CN116052088B

Movatterモバイル変換

Info

Publication number: CN116052088B
Application number: CN202310200539.0A
Authority: CN
Inventors: 茹冰倩; 李早; 金昭; 储文峰
Original assignee: Hefei University of Technology
Current assignee: Hefei University of Technology
Priority date: 2023-03-06
Filing date: 2023-03-06
Publication date: 2023-06-16
Anticipated expiration: 2043-03-06
Also published as: CN116052088A

Abstract

The invention relates to the technical field of spatial vitality evaluation, solves the technical problem of lower accuracy of current vitality spatial evaluation, in particular to a vitality spatial measurement method based on point cloud, which comprises the following steps: acquiring real public space point cloud data in a community to be measured at a measurement time point; preprocessing the public space point cloud data to obtain preprocessed public space simplified point cloud data; inputting the common space simplified point cloud data into an improved deep neural network for characteristic analysis to obtain a plurality of point cloud blocks of different categories, and screening human point cloud from the point cloud blocks according to human skeleton characteristics; and counting the number of public space personnel in the community to be measured according to the labeled human body point cloud. According to the invention, the human body point cloud is extracted from the real public space point cloud data by adopting the convolutional neural network of the attention mechanism of the embedded graph, and the spatial vitality is comprehensively evaluated from the number of people in the public space and the motion state of the people, so that the vitality spatial evaluation accuracy is improved.

Description

Translated fromChinese

基于点云的活力空间测度方法、系统及计算机设备Vitality space measurement method, system and computer equipment based on point cloud

技术领域technical field

本发明涉及空间活力评测技术领域，尤其涉及一种基于点云的活力空间测度方法、系统及计算机设备。The invention relates to the technical field of space vitality evaluation, in particular to a method, system and computer equipment for measuring vitality space based on point clouds.

背景技术Background technique

公共空间活力是指公共空间中可观察到的人及其活动，因而对公共空间活力进行评测能够直观的反映某个社区的空间品质，以便帮助社区规划者和管理者定量评价一个规划区域或改造区域的设计策略及改建模式，进而对社区的发展建设具有重要意义。目前，现有的公共空间活力的测度方法主要有以下两种：The vitality of public space refers to the observable people and their activities in the public space. Therefore, the evaluation of the vitality of public space can directly reflect the spatial quality of a certain community, so as to help community planners and managers to quantitatively evaluate a planning area or renovation. The design strategy and reconstruction mode of the region are of great significance to the development and construction of the community. At present, there are two main methods for measuring the vitality of public space:

一种是利用手机信令数据以及POI密度等多源数据的统计分析，以人群活动密度作为测度指标直接评测公共空间活力指数，但该种测度方法仅从人群聚集程度为指标对空间活力进行了评测，忽视了对人体活动状态的评价，存在一定的片面性，从而降低了空间活力指数的准确性；One is to use the statistical analysis of multi-source data such as mobile phone signaling data and POI density to directly evaluate the vitality index of public space with crowd activity density as the measurement index, but this measurement method only evaluates the space vitality from the degree of crowd gathering The evaluation ignores the evaluation of human activity status, which has a certain one-sidedness, thus reducing the accuracy of the spatial vitality index;

一种是从公共空间的设施构成、环境舒适度以及功能混合性等方面间接的评测公共空间活力指数，但该种测度方法仅根据人类活动所需环境设施的客观角度对空间活力进行了理论评测，没有进行事实验证，使得空间活力指数易于出现较大偏差。One is to indirectly evaluate the public space vitality index from the aspects of public space facility composition, environmental comfort, and functional mix, but this measurement method only theoretically evaluates the space vitality based on the objective perspective of the environmental facilities required by human activities , without factual verification, making the spatial vitality index prone to large deviations.

发明内容Contents of the invention

针对现有技术的不足，本发明提供了一种基于点云的活力空间测度方法、系统及计算机设备，解决了目前活力空间评测准确度较低的技术问题，达到了通过根据真实的包含人员状态的公共空间点云数据对公共空间活力进行评测以提高空间活力评测准确度的目的。Aiming at the deficiencies of the prior art, the present invention provides a point cloud-based vitality space measurement method, system and computer equipment, which solves the technical problem of low accuracy in the current vitality space evaluation, and achieves The public space point cloud data is used to evaluate the public space vitality to improve the accuracy of space vitality evaluation.

为解决上述技术问题，本发明提供了如下技术方案：一种基于点云的活力空间测度方法，包括以下步骤：In order to solve the above-mentioned technical problems, the present invention provides the following technical solutions: a method for measuring vitality space based on point clouds, comprising the following steps:

S1、获取测度时间点下由图像采集设备所采集的待测度社区内公共空间点云数据；S1. Obtain the point cloud data of the public space in the community to be measured collected by the image acquisition device at the measurement time point;

S2、获取测度时间点下由图像采集设备所采集的待测度社区内公共空间点云数据；S2. Obtain the point cloud data of the public space in the community to be measured collected by the image acquisition device at the measurement time point;

S3、将所述公共空间精简点云数据输入改进的深度神经网络进行特征分析以获得若干个不同类别的点云块，再根据人体骨架特征从所述点云块中筛选出人体点云并进行标注；S3. Input the simplified point cloud data in the public space into the improved deep neural network for feature analysis to obtain several different types of point cloud blocks, and then screen out the human body point cloud from the point cloud blocks according to the characteristics of the human skeleton and perform mark;

S4、根据标注的人体点云统计所述待测度社区内的公共空间人员数量，并计算所述公共空间人员数量与所述待测度社区内的常住人员数量的比值R；S4. Count the number of people in the public space in the community to be measured according to the marked human body point cloud, and calculate the ratio R of the number of people in the public space to the number of permanent residents in the community to be measured;

S5、分析每个所述人体点云的姿态以统计公共空间中运动人员数量，并计算运动人员数量与所述公共空间人员数量的比值Y；S5. Analyzing the posture of each point cloud of the human body to count the number of athletes in the public space, and calculating the ratio Y of the number of athletes to the number of people in the public space;

S6、根据预设的外出人员阈值W和活跃度阈值H对所述待测度社区的活力空间进行评测；S6. Evaluate the vitality space of the community to be measured according to the preset out-going threshold W and activity threshold H;

若R＞W且Y＞H，则将该测度时间点下所述待测度社区内的公共空间标记为“高活力空间”；If R>W and Y>H, mark the public space in the community to be measured under the measurement time point as "high vitality space";

若R＞W且Y≤H，则将该测度时间点下所述待测度社区内的公共空间标记为“中活力空间”；If R>W and Y≤H, mark the public space in the community to be measured under the measurement time point as "medium vitality space";

若R≤W且Y≤H，则将该测度时间点下所述待测度社区内的公共空间标记为“低活力空间”。If R≤W and Y≤H, the public space in the community to be measured at the measurement time point is marked as "low vitality space".

进一步地，在步骤S2中，对公共空间点云数据进行预处理，得到预处理后的公共空间精简点云数据的具体过程包括以下步骤：Further, in step S2, the specific process of preprocessing the public space point cloud data to obtain the preprocessed public space simplified point cloud data includes the following steps:

S21、对所述公共空间点云数据进行噪声剔除操作，得到去噪后公共空间点云数据；S21. Perform a noise removal operation on the public space point cloud data to obtain public space point cloud data after denoising;

S22、对所述去噪后公共空间点云数据进行稀疏化操作，得到公共空间精简点云数据。S22. Perform a thinning operation on the denoised public space point cloud data to obtain simplified public space point cloud data.

进一步地，在步骤S3中，将所述公共空间精简点云数据输入改进的深度神经网络进行特征分析以获得若干个不同类别的点云块，再根据人体骨架特征从所述点云块中筛选出人体点云并进行标注的具体过程包括以下步骤：Further, in step S3, the simplified point cloud data in the public space is input into the improved deep neural network for feature analysis to obtain several different types of point cloud blocks, and then screened from the point cloud blocks according to the characteristics of the human skeleton The specific process of extracting the point cloud of the human body and labeling it includes the following steps:

S31、根据所述公共空间精简点云数据采用编码卷积单元进行多尺度邻域特征分析以提取全局特征信息；S31. According to the simplified point cloud data in the public space, a coded convolution unit is used to perform multi-scale neighborhood feature analysis to extract global feature information;

S32、采用图注意力单元对所述全局特征信息进行分析，得到不同尺度下的局部特征信息；S32. Using a graph attention unit to analyze the global feature information to obtain local feature information at different scales;

S33、根据所述全局特征信息和局部特征信息采用特征融合与分类单元进行语义分割，得到不同类别的点云块；S33. Using feature fusion and classification units to perform semantic segmentation according to the global feature information and local feature information, to obtain point cloud blocks of different categories;

S34、根据人体骨架特征采用阈值法从所述点云块中筛选出人体点云并进行标注。S34. Screen out the point cloud of the human body from the point cloud block by using a threshold method according to the characteristics of the human skeleton and mark it.

进一步地，在步骤S5中，分析每个所述人体点云的姿态以统计公共空间中运动人员数量的具体过程包括以下步骤：Further, in step S5, the specific process of analyzing the posture of each said human body point cloud to count the number of athletes in the public space includes the following steps:

根据人体骨架结构从每个所述人体点云中提取人体关键关节点；Extract key joint points of the human body from each of the human body point clouds according to the human body skeleton structure;

计算所述人体关键关节点中任意相邻两个关节点之间的三维距离，并将大于预设阈值的数据判定为运动特征数据；Calculating the three-dimensional distance between any two adjacent joint points in the key joint points of the human body, and determining data greater than a preset threshold as motion feature data;

根据所述运动特征数据确定人体运动姿态特征向量；Determining a human body motion posture feature vector according to the motion feature data;

根据所述人体运动姿态特征向量的累积得到公共空间中运动人员数量。The number of athletes in the public space is obtained according to the accumulation of the feature vectors of the human body movement postures.

进一步地，在步骤S6之后还包括：Further, after step S6, it also includes:

S7、将所述公共空间精简点云数据投影至对应的地理信息图进行空间降维，并对人体点云对应的坐标点绘制相应的颜色，得到用于表示空间活跃度的活力热点图。S7. Project the simplified point cloud data in the public space to the corresponding geographic information map for spatial dimensionality reduction, and draw corresponding colors for the coordinate points corresponding to the human body point cloud to obtain a vitality heat map for representing the spatial activity.

进一步地，在步骤S7中，将所述公共空间精简点云数据投影至对应的地理信息图进行空间降维，并对所述人体点云对应的坐标点绘制相应的颜色，得到用于表示空间活跃度的活力热点图的具体过程包括以下步骤：Further, in step S7, the public space condensed point cloud data is projected to the corresponding geographic information map for spatial dimensionality reduction, and corresponding colors are drawn for the coordinate points corresponding to the human body point cloud, so as to obtain The specific process of the activity heat map includes the following steps:

S71、将所述公共空间精简点云数据沿z轴正投影至对应的地理信息图进行空间降维，得到二维点云图；S71. Forward projecting the simplified point cloud data in the public space to the corresponding geographic information map along the z-axis to perform spatial dimension reduction to obtain a two-dimensional point cloud map;

S72、根据每个所述人体点云的姿态在所述二维点云图中对应的格栅处绘制相应的颜色；S72. Draw a corresponding color at the corresponding grid in the two-dimensional point cloud image according to the pose of each point cloud of the human body;

S73、重复上述步骤将所述二维点云图中所有与人体点云对应的格栅绘制上相应的颜色，得到用于表示空间活跃度的活力热点图。S73. Repeat the above steps to draw corresponding colors on all the grids corresponding to the human body point cloud in the two-dimensional point cloud image to obtain a vitality heat map for representing the spatial activity.

进一步地，所述人体关键关节点包括15个，分别是头部、颈部、左肩、右肩、骶椎、左肘、左手、右肘、右手、左髋、右髋、左膝、右膝、左脚和右脚对应的关节点。Further, the key joint points of the human body include 15 points, namely head, neck, left shoulder, right shoulder, sacrum, left elbow, left hand, right elbow, right hand, left hip, right hip, left knee, right knee , the joint points corresponding to the left foot and the right foot.

本发明还提供了一种用于实现上述基于点云的活力空间测度方法的系统，包括：The present invention also provides a system for realizing the above point cloud-based vitality space measurement method, including:

点云数据获取模块，所述点云数据获取模块用于获取测度时间点下由图像采集设备所采集的待测度社区内公共空间点云数据；A point cloud data acquisition module, the point cloud data acquisition module is used to obtain the public space point cloud data collected by the image acquisition device under the measurement time point in the community to be measured;

点云数据预处理模块，所述点云数据预处理模块用于对公共空间点云数据进行预处理，得到预处理后的公共空间精简点云数据；A point cloud data preprocessing module, the point cloud data preprocessing module is used to preprocess the public space point cloud data, and obtain the preprocessed public space simplified point cloud data;

人体点云提取模块，所述人体点云提取模块用于将所述公共空间精简点云数据输入改进的深度神经网络进行特征分析以获得若干个不同类别的点云块，再根据人体骨架特征从所述点云块中筛选出人体点云并进行标注；A human body point cloud extraction module, the human body point cloud extraction module is used to input the simplified point cloud data of the public space into the improved deep neural network for feature analysis to obtain several different types of point cloud blocks, and then according to the human body skeleton features from Human body point cloud is screened out from the point cloud block and marked;

第一计算模块，所述第一计算模块用于根据标注的人体点云统计所述待测度社区内的公共空间人员数量，并计算所述公共空间人员数量与所述待测度社区内的常住人员数量的比值R；The first calculation module, the first calculation module is used to count the number of people in the public space in the community to be measured according to the marked human body point cloud, and calculate the number of people in the public space and the number of people in the community to be measured The ratio R of the number of permanent residents;

第二计算模块，所述第二计算模块用于分析每个所述人体点云的姿态以统计公共空间中运动人员数量，并计算运动人员数量与所述公共空间人员数量的比值Y；A second calculation module, the second calculation module is used to analyze the posture of each of the human body point clouds to count the number of athletes in the public space, and calculate the ratio Y of the number of athletes to the number of people in the public space;

活力评测模块，所述活力评测模块用于根据预设的外出人员阈值W和活跃度阈值H对所述待测度社区的活力空间进行评测。A vitality evaluation module, the vitality evaluation module is used to evaluate the vitality space of the community to be measured according to the preset threshold W of people going out and the threshold H of activity.

进一步地，还包括：Further, it also includes:

活力热点图绘制模块，所述活力热点图绘制模块用于将所述公共空间精简点云数据投影至对应的地理信息图进行空间降维，并对人体点云对应的坐标点绘制相应的颜色，得到用于表示空间活跃度的活力热点图。A vitality heat map drawing module, the vitality heat map drawing module is used to project the simplified point cloud data of the public space to the corresponding geographic information map for spatial dimensionality reduction, and draw corresponding colors to the coordinate points corresponding to the human body point cloud, Get a vitality heat map for representing spatial activity.

本发明还提供了一种计算机设备，包括处理器和存储器，所述存储器用于存储计算机程序，所述计算机程序被所述处理器执行时实现上述基于点云的活力空间测度方法。The present invention also provides a computer device, including a processor and a memory, the memory is used to store a computer program, and when the computer program is executed by the processor, the above method for measuring vitality space based on point cloud is realized.

借由上述技术方案，本发明提供了一种基于点云的活力空间测度方法、系统及计算机设备，至少具备以下有益效果：By virtue of the above technical solutions, the present invention provides a point cloud-based vitality space measurement method, system and computer equipment, which at least have the following beneficial effects:

1、本发明通过根据雷达采集的待测度社区内真实的公共空间点云数据，采用嵌入图注意力机制的卷积神经网络对公共空间点云数据进行分类提取人体点云，得到测度时间点下的公共空间人员数量，随后根据人体骨架结构特征确定人体点云中每个点云块的姿态特征，得到待测度社区公共空间中的运动人员数量，最后根据公共空间人员数量与待测度社区内的常住人员数量的比值以及运动人员数量与公共空间人员数量的比值，从两个方面对待测度社区内公共空间的活力进行了客观评测，从而提高了活力空间评测准确度。1. According to the real public space point cloud data collected by the radar in the community to be measured, the present invention uses the convolutional neural network embedded in the graph attention mechanism to classify the public space point cloud data and extract the human body point cloud to obtain the measurement time point The number of people in the public space, and then determine the posture characteristics of each point cloud block in the human body point cloud according to the structural characteristics of the human body skeleton, and obtain the number of athletes in the public space of the community to be measured. The ratio of the number of permanent residents in the community and the ratio of the number of athletes to the number of people in the public space objectively evaluate the vitality of the public space in the community from two aspects, thereby improving the accuracy of the evaluation of the vitality space.

2、本发明通过将待测度社区内真实的公共空间精简点云数据投影至对应的地理信息图进行空间降维，并结合人体点云中每个点云块的姿态，绘制用于表示待测度社区内公共空间活跃度的活力热点图，实现了公共空间信息的展示，为后续社区公共空间活力的提升提供了强有力的支撑。2. The present invention performs spatial dimensionality reduction by projecting the simplified point cloud data of the real public space in the community to be measured to the corresponding geographic information map, and combines the posture of each point cloud block in the human body point cloud to draw the The vitality heat map, which measures the activity of public space in the community, realizes the display of public space information and provides strong support for the subsequent improvement of the vitality of public space in the community.

附图说明Description of drawings

此处所说明的附图用来提供对本申请的进一步理解，构成本申请的一部分，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。在附图中：The drawings described here are used to provide a further understanding of the application and constitute a part of the application. The schematic embodiments and descriptions of the application are used to explain the application and do not constitute an improper limitation to the application. In the attached picture:

图1为本发明实施例一中活力空间测度方法的流程图；Fig. 1 is a flow chart of the vitality space measuring method in the first embodiment of the present invention;

图2为本发明实施例一中改进的深度神经网络的结构框图；Fig. 2 is the structural block diagram of the improved deep neural network in the first embodiment of the present invention;

图3为本发明实施例一中提取人体点云的流程图；Fig. 3 is the flowchart of extracting the human body point cloud in the first embodiment of the present invention;

图4为本发明实施例一中人体骨架关键关节点结构示意图；4 is a schematic structural diagram of the key joint points of the human skeleton in Embodiment 1 of the present invention;

图5为本发明实施例一中活力空间测度系统的原理框图；Fig. 5 is a functional block diagram of the vitality space measurement system in Embodiment 1 of the present invention;

图6为本发明实施例二中活力空间测度方法的流程图；Fig. 6 is a flow chart of the vitality space measurement method in the second embodiment of the present invention;

图7为本发明实施例二中活力空间测度系统的原理框图；Fig. 7 is a functional block diagram of the vitality space measuring system in the second embodiment of the present invention;

图8为本发明实施例中计算机设备的内部结构框图。Fig. 8 is a block diagram of the internal structure of the computer device in the embodiment of the present invention.

图中：10、点云数据获取模块；20、点云数据预处理模块；30、人体点云提取模块；40、第一计算模块；50、第二计算模块；60、活力评测模块；70、活力热点图绘制模块。In the figure: 10, point cloud data acquisition module; 20, point cloud data preprocessing module; 30, human body point cloud extraction module; 40, first calculation module; 50, second calculation module; 60, vitality evaluation module; 70, Vitality heat map drawing module.

具体实施方式Detailed ways

为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图和具体实施方式对本发明作进一步详细的说明。借此对本申请如何应用技术手段来解决技术问题并达成技术功效的实现过程能充分理解并据以实施。In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. In this way, the realization process of how the application applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

场景概述Scenario overview

随着城镇化的发展，新小区不断拔地而起的同时老旧小区的数量也越来越多，由于老旧小区建造年代悠久、基础设施落后，致使居民生活条件差，与周围新小区极不协调，若拆除重建则不符合可持续发展原则，故需对老旧小区进行合理改造。老旧小区的改造重点是道路、景观广场和健身广场等公共空间区域，而公共空间的活力指数能够直观的反映某个老旧小区的空间品质，进而能够为社区改造者和管理者提供决策性指导意义。With the development of urbanization, the number of old communities is also increasing while new communities are constantly rising. Due to the long construction period and backward infrastructure of the old communities, the living conditions of the residents are poor, which is very different from the surrounding new communities. If it is not coordinated, demolition and reconstruction will not conform to the principle of sustainable development, so it is necessary to carry out reasonable reconstruction of the old community. The renovation of old communities focuses on public space areas such as roads, landscape squares, and fitness squares, and the vitality index of public spaces can directly reflect the spatial quality of an old community, which in turn can provide decision-making information for community renovators and managers. Guiding significance.

在对老旧小区公共空间活力进行测度时，为了准确评测老旧小区公共空间的活力指数，不仅要评测公共空间聚焦的人员数量还要考虑人员的运动状态，为此，本申请提出根据待测度社区真实的公共空间点云数据统计公共空间聚集人员数量及其运动状态，实现对公共空间活力的全面评测，以提高活力空间评测的准确度。When measuring the vitality of the public space in the old community, in order to accurately evaluate the vitality index of the public space in the old community, it is necessary not only to evaluate the number of people in the public space but also to consider the movement status of the people. The point cloud data of the real public space in the community counts the number of people gathered in the public space and their movement status, so as to realize a comprehensive evaluation of the vitality of the public space and improve the accuracy of the evaluation of the vitality space.

需要说明的是，上述老旧小区公共空间活力评测仅仅是本申请实施例提供的活力空间测度方法的一种示例性应用场景，在本申请实施例中并不限定活力空间测度方法的具体应用场景，例如，还可以应用在旅游景区空间活力的评测中。It should be noted that the above public space vitality evaluation of old residential quarters is only an exemplary application scenario of the vitality space measurement method provided by the embodiment of the present application, and the specific application scenario of the vitality space measurement method is not limited in the embodiment of the present application , for example, can also be applied to the evaluation of the spatial vitality of tourist attractions.

实施例一Embodiment one

请参照图1-图4，示出了本实施例的一种具体实施方式，本实施例通过根据真实的公共空间点云数据提取公共空间人员数量，并根据点云块姿态特征确定运动人员数量，从公共空间人员数量与待测度社区内的常住人员数量的比值以及运动人员数量与公共空间人员数量的比值两个方面对公共空间活力进行客观评测，提高了活力空间评测的准确度。Please refer to FIG. 1-FIG. 4, which shows a specific implementation of this embodiment. This embodiment extracts the number of people in public space according to the real public space point cloud data, and determines the number of sports people according to the point cloud block posture characteristics , from the ratio of the number of people in the public space to the number of permanent residents in the community to be measured, and the ratio of the number of sports people to the number of people in the public space, the vitality of the public space is objectively evaluated, which improves the accuracy of the evaluation of the vitality space.

如图1所示，一种基于点云的活力空间测度方法，包括以下步骤：As shown in Figure 1, a point cloud-based vitality space measurement method includes the following steps:

S1、获取测度时间点下由图像采集设备所采集的待测度社区内公共空间点云数据。S1. Obtain the point cloud data of the public space in the community to be measured collected by the image collection device at the measurement time point.

具体的，通过采用搭载激光扫描仪的无人机在测度时间点下对待测度社区内的公共空间进行扫描，得到包含公共空间内所有物体表面特征信息的三维点云数据

，并将三维点云数据/>

实时传输至计算机设备，为后续进行空间活力评测提供数据支撑，其中，三维点云数据/>

可表示为：/>

，式中，/>

表示三维点云数据/>

中所含有的信息点的总数量。Specifically, by using a drone equipped with a laser scanner to scan the public space in the community to be measured at the measurement time point, the 3D point cloud data containing the surface feature information of all objects in the public space is obtained

, and the 3D point cloud data />

Real-time transmission to computer equipment to provide data support for subsequent space vitality evaluation, among which, 3D point cloud data/>

Can be expressed as: />

, where, />

Represents 3D point cloud data/>

The total number of information points contained in .

值得注意的是，活力空间的评测主要有三个评测指标：人员数量、活动状态和停留时间，也就是说，公共空间的活力指数与人密切相关，为此，本实施例根据人们的作息规律选取从早上六点到晚上十点每个整点时刻作为测度时间点

。It is worth noting that there are three main evaluation indicators for the evaluation of vitality space: the number of people, activity status and stay time. That is to say, the vitality index of public space is closely related to people. Therefore, this embodiment selects Every hour from 6:00 am to 10:00 pm is used as the measurement time point

.

S2、对公共空间点云数据进行预处理，得到预处理后的公共空间精简点云数据。S2. Preprocessing the public space point cloud data to obtain the preprocessed public space simplified point cloud data.

通过扫描获取的三维点云数据

中包含有一些没有价值的噪声点和冗余点，并且具有很高的密度，如果直接进行语义分割处理则需要耗费大量的时间，致使空间活力评测效率较低，为此，需要通过计算机设备对接收到的三维点云数据/>

进行一系列预处理操作，减小点云数据量的同时确保点云精度，提高后续获取公共空间内人体点云数据的效率及精确性。3D point cloud data acquired by scanning

contains some worthless noise points and redundant points, and has a high density. If the semantic segmentation process is directly performed, it will take a lot of time, resulting in a low efficiency of spatial vitality evaluation. Therefore, it is necessary to use computer equipment to Received 3D point cloud data/>

Perform a series of preprocessing operations to reduce the amount of point cloud data while ensuring the accuracy of the point cloud, and improve the efficiency and accuracy of subsequent acquisition of human body point cloud data in public spaces.

本实施例中，对公共空间点云数据进行预处理的具体过程包括：In this embodiment, the specific process of preprocessing the public space point cloud data includes:

S21、对公共空间点云数据进行噪声剔除操作，得到去噪后公共空间点云数据。S21. Perform a noise removal operation on the point cloud data in the public space to obtain the point cloud data in the public space after denoising.

通过高斯滤波器依次计算公共空间点云数据

内每个点到其领域内最近的/>

个邻近点之间高程距离的平均值/>

，并计算公共空间点云数据/>

中所有点的/>

的均值/>

以及标准差/>

；然后，遍历公共空间点云数据/>

中每个点以判断它们的/>

是否在标准范围

之外，如果在标准差范围之外，则判定该点为噪声点并将其从数据集中剔除，最终得到去噪后公共空间点云数据。Sequentially calculate public space point cloud data through Gaussian filter

within each point to the nearest /> within its domain

The average of the elevation distances between neighboring points/>

, and calculate the public space point cloud data/>

/> for all points in

mean of />

and the standard deviation />

; Then, traverse the public space point cloud data />

Each point in to judge their />

Is it within the standard range

In addition, if it is outside the standard deviation range, the point is judged to be a noise point and removed from the data set, and finally the denoised public space point cloud data is obtained.

S22、对去噪后公共空间点云数据进行稀疏化操作，得到公共空间精简点云数据。S22. Perform a thinning operation on the denoised public space point cloud data to obtain simplified public space point cloud data.

将去噪后公共空间点云划分为多个大小一致的网格，落在每个网格内的所有点组成点云网格，当点云网格的平均网格密度

大于预设阈值时，计算点云网格中所有点的重心，并以距离重心距离最短的点近似代替点云网格中所有的点，采用相同的方法遍历去噪后公共空间点云数据，最终得到确保点云拓扑结构和精度不变的前提下，点云数量明显稀疏化的公共空间精简点云数据。Divide the denoised public space point cloud into multiple grids of the same size, and all the points falling in each grid form a point cloud grid. When the average grid density of the point cloud grid is

When it is greater than the preset threshold, calculate the center of gravity of all points in the point cloud grid, and approximately replace all points in the point cloud grid with the point with the shortest distance from the center of gravity, and use the same method to traverse the public space point cloud data after denoising, Finally, under the premise of ensuring that the topology and accuracy of the point cloud remain unchanged, the public space condensed point cloud data with a significantly sparse number of point clouds is obtained.

由于原始的公共空间点云数据中包含有建筑点、植物点、娱乐设施点和人体点等多种不同类别的点云，因而接下来需要对预处理后的公共空间精简点云数据进行分类，以便精准提取用于评测公共空间活跃度的人体点云。Since the original public space point cloud data contains many different types of point clouds such as building points, plant points, entertainment facility points, and human body points, it is necessary to classify the preprocessed public space simplified point cloud data. In order to accurately extract the point cloud of the human body for evaluating the activity of public spaces.

S3、将公共空间精简点云数据输入改进的深度神经网络进行特征分析以获得若干个不同类别的点云块，再根据人体骨架特征从点云块中筛选出人体点云并进行标注。S3. Input the simplified point cloud data in the public space into the improved deep neural network for feature analysis to obtain several different types of point cloud blocks, and then select the human body point cloud from the point cloud blocks according to the characteristics of the human skeleton and mark them.

下面将结合图2示出的改进的深度神经网络的结构框图对从公共空间精简点云数据中提取人体点云块的具体过程进行详细说明，如图3所示，具体包括以下步骤：The specific process of extracting human body point cloud blocks from the simplified point cloud data in the public space will be described in detail below in conjunction with the structural block diagram of the improved deep neural network shown in Figure 2, as shown in Figure 3, which specifically includes the following steps:

S31、根据公共空间精简点云数据采用编码卷积单元进行多尺度邻域特征分析以提取全局特征信息。S31. Using the coded convolution unit to perform multi-scale neighborhood feature analysis according to the simplified point cloud data in the public space to extract global feature information.

如图2所示，将包含有m个点的公共空间精简点云数据输入改进的深度神经网络，且待测度社区内公共空间精简点云中的每个点具有可以记为

，其中，/>

表示第/>

个点的坐标信息，/>

表示第/>

个点相对于第/>

个场景的位置；本实施例通过利用包括三层卷积层Conv和三层激活层Concat的多层感知机MLP(64,64)从公共空间精简点云数据图像像素中提取特征，得到每个点的64维特征；再通过特征语义编码将特征维度上升至1024维特征空间，且层级之间设有一个池化层，具有减小数据尺寸、加快计算的作用，还能很好的聚合局部特征，防止出现过拟合现象；最后使用最大池化层/>

进行池化操作以得到全局特征信息。As shown in Figure 2, the data of the public space simplified point cloud containing m points is input into the improved deep neural network, and each point in the public space simplified point cloud in the community to be measured has a value that can be written as

, where />

Indicates the first />

coordinate information of a point, />

Indicates the first />

point relative to />

The position of each scene; this embodiment extracts features from the public space simplified point cloud data image pixels by utilizing a multi-layer perceptron MLP (64,64) comprising three layers of convolutional layers Conv and three layers of activation layers Concat, to obtain each The 64-dimensional feature of the point; then the feature dimension is raised to a 1024-dimensional feature space through feature semantic coding, and a pooling layer is set between the layers, which can reduce the data size and speed up the calculation. features to prevent overfitting; finally use the maximum pooling layer />

A pooling operation is performed to obtain global feature information.

需要说明的是，由于图像的局部区域是相关联的，因而多层感知机MLP采用权重共享机制，减少了网络参数，降低网络复杂性，提高了全局特征的提取效率。It should be noted that since the local areas of the image are associated, the multi-layer perceptron MLP adopts a weight sharing mechanism, which reduces network parameters, reduces network complexity, and improves the extraction efficiency of global features.

S32、采用图注意力单元对全局特征信息进行分析，得到不同尺度下的局部特征信息。S32. Using the graph attention unit to analyze the global feature information to obtain local feature information at different scales.

继续参考图2，通过采用包括自注意力模块和偏置注意力模块的四层注意力层

对全局特征信息的局部几何信息进行分析，得到不同尺度下的局部特征信息。Continuing to refer to Figure 2, by adopting a four-layer attention layer including a self-attention module and a bias attention module

The local geometric information of the global feature information is analyzed to obtain the local feature information at different scales.

S33、根据全局特征信息和局部特征信息采用特征融合与分类单元进行语义分割，得到不同类别的点云块。S33. Semantic segmentation is performed using feature fusion and classification units according to the global feature information and local feature information, to obtain point cloud blocks of different categories.

继续参考图2，通过由依次堆叠的三层全卷积层

、一层随机失活层/>

和一层全卷积层/>

组成的特征融合与分类单元对全局特征信息和局部特征信息进行语义分割，最终得到若干个不同类别的点云块，主要包括建筑、植物和人体等类别。Continuing to refer to Figure 2, by stacking three layers of full convolutional layers in sequence

, a random deactivation layer/>

and a fully convolutional layer />

The composed feature fusion and classification unit performs semantic segmentation on the global feature information and local feature information, and finally obtains several different types of point cloud blocks, mainly including buildings, plants, and human bodies.

S34、根据人体骨架特征采用阈值法从点云块中筛选出人体点云并进行标注。S34. Screen out the point cloud of the human body from the point cloud block by using a threshold method according to the characteristics of the human skeleton and mark it.

继续参考图2，通过标签标注单元提取人体点云数据中的有用信息计算人体特征描述子

，同时根据每个点云块的形状特征计算点云块的特征描述子/>

，然后依次比对每个点云块的特征描述子/>

与人体特征描述子/>

的相似度/>

，并筛选出相似度/>

大于预设相似度阈值/>

的点云块，并标注为“1”，否则标注为“0”，最终获得标注有标签的点云块。其中，相似度/>

的表达式如下所示：Continuing to refer to Figure 2, the human body feature descriptor is calculated by extracting useful information in the human body point cloud data through the tagging unit

, and at the same time calculate the feature descriptor of the point cloud block according to the shape feature of each point cloud block />

, and then sequentially compare the feature descriptors of each point cloud block/>

and human body feature descriptors/>

similarity of />

, and filter out the similarity />

Greater than the preset similarity threshold />

The point cloud block of , and mark it as "1", otherwise mark it as "0", and finally get the point cloud block marked with the label. Among them, similarity />

The expression for is as follows:

上式中，

表示人体点云形状特征，/>

表示采样点云块/>

的形状特征。In the above formula,

Represents the shape feature of the point cloud of the human body, />

Represents a sampling point cloud block />

shape features.

需要说明的是，相似度阈值

的取值可根据实际分类经验及精度要求进行设置，在此不作具体限定。It should be noted that the similarity threshold

The value of can be set according to actual classification experience and accuracy requirements, and is not specifically limited here.

S4、根据标注的人体点云统计待测度社区内的公共空间人员数量，并计算公共空间人员数量与待测度社区内的常住人员数量的比值R。S4. Count the number of people in the public space in the community to be measured according to the marked human body point cloud, and calculate the ratio R of the number of people in the public space to the number of permanent residents in the community to be measured.

在步骤S34中将人体点云块的标签标注为“1”，因而统计标注为“1”的点云块的数量，即为待测度社区内的公共空间人员数量；根据人口大普查或常住人口登记表可获得待测度社区内的常住人员数量，经计算可得到公共空间人员数量与待测度社区内的常住人员数量的比值R。In step S34, the label of the point cloud block of the human body is marked as "1", so the number of point cloud blocks marked as "1" is counted, which is the number of people in the public space in the community to be measured; according to the population census or permanent residents The population registration form can obtain the number of permanent residents in the community to be measured, and the ratio R of the number of people in the public space to the number of permanent residents in the community to be measured can be obtained through calculation.

S5、分析每个人体点云的姿态以统计公共空间中运动人员数量，并计算运动人员数量与公共空间人员数量的比值Y。S5. Analyze the posture of each human body point cloud to count the number of athletes in the public space, and calculate the ratio Y of the number of athletes to the number of people in the public space.

本实施例中，统计公共空间中运动人员数量的具体步骤包括：In this embodiment, the specific steps of counting the number of athletes in the public space include:

第一步，根据人体骨架结构从人体点云的每个点云块中提取人体关键关节点。In the first step, the key joint points of the human body are extracted from each point cloud block of the human body point cloud according to the human skeleton structure.

根据人体骨架结构可知，每个关节点相对于彼此的位置限定了人体的姿态，也就是说，用于限定骨架的关节点越多可以估计的人体姿态就越多，但这将导致人体姿态的估算时间越长，为此，本实施例选取15个关键关节点对人体点云中每个点云块的姿态进行估算。如图4所示，15个关键关节点分别是头关节点

、颈部关节点/>

、左肩关节点/>

、右肩关节点/>

、骶椎关节点/>

、左肘关节点/>

、左手关节点/>

、右肘关节点/>

、右手关节点/>

、左髋关节点/>

、右髋关节点/>

、左膝关节点/>

、右膝关节点/>

、左脚关节点/>

、右脚关节点/>

。According to the structure of the human skeleton, the position of each joint point relative to each other defines the posture of the human body, that is to say, the more joint points used to define the skeleton, the more human postures can be estimated, but this will lead to the The longer the estimation time, for this reason, this embodiment selects 15 key joint points to estimate the pose of each point cloud block in the human body point cloud. As shown in Figure 4, the 15 key joint points are head joint points

, neck joint point />

, left shoulder joint />

, right shoulder joint />

, sacral joint point/>

, left elbow joint />

, left hand joint />

, right elbow joint />

, right hand joint point />

, left hip joint />

, right hip joint />

, left knee point />

, right knee joint />

, left foot joint />

, right foot joint />

.

第二步，计算人体关键关节点中任意相邻两个关节点之间的三维距离，并将大于预设阈值的数据判定为运动特征数据。例如通过计算左肩关节点

和左肘关节点/>

以及左肘关节点/>

和左手关节点/>

的三维距离，可判定人体左臂的运动姿态。The second step is to calculate the three-dimensional distance between any two adjacent joint points in the key joint points of the human body, and determine the data greater than the preset threshold as motion feature data. For example by calculating the left shoulder joint point

and left elbow joint />

and the left elbow point />

and the left hand joint node />

The three-dimensional distance can determine the motion posture of the left arm of the human body.

第三步，根据运动特征数据确定人体运动姿态特征向量；也就是说，根据人体各个主干部位的运动特征可综合分析出人体运动姿态，例如下蹲、奔跑、静止站立以及弯腰等姿态。The third step is to determine the feature vector of human motion posture according to the motion feature data; that is to say, according to the motion characteristics of each main part of the human body, the motion posture of the human body can be comprehensively analyzed, such as squatting, running, standing still, and bending over.

第四步，根据人体运动姿态特征向量的累积，可得到公共空间中运动人员数量。In the fourth step, according to the accumulation of the feature vectors of human motion postures, the number of sports people in the public space can be obtained.

根据上述步骤统计出公共空间中运动人员数量后，经计算可得到运动人员数量与公共空间人员数量的比值Y。After counting the number of athletes in the public space according to the above steps, the ratio Y of the number of athletes to the number of people in the public space can be obtained through calculation.

S6、根据预设的外出人员阈值W和活跃度阈值H对待测度社区的活力空间进行评测；S6. Evaluate the vitality space of the community to be measured according to the preset out-going threshold W and activity threshold H;

若R＞W且Y＞H，则将该测度时间点下待测度社区内的公共空间标记为“高活力空间”；If R>W and Y>H, mark the public space in the community to be measured at the measurement time point as "high vitality space";

若R＞W且Y≤H，则将该测度时间点下待测度社区内的公共空间标记为“中活力空间”；If R>W and Y≤H, mark the public space in the community to be measured at the measurement time point as "medium vitality space";

若R≤W且Y≤H，则将该测度时间点下待测度社区内的公共空间标记为“低活力空间”。If R≤W and Y≤H, the public space in the community to be measured at the measurement time point is marked as "low vitality space".

请参照图5，本实施例还提供一种用于实现上述基于点云的活力空间测度方法的系统，包括：Please refer to FIG. 5, this embodiment also provides a system for implementing the above point cloud-based vitality space measurement method, including:

点云数据获取模块10，用于获取测度时间点下由图像采集设备所采集的待测度社区内公共空间点云数据；The point clouddata acquisition module 10 is used to acquire the public space point cloud data in the community to be measured collected by the image acquisition device at the measurement time point;

点云数据预处理模块20，用于对公共空间点云数据进行预处理，得到预处理后的公共空间精简点云数据；The point clouddata preprocessing module 20 is used to preprocess the public space point cloud data, and obtain the preprocessed public space simplified point cloud data;

人体点云提取模块30，用于将公共空间精简点云数据输入改进的深度神经网络进行特征分析以获得若干个不同类别的点云块，再根据人体骨架特征从点云块中筛选出人体点云并进行标注；The human body pointcloud extraction module 30 is used to input the simplified point cloud data in the public space into the improved deep neural network for feature analysis to obtain several different types of point cloud blocks, and then screen the human body points from the point cloud blocks according to the characteristics of the human body skeleton cloud and label it;

第一计算模块40，用于根据标注的人体点云统计待测度社区内的公共空间人员数量，并计算公共空间人员数量与待测度社区内的常住人员数量的比值R；Thefirst calculation module 40 is used to count the number of people in the public space in the community to be measured according to the marked human body point cloud, and calculate the ratio R of the number of people in the public space to the number of permanent residents in the community to be measured;

第二计算模块50，用于分析人体点云中每个点云块的姿态以统计公共空间中运动人员数量，并计算运动人员数量与公共空间人员数量的比值Y；Thesecond calculation module 50 is used to analyze the posture of each point cloud block in the human body point cloud to count the number of athletes in the public space, and calculate the ratio Y of the number of athletes to the number of people in the public space;

活力评测模块60，用于根据预设的外出人员阈值W和活跃度阈值H对待测度社区的活力空间进行评测；Thevitality evaluation module 60 is used to evaluate the vitality space of the community to be measured according to the preset threshold W of out-goers and the threshold H of activity;

通过本实施例，通过根据雷达采集的待测度社区内真实的公共空间点云数据，采用嵌入图注意力机制的卷积神经网络对公共空间点云数据进行分类提取人体点云，得到测度时间点下的公共空间人员数量，随后根据人体骨架结构特征确定人体点云中每个点云块的姿态特征，得到待测度社区公共空间中的运动人员数量，最后根据公共空间人员数量与待测度社区内的常住人员数量的比值以及运动人员数量与公共空间人员数量的比值，从两个方面对待测度社区内公共空间的活力进行了客观评测，从而提高了活力空间评测准确度。Through this embodiment, according to the real public space point cloud data collected by the radar in the community to be measured, the convolutional neural network embedded in the graph attention mechanism is used to classify the public space point cloud data and extract the human body point cloud to obtain the measurement time The number of people in the public space under the point, and then determine the posture characteristics of each point cloud block in the human body point cloud according to the structural characteristics of the human body skeleton, and obtain the number of athletes in the public space of the community to be measured. Finally, according to the number of people in the public space and the number of people to be measured The ratio of the number of permanent residents in the community and the ratio of the number of athletes to the number of people in the public space objectively evaluate the vitality of the public space in the community from two aspects, thereby improving the accuracy of the evaluation of the vitality space.

实施例二Embodiment two

本实施例所提供的实施方式是在实施例一的基础上做出的，相同部分能够解决相同的技术问题，并且具有相同的有益效果，相互参见即可，在本实施例中将不再展开详细赘述。The implementation provided in this embodiment is made on the basis of Embodiment 1. The same parts can solve the same technical problem and have the same beneficial effect, just refer to each other, and will not be expanded in this embodiment Go into details.

请参照图6，示出了本实施例的一种具体实施方式，本实施例通过根据待测度社区内公共空间在地理信息图中的二维坐标对真实的公共空间精简点云数据进行空间降维，并根据公共空间内真实的人体状态绘制活力热点图，为后续提升公共空间活力提供了强有力的数据支撑。Please refer to FIG. 6, which shows a specific implementation of this embodiment. In this embodiment, according to the two-dimensional coordinates of the public space in the geographic information map in the community to be measured, the real public space simplified point cloud data is spatially analyzed. Dimensionality reduction, and draw a vitality hotspot map based on the real human body state in the public space, providing a strong data support for the subsequent improvement of the vitality of the public space.

图6所示，一种基于点云的活力空间测度方法，包括以下步骤：As shown in Figure 6, a point cloud-based vitality space measurement method includes the following steps:

S7、将公共空间精简点云数据投影至对应的地理信息图进行空间降维，并对人体点云对应的坐标点绘制相应的颜色，得到用于表示空间活跃度的活力热点图。S7. Project the simplified point cloud data of the public space to the corresponding geographic information map for spatial dimensionality reduction, and draw corresponding colors for the coordinate points corresponding to the human body point cloud to obtain a vitality heat map for representing the spatial activity.

S71、将公共空间精简点云数据沿z轴正投影至对应的地理信息图进行空间降维，得到二维点云图。S71. Orthographically project the simplified point cloud data in the public space to the corresponding geographic information map along the z-axis to perform spatial dimensionality reduction to obtain a two-dimensional point cloud map.

具体的，找到待测度社区内公共空间在地理信息图中的二维坐标，并将公共空间精简点云数据与待测度社区内公共空间在地理信息图中的位置进行配准，再将公共空间精简点云数据沿z轴正投影至对应的地理信息图进行空间降维，最终得到包含真实人体运动状态的二维点云图。Specifically, find the two-dimensional coordinates of the public space in the community to be measured in the geographic information map, and register the simplified point cloud data of the public space with the position of the public space in the community to be measured in the geographic information map, and then The simplified point cloud data in the public space is orthographically projected along the z-axis to the corresponding geographic information map for spatial dimensionality reduction, and finally a two-dimensional point cloud image containing the real human motion state is obtained.

S72、根据人体点云中每个点云块的姿态在二维点云图中对应的格栅处绘制相应的颜色。S72. Draw a corresponding color at the corresponding grid in the two-dimensional point cloud image according to the posture of each point cloud block in the human body point cloud.

根据人体点云数据的当前姿态特征采用相应的颜色对二维点云图中对应的格栅进行标识，颜色越深代表相应区域人员运动特征越明显，颜色越淡代表相应区域人员静止特征越明显，且无色区域代表没有人员聚集。According to the current posture characteristics of the human point cloud data, the corresponding color is used to mark the corresponding grid in the two-dimensional point cloud image. The darker the color, the more obvious the movement characteristics of the people in the corresponding area, and the lighter the color, the more obvious the static characteristics of the people in the corresponding area. And the colorless area represents no gathering of people.

S73、重复上述步骤将二维点云图中所有与人体点云对应的格栅绘制上相应的颜色，得到用于表示空间活跃度的活力热点图。S73. Repeat the above steps to draw corresponding colors on all the grids corresponding to the human body point cloud in the two-dimensional point cloud image to obtain a vitality heat map for representing the spatial activity.

通过绘制表示待测度社区内公共空间活跃度的活力热点图并输出，能够为社区改造人员提供决策支撑，从而提高了社区公共空间活力提升的工作效率，具有较高的社会价值和应用前景。By drawing and outputting a vitality heat map representing the activity of public space in the community to be measured, it can provide decision-making support for community renovation personnel, thereby improving the work efficiency of community public space vitality improvement, and has high social value and application prospects.

请参照图7，本实施例还提供一种用于实现上述基于点云的活力空间测度方法的系统，包括：Please refer to FIG. 7 , this embodiment also provides a system for implementing the above point cloud-based vitality space measurement method, including:

若R≤W且Y≤H，则将该测度时间点下待测度社区内的公共空间标记为“低活力空间”；If R≤W and Y≤H, mark the public space in the community to be measured at the measurement time point as "low vitality space";

活力热点图绘制模块70，用于将公共空间精简点云数据投影至对应的地理信息图进行空间降维，并对人体点云对应的坐标点绘制相应的颜色，得到用于表示空间活跃度的活力热点图。The vitality heatmap drawing module 70 is used to project the simplified point cloud data in the public space to the corresponding geographic information map for spatial dimensionality reduction, and draw corresponding colors to the coordinate points corresponding to the human body point cloud to obtain the data used to represent the spatial activity. Vitality heat map.

通过本实施例，通过根据雷达采集的待测度社区内真实的公共空间点云数据，采用嵌入图注意力机制的卷积神经网络对公共空间点云数据进行分类提取人体点云，得到测度时间点下的公共空间人员数量，随后根据人体骨架结构特征确定人体点云中每个点云块的姿态特征，得到待测度社区公共空间中的运动人员数量，再通过将待测度社区内真实的公共空间精简点云数据投影至对应的地理信息图进行空间降维，并结合人体点云中每个点云块对应的运动姿态特征向量确定人体状态，绘制用于表示待测度社区内公共空间活跃度的活力热点图，实现了公共空间信息的展示，为后续社区公共空间活力的提升提供了强有力的支撑。Through this embodiment, according to the real public space point cloud data collected by the radar in the community to be measured, the convolutional neural network embedded in the graph attention mechanism is used to classify the public space point cloud data and extract the human body point cloud to obtain the measurement time The number of people in the public space under the point, and then determine the posture characteristics of each point cloud block in the human body point cloud according to the structural characteristics of the human body skeleton, and obtain the number of athletes in the public space of the community to be measured, and then pass the real The public space simplified point cloud data is projected to the corresponding geographic information map for spatial dimensionality reduction, and the motion posture feature vector corresponding to each point cloud block in the human body point cloud is used to determine the human body state, which is drawn to represent the public space in the community to be measured. The vitality heat map of spatial activity realizes the display of public space information and provides strong support for the subsequent improvement of the vitality of public space in the community.

本实施例还提供了一种计算机设备，下面将结合图8对计算机设备的内部结构进行说明，该计算机设备包括通过系统总线连接的处理器、存储器、网络接口、显示屏以及输入装置。This embodiment also provides a computer device. The internal structure of the computer device will be described below with reference to FIG. 8 . The computer device includes a processor connected through a system bus, a memory, a network interface, a display screen, and an input device.

处理器是计算机设备的控制中心，利用各种接口和线路连接整个设备的各个部分，并通过运行或执行存储在存储器内的计算机可读指令和/或模块，以及调用存储在存储器内的数据，实现计算机设备的各种功能；此处所涉及的处理器可以是中央处理单元CPU，还可以是其它通用处理器、数字信号处理器DSP、专用集成电路ASIC、现成可编程门阵列FPGA或者其它可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等；其中，通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。The processor is the control center of the computer equipment, which uses various interfaces and lines to connect various parts of the entire equipment, and by running or executing computer-readable instructions and/or modules stored in the memory, and calling data stored in the memory, Realize various functions of computer equipment; the processor involved here can be a central processing unit CPU, or other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable Logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.; wherein, the general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

存储器用于存储计算机可读指令和/或模块，主要包括存储介质和内存储器，存储介质可以是非易失性存储介质，也可以是易失性存储介质，存储介质存储有操作系统，还可存储有计算机可读指令，该计算机可读指令被处理器执行时，可使得处理器实现基于点云的活力空间测度方法，例如，图1和图6所示的步骤S1至步骤S7及该方法的其它扩展和相关步骤的延伸；或者，处理器执行计算机可读指令时实现上述实施例中基于点云的活力空间测度系统的各模块/单元的功能，如图7所示模块10至模块70的功能，为避免重复，这里不再展开赘述。The memory is used to store computer-readable instructions and/or modules, mainly including a storage medium and an internal memory. The storage medium can be a non-volatile storage medium or a volatile storage medium. The storage medium stores an operating system and can also store There are computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor can be implemented to measure the vitality space based on the point cloud, for example, steps S1 to S7 shown in Figure 1 and Figure 6 and the method Other extensions and extensions of related steps; or, when the processor executes computer-readable instructions, the functions of each module/unit of the point cloud-based vitality space measurement system in the above-mentioned embodiments are realized, as shown in Figure 7.Modules 10 to 70 function, in order to avoid repetition, it is not repeated here.

网络接口用于与外部服务器通过网络连接通信；显示屏可以是液晶显示屏或者电子墨水显示屏；输入装置可以是显示屏上覆盖的触摸层，也可以是计算机设备外壳上设置的按键、轨迹球或触控板，还可以是外接的键盘、触控板或鼠标等。The network interface is used to communicate with an external server through a network connection; the display screen can be a liquid crystal display screen or an electronic ink display screen; the input device can be a touch layer covered on the display screen, or a button or a trackball set on the casing of the computer equipment or a touchpad, or an external keyboard, touchpad, or mouse.

需要说明的是，存储器可以集成在处理器中，也可以与处理器分开设置，图8中所示出的结构，仅仅是与本申请方案相关的部分结构的示意框图，并不构成对本申请方案所应用于其上的计算机设备的限定，具体的计算机设备可以包括比图中所示更多或更少的部件，或者组合某些部件，或者采用不同的部件布置。It should be noted that the memory can be integrated in the processor, or it can be set separately from the processor. The structure shown in FIG. 8 is only a schematic block diagram of a part of the structure related to the solution of this application, and does not constitute a reference to the solution of this application. The limitations of the computer device to which it applies, a specific computer device may include more or fewer components than shown in the figure, or combine certain components, or adopt a different arrangement of components.

上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。通过以上实施方式的描述，本领域的普通技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上所述的一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，或者网络设备等)执行本申请各个实施例所述的方法。The serial numbers of the above embodiments of the present application are for description only, and do not represent the advantages and disadvantages of the embodiments. Through the description of the above embodiments, those of ordinary skill in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product in essence or the part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM) as described above. , magnetic disk, optical disk), including several instructions to enable a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present application.

以上实施方式对本发明进行了详细介绍，本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本发明的限制。The above embodiments have described the present invention in detail. The principles and implementation methods of the present invention have been described by using specific examples herein. The descriptions of the above embodiments are only used to help understand the method of the present invention and its core idea; meanwhile, for Those skilled in the art will have changes in the specific implementation and scope of application according to the idea of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.