes in the third node set into multi-layer sub-units from near to far, centered on the target node. The set is traversed from near to far through multiple layers of unit subsets. When the maximum node value in the first unit subset is not greater than the target threshold, and the maximum node value in the second unit subset is greater than the target threshold, or when the maximum node value in the first unit subset is greater than the maximum node value in the second unit subset, the second unit subset is taken as the outermost unit subset corresponding to the target node. Here, the first unit subset is any unit subset in the multi-layer unit subset, and the second unit subset is the unit subset above the first unit subset. All nodes in all unit subsets between the target node and the second unit subset are taken as associated nodes of the target node. The target node and its associated nodes are removed from the third node set.
[0009] Optionally, for each target node, a first-layer subset of units in the multi-layer unit subset corresponding to the target node is determined from near to far; at least two nodes located on the surface of the target part are determined from the first-layer unit subset as first target associated nodes, and at least one node located inside the target part is determined from the first-layer unit subset as second target associated nodes; the magnitude of the view vector is determined based on the coordinates of the target node and the first target associated node, and the direction of the view vector is determined based on the second target associated node; for each target node, the distance between every two associated nodes among the associated nodes of the target node is determined, and the pair of associated nodes with the largest distance is determined as the third target associated node, and the feature size is determined based on the coordinates of the third target associated node.
[0010] Optionally, for each target node, the cloud map background corresponding to the target node is determined, the cloud map color band is determined based on the target threshold, a local cloud map corresponding to the target node is generated based on the view vector and feature size, and data annotations are added to the local cloud map.
[0011] Optionally, when the target part is a hollow structure, a preset target section distance is obtained, which is the distance between the target section positioning point and the target node; for each target node, the target section positioning point is determined based on the target node's coordinates, the target section distance, and the view vector; a sectional view of the target node is generated based on the target section positioning point and the view vector; and the sectional view is saved.
[0012] Optionally, the information of the target node and associated nodes is stored in the form of target variables. The type of the target variable includes at least one of the following: nested array, list, or dictionary. The information includes at least one of the following: target data type, target threshold, surface type of the target node, target node number, target node value, number of the multi-layer unit subset corresponding to the associated node, view vector, feature size, and profile location point.
[0013] Optionally, in response to a retrieval command for the target object, a local cloud map of the target node corresponding to the retrieval command is retrieved, and information about the target node and associated nodes is retrieved; a processing report file in the target format is generated based on the local cloud map, the information about the target node and associated nodes.
[0014] According to another aspect of the embodiments of this application, a data post-processing apparatus is also provided, comprising: an acquisition module, configured to acquire cloud map data of a target part and establish a target node set based on the cloud map data; a filtering module, configured to acquire filtering information set by the target object, determine multiple target nodes from the target node set based on the filtering information, and determine the associated nodes of each target node; a determination module, configured to determine the view vector and feature size corresponding to each target node based on the associated nodes of each target node, wherein the view vector is used to determine the view direction of the local cloud map corresponding to the target node, and the feature size is used to determine the scaling ratio of the local cloud map corresponding to the target node; and a generation module, configured to generate a local cloud map corresponding to each target node based on the view vector and feature size, save the local cloud map, and save the information of the target node and associated nodes.
[0015] According to another aspect of the embodiments of this application, a data post-processing apparatus is also provided, the apparatus comprising: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-described data post-processing method through the computer program.
[0016] In this embodiment, firstly, cloud map data of the target part is acquired, and a target node set is established based on the cloud map data. Then, filtering information set for the target object is acquired, and multiple target nodes are determined from the target node set based on the filtering information, along with associated nodes for each target node. Based on the associated nodes of each target node, the view vector and feature size corresponding to each target node are determined. The view vector is used to determine the view direction of the local cloud map corresponding to the target node, and the feature size is used to determine the scaling ratio of the local cloud map corresponding to the target node. Based on the view vector and feature size, a local cloud map corresponding to each target node is generated, and the local cloud map is saved, along with information about the target node and associated nodes. The user only needs to provide filtering information, and the entire post-processing process can be automated, resulting in high efficiency. The filtering process simultaneously analyzes the target node and its associated nodes, avoiding interference from associated nodes in the filtering results. By analyzing the view vector and feature size, a more accurate local cloud map can be generated. This application effectively solves the technical problem that the post-processing results of the cloud map data in the finite element simulation analysis process often fail to meet the requirements. Attached Figure Description
[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:
[0018] Figure 1 This is a flowchart illustrating an optional data post-processing method according to an embodiment of this application;
[0019] Figure 2 This is a schematic diagram of an optional stress distribution of a part according to an embodiment of this application;
[0020] Figure 3 This is a schematic diagram of an optional node filtering process according to an embodiment of this application;
[0021] Figure 4 This is a schematic diagram of an optional definition of a viewpoint vector according to an embodiment of this application;
[0022] Figure 5 This is a schematic diagram of an optional data post-processing apparatus according to an embodiment of this application. Detailed Implementation
[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.
[0024] It should be noted that the terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0025] Example 1
[0026] For complex-shaped parts, such as subframes, steering knuckles, and trapezoidal arms in passenger car chassis, there are usually multiple high-stress points, distributed across different locations such as the upper and lower surfaces. If the part is a hollow structure, the high-stress points may also be distributed across different locations on the inner surface of the cavity. During the part design process, it is necessary to pay attention to all high-stress locations to assess and improve each one. However, related technologies that automatically extract the highest stress often only filter out one location, which cannot meet the post-processing requirements of such parts. Even if more nodes are extracted besides the highest stress point, it may still be impossible to accurately capture the location of interest, for the following reasons:
[0027] Figure 1This diagram shows a partial schematic of the cavity of a hollow cast trapezoidal arm. Assuming the stress of interest is above 200 MPa, there are four high-stress zones within the cavity. Points A, B, C, and D represent the stress peak locations of these four zones. Point A is located on the upper surface of the part with a stress of 350 MPa; point B is located on the side of the part with a stress of 300 MPa; point C is located on the lower surface of the part with a stress of 280 MPa; and point D is located on the upper surface of the inner cavity of the part with a stress of 240 MPa. Near point A, the stress on the part gradually decreases outwards from A. Therefore, there will be many nodes exceeding 200 MPa, such as 330 MPa, 320 MPa, 300 MPa, 280 MPa, 260 MPa, etc., until it drops below 200 MPa. However, these nodes actually represent the same location as A and do not need to be extracted entirely. But if extraction is performed by sorting by stress magnitude, these indefinite numbers of nodes will be ranked before B, C, and D, constituting interference data for stress extraction and making it difficult to identify and extract the data from nodes B, C, and D.
[0028] Furthermore, when outputting contour maps of these high-stress areas, if a fixed perspective is used, some areas will be obscured. For example, in a top view, area A might be visible, but areas B on the side, C on the lower surface, and D on the upper surface of the inner cavity would be obscured. If drawn to a fixed scale, drawing a global view might result in unclear local representations, while drawing a local view requires knowing the location of the local areas (A, B, C, D). Therefore, post-processing of these calculation results usually requires manual operation. First, different stress levels are set to different display color bands, the viewing angle and cross-section are manually adjusted, and then the stress contour map is manually observed to identify each high-stress area in turn, read the peak stress, and save the image. For a part with a complex shape, identifying readings and drawing data for each working condition and location in each version of the design usually takes several hours, and omissions are still possible.
[0029] To address the aforementioned issues, this application provides an automated data post-processing method. Users only need to provide filtering information, and the entire post-processing process can be completed automatically with high efficiency. The filtering process simultaneously analyzes the target node and its associated nodes, avoiding interference from associated nodes on the filtering results. By analyzing the view vector and feature size, a more accurate local cloud map can be generated, thereby meeting the user's needs.
[0030] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0031] Figure 1 This is a flowchart illustrating an optional data post-processing method according to an embodiment of this application, such as... Figure 1 As shown, the method includes at least steps S102-S108, wherein:
[0032] Step S102: Obtain the cloud map data of the target part, and establish a target node set based on the cloud map data.
[0033] As an optional implementation, in response to the selection command of the target object, the finite element simulation file corresponding to the selection command can be read to obtain the cloud map data of the target part. The finite element simulation file includes at least: a finite element model file and a finite element result file.
[0034] For example, when a user enters a file path in the human-computer interaction interface, the data processing device can automatically read the corresponding finite element model file and finite element result file based on the file path to obtain the cloud map data of the target part.
[0035] Subsequently, a target node set can be established based on the cloud map data. The target node set includes: a first node set and a second node set. When the target part is a hollow structure, the first node set includes: an outer surface node set consisting of all outer surface nodes of the target part, denoted as {nest_outer_total}, and an inner surface node set consisting of all inner surface nodes of the target part, denoted as {nest_inner_total}. When the target part is a solid structure, the first node set includes: the outer surface node set. The second node set includes nodes in the target part that do not need to be of interest, denoted as {nest_release}.
[0036] Understandably, if such a set of nodes has already been created in the finite element model file or finite element result file, it can be called directly.
[0037] Step S104: Obtain the filtering information set by the target object, determine multiple target nodes from the target node set based on the filtering information, and determine the associated nodes of each target node.
[0038] Specifically, the system can receive filtering information input from the target object. This filtering information includes at least the target data type and the target threshold. For example, if the target part requires attention to Mises stress above 200 MPa, the user can input the target data type as Mises stress and the target threshold as S0 = 200 MPa in the human-machine interface.
[0039] Subsequently, as an optional implementation, the target nodes and their associated nodes can be filtered from the target node set through the following steps.
[0040] First, determine the difference between the first and second node sets as the third node set. Then, repeat the following process to determine multiple target nodes in the target node set and the associated nodes of each target node:
[0041] Sort each node in the third node set from largest to smallest based on the value of the target data type; the process ends when the value of the first sorted node is not greater than the target threshold; when the value of the first sorted node is greater than the target threshold, the first sorted node is taken as the target node, and the other nodes in the third node set are divided into multi-level unit subsets from near to far, centered on the target node; traverse the multi-level unit subsets from near to far, when the maximum node value in the first unit subset is not greater than the target threshold, and the maximum node value in the second unit subset is greater than the target threshold, or when the maximum node value in the first unit subset is greater than the maximum node value in the second unit subset, the second unit subset is taken as the outermost unit subset corresponding to the target node, where the first unit subset is any unit subset in the multi-level unit subset, and the second unit subset is the unit subset above the first unit subset; all nodes in the unit subsets between the target node and the second unit subset are taken as the associated nodes of the target node, and the target node and its associated nodes are removed from the third node set.
[0042] The following analysis Figure 1 The process of identifying the peak points A, B, C, and D of four high-stress regions in a part that exceed the stress threshold of 200 MPa serves as an example to illustrate the above screening process.
[0043] (1) Process the outer surface nodes and inner surface nodes in turn to establish a third node set {U}. When processing the outer surface nodes, {U} = {nest_outer_total} - {nest_release}, and when processing the inner surface nodes, {U} = {nest_inner_total} - {nest_release}.
[0044] (2) Sort the stresses of each node in the third node set {U} from largest to smallest. Assume that the stress at the point of maximum stress A is S. A If S ≤ S0, it means that the peak stress at all nodes is not greater than the stress threshold, i.e., there is no location of interest in the target part, and the data post-processing workflow can be terminated directly; if S A If S ≥ 0, then point A will be the first target node.
[0045] (3) Figure 3 As shown, the mesh expands outward layer by layer in a loop, and the associated nodes of the target node A are determined by gradient:
[0046] Identify all nodes in the first-layer cell subset {Ea1} adjacent to the target node A in sequence, and denote them as the first-layer surrounding node set {Na1}; identify all nodes in the second-layer cell subset {Ea2}, and denote them as the second-layer surrounding node set {Na2}, ...;
[0047] Obtain the maximum stress S in {Ea1}, {Ea2}, ... respectively. A1 S A2 ..., if S A2 >S0 and S A2 <S A1 The process continues iterating through the next layer of element subsets and nodes; when the iteration reaches the nth layer, the stress drops below the threshold, i.e., S. An When S ≤ S0, the loop exits; or another nearby peak is encountered, and the stress gradient changes, i.e., S An >S A(n-1) When the loop ends, the set of associated nodes of the target node A is recorded as {Na1}+{Na2}+…{Na(n-1)}.
[0048] (4) Update the third node set {U}={U}-({Na1}+{Na2}+…{Na(n-1)}), that is, after removing all the interference data near the target node A, enter the loop to determine the next peak stress, obtain the second target node B, and obtain the associated nodes of the target node B layer by layer.
[0049] Loop through and update the array {U}. Once all target nodes A, B, C, and D are found, exit the loop.
[0050] It should be noted that the above process is illustrated using the screening of peak stress nodes as an example and is not intended to be limiting. In practical applications, other data types can also be screened, and the same method can be used to screen valley values.
[0051] To facilitate data management, in some optional embodiments of this application, information about target nodes and associated nodes can be stored as target variables. The target variable type includes at least one of the following: nested array, list, or dictionary. The information includes at least one of the following: target data type, target threshold, surface type of the target node, target node number, target node value, number of the multi-layer unit subset corresponding to the associated node, view vector, feature size, and profile location point. All nodes can be identified by node number, and all unit subsets can be identified by unit number.
[0052] Step S106: Determine the view vector and feature size corresponding to each target node based on the associated nodes of each target node. The view vector is used to determine the view direction of the local cloud map corresponding to the target node, and the feature size is used to determine the scaling ratio of the local cloud map corresponding to the target node.
[0053] To avoid the problem that the location of interest in a complex model may be occluded due to an inappropriate viewpoint, this application provides an optional viewpoint definition method when generating a local cloud map of the target node, which can adapt to the viewpoint at different locations.
[0054] Specifically, for each target node, the first layer of unit subsets in the multi-layer unit subset corresponding to the target node is determined from near to far; at least two nodes located on the surface of the target part are determined from the first layer of unit subset as first target associated nodes, and at least one node located inside the target part is determined from the first layer of unit subset as second target associated nodes; the magnitude of the view vector is determined based on the coordinates of the target node and the first target associated node, and the direction of the view vector is determined based on the second target associated node.
[0055] like Figure 4 As shown, for target node Na, two first target associated nodes M1 and M2 located on the surface of the entity and a second target associated node P located inside the entity can be selected from the first layer of the associated unit subset. Then, the spatial orientation and positive and negative directions of the view vector of target node Na are calculated through the following steps.
[0056] (1) First, extract the coordinates of each node:
[0057] Na(x1,y1,z1), M1(x2,y2,z2), M2(x3,y3,z3), P(x4,y4,z4);
[0058] (2) Next, calculate the direction vector.
[0059] Calculate the vectors in each direction, starting from node Na and ending at nodes M1, M2, and P respectively:
[0060]
[0061]
[0062]
[0063] Calculate vectors and vector product
[0064]
[0065] That is, a vector perpendicular to the plane formed by points Na, M1, and M2, with two possible directions: one pointing outwards from the object, and the other pointing inwards from the object. Figure 4 The middle part is represented by solid lines and dashed lines.
[0066] (3) Finally, determine the positive and negative directions and calculate the view vector.
[0067] pass and Dot product Positive and negative judgment The direction, if t>0, then If t < 0, then
[0068] To address the issue that drawing only the overall cloud map of a large-size model might result in unclear descriptions of local details, this application also provides a rule for defining feature dimensions to adaptively adjust the scale of the cloud map drawing.
[0069] Specifically, for each target node, the distance between every two associated nodes in the associated nodes of the target node is determined, and the pair of associated nodes with the largest distance is taken as the third target associated node. The feature size is determined based on the coordinates of the third target associated node.
[0070] Since the pair of related nodes with the largest distance can reflect the actual size of the area where the target node is located, the image display range of the final generated local cloud map is set to a preset multiple of the feature size. The specific multiple can be set by the user according to experience, so that a local cloud map with a moderate display range and clear details can be generated.
[0071] Considering the difficulty in drawing the inner wall of the cavity of hollow parts, this application embodiment also provides an adaptive activation function for the cross-sectional view to draw the cloud map of the inner surface of the cavity.
[0072] Specifically, when the target part is a hollow structure, a preset target section distance h is obtained. The target section distance h is the distance between the target section positioning point and the target node, where 0
[0073] For each target node, determine the target profile location point based on the target node's coordinates, target profile distance, and view vector; generate a sectional view of the target node based on the target profile location point and view vector; and save the sectional view.
[0074] Step S108: Generate a local cloud map corresponding to each target node based on the view vector and feature size, save the local cloud map, and save the information of the target node and associated nodes.
[0075] As an optional implementation, for each target node, a background cloud map corresponding to the target node can be determined, and a color band of the cloud map can be determined based on a target threshold. A local cloud map corresponding to the target node can be generated based on the view vector and feature size, and data annotations can be added to the local cloud map. Finally, the generated local cloud map and the array variables corresponding to the target node can be written to a file at a specified path.
[0076] Optionally, after data processing is completed, in response to the retrieval command of the target object, the local cloud map of the target node corresponding to the retrieval command can be retrieved, and the information of the target node and associated nodes can be retrieved; based on the local cloud map, the information of the target node and associated nodes, a processing report file in the target format, such as Excel, PPT, Word, etc., can be generated.
[0077] In this embodiment, firstly, cloud map data of the target part is acquired, and a target node set is established based on the cloud map data. Then, filtering information set for the target object is acquired, and multiple target nodes are determined from the target node set based on the filtering information, along with associated nodes for each target node. Based on the associated nodes of each target node, the view vector and feature size corresponding to each target node are determined. The view vector is used to determine the view direction of the local cloud map corresponding to the target node, and the feature size is used to determine the scaling ratio of the local cloud map corresponding to the target node. Based on the view vector and feature size, a local cloud map corresponding to each target node is generated, and the local cloud map is saved, along with information about the target node and associated nodes. The user only needs to provide filtering information, and the entire post-processing process can be automated, resulting in high efficiency. The filtering process simultaneously analyzes the target node and its associated nodes, avoiding interference from associated nodes in the filtering results. By analyzing the view vector and feature size, a more accurate local cloud map can be generated. This application effectively solves the technical problem that the post-processing results of the cloud map data in the finite element simulation analysis process often fail to meet the requirements.
[0078] Example 2
[0079] According to embodiments of this application, a data post-processing apparatus for implementing the above-described data post-processing method is also provided, such as... Figure 5 As shown, the data post-processing device includes at least an acquisition module 51, a filtering module 52, a determination module 53, and a generation module 54, wherein:
[0080] The acquisition module 51 is used to acquire the cloud map data of the target part and establish a target node set based on the cloud map data.
[0081] As an optional implementation, the acquisition module can respond to the selection command of the target object, read the finite element simulation file corresponding to the selection command, and obtain the cloud map data of the target part. The finite element simulation file includes at least: a finite element model file and a finite element result file.
[0082] Subsequently, the acquisition module can establish a target node set based on the cloud map data. The target node set includes a first node set and a second node set. When the target part is a hollow structure, the first node set includes an outer surface node set consisting of all outer surface nodes and an inner surface node set consisting of all inner surface nodes. When the target part is a solid structure, the first node set includes the outer surface node set. The second node set includes nodes in the target part that do not require attention. Understandably, if such a node set has already been established in the finite element model file or finite element result file, it can be directly called.
[0083] The filtering module 52 is used to obtain the filtering information set by the target object, determine multiple target nodes from the target node set based on the filtering information, and determine the associated nodes of each target node.
[0084] Specifically, the filtering module can receive filtering information input from the target object, wherein the filtering information includes at least: the target data type and the target threshold.
[0085] The filtering module can then filter target nodes and their associated nodes from the target node set using the following steps.
[0086] First, determine the difference between the first and second node sets as the third node set. Then, repeat the following process to determine multiple target nodes in the target node set and the associated nodes of each target node:
[0087] Sort each node in the third node set from largest to smallest based on the value of the target data type; the process ends when the value of the first sorted node is not greater than the target threshold; when the value of the first sorted node is greater than the target threshold, the first sorted node is taken as the target node, and the other nodes in the third node set are divided into multi-level unit subsets from near to far, centered on the target node; traverse the multi-level unit subsets from near to far, when the maximum node value in the first unit subset is not greater than the target threshold, and the maximum node value in the second unit subset is greater than the target threshold, or when the maximum node value in the first unit subset is greater than the maximum node value in the second unit subset, the second unit subset is taken as the outermost unit subset corresponding to the target node, where the first unit subset is any unit subset in the multi-level unit subset, and the second unit subset is the unit subset above the first unit subset; all nodes in the unit subsets between the target node and the second unit subset are taken as the associated nodes of the target node, and the target node and its associated nodes are removed from the third node set.
[0088] To facilitate data management, in some optional embodiments of this application, information about target nodes and associated nodes can be stored as target variables. The target variable type includes at least one of the following: nested array, list, or dictionary. The information includes at least one of the following: target data type, target threshold, surface type of the target node, target node number, target node value, number of the multi-layer unit subset corresponding to the associated node, view vector, feature size, and profile location point. All nodes can be identified by node number, and all unit subsets can be identified by unit number.
[0089] The determination module 53 is used to determine the view vector and feature size corresponding to each target node based on the associated nodes of each target node. The view vector is used to determine the view direction of the local cloud map corresponding to the target node, and the feature size is used to determine the scaling ratio of the local cloud map corresponding to the target node.
[0090] To avoid the problem that the location of interest in a complex model may be occluded due to an inappropriate viewpoint, this application provides an optional viewpoint definition method when generating a local cloud map of the target node, which can adapt to the viewpoint at different locations.
[0091] Specifically, for each target node, the determining module determines the first layer of unit subsets from near to far in the multi-layer unit subset corresponding to the target node; determines at least two nodes located on the surface of the target part as first target associated nodes from the first layer of unit subsets, and determines at least one node located inside the target part as a second target associated node from the first layer of unit subsets; determines the magnitude of the view vector based on the coordinates of the target node and the first target associated node, and determines the direction of the view vector based on the second target associated node.
[0092] To address the issue that drawing only the overall cloud map of a large-size model might result in unclear descriptions of local details, this application also provides a rule for defining feature dimensions to adaptively adjust the scale of the cloud map drawing.
[0093] Specifically, for each target node, the determination module determines the distance between every two associated nodes among the associated nodes of the target node, takes the pair of associated nodes with the largest distance as the third target associated node, and determines the feature size based on the coordinates of the third target associated node.
[0094] Since the pair of related nodes with the largest distance can reflect the actual size of the area where the target node is located, the image display range of the final generated local cloud map is set to a preset multiple of the feature size. The specific multiple can be set by the user according to experience, so that a local cloud map with a moderate display range and clear details can be generated.
[0095] Considering the difficulty in drawing the inner wall of the cavity of hollow parts, this application embodiment also provides an adaptive activation function for the cross-sectional view to draw the cloud map of the inner surface of the cavity.
[0096] Specifically, when the target part is a hollow structure, the module determines the preset target profile distance h, where h is the distance between the target profile positioning point and the target node, and 0
[0097] The generation module 54 is used to generate a local cloud map corresponding to each target node based on the view vector and feature size, save the local cloud map, and save the information of the target node and associated nodes.
[0098] As an optional implementation, for each target node, the generation module can determine the background cloud map corresponding to the target node, determine the cloud map color band based on the target threshold, generate a local cloud map corresponding to the target node based on the view vector and feature size, and add data annotations to the local cloud map. Finally, the generated local cloud map and the array variables corresponding to the target node can be written to a file at a specified path.
[0099] Optionally, the generation module can also respond to the retrieval command of the target object, retrieve the local cloud map of the target node corresponding to the retrieval command, and retrieve the information of the target node and related nodes; and generate a processing report file in the target format, such as Excel, PPT, Word, etc., based on the local cloud map, the information of the target node and related nodes.
[0100] It should be noted that each module in the data post-processing device in this application embodiment corresponds one-to-one with each implementation step in the data post-processing method in embodiment 1. Since embodiment 1 has been described in detail, some details not shown in this embodiment can be referred to embodiment 1, and will not be elaborated further here.
[0101] Example 3
[0102] According to an embodiment of this application, a non-volatile storage medium is also provided, which includes a stored program, wherein the device where the non-volatile storage medium is located executes the data post-processing method in Embodiment 1 by running the program.
[0103] Specifically, the device containing the non-volatile storage medium executes the following steps by running the program: acquiring cloud map data of the target part, and establishing a target node set based on the cloud map data; acquiring the filtering information set for the target object, determining multiple target nodes from the target node set based on the filtering information, and determining the associated nodes of each target node; determining the view vector and feature size corresponding to each target node based on the associated nodes of each target node, wherein the view vector is used to determine the view direction of the local cloud map corresponding to the target node, and the feature size is used to determine the scaling ratio of the local cloud map corresponding to the target node; generating a local cloud map corresponding to each target node based on the view vector and feature size, saving the local cloud map, and saving the information of the target node and associated nodes.
[0104] According to an embodiment of this application, a processor is also provided for running a program, wherein the program executes the data post-processing method in embodiment 1 during runtime.
[0105] Specifically, the program executes the following steps during runtime: acquiring the cloud map data of the target part, and establishing a target node set based on the cloud map data; acquiring the filtering information set by the target object, determining multiple target nodes from the target node set based on the filtering information, and determining the associated nodes of each target node; determining the view vector and feature size corresponding to each target node based on the associated nodes of each target node, wherein the view vector is used to determine the view direction of the local cloud map corresponding to the target node, and the feature size is used to determine the scaling ratio of the local cloud map corresponding to the target node; generating a local cloud map corresponding to each target node based on the view vector and feature size, saving the local cloud map, and saving the information of the target node and associated nodes.
[0106] According to an embodiment of this application, an electronic device is also provided, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the data post-processing method of embodiment 1 through the computer program.
[0107] Specifically, the processor is configured to execute the following steps via a computer program: acquire cloud map data of the target part, and establish a target node set based on the cloud map data; acquire filtering information set for the target object, determine multiple target nodes from the target node set based on the filtering information, and determine the associated nodes of each target node; determine the view vector and feature size corresponding to each target node based on the associated nodes of each target node, wherein the view vector is used to determine the view direction of the local cloud map corresponding to the target node, and the feature size is used to determine the scaling ratio of the local cloud map corresponding to the target node; generate a local cloud map corresponding to each target node based on the view vector and feature size, save the local cloud map, and save the information of the target node and associated nodes.
[0108] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0109] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0110] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.
[0111] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0112] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0113] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.
[0114] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.
Claims
1. A data post-processing method, characterized in that, include: Obtain cloud map data of the target part, and establish a target node set based on the cloud map data. The target node set includes a first node set and a second node set. When the target part is a hollow structure, the first node set includes an outer surface node set consisting of all outer surface nodes of the target part and an inner surface node set consisting of all inner surface nodes of the target part. When the target part is a solid structure, the first node set includes the outer surface node set. The second node set includes nodes in the target part that do not need to be considered. Receive filtering information input from the target object, wherein the filtering information includes at least: target data type and target threshold; The difference between the first set of nodes and the second set of nodes is determined as the third set of nodes; Repeat the following process to determine multiple target nodes in the target node set and the associated nodes of each target node: Sort each node in the third node set from largest to smallest based on the value of the target data type; The process ends when the value of the first sorted node is not greater than the target threshold. When the value of the first-ranked node is greater than the target threshold, the first-ranked node is taken as the target node, and the other nodes in the third node set are divided into multi-level unit subsets from near to far, with the target node as the center. Traverse the multi-layered unit subsets from near to far. When the maximum node value in the first unit subset is not greater than the target threshold and the maximum node value in the second unit subset is greater than the target threshold, or when the maximum node value in the first unit subset is greater than the maximum node value in the second unit subset, the second unit subset is taken as the outermost unit subset corresponding to the target node. Here, the first unit subset is any unit subset in the multi-layered unit subsets, and the second unit subset is the unit subset above the first unit subset. The nodes in all unit subsets between the target node and the second unit subset are taken as the associated nodes of the target node, and the target node and its associated nodes are removed from the third node set. Based on the associated nodes of each target node, a view vector and feature size corresponding to each target node are determined, wherein the view vector is used to determine the view direction of the local cloud map corresponding to the target node, and the feature size is used to determine the scaling ratio of the local cloud map corresponding to the target node; A local cloud map corresponding to each target node is generated based on the view vector and the feature size. The local cloud map is saved, along with the information of the target node and the associated node.
2. The method according to claim 1, characterized in that, Acquire cloud map data of the target part, and establish a target node set based on the cloud map data, including: In response to the selection command of the target object, the finite element simulation file corresponding to the selection command is read to obtain the cloud map data of the target part, wherein the finite element simulation file includes at least: a finite element model file and a finite element result file; A target node set is established based on the cloud map data.
3. The method according to claim 2, characterized in that, Based on the associated nodes of each target node, the view vector and feature size corresponding to each target node are determined, including: For each target node, determine the first layer of unit subsets from near to far within the multi-layer unit subset corresponding to the target node; determine at least two nodes located on the surface of the target part as first target associated nodes from the first layer of unit subsets, and determine at least one node located inside the target part as a second target associated node from the first layer of unit subsets; determine the magnitude of the view vector based on the coordinates of the target node and the first target associated node, and determine the direction of the view vector based on the second target associated node; For each target node, determine the distance between every two associated nodes among the associated nodes of the target node, take the pair of associated nodes with the largest distance as the third target associated node, and determine the feature size based on the coordinates of the third target associated node.
4. The method according to claim 2, characterized in that, Based on the view vector and the feature size, a local cloud map is generated for each target node, including: For each target node, a cloud map background corresponding to the target node is determined, and a cloud map color band is determined based on the target threshold. A local cloud map corresponding to the target node is generated based on the view vector and the feature size, and data annotations are added to the local cloud map.
5. The method according to claim 1, characterized in that, The method further includes: When the target part is a hollow structure, a preset target section distance is obtained, wherein the target section distance is the distance between the target section positioning point and the target node; For each target node, the target profile positioning point is determined based on the target node's coordinates, the target profile distance, and the view vector; A cross-sectional view of the target node is generated based on the target profile positioning points and the view vector; Save the sectional view.
6. The method according to claim 2, characterized in that, The information of the target node and the associated nodes is saved, including: The information of the target node and the associated node is stored in the form of target variables, wherein the type of the target variable includes at least one of the following: nested array form, list form, dictionary form, and the information includes at least one of the following: the target data type, the target threshold, the surface type where the target node is located, the number of the target node, the value of the target node, the number of the multi-layer unit subset corresponding to the associated node, the view vector, the feature size, and the profile positioning point.
7. The method according to claim 1, characterized in that, The method further includes: In response to the retrieval command of the target object, the local cloud map of the target node corresponding to the retrieval command is retrieved, and the information of the target node and the associated node is retrieved; A processing report file in the target format is generated based on the information of the local cloud map, the target node, and the associated nodes.
8. A data post-processing apparatus, characterized in that, include: An acquisition module is used to acquire cloud map data of a target part and establish a target node set based on the cloud map data. The target node set includes a first node set and a second node set. When the target part is a hollow structure, the first node set includes an outer surface node set consisting of all outer surface nodes of the target part and an inner surface node set consisting of all inner surface nodes of the target part. When the target part is a solid structure, the first node set includes the outer surface node set. The second node set includes nodes in the target part that do not require attention. A filtering module is used to receive filtering information input by a target object, wherein the filtering information includes at least: target data type and target threshold; determine the difference between the first node set and the second node set as the third node set; repeatedly execute the following process to determine multiple target nodes in the target node set and the associated nodes of each target node: sort each node in the third node set from largest to smallest based on the value of the target data type; when the value of the first sorted node is not greater than the target threshold, the process ends; when the value of the first sorted node is greater than the target threshold, take the first sorted node as the target node, and divide the other nodes in the third node set into multi-level unit subsets from near to far, with the target node as the center; Traverse the multi-layered unit subsets from near to far. When the maximum node value in the first unit subset is not greater than the target threshold, and the maximum node value in the second unit subset is greater than the target threshold, or when the maximum node value in the first unit subset is greater than the maximum node value in the second unit subset, take the second unit subset as the outermost unit subset corresponding to the target node. Here, the first unit subset is any unit subset in the multi-layered unit subsets, and the second unit subset is the unit subset above the first unit subset. Take all nodes in the unit subsets between the target node and the second unit subset as the associated nodes of the target node, and remove the target node and the associated nodes of the target node from the third node set. The determination module is used to determine the view vector and feature size corresponding to each target node based on the associated nodes of each target node, wherein the view vector is used to determine the view direction of the local cloud map corresponding to the target node, and the feature size is used to determine the scaling ratio of the local cloud map corresponding to the target node; The generation module is used to generate a local cloud map corresponding to each target node based on the view vector and the feature size, save the local cloud map, and save the information of the target node and the associated node.
9. A data post-processing device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the data post-processing method of any one of claims 1 to 7 through the computer program.