摘要：【中文】本发明公开了一种基于装配式理念的逆作法，涉及建筑施工技术领域，包括以下步骤：将底筒锤击打入地下，将底筒内部的土体挖出；将多片式护筒依次放置在底筒的上方，并向下锤击位于上层多片式护筒，将底筒和多片式护筒内的土体挖出；在底筒的下方挖桩基；在底筒和多片式护筒内加入内衬材料，插入钢筋，浇灌混凝土；在第一层多片式护筒的上方装配第一层梁板；在第一层梁板的一侧向下开挖地下空间，将第一层多片式护筒和及其所在地层的内衬材料拆除，然后在第二层多片式护筒的上方装配第二层梁板；拆除底筒和及其所在地层的内衬材料。本发明的有益效果是，装配式构件是在工厂里预制的，能最大限度地改善墙体开裂、渗漏等质量通病，减少反工成本。 【EN】The invention discloses a reverse construction method based on an assembly type concept, which relates to the technical field of building construction and comprises the following steps: hammering the bottom cylinder into the ground, and digging out soil inside the bottom cylinder; sequentially placing the multi-piece protective barrels above the bottom barrel, hammering the multi-piece protective barrels on the upper layer downwards, and digging out soil in the bottom barrel and the multi-piece protective barrels; digging a pile foundation below the bottom barrel; adding lining materials into the bottom barrel and the multi-piece protective barrel, inserting steel bars, and pouring concrete; assembling a first layer of beam plates above the first layer of multi-piece pile casing; downwards excavating an underground space at one side of the first layer of beam slab, removing the first layer of multi-piece type casing and lining materials of a stratum where the first layer of multi-piece type casing is located, and then assembling a second layer of beam slab above the second layer of multi-piece type casing; and removing the bottom cylinder and the lining material of the stratum where the bottom cylinder is located. The invention has the advantages that the assembly type components are prefabricated in a factory, the common quality problems of wall cracking, leakage and the like can be improved to the maximum extent, and the reverse construction cost is reduced.

摘要：【中文】本发明公开了一种带剪力齿的装配式剪力墙，涉及建筑施工设计技术领域，包括至少两个剪力墙模块，两相邻所述剪力墙模块的相对一端均设置至少一个剪力齿，至少一个所述剪力墙模块设有与剪力齿相通的浇注孔。本发明的有益效果是，墙体两端带剪力齿，浇筑完成后在上下墙之间形成质量可靠，可直观检测的抗剪键，抗剪键为整体受力，很好连接上下墙；剪力墙自带浇注孔，可采用自密实混凝土浇筑，上下墙体整体性和抗剪性能优异。 【EN】The invention discloses an assembled shear wall with shear teeth, and relates to the technical field of building construction design. The invention has the beneficial effects that the two ends of the wall body are provided with the shear teeth, after the pouring is finished, a shear key with reliable quality and visual detection is formed between the upper wall and the lower wall, and the shear key is integrally stressed and is well connected with the upper wall and the lower wall; the shear wall is provided with the pouring hole, self-compacting concrete can be adopted for pouring, and the integrity and the shearing resistance of the upper wall body and the lower wall body are excellent.

摘要：【中文】本发明公开一种基于传感信息融合的无人驾驶避障方法及系统，本系统用于实现本方法，本方法包括获取在时间段[t₁,t₂]间无人车行驶距离；获取无人车在时间t₁的行驶距离和在时间t₂的距离计算在时间段[t₁,t₂]间无人车行驶距离与无人车至障碍物的距离差值的比例值；将该比例值与预定范围进行比较，若该比例值在预定范围内，则将无人车行驶距离与无人车至障碍物的距离差值加权平均以获取无人车至障碍物的修正距离若该比例值不在预定范围内，则视无人车行驶距离为无人车至障碍物的修正距离根据无人车至障碍物的修正距离控制无人车的避障动作。本发明减少由障碍物深度信息的噪声污染和环境变化导致无人车至障碍物距离的测量误差，提高无人车安全性。 【EN】The invention discloses an unmanned obstacle avoidance method and system based on sensing information fusion₁,t₂]The running distance of the unmanned vehicle is reduced; obtaining the time t of the unmanned vehicle₁Distance traveledAnd at time t₂Is a distance ofCalculating over a time period t₁,t₂]Distance between unmanned vehicles and unmanned vehiclesA proportional value of a distance difference of the obstacle; comparing the ratio value with a preset range, and if the ratio value is in the preset range, carrying out weighted average on the driving distance of the unmanned vehicle and the distance difference between the unmanned vehicle and the obstacle to obtain the corrected distance between the unmanned vehicle and the obstacleIf the ratio value is not in the preset range, the distance between the unmanned vehicle and the obstacle is considered as the correction distanceAccording to the corrected distance from the unmanned vehicle to the obstacleAnd controlling the obstacle avoidance action of the unmanned vehicle. The invention reduces the measurement error of the distance between the unmanned vehicle and the obstacle caused by the noise pollution of the depth information of the obstacle and the environmental change, and improves the safety of the unmanned vehicle.

摘要：【中文】本发明公开了一种基于深度置信网络的电网虚拟数据注入攻击的检测方法，包括：S1：构建深度置信网络检测模型，所述深度置信网络检测模型包括有：若干层RBM网络、单层BP神经网络，所述RBM网络采用全连接方式连接；S2：获取IEEE标准节点测量数据，对获取数据添加随机噪声、攻击向量生成攻击数据；S3：将攻击数据进行归一化后的数据按设定比例分为测试数据集和训练数据集；S4：利用无监督学习对RBM网络进行从上而下逐层训练，并通过BP神经网络反向传播误差进行模型参数调整，得到训练后的深度置信网络检测模型；S5：将测试数据集输入训练后的深度置信网络检测模型，输出预测结果。本发明克服了传统检测方法对检测阈值的依赖，实现对多种攻击模式检测。 【EN】The invention discloses a method for detecting power grid virtual data injection attack based on a deep belief network, which comprises the following steps: s1: constructing a deep confidence network detection model, wherein the deep confidence network detection model comprises the following steps: the RBM network comprises a plurality of layers of RBM networks and a single-layer BP neural network, wherein the RBM networks are connected in a full connection mode; s2: acquiring IEEE standard node measurement data, and adding random noise and an attack vector to the acquired data to generate attack data; s3: dividing the data after normalization of the attack data into a test data set and a training data set according to a set proportion; s4: the method comprises the steps that an RBM (radial basis function) network is trained layer by layer from top to bottom by unsupervised learning, model parameters are adjusted through back propagation errors of a BP (back propagation) neural network, and a trained deep belief network detection model is obtained; s5: and inputting the test data set into the trained deep confidence network detection model, and outputting a prediction result. The invention overcomes the dependence of the traditional detection method on the detection threshold value and realizes the detection of various attack modes.

摘要：【中文】本发明公开了一种基于IMU的uwb定位异常值处理方法，包括步骤：S1.获取无人机的IMU数据；S2.计算无人机的姿态角度；S3.计算X、Y、Z轴的加速度以及△t时间内X、Y、Z轴上的位移，估计出无人机的位移值Q；对uwb测量值进行异常检测；S5.对异常的uwb测量值进行数据融合，修正uwb测量值。本发明解决了现有的原始数据预处理过程中的异常值判别不准确以及异常值修正的精度地等问题，提高了测量硬件数据准确度。而且本发明不需要外加额外硬件设备，增强了uwb传感器在复杂环境下测量数据的稳定性，同时为后面的uwb定位解算提供有效精确的测量输出数据，提高uwb定位的稳定性，快速性以及实时性。 【EN】The invention discloses an IMU-based uwb positioning abnormal value processing method which comprises the steps of S1, obtaining IMU data of an unmanned aerial vehicle, S2, calculating an attitude angle of the unmanned aerial vehicle, S3, calculating X, Y, Z axis acceleration and X, Y, Z axis displacement within △ t time, estimating a displacement value Q of the unmanned aerial vehicle, carrying out abnormal detection on uwb measurement values, and S5, carrying out data fusion on the abnormal uwb measurement values and correcting the uwb measurement values.

摘要：【中文】本发明提出一种基于轻量型神经网络的无人机自主避障方法，包括以下步骤：采集模拟无人机搭载相机飞行的视频数据作为训练数据；对训练数据进行预处理；采用轻量型卷积神经网络架构构建卷积神经网络，将经过预处理的训练数据输入卷积神经网络中进行训练；将完成训练的卷积神经网络应用于无人机的处理器中，无人机中的单目相机将实时采集的视频帧数据传输给处理器中，视频帧数据通过处理器中的卷积神经网络后输出得到碰撞概率，处理器根据输出的碰撞概率对当前无人机的飞行速度进行调制，且当无人机飞行速度降低至预设的最低速度时，无人机沿机身的y轴平移，实现无人机的自主避障。本发明还提出了一种基于轻量型神经网络的无人机自主避障系统。 【EN】The invention provides an unmanned aerial vehicle autonomous obstacle avoidance method based on a light weight type neural network, which comprises the following steps: collecting video data simulating the flight of a camera carried by an unmanned aerial vehicle as training data; preprocessing training data; constructing a convolutional neural network by adopting a lightweight convolutional neural network architecture, and inputting preprocessed training data into the convolutional neural network for training; in being applied to unmanned aerial vehicle's treater with the convolution neural network who accomplishes the training, monocular camera among the unmanned aerial vehicle transmits the video frame data of real-time collection for the treater in, the video frame data is exported behind the convolution neural network in the treater and is obtained the collision probability, the treater modulates current unmanned aerial vehicle's flying speed according to the collision probability of output, and when unmanned aerial vehicle flying speed reduced to predetermined minimum velocity, unmanned aerial vehicle translated along the y axle of fuselage, realize unmanned aerial vehicle's autonomic obstacle avoidance. The invention further provides an unmanned aerial vehicle autonomous obstacle avoidance system based on the light weight type neural network.

摘要：【中文】本发明公开了一种基于GPU加速的无人机视觉SLAM方法，首先进行相机图像信息的读取和预处理；执行前端任务：采用特征点法估计位姿；包括：相机图像的特征提取和特征匹配，采用ICP算法估算位姿；进行后端优化：基于图优化的后端优化进而优化位姿；进行词袋回环检测，构建和优化位姿图；其中步骤相机图像的特征提取和特征匹配在GUP上进行，相机图像的特征提取和特征匹配之后的步骤使用OpenCV实现CUDA并行化。本发明方法在现有的嵌入式小型GPU的基础上完成视觉SLAM前端部分的特征提取和特征匹配任务，SLAM算法的其它部分集中在CPU进行并行计算，在不降低定位精度的前提下，提高算法计算速度，减少无人机的体积与重量。 【EN】The invention discloses an unmanned aerial vehicle vision SLAM method based on GPU acceleration, which comprises the steps of firstly reading and preprocessing camera image information; executing a front-end task: estimating the pose by adopting a characteristic point method; the method comprises the following steps: extracting and matching the features of the camera image, and estimating the pose by adopting an ICP (inductively coupled plasma) algorithm; carrying out back-end optimization: optimizing the pose based on the back-end optimization of the graph optimization; performing bag-of-words loop detection, and constructing and optimizing a pose graph; the method comprises the steps of performing feature extraction and feature matching of camera images on GUP, and using OpenCV to realize CUDA parallelization in the steps after the feature extraction and the feature matching of the camera images. The method completes the task of feature extraction and feature matching of the front end part of the visual SLAM on the basis of the existing embedded small GPU, and other parts of the SLAM algorithm are centralized in a CPU for parallel calculation, so that the calculation speed of the algorithm is improved and the volume and the weight of the unmanned aerial vehicle are reduced on the premise of not reducing the positioning precision.

摘要：【中文】本发明公开了一种基于深度点线特征的无人机视觉里程计方法，包括以下步骤：S1：进行深度相机的标定，获取深度相机的内参矩阵和畸变系数；S2：利用深度相机获取视觉图像，提取视觉图像的关键点特征，并将关键点的像素坐标转化为三维坐标；S3：提取视觉图像的线特征；S4：利用平均视差法和跟踪质量法在视觉图像中提取当前的关键帧；S5：利用已提取的关键点特征和线特征对视觉图像相邻帧进行特征匹配，利用ICP算法估计设备的运动位姿；S6：对估计的运动位姿进行局部优化，输出位姿。本发明提高了无人机位姿估计的准确度。 【EN】The invention discloses an unmanned aerial vehicle visual odometer method based on depth point-line characteristics, which comprises the following steps: s1: calibrating the depth camera to obtain an internal reference matrix and a distortion coefficient of the depth camera; s2: acquiring a visual image by using a depth camera, extracting key point characteristics of the visual image, and converting pixel coordinates of key points into three-dimensional coordinates; s3: extracting line features of the visual image; s4: extracting a current key frame from the visual image by using an average parallax method and a tracking quality method; s5: performing feature matching on adjacent frames of the visual image by using the extracted key point features and the extracted line features, and estimating the motion pose of the equipment by using an ICP (inductively coupled plasma) algorithm; s6: and carrying out local optimization on the estimated motion pose and outputting the pose. The invention improves the accuracy of pose estimation of the unmanned aerial vehicle.

摘要：【中文】本发明涉及一种基于高分子表面改性制备超疏水纸张的方法，依次包括以下步骤：（1）将纸张基材表面清洁，按设计要求裁剪备用；（2）称取聚乙烯/聚氯乙烯/聚苯乙烯颗粒，加入含有二甲苯溶剂的烧杯中，搅拌、加热使高分子充分溶解，形成透明均匀溶液，再加入丙酮持续搅拌至混合均匀，配成浸渍液；（3）将准备好的纸张基材竖直插入上述浸渍液中，用浸渍提拉机设置浸渍次数为1‑5次，浸渍时间为1‑5min，浸渍完成后匀速将滤纸提拉出来，烘干，即得接触角≥150°的超疏水纸张；本发明制备方法简单，所需制备时间短、成本低、环境友好，可以利用各种纸张基材制得超疏水纸张，产品稳定性较好，室外放置三月仍具有良好的自清洁效果，适合工业化规模化生产。 【EN】The invention relates to a method for preparing super-hydrophobic paper based on polymer surface modification, which sequentially comprises the following steps: (1) cleaning the surface of a paper substrate, and cutting the paper substrate for later use according to design requirements; (2) weighing polyethylene/polyvinyl chloride/polystyrene particles, adding the polyethylene/polyvinyl chloride/polystyrene particles into a beaker containing a xylene solvent, stirring and heating to fully dissolve macromolecules to form a transparent uniform solution, and adding acetone to continuously stir until the mixture is uniformly mixed to prepare an impregnation solution; (3) vertically inserting the prepared paper substrate into the impregnation liquid, setting the impregnation times to be 1-5 times by using an impregnation lifting machine, wherein the impregnation time is 1-5min, lifting and pulling out the filter paper at a constant speed after the impregnation is finished, and drying to obtain the super-hydrophobic paper with a contact angle of more than or equal to 150 ℃; the preparation method is simple, short in required preparation time, low in cost and environment-friendly, the super-hydrophobic paper can be prepared by utilizing various paper substrates, the product stability is good, the self-cleaning effect is good even after the super-hydrophobic paper is placed outdoors for three months, and the method is suitable for industrial large-scale production.

摘要：【中文】本发明属于网络技术领域，是一种Ad Hoc路由中选择使用较少的节点以减少通信干扰的方法，其特征在于：包括下列步骤：第一步，在MANETs网络中使用基于位置的路由；第二步，引入保守邻域范围；第三步，选择使用较少的节点做为下一跳节点。本发明提出了一种新的方法即GBR‑CNR‑LU，来提高GBR方法的性能，仿真实验表明提出的方法显著提高了包传递的性能，确保了更高的网络稳定性。显著提高了包传递的性能，确保了更高的网络稳定，提高了通信可靠性。 【EN】The invention belongs to the technical field of networks, and discloses a method for reducing communication interference by selecting and using fewer nodes in an Ad Hoc route, which is characterized by comprising the following steps: comprises the following steps: a first step of using location-based routing in a MANETs network; secondly, introducing a conservative neighborhood range; and thirdly, selecting and using fewer nodes as next hop nodes. The invention provides a new method, namely GBR-CNR-LU, to improve the performance of GBR method, and simulation experiments show that the proposed method significantly improves the packet transfer performance and ensures higher network stability. The method has the advantages of remarkably improving the performance of packet transmission, ensuring higher network stability and improving the communication reliability.