481 results about "Pyramid" patented technology

The invention discloses a sparse view three-dimensional reconstruction method and system based on dynamic occlusion perception and multi-prior fusion. The sparse view three-dimensional reconstruction method comprises the steps of obtaining a sparse view; extracting multi-scale geometric features from the sparse view through a feature pyramid network, constructing a probabilistic depth estimation module, and generating an initial sparse point cloud based on the probabilistic depth estimation module; based on the initial sparse point cloud, a dynamic occlusion perception attention mask is introduced, and occlusion area mismatching propagation is inhibited through cross-view visibility analysis and a multi-stage confidence aggregation strategy; and fusing geometric priori, semantic priori and implicit priori, carrying out multi-source priori collaborative constraint through an adaptive weight distribution strategy, enhancing the geometric accuracy of the weak texture region, repairing the occlusion region, and outputting a three-dimensional reconstruction image of the sparse view. According to the invention, the integrity and precision of sparse view reconstruction are improved.

The invention discloses a 3DTiles data storage method and a map data query method.The 3DTiles data storage method comprises the following steps that (1) an LOD hierarchy pyramid type hierarchy table is established, and an LOD range index is created; 2) generating a three-dimensional object into an agent point, and segmenting a tile according to a KD-tree structure to construct a three-dimensional KD-tree index tree; and 3) storing the data of the KD-tree leaf node into a GridFS of MongoDB multi-mode storage, mapping a file path into a unique hash value, writing the unique hash value into a corresponding node according to a KD-tree node storage structure, and creating a geographic spatial index. The method creatively solves the problems that file system management is difficult due to the fact that the number of files is large, a hierarchical structure based on a file directory is difficult to support efficient spatial range retrieval, and the spatial query response time is reduced to the millisecond level in combination with a three-dimensional KD-tree index and a MongoDB geographic spatial index.

The invention discloses a crack segmentation method and system based on a dynamic receptive field and multi-scale semantic aggregation, and relates to the technical field of computer vision. The method comprises the following steps: inputting a crack image, and simultaneously capturing local details and global structural features of a crack through a dynamic snakelike Mama module: dynamically adjusting the shape of a convolution kernel to adapt to the geometric change of the crack, inputting the obtained features into a spatial pyramid pooling layer to extract multi-scale context information, and outputting feature representation fused with a long-range dependency relationship; based on the feature representation fused with the long-range dependency relationship, an interaction relationship between local details and global semantics is established through a multi-scale semantic aggregation module, background noise interference is suppressed through a parallel supervision attention mechanism, and a pixel-level crack segmentation result is generated through a lightweight segmentation head. According to the method, fine cracks can be segmented more accurately in a complex background environment, and meanwhile, the conditions of wrong segmentation and missing segmentation are effectively relieved.

The invention discloses a cross-source data three-dimensional reconstruction method and system based on improved Gaussian sputtering, and the method comprises the steps: collecting an unmanned plane inclined image and a ground panoramic image of a target region, and constructing a time-space correlation data set; based on multi-view geometric constraints, space-time coding matching point pairs are established through an adaptive feature pyramid, intelligent incremental cross-source data sparse reconstruction is carried out, and point cloud and camera parameters are output; adopting improved Gaussian sputtering, compressing a three-dimensional Gaussian kernel into a two-dimensional Gaussian primitive through double tangent vector constraint, and fitting surface geometry to realize multi-scale reconstruction; and optimizing primitive parameters by using a differentiatable renderer, completing multi-scale fine reconstruction through gradient back propagation, and generating a high-precision three-dimensional model. According to the method, multi-scale accurate geometric prior input and accurate camera poses are provided for three-dimensional reconstruction, the dependence on professional manual operation in a traditional three-dimensional reconstruction method is greatly reduced, and meanwhile, the geometric accuracy and visual fidelity of a reconstruction result are remarkably improved.

The invention discloses a bearing fault diagnosis method based on pyramid convolution and multi-scale fusion, relates to the technical field of artificial intelligence, and solves the problems that an existing method is sensitive to signal distribution change under complex working conditions, a model needs to be repeatedly trained and the reasoning efficiency is low. According to the scheme, a vibration signal is sampled through a sliding window and converted into a time-frequency graph, a feature extraction network combining multi-scale expansion convolution and channel attention is constructed, multi-scale weighted channel features are fused, cross entropy loss training is used, and a verification set is combined to screen an optimal model for fault classification. According to the method, the diagnosis stability and the reasoning efficiency under the cross-working condition are remarkably improved.

The invention relates to a composite microprism reflective film integrated with various triangular pyramids and a mold manufacturing method thereof, the surface of the microprism reflective film is formed by an array of identical parallelogram unit bodies, and each parallelogram unit body is composed of three identical triangular pyramids A, three identical triangular pyramids B, a triangular pyramid C and a triangular pyramid D, the bottom surfaces of the triangular pyramid A, the triangular pyramid B, the triangular pyramid C and the triangular pyramid D are identical equilateral triangles, the inclination angles of the three pyramid surfaces of the triangular pyramid A are alpha / 2 and alpha / 2 and beta / 2 + respectively, the inclination angles of the three pyramid surfaces of the triangular pyramid B are alpha / 2 and alpha / 2 and beta / 2-respectively, the inclination angles of the three pyramid surfaces of the triangular pyramid C are beta / 2 + respectively, and the inclination angles of the three pyramid surfaces of the triangular pyramid D are beta / 2-respectively. Greater than zero. According to the method, the performance under different application conditions can be reasonably optimized, so that wide-angle performance improvement and optimization and balance of overall comprehensive performance can be realized.

The invention discloses a method for detecting the quality of a functional layer of a building outer wall by an unmanned aerial vehicle, and particularly relates to the technical field of building outer wall detection.The method comprises the steps that firstly, an infrared image of a building outer wall facing object is collected through the unmanned aerial vehicle, pixel points and remaining pixel points of a serious hollowing area are determined, and the abnormal degree of the remaining pixel points is calculated; clustering is carried out by adopting a defect probability-based region growing method; based on seed point screening of morphological preprocessing, structural elements of different sizes are comprehensively determined to be used through weighted summation calculation according to the jitter frequency of the unmanned aerial vehicle and the image resolution, and noise is filtered step by step. Generating a multi-scale pyramid for the original image, and detecting candidate seed points at each level; sorting and preferentially selecting the candidate seed points based on the morphological closure degree and the shape regularity of each candidate seed point; pixel points in the neighborhood of the growth seed points are merged to obtain all hollow defect connected domains; and judging the quality condition of the building outer wall functional layer according to the area of the hollowing defect connected domain.

The invention discloses an AI semantic understanding-based zero-code multi-terminal application automatic construction method, which comprises the following steps of: receiving a UI design draft image and layer metadata uploaded by a user, respectively extracting visual features and structural features through a double-branch feature extractor based on an AI semantic understanding technology, and establishing an AI semantic understanding model; a cross-modal attention module and a cavity space pyramid pooling module are combined to generate an enhanced feature graph, and a high-precision UI component mask and an interaction dependency graph are generated; according to target end equipment parameters, dynamic weights are generated through a multi-layer perceptron, layout constraints of the interaction dependency graph are adjusted, a layout target function is optimized, and component overlapping and visual unbalance are minimized; based on the predefined control library and the mapping rule, the UI component is mapped into the atomic control of the target platform, the interaction logic is converted into the event-action chain, and the target platform code is generated, and the automation degree and the cross-platform consistency of zero code development are improved through dynamic adaptation of the target end equipment and automatic code generation.

The invention belongs to the field of three-dimensional target detection, and particularly relates to a lightweight Mama three-dimensional target detection method based on columnar point cloud representation, which mainly consists of three parts: a spatial feature processing module based on Hilbert serialization and dynamic grouping, a Mama module based on a selective state space and a lightweight pyramid feature fusion module. The Hilbert serialization module keeps spatial locality through a serialization mode, and is helpful for capturing a structural relationship between adjacent points in the point cloud, so that the modeling effect of local feature integration and context information is improved; the Mama module adopts an input-dependent dynamic modeling mechanism, can provide higher modeling efficiency and stronger long-range dependent modeling capability when processing a large-scale volume column sequence, and is particularly suitable for a sparse point cloud scene; the lightweight pyramid fusion module enhances the perception capability of a target boundary through single-round up-down sampling and feature fusion, and improves the detection performance of a small target, thereby effectively balancing the efficiency and precision of 3D target detection.

The invention relates to an OpenGL (Open Graphics Library)-based large-scale three-dimensional model object quick pickup method. The method comprises the following steps: converting an interactive operation of a user into a three-dimensional space structure; a CPU constructs octree space acceleration structures based on a bounding box of an object in a large-scale three-dimensional model, and leaf nodes, intersecting with a pickup ray or a pickup view pyramid, of an axis alignment bounding box in each octree space acceleration structure are screened out to serve as candidate leaf nodes; collecting triangles contained in all candidate leaf nodes, and constructing a candidate triangle set; dynamically allocating thread groups and threads of the GPU based on the number of candidate triangles in the candidate triangle set; and performing highlight display on the candidate triangles intersected with the pickup ray or the pickup view cone, and feeding back IDs of the corresponding candidate triangles. According to the method, the data transmission quantity and the video memory access pressure are remarkably reduced, the pickup response time is greatly shortened in a large-scale three-dimensional model scene, and the requirements of real-time interaction and visual operation can be met.

The invention relates to a microprism reflective film combined by three kinds of triangular pyramids and a manufacturing method of a mold of the microprism reflective film, the surface of the microprism reflective film is formed by an array of identical parallelogram unit bodies (1), each parallelogram unit body (1) is composed of four identical triangular pyramids A, one triangular pyramid B and three identical triangular pyramids C, the directions of the four triangular pyramids A and the directions of the three triangular pyramids C and the triangular pyramids B form 180 degrees, and the three triangular pyramids C and the triangular pyramids B are arranged among the four triangular pyramids A in a staggered manner; the bottom surfaces of the triangular pyramids B and C are the same equilateral triangles, the inclination angles of the three pyramid surfaces of each triangular pyramid A are alpha / 2, the inclination angles of the pyramid surfaces of the triangular pyramids B are beta / 2, and the inclination angles of the three pyramid surfaces of the triangular pyramids C are alpha / 2, alpha / 2 and beta / 2 respectively. According to the method, the performance under different application conditions can be reasonably optimized, so that wide-angle performance improvement and optimization and balance of overall comprehensive performance can be realized.

The invention provides an intelligent machine tool cutter wear state recognition and machining parameter adjustment method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting the working condition data of a machine tool machining process, carrying out the sliding window segmentation, denoising and normalization processing, extracting a time sequence feature sequence through a bidirectional gating circulation unit, and carrying out the recognition of a machining parameter; the method comprises the following steps: acquiring an intrinsic mode component by combining Hilbert-Huang transform decomposition, extracting a frequency domain feature sequence by utilizing four-layer spatial pyramid pooling, determining a fusion weight based on mutual information entropy to obtain a tool wear state feature vector, and inputting the feature vector into a pre-training extreme learning machine to predict the wear loss and the residual life. And determining an initial adjustment scheme in combination with observation sharing information of a parallel agent structure, predicting a processing quality score through a capsule network and feeding back the score as a reward signal, and obtaining an optimization correction strategy for adjusting working parameters of a machine tool.

The invention relates to a map visualization rendering method and system based on a WebGPU engine and computer equipment, and the method comprises the steps: obtaining map data in a unified format; inputting the map data into a rendering layer, and performing layer division on the map data according to spatial levels and element types to obtain multiple pieces of layer data; distributing an independent rendering thread for each layer data; performing block processing on the map data to obtain picture blocks with pyramid structures; and calling a corresponding rendering thread in the current view range and the scaling to load the corresponding picture block for rendering display. By means of the method and device, the problem that the map visualization rendering performance is poor is solved.

The invention discloses a lightweight human body posture estimation method based on PLES-YOLO, and belongs to the field of computer vision, and the method comprises the following steps: carrying out the feature extraction of an input image through employing an improved down-sampling module, and obtaining an initial feature map; based on the initial feature map, a multi-scale feature fusion module is adopted to carry out progressive feature enhancement, and multi-scale fusion features are obtained; based on the multi-scale fusion features, performing feature optimization by adopting a spatial pyramid pooling module combined with an attention mechanism to obtain weighted multi-scale features; and on the basis of the weighted multi-scale features, a lightweight shared convolution detection head is adopted to predict human body key point coordinates, and a final attitude estimation result is output.

The invention provides an intelligent visual inspection method and system for hardware stamping die production, and relates to the field of hardware stamping die production, and the method comprises the steps: obtaining a die current state image of a to-be-detected hardware stamping die and a defect-free die state reference image, and carrying out the geometric alignment; and then calculating a difference graph to obtain significance distribution of a potential defect area, carrying out multi-scale difference feature hierarchical coding by using a pyramid network, and separating normal wear artifacts from real defects. Furthermore, a feature receptive field anchoring mechanism is introduced, and the attention weight is dynamically adjusted according to the geometric topology of the mold. And finally, a lightweight classifier is used to make a decision on the enhanced difference features, and sub-millimeter-level anomaly recognition is realized. In this way, the sensitivity to tiny changes of the hardware stamping die can be effectively improved, and defects can be found in the early stage.

The invention discloses a double-branch multi-scale modeling semantic segmentation method based on a tunnel scene, and the method comprises the following steps: S1, collecting a tunnel face image in a tunnel, constructing a pixel-level annotation data set, and improving the sample diversity through data enhancement; and S2, establishing a context path of the double-branch semantic segmentation model, performing fast down-sampling by using a residual network to extract high-dimensional features, designing a spatial symmetric pyramid module to enhance semantic perception, and finally fusing and outputting average pooling up-sampling and spatial pyramid pooling features. And S3, establishing a spatial path of the double-branch semantic segmentation model, performing down-sampling and pooling processing on the high-dimensional feature map, and adding a dynamic sensing context module to extract a multi-scale feature fusion output result. And S4, carrying out feature fusion on the obtained feature information, and finally predicting a segmented image. According to the invention, the dynamic context sensing module and the space symmetric pyramid module are designed, so that the accuracy of multi-scale classification in a complex scene can be effectively improved.

The invention relates to a microprism reflective film integrated with three types of triangular pyramid combinations and a manufacturing method of a mold of the microprism reflective film, which are characterized in that the surface of the microprism reflective film is formed by the same parallelogram unit array, and each parallelogram unit consists of four same triangular pyramids A, two mixed triangular pyramids B and two mixed triangular pyramids C, the bottom surfaces of the triangular pyramids A, the mixed triangular pyramids B and the mixed triangular pyramids C are the same equilateral triangles, the inclination angles of the three pyramid surfaces of each triangular pyramid A are alpha / 2, the inclination angles of the pyramid surfaces of the mixed triangular pyramids B are alpha / 2, alpha / 2 and beta L, and the inclination angles of the three pyramid surfaces of the mixed triangular pyramids C are alpha / 2, alpha / 2 and beta R; according to the method, the performance under different application conditions can be reasonably optimized, so that wide-angle performance improvement and optimization and balance of overall comprehensive performance can be realized.

The invention provides a liquid crystal display screen detection method, device and equipment and a storage medium, and the method comprises the steps: carrying out the hierarchical scale feature extraction of an image collected by a liquid crystal display screen through a multistage object pyramid structure, and obtaining a multi-scale defect feature representation; encoding the multi-scale features into a unified high-dimensional space by adopting a comparison encoding algorithm, and calculating a feature vector distance relationship to obtain comparison encoding feature vectors; inputting the feature vector into a defect detection model, and performing task specific activation through a coordinate convolution positioning branch and a dynamic snakelike convolution class branch to obtain a decoupling detection result; and according to a decoupling detection result, identifying and classifying defect types through a detection head, and outputting a detection result. According to the method, defects of different scales are captured through multi-stage pyramid structure extraction, discriminative enhancement of feature representation is achieved through high-dimensional space mapping of a comparison coding algorithm, and positioning precision and classification accuracy are improved through decoupling processing of coordinate convolution and dynamic snakelike convolution.

The invention relates to a composite microprism reflective film with three triangular pyramid structures and a manufacturing method of a mold of the composite microprism reflective film, and is characterized in that the surface of the microprism reflective film is formed by an array of identical parallelogram unit bodies, and each parallelogram unit body is composed of two identical triangular pyramids A, four mixed triangular pyramids B and two mixed triangular pyramids C, the bottom surfaces of the triangular pyramids A, the mixed triangular pyramids B and the mixed triangular pyramids C are the same equilateral triangles, the inclination angles of the three pyramid surfaces of each triangular pyramid A are alpha / 2, the inclination angles of the pyramid surfaces of the mixed triangular pyramids B are alpha / 2, alpha / 2 and beta / 2, and the inclination angles of the three pyramid surfaces of the mixed triangular pyramids C are alpha / 2, beta / 2 and beta / 2 respectively; the area ratio of the triangular pyramid A to the triangular pyramid B to the triangular pyramid C is 1: 2: 1, and the performance under different application conditions can be reasonably optimized, so that the wide-angle performance can be improved, and the optimization and balance of the overall comprehensive performance can be realized.

The invention discloses an AI-based historical defective fresco digital restoration system, and relates to the technical field of AI restoration, a two-channel filter and a U-Net network are adopted to extract a fresco damage boundary, and edge detection is enhanced in combination with manual labeling; calculating a multi-scale Hurst index, and constructing a matrix quantization fractal feature change rule; dynamically generating a probabilistic grammar rule set based on the fractal incidence matrix, and defining a state transition probability function of texture growth; recursively executing texture generation driven by grammar rules from boundary points, and introducing random disturbance attenuating along with depth to simulate a stroke natural form; textures are generated through Laplacian pyramid layered correction, and the phase of a reference image is aligned in a Fourier domain to reserve structural consistency; and solving an affine transformation matrix with unchanged illumination in the transition zone, and globally optimizing color connection between the restoration area and the original wall painting. According to the method, the dynamic grammar rule base is established by analyzing the fractal features of the residual strokes, so that the generated texture follows creation logic, and the visual coherence of a recovery area is improved.

The invention provides a space-spectrum Mama hyperspectral image classification method based on a pyramid structure. The problem that an existing method is insufficient in performance in classification of complex backgrounds and fine-grained ground objects is mainly solved. Comprising the following steps: 1) acquiring a hyperspectral image, and constructing a training set and a test set; 2) designing a double-branch model based on multi-scale space-spectrum adaptive fusion, and extracting multi-scale space and spectrum feature information in parallel; 3) respectively designing a spatial feature extraction module and a spectral feature extraction module, capturing target information in a spatial domain and a spectral domain, and optimizing fusion of spatial spectral features by using a multi-scale adaptive weighting mechanism; 4) constructing a space spectrum interactive fusion module for deep interactive fusion of features extracted by space and spectrum branches; and 5) training the model until convergence, and obtaining a final classification result by using the model. The method can effectively improve the processing capability of the complex background in the image, enhance the ground feature classification precision, and significantly improve the hyperspectral image classification performance.

The invention proposes a three-dimensional modeling method based on multi-view image fusion and AI semantic material decoupling, and the method comprises the steps: employing a self-adaptive overlapped region weighted fusion strategy in an image preprocessing stage through an improved pyramid block matching algorithm, dynamically adjusting the gray value of a pixel through an inverse distance weighting function, and carrying out the dynamic adjustment of the gray value of the pixel; exposure differences and splicing gaps among multi-view images are eliminated, building structure features are extracted through a graph convolutional network by constructing a two-channel deep learning network and a space geometric channel, semantic segmentation of eaves and beam column components is achieved, and in the two-channel deep learning network, a material decoupling channel adopts an adversarial generative network; according to the method, the texture alignment precision can be improved to 0.3 pixel level in an ancient building digital case, the material attribute restoration error is lower than 8%, the model is supported to directly perform weathering simulation and virtual restoration operation in a Unity / Unreal engine, and a new technical normal form is provided for digital protection and activation utilization of cultural heritage.

The invention discloses a three-dimensional scene reconstruction method driven by monocular implicit depth features. The method comprises the following steps: extracting multi-view monocular depth features through a frozen monocular depth encoder; realizing cross-view feature alignment by using a geometry-vision similarity fusion module; generating a multi-scale monocular implicit depth feature through a multi-scale feature adaptive and processing module; a context modeling module and a feature pyramid network are adopted to extract multi-view context features; carrying out one-stage fusion with a monocular implicit depth feature to obtain a fusion feature; generating a depth map through the multi-view stereo depth estimation network; under the constraint of a depth map, generating spatial point features through multi-source feature projection sampling; performing two-stage fusion with the fusion features to generate point-level implicit features; optimizing the features through a frequency-space local-global gating mechanism; respectively inputting the optimization features into a Gaussian sputtering explicit rendering branch and a volume implicit rendering branch; and carrying out weighted fusion on the rendering result to generate a new view image.

The invention relates to the technical field of computer vision, in particular to a complex scene object recognition system based on multi-scale feature fusion, which comprises a feature extraction module, a Riemannian geometric feature pyramid module, a candidate region generation module and an instance recognition and positioning module, and is characterized in that the feature extraction module extracts multi-scale spatial features; the Riemannian geometric feature pyramid module maps spatial features to a Riemannian manifold space, calculates curvature distribution of feature manifolds, dynamically adjusts feature extraction levels and fusion strategies, and realizes geometric consistent fusion of different scale features through geodesic line feature fusion; the candidate region generation module generates an object candidate region based on the fusion features; and the instance identification and positioning module completes final object classification and positioning, introduces a differential geometry theory to construct a Riemannian geometric feature pyramid, realizes geometric representation and adaptive fusion of features, solves the problems of scale change, partial occlusion, background interference and the like in a complex scene, and effectively improves the accuracy and robustness of object identification.

The invention relates to an efficient concrete crack detection method, device and system. The method comprises the following steps: acquiring an image of a to-be-detected concrete beam; generating a hierarchical grid backbone network of redundant features based on linear transformation, and performing feature extraction processing on the to-be-detected concrete beam image to obtain multi-scale feature information; performing feature fusion processing on the multi-scale feature information based on a geometric perception dynamic feature pyramid network to obtain fused feature information; based on task alignment of the dynamic detection head and classification and positioning processing of the fusion feature information, the target crack information corresponding to the to-be-detected concrete beam image is obtained, the concrete crack detection accuracy is improved, the weak texture feature extraction and retention capability is significantly enhanced, the leak detection risk of fine cracks is effectively reduced, and the detection efficiency is improved. The anti-interference capability under a complex background is enhanced, the false detection rate is reduced, mismatching of classification and positioning tasks is avoided, and the crack positioning precision is improved.

The invention discloses a small target detection method based on a bidirectional enhancement mechanism and loss optimization, and the method comprises the steps: replacing a key lower sampling layer with a space depth conversion convolution (SPDConv) module in a backbone network part, so as to maintain the fine-grained features of a small target; an HGBlockPPA module integrating multi-scale pooling and double-path attention is introduced at the tail end of the network, and the expression ability of defect features is enhanced. In the feature fusion stage, a small target enhanced pyramid structure is constructed, a high-resolution shallow-layer feature map P2 is introduced and fused, a bidirectional feature propagation path is established, and feature refining and small target feature response enhancement are realized in combination with a multi-layer feature splicing and re-parameterization convolution module. Meanwhile, a Shape-NWD-SD composite loss function is designed, and a bounding box is modeled as Gaussian distribution. According to the method, the real-time performance of the model is kept, and meanwhile, the detection recall rate and the positioning precision of the small target are remarkably improved.

The invention provides an anti-shake motor, a camera module and electronic equipment, and relates to the technical field of shooting equipment. The anti-shake motor includes a base and a first bracket. The first support is movably connected with the base and can rotate around a first shaft relative to the base. The first axis is parallel to the second direction. The anti-shake motor further comprises a positioning ball. The positioning ball is movably connected between the base and the first support. The base is provided with a positioning groove. The positioning groove comprises three limiting faces. And the three limiting surfaces form three side surfaces of the triangular pyramid. The positioning balls and the positioning grooves are arranged in the second direction, the positioning balls are movably connected into the positioning grooves and make contact with the three limiting faces at the same time, and the first shaft penetrates through the center of the space defined by the three limiting faces. According to the anti-shake motor, positioning of the first shaft is achieved through cooperation of the positioning balls and the positioning grooves, friction force between the first support and the base is small, the first support is not prone to clamping stagnation when moving relative to the base, and the control precision of the anti-shake motor is high.

The connecting rod coupling type pyramid lattice structure comprises a core body and a connecting assembly, the core body comprises a plurality of first unit cells and a plurality of second unit cells, each first unit cell comprises a first truss, a second truss, a first straight connecting rod and a first curved connecting rod, and the first trusses and the second trusses are connected in an inserted mode to form a rectangular pyramid structure; the first unit cell comprises a first truss, a second truss, a first straight connecting rod and a first curved connecting rod, the first straight connecting rod is arranged between two inclined rods of the first truss, the first curved connecting rod is arranged between two inclined rods of the second truss, and the second unit cell comprises a third truss, a fourth truss, a second straight connecting rod and a second curved connecting rod; the second curved connecting rod is arranged between the two inclined rods of the fourth truss; the connecting assembly comprises a top frame and a bottom frame which are arranged in a checkerboard shape, the top frame is provided with a plurality of first mounting parts, and the bottom frame is provided with a plurality of second mounting parts; in this way, the energy absorption effect is better, the good shape and integrity are kept, and expandability is high.

The invention discloses a novel structure construction method using a gradient triple-period minimal surface lattice. The novel structure construction method comprises the following steps: defining a mathematical relationship of an entity part of a TPMS lattice according to a selected scene; the standard TPMS lattice in the original cube is deformed and matched into the pyramid space, a transition structure is formed, and a final TPMS distortion structure is output; connecting the upper-layer small unit lattices with the lower-layer large unit lattices by the TPMS twisted structure to generate gradient lattices without geometric defects; the gradient between different unit numbers is controlled by controlling the size of the top plane of the pyramid; the size of the transition area is adjusted, the transformation completion distance of the substructure is changed, and then the elastic modulus is adjusted and controlled; the number of layers of the base structure in the transition area is increased, and the buffering distance of structure transformation is prolonged. The problems that in the prior art, a gradient lattice transition area has geometric defects, the adjustable range of elastic modulus is small, and stress is concentrated are solved, and the stability and durability of the structure are improved.

The invention relates to a microprism reflective film with three kinds of triangular pyramid combinations and a manufacturing method of a mold of the microprism reflective film, the surface of the microprism reflective film is formed by an array of identical parallelogram unit bodies (1), each parallelogram unit body (1) is composed of four identical triangular pyramids A, one triangular pyramid B and three identical triangular pyramids C, the directions of the four triangular pyramids A and the directions of the three triangular pyramids C and the triangular pyramids B form 180 degrees, and the three triangular pyramids C and the triangular pyramids B are arranged among the four triangular pyramids A in a staggered manner; the bottom surfaces of the triangular pyramids A, B and C are the same equilateral triangles, the inclination angles of the three pyramids of each triangular pyramid A are alpha / 2, the inclination angles of the three pyramids of each triangular pyramid B are beta-alpha / 2, and the inclination angles of the three pyramids of each triangular pyramid C are alpha / 2, alpha / 2 and beta-alpha / 2 respectively. According to the method, the performance under different application conditions can be reasonably optimized, so that wide-angle performance improvement and optimization and balance of overall comprehensive performance can be realized.