CN116828156B - Geospatial event acquisition method, system, equipment, medium and acquisition box - Google Patents

Geospatial event acquisition method, system, equipment, medium and acquisition box Download PDF

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CN116828156B
CN116828156B CN202311107558.5A CN202311107558A CN116828156B CN 116828156 B CN116828156 B CN 116828156B CN 202311107558 A CN202311107558 A CN 202311107558A CN 116828156 B CN116828156 B CN 116828156B
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CN116828156A (en
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李亚东
曹明兰
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Beijing Zhongyuda Information Technology Co ltd
Beijing University of Technology
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Beijing University of Technology
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Abstract

本发明提出了地理空间事件的采集方法、系统、设备、介质及采集盒,涉及空间信息技术领域。其包括:执行循环过程直至满足对象检测模型达到收敛的预设条件;其中,循环过程包括:根据无人机图传的实时画面,控制该无人机持续采集包括监测目标的第一图像数据;将根据第一图像数据进行人工标注所得到的样本数据在云服务器中进行构建并训练对应的对象检测模型。根据收敛的对象检测模型对第二图像数据进行计算推理,得到监测目标对应的空间数据,其中,第二图像数据为在对象检测模型收敛后,无人机采集得到的包括监测目标的图像数据。根据空间数据生成对应的地理空间事件。该方案能够保证低耦合的情况下,实时的进行生成地理空间事件数据。

The present invention proposes a method, system, device, medium and collection box for collecting geospatial events, and relates to the field of spatial information technology. It includes: executing a loop process until the preset condition that the object detection model reaches convergence is met; wherein the loop process includes: according to the real-time picture of the drone image transmission, controlling the drone to continuously collect first image data including the monitoring target; constructing and training the corresponding object detection model in the cloud server based on the sample data obtained by manual annotation according to the first image data. According to the converged object detection model, the second image data is calculated and inferred to obtain the spatial data corresponding to the monitoring target, wherein the second image data is the image data including the monitoring target collected by the drone after the object detection model converges. Generate the corresponding geospatial event according to the spatial data. This solution can ensure the real-time generation of geospatial event data under low coupling.

Description

地理空间事件的采集方法、系统、设备、介质及采集盒Method, system, device, medium and collection box for collecting geospatial events

技术领域Technical Field

本发明涉及空间信息技术领域,具体而言,涉及地理空间事件的采集方法、系统、设备、介质及采集盒。The present invention relates to the field of spatial information technology, and in particular to a method, system, device, medium and collection box for collecting geospatial events.

背景技术Background technique

随着地理空间信息理论与技术的不断发展,智慧城市建设也在逐步推进。在这个背景下,社会对地理空间数据的需求也从历史、现状性基础数据逐渐提升到了实时性数据的层面。With the continuous development of geospatial information theory and technology, the construction of smart cities is also gradually advancing. In this context, the society's demand for geospatial data has gradually increased from historical and current basic data to real-time data.

目前通常采用的方法是先利用无人机进行外业航测,然后回到机房使用专业软件对无人机航测数据进行一系列处理,以获取DOM、DEM等标准产品。最后,利用这些标准产品进行应用和分析。这种方式不仅影响了数据的实时性,而且难以形成事件类型的数据。The commonly used method at present is to use drones to conduct field surveys first, and then use professional software to process the drone survey data in the computer room to obtain standard products such as DOM and DEM. Finally, these standard products are used for application and analysis. This method not only affects the real-time nature of the data, but also makes it difficult to form event-type data.

发明内容Summary of the invention

本发明的目的在于提供地理空间事件的采集方法、系统、设备、介质及采集盒,其能够保证低耦合的情况下,实时的进行生成地理空间事件数据。The purpose of the present invention is to provide a method, system, device, medium and collection box for collecting geospatial events, which can ensure the real-time generation of geospatial event data under low coupling.

本发明是这样实现的:The present invention is achieved in that:

第一方面,本申请提供一种地理空间事件的采集方法,包括以下步骤:In a first aspect, the present application provides a method for collecting geospatial events, comprising the following steps:

执行循环过程直至满足预设条件;其中,上述预设条件为对象检测模型达到收敛,上述循环过程包括:根据无人机图传的实时画面,控制该无人机持续采集包括监测目标的第一图像数据;根据上述第一图像数据进行人工标注,得到对应的样本数据;根据上述样本数据在云服务器中进行构建并训练对应的对象检测模型。根据收敛的对象检测模型对第二图像数据进行计算推理,得到监测目标对应的空间数据,其中,第二图像数据为在对象检测模型收敛后,无人机采集得到的包括监测目标的图像数据。根据上述空间数据生成对应的地理空间事件。The loop process is executed until the preset conditions are met; wherein the preset conditions are that the object detection model reaches convergence, and the loop process includes: according to the real-time picture of the drone image transmission, the drone is controlled to continuously collect the first image data including the monitoring target; according to the first image data, manual annotation is performed to obtain the corresponding sample data; according to the sample data, the corresponding object detection model is constructed and trained in the cloud server. According to the converged object detection model, the second image data is calculated and inferred to obtain the spatial data corresponding to the monitoring target, wherein the second image data is the image data including the monitoring target collected by the drone after the object detection model converges. The corresponding geospatial event is generated according to the above spatial data.

进一步地,基于前述方案,该方法还包括以下步骤:根据GIS空间分析算法对上述地理空间事件进行空间分析,得到对应的事件分析结果。Furthermore, based on the aforementioned scheme, the method further includes the following steps: performing spatial analysis on the above-mentioned geographic spatial events according to a GIS spatial analysis algorithm to obtain corresponding event analysis results.

进一步地,基于前述方案,上述空间数据包括几何形状信息、位置信息、事件标识、事件类别和时间信息中的至少一种。Further, based on the aforementioned scheme, the above-mentioned spatial data includes at least one of geometric shape information, location information, event identification, event category and time information.

第二方面,本申请提供一种地理空间事件的采集方法,包括以下步骤:In a second aspect, the present application provides a method for collecting geospatial events, comprising the following steps:

执行循环过程直至满足预设条件;其中,上述预设条件为对象检测模型收敛,上述循环过程包括:基于地面站观测到的无人机图传的实时画面,规划无人机的飞行线路,并将无人机在该飞行线路持续采集的包括监测目标的第三图像数据,发送至云服务器中;基于上述第三图像数据进行人工标注,得到对应的样本数据,以在云服务器中进行训练和验证对应的对象检测模型。采集盒在收到训练至收敛的对象检测模型后,利用该对象检测模型对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,得到对应的空间数据,并将基于上述空间数据生成的地理空间事件发送至云服务器,以供云服务进行空间分析。The loop process is executed until the preset conditions are met; wherein the preset conditions are that the object detection model converges, and the loop process includes: planning the flight route of the drone based on the real-time images of the drone image transmission observed by the ground station, and sending the third image data including the monitoring target continuously collected by the drone during the flight route to the cloud server; performing manual annotation based on the third image data to obtain the corresponding sample data to train and verify the corresponding object detection model in the cloud server. After receiving the object detection model trained to convergence, the acquisition box uses the object detection model to detect the target object on the real-time images of the event area to be monitored newly collected by the drone, obtain the corresponding spatial data, and send the geospatial events generated based on the above spatial data to the cloud server for the cloud service to perform spatial analysis.

进一步地,基于前述方案,上述将基于上述空间数据生成的地理空间事件发送至云服务器时包括:基于预定的GIS事件模型将上述空间数据进行转换为符合OGC空间数据标准的数据,并将该数据存储至对应的存储器中和发送至云服务器。Further, based on the above-mentioned scheme, when the geospatial events generated based on the above-mentioned spatial data are sent to the cloud server, it includes: converting the above-mentioned spatial data into data that conforms to the OGC spatial data standard based on a predetermined GIS event model, and storing the data in a corresponding memory and sending it to the cloud server.

第三方面,本申请提供一种采集盒,其包括:In a third aspect, the present application provides a collection box, comprising:

接收模块,被配置为:接收云服务器训练至收敛的对象检测模型,其中云服务器训练上述对象检测模型的步骤为:持续接收无人机在执行对应飞行线路中实时采集的包括监测目标的第四图像数据,并基于对应人员对该第四图像数据持续进行人工标注所得到的样本数据,进行训练和验证对应的对象检测模型,直至上述对象检测模型收敛为止。处理模块,被配置为:利用接收的云服务器训练至收敛的对象检测模型,对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,以得到目标事件对应的空间数据。发送模块,被配置为:将上述空间数据发送至云服务器和/或目标终端设备,以供云服务和/或目标终端设备进行空间分析。The receiving module is configured to: receive the object detection model trained to convergence by the cloud server, wherein the step of the cloud server training the above object detection model is: continuously receiving the fourth image data including the monitoring target collected in real time by the drone during the execution of the corresponding flight route, and training and verifying the corresponding object detection model based on the sample data obtained by the corresponding personnel continuously manually annotating the fourth image data, until the above object detection model converges. The processing module is configured to: use the received object detection model trained to convergence by the cloud server to perform target object detection on the real-time picture of the event area to be monitored newly collected by the drone to obtain the spatial data corresponding to the target event. The sending module is configured to: send the above spatial data to the cloud server and/or the target terminal device for the cloud service and/or the target terminal device to perform spatial analysis.

第四方面,本申请提供一种地理空间事件的实时采集系统,其包括:In a fourth aspect, the present application provides a real-time collection system for geospatial events, which includes:

模型训练模块,被配置为:执行循环过程直至满足预设条件;其中,上述预设条件为对象检测模型达到收敛,上述循环过程包括:根据无人机图传的实时画面,控制该无人机持续采集包括监测目标的第一图像数据;根据上述第一图像数据进行人工标注,得到对应的样本数据;根据上述样本数据在云服务器中进行构建并训练对应的对象检测模型。空间数据生成模块,被配置为:根据收敛的对象检测模型对第二图像数据进行计算推理,得到监测目标对应的空间数据,其中,第二图像数据为在对象检测模型收敛后,无人机采集得到的包括监测目标的图像数据。空间事件生成模块,被配置为:根据上述空间数据生成对应的地理空间事件。The model training module is configured to: execute a loop process until a preset condition is met; wherein the preset condition is that the object detection model reaches convergence, and the loop process includes: controlling the drone to continuously collect first image data including the monitoring target according to the real-time picture transmitted by the drone; manually annotating the first image data to obtain corresponding sample data; and constructing and training the corresponding object detection model in the cloud server according to the sample data. The spatial data generation module is configured to: perform computational reasoning on the second image data according to the converged object detection model to obtain spatial data corresponding to the monitoring target, wherein the second image data is the image data including the monitoring target collected by the drone after the object detection model converges. The spatial event generation module is configured to: generate corresponding geospatial events according to the above spatial data.

第五方面,本申请提供一种地理空间事件的实时采集系统,其包括:无人机、采集盒、地面站和云服务器。In a fifth aspect, the present application provides a real-time collection system for geospatial events, which includes: a drone, a collection box, a ground station and a cloud server.

上述无人机,用于接收地面站的控制信号,以在待监测事件区域执行对应的飞行线路,并将获取的实时画面传输至上述地面站进行显示,以及将采集的包括监测目标的图像数据发送至上述云服务。上述地面站,用于接收并显示无人机获取的实时画面,并响应于预定操作指令,向无人机发送对应的控制信号。上述采集盒,用于接收云服务器训练至收敛的对象检测模型,并基于该对象检测模型,对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,以得到对应的空间数据,并用以将上述空间数据发送至云服务器。上述云服务器,用于持续接收无人机在执行对应飞行线路中实时采集的包括监测目标的第五图像数据,并基于对应人员对该第五图像数据持续进行人工标注所得到的样本数据,进行训练和验证对应的对象检测模型,直至上述对象检测模型收敛为止;以及用以将训练至收敛的对象检测模型发送至上述采集盒;还用于接收采集盒发送的空间数据,以进行空间分析。The above-mentioned UAV is used to receive the control signal of the ground station to execute the corresponding flight route in the event area to be monitored, and transmit the acquired real-time picture to the above-mentioned ground station for display, and send the collected image data including the monitoring target to the above-mentioned cloud service. The above-mentioned ground station is used to receive and display the real-time picture acquired by the UAV, and send the corresponding control signal to the UAV in response to the predetermined operation instruction. The above-mentioned acquisition box is used to receive the object detection model trained to convergence by the cloud server, and based on the object detection model, perform target object detection on the real-time picture of the event area to be monitored newly acquired by the UAV to obtain corresponding spatial data, and send the above-mentioned spatial data to the cloud server. The above-mentioned cloud server is used to continuously receive the fifth image data including the monitoring target collected in real time by the UAV in the execution of the corresponding flight route, and train and verify the corresponding object detection model based on the sample data obtained by the corresponding personnel continuously manually annotating the fifth image data until the above-mentioned object detection model converges; and send the object detection model trained to convergence to the above-mentioned acquisition box; and also receive the spatial data sent by the acquisition box for spatial analysis.

第六方面,本申请提供一种电子设备,包括至少一个处理器、至少一个存储器和数据总线;其中:上述处理器与上述存储器通过上述数据总线完成相互间的通信;上述存储器存储有被上述处理器执行的程序指令,上述处理器调用上述程序指令以执行如上述第一方面和第二方面中任一项所述的方法。In a sixth aspect, the present application provides an electronic device comprising at least one processor, at least one memory and a data bus; wherein: the processor and the memory communicate with each other via the data bus; the memory stores program instructions to be executed by the processor, and the processor calls the program instructions to execute a method as described in any one of the first and second aspects above.

第七方面,本申请提供一种计算机可读存储介质,其上存储有计算机程序,该计算机程序被处理器执行时实现如上述第一方面和第二方面中任一项所述的方法。In a seventh aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of the first and second aspects above.

相对于现有技术,本发明至少具有如下优点或有益效果:Compared with the prior art, the present invention has at least the following advantages or beneficial effects:

其首先根据无人机图传的实时画面,控制该无人机持续采集包括监测目标的图像数据,然后对该图像数据进行人工标注后,在云服务器中进行训练对应的对象检测模型,从而可以快速获得与监测目标相关的收敛的对象检测模型。进而,接下来就可以利用该收敛的对象检测模型,对无人机接下来获取的图像数据进行计算推理,以得到能够用以生成地理空间事件的检测目标对应的空间数据。即,该技术方案能够可以简单方便的获取到实时性强的地理空间事件,便于对突发性的事件进行监测。First, based on the real-time image transmitted by the drone, the drone is controlled to continuously collect image data including the monitored target. Then, after manually annotating the image data, the corresponding object detection model is trained in the cloud server, so that a converged object detection model related to the monitored target can be quickly obtained. Then, the converged object detection model can be used to perform computational reasoning on the image data acquired by the drone next, so as to obtain spatial data corresponding to the detection target that can be used to generate geospatial events. In other words, this technical solution can easily and conveniently obtain real-time geospatial events, which is convenient for monitoring sudden events.

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

为了更清楚地说明本发明实施例的技术方案,下面将对实施例中所需要使用的附图作简单地介绍,应当理解,以下附图仅示出了本发明的某些实施例,因此不应被看作是对范围的限定,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他相关的附图。In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

图1为本发明一实施例提供的地理空间事件的采集方法的流程图;FIG1 is a flow chart of a method for collecting geospatial events provided by an embodiment of the present invention;

图2为本发明又一实施例提供的地理空间事件的采集方法的流程图;FIG2 is a flow chart of a method for collecting geospatial events provided by another embodiment of the present invention;

图3为本发明一实施例提供的采集盒的结构框图;FIG3 is a structural block diagram of a collection box provided by an embodiment of the present invention;

图4为本发明一实施例提供的地理空间事件的实时采集系统的结构框图;FIG4 is a structural block diagram of a real-time collection system for geospatial events provided by an embodiment of the present invention;

图5为本发明一实施例提供的地理空间事件的实时采集系统一实施例的对应的信令图;FIG5 is a corresponding signaling diagram of an embodiment of a real-time collection system for geospatial events provided by an embodiment of the present invention;

图6为本发明一实施例提供的一种电子设备的结构框图。FIG6 is a structural block diagram of an electronic device provided by an embodiment of the present invention.

图标:101、处理器;102、存储器;103、数据总线。Icon: 101, processor; 102, memory; 103, data bus.

具体实施方式Detailed ways

为使本申请实施例的目的、技术方案和优点更加清楚,下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本申请一部分实施例,而不是全部的实施例。通常在此处附图中描述和示出的本申请实施例的组件可以以各种不同的配置来布置和设计。In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

下面结合附图,对本申请的一些实施方式作详细说明。在不冲突的情况下,下述的各个实施例及实施例中的各个特征可以相互组合。同时,在本申请的描述中,出现的术语“第一”、“第二”等仅用于区分描述,而不能理解为指示或暗示相对重要性。Some embodiments of the present application are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and the features in the embodiments may be combined with each other. At the same time, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

实施例1Example 1

在现有技术中,是对感兴趣的目标物进行预先训练模型,然后将该模型设置在无人机上,然后利用该无人机去现场进行监测目标物。然而,这种方式不能做到利用无人机进行实时监测,只适合对通用性的目标物进行监测,却不能胜任突发性的事件的监测任务。In the prior art, a model is pre-trained for the target object of interest, and then the model is set on a drone, which is then used to monitor the target object on site. However, this method cannot achieve real-time monitoring using drones, and is only suitable for monitoring general targets, but not for monitoring sudden events.

为了应对上述问题,在本申请实施例的第一方面,提供了一种地理空间事件的采集方法,其通过优化处理流程,从而能够获取实时性强的地理空间事件,便于对突发性的事件进行监测。In order to address the above-mentioned problems, in a first aspect of an embodiment of the present application, a method for collecting geospatial events is provided, which can obtain real-time geospatial events by optimizing the processing flow, thereby facilitating the monitoring of sudden events.

请参阅图1,该一种地理空间事件的采集方法包括以下步骤:Referring to FIG. 1 , the method for collecting geospatial events includes the following steps:

步骤S101:执行循环过程直至满足预设条件;其中,所述预设条件为对象检测模型达到收敛,所述循环过程包括:根据无人机图传的实时画面,控制该无人机持续采集包括监测目标的第一图像数据;根据所述第一图像数据进行人工标注,得到对应的样本数据;根据所述样本数据在云服务器中进行构建并训练对应的对象检测模型。Step S101: Execute a loop process until a preset condition is met; wherein the preset condition is that the object detection model reaches convergence, and the loop process includes: according to the real-time picture of the drone image transmission, controlling the drone to continuously collect first image data including the monitoring target; manually annotating according to the first image data to obtain corresponding sample data; and constructing and training a corresponding object detection model in the cloud server according to the sample data.

上述步骤中,在控制无人机飞行到待检测事件区域(包括监测目标的区域),进行实时采集包括监测目标的图像数据,然后利用采集的包括监测目标的图像数据,用以了解现场的具体情况。接着,就可以根据监测目标进行观察图传的实时画面,进行操控无人机进行执行相应的飞行线路,并且在途中进行不断采集包括监测目标的第一图像数据。示例性地,可以通过操作人员在地面站的显示器上进行观测无人机图传的实时画面,然后人工的进行观察图传的实时画面,进而根据观察的情况实时操控无人机进行执行相应的飞行线路,从而可以结合人工的因素利用无人机精准的获取大量的包括监测目标的第一图像数据。In the above steps, when the drone is controlled to fly to the event area to be detected (including the area of the monitoring target), real-time image data including the monitoring target is collected, and then the collected image data including the monitoring target is used to understand the specific situation on the scene. Then, the real-time picture of the image transmission can be observed according to the monitoring target, and the drone can be controlled to execute the corresponding flight route, and the first image data including the monitoring target can be continuously collected on the way. Exemplarily, the operator can observe the real-time picture of the drone image transmission on the display of the ground station, and then manually observe the real-time picture of the image transmission, and then control the drone in real time to execute the corresponding flight route according to the observed situation, so that a large amount of first image data including the monitoring target can be accurately obtained by using the drone in combination with artificial factors.

在将第一图像数据上传至云服务器后,相应的工作人员可以在云服务器中对该第一图像数据进行人工标注处理,也可以利用客户端工具不停的从云服务器中获取最新的第一图像数据,并进行标注,用以得到对应的样本数据。然后,在获取了样本数据的基础上,即可开始利用该样本数据在云服务器中进行构建并训练对应的对象检测模型,其在训练的过程中需不断的获取新的样本数据,并进行训练和验证该模型,直至模型收敛后跳出循环。After uploading the first image data to the cloud server, the corresponding staff can manually annotate the first image data in the cloud server, or use the client tool to continuously obtain the latest first image data from the cloud server and annotate it to obtain the corresponding sample data. Then, based on the obtained sample data, the corresponding object detection model can be constructed and trained in the cloud server using the sample data. During the training process, new sample data needs to be continuously obtained, and the model needs to be trained and verified until the model converges and exits the loop.

需要说明的是,之所以需要训练对象检测模型,主要是因为监测目标的提前不既定导致的,其是需要根据实际情况需要进行选定监测目标,因此不能获取提前训练好的对象检测模型进行检测识别。例如,监测目标可能是在自然灾害、交通事故、火灾、治安事件等情况下临时确定的具有特定性的对象目标,是不可能用通用的对象检测模型进行识别检测,进而对其进行监测的。It should be noted that the reason why object detection models need to be trained is mainly because the monitoring targets are not determined in advance. The monitoring targets need to be selected according to the actual situation, so it is impossible to obtain pre-trained object detection models for detection and identification. For example, the monitoring target may be a specific object target temporarily determined in the case of natural disasters, traffic accidents, fires, public security incidents, etc. It is impossible to use a general object detection model to identify and detect it, and then monitor it.

步骤S102:根据收敛的对象检测模型对第二图像数据进行计算推理,得到监测目标对应的空间数据,其中,第二图像数据为在对象检测模型收敛后,无人机采集得到的包括监测目标的图像数据;Step S102: performing calculation and reasoning on the second image data according to the converged object detection model to obtain spatial data corresponding to the monitoring target, wherein the second image data is image data including the monitoring target collected by the drone after the object detection model converges;

步骤S103:根据所述空间数据生成对应的地理空间事件。Step S103: Generate corresponding geospatial events according to the spatial data.

在获得了收敛的对象检测模型后,即可开始进一步的利用无人机对包括监测目标的待监测区域进行监测,即可以将接下来无人机获取的第二图像数据进行计算推理,从而得到监测目标对应的空间数据,以进一步的利用该空间数据生成对应的地理空间事件。其中,在本发明的一些实现方式中,所述空间数据包括几何形状信息、位置信息、事件标识、事件类别和时间信息中的至少一种。当然,具体的空间数据也可以根据需要进行自主选择,此处不对空间数据所包括的具体数据作严格限定。例如,识别获取的空间数据可能不仅是坐标,还有形状,其中,若形状是点的话,其表现形式可以是坐标,但形状也有可能是线,或面,则采用对应的表现形式。总之,空间数据到底包括哪些,是需要根据实际情况需要进行选择适用的。示例性地,后续处理也可以是利用检测的目标对象的图像坐标和无人机瞬时POS数据进行坐标处理,然后将监测到的坐标数据与时间和ID等打包成EVENT事件发送给云端。After obtaining the converged object detection model, the drone can be further used to monitor the monitored area including the monitoring target, that is, the second image data obtained by the drone can be calculated and inferred to obtain the spatial data corresponding to the monitoring target, so as to further use the spatial data to generate the corresponding geospatial event. Among them, in some implementations of the present invention, the spatial data includes at least one of geometric shape information, location information, event identification, event category and time information. Of course, the specific spatial data can also be selected autonomously as needed, and the specific data included in the spatial data is not strictly limited here. For example, the spatial data obtained by identification may not only be coordinates, but also shapes. If the shape is a point, its expression form can be coordinates, but the shape may also be a line or a surface, and the corresponding expression form is adopted. In short, what the spatial data includes needs to be selected and applied according to the actual situation. Exemplarily, the subsequent processing can also be to use the image coordinates of the detected target object and the instantaneous POS data of the drone for coordinate processing, and then the monitored coordinate data is packaged with time and ID into EVENT events and sent to the cloud.

需要说明的是,第一图像数据和第二图像数据从本质上来说都是无人机获取的包括监测目标的图像数据,只是说第一图像数据是用以训练对象模型的过程中获取的图像数据,第二图像数据是在获得了收敛的对象检测模型后,提供给对象检测模型进行目标对象检测的图像数据。因此,若基于第一图像数据的获取过程是:操作人员在地面站的显示器上进行观测无人机图传的实时画面,然后人工的进行观察图传的实时画面,进而操控无人机进行执行相应的飞行线路,从而可以结合人工的因素利用无人机精准的获取大量的包括监测目标的第一图像数据;则对应的,第二图像数据的获取过程可以是:在获得了收敛的对象检测模型后,通知对应的操作人员,然后操作人员将可以开始重新规划无人机的飞行线路,然后去获取第二图像数据,并利用收敛的对象检测模型进行目标对象检测。It should be noted that the first image data and the second image data are essentially image data including monitoring targets acquired by the drone, but the first image data is image data acquired in the process of training the object model, and the second image data is image data provided to the object detection model for target object detection after the converged object detection model is obtained. Therefore, if the acquisition process based on the first image data is: the operator observes the real-time image of the drone image transmission on the display of the ground station, and then manually observes the real-time image of the image transmission, and then controls the drone to execute the corresponding flight route, so that the drone can be combined with artificial factors to accurately acquire a large amount of first image data including monitoring targets; then correspondingly, the acquisition process of the second image data can be: after obtaining the converged object detection model, notify the corresponding operator, and then the operator will be able to start re-planning the flight route of the drone, and then acquire the second image data, and use the converged object detection model to detect the target object.

另外,对于地理空间事件而言,在一些实施例中,可以理解为发生在某个时间和地点的具体事情,如自然灾害、交通事故、火灾、治安事件等。此时,其为采用地理信息空间数据模型组织和描述的一种空间数据类型,一般具有空间特征、属性特征和时间特征。其中空间特征主要描述了其地理位置空间坐标、几何形状等;属性特征主要描述了它的行业业务信息,例如,名称、类型等;时间特征描述了其发生的时间信息,包括事件发生的具体时间、持续时间以及事件时序等方面的信息。In addition, for geospatial events, in some embodiments, they can be understood as specific things that occur at a certain time and place, such as natural disasters, traffic accidents, fires, public security incidents, etc. At this time, it is a spatial data type organized and described by the geographic information spatial data model, generally with spatial characteristics, attribute characteristics and time characteristics. Among them, the spatial characteristics mainly describe its geographical location spatial coordinates, geometric shape, etc.; the attribute characteristics mainly describe its industry business information, such as name, type, etc.; the time characteristics describe the time information of its occurrence, including the specific time of the event, duration, and event sequence information.

基于前述方案,在本发明的一些实现方式中,在获得了地理空间事件后,上述方法还包括以下步骤:根据GIS空间分析算法对所述地理空间事件进行空间分析,得到对应的事件分析结果。从而,后续可以利用该事件分析结果直接在服务器中进行应用事件,也可以发送或拷贝至单机设备上进行应用事件。即,后续就可以在相应的服务器或终端设备上按地理信息系统空间分析的方式进行各种应用分析了。Based on the above scheme, in some implementations of the present invention, after obtaining the geospatial event, the above method further includes the following steps: spatially analyzing the geospatial event according to the GIS spatial analysis algorithm to obtain the corresponding event analysis result. Thus, the event analysis result can be used to directly apply the event in the server, or it can be sent or copied to a stand-alone device to apply the event. That is, various application analyses can be performed on the corresponding server or terminal device in the manner of geographic information system spatial analysis.

如图2所示,在本实施例的第二方面中,基于相同的发明构思,本申请实施例还提供了一种地理空间事件的采集方法,其包括以下步骤:As shown in FIG. 2 , in the second aspect of this embodiment, based on the same inventive concept, this embodiment of the present application further provides a method for collecting geospatial events, which includes the following steps:

步骤S201:执行循环过程直至满足预设条件;其中,所述预设条件为对象检测模型收敛,所述循环过程包括:基于地面站观测到的无人机图传的实时画面,规划无人机的飞行线路,并将无人机在该飞行线路持续采集的包括监测目标的第三图像数据,发送至云服务器中;基于所述第三图像数据进行人工标注,得到对应的样本数据,以在云服务器中进行训练和验证对应的对象检测模型;Step S201: executing a loop process until a preset condition is met; wherein the preset condition is that the object detection model converges, and the loop process includes: planning the flight route of the drone based on the real-time image transmitted by the drone observed by the ground station, and sending the third image data including the monitoring target continuously collected by the drone on the flight route to the cloud server; manually annotating the third image data to obtain corresponding sample data, so as to train and verify the corresponding object detection model in the cloud server;

步骤S202:采集盒在收到训练至收敛的对象检测模型后,利用该对象检测模型对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,得到对应的空间数据,并将基于所述空间数据生成的地理空间事件发送至云服务器,以供云服务进行空间分析。其中,若该空间数据仅包括目标对象的坐标和几何形状,则可以进一步结合目标对象的属性进行打包生成地理空间事件。Step S202: After receiving the object detection model trained to convergence, the acquisition box uses the object detection model to detect the target object on the real-time image of the event area to be monitored newly acquired by the drone, obtains the corresponding spatial data, and sends the geospatial events generated based on the spatial data to the cloud server for the cloud service to perform spatial analysis. If the spatial data only includes the coordinates and geometric shape of the target object, it can be further packaged and combined with the attributes of the target object to generate a geospatial event.

在第一方面公开的地理空间事件的采集方法的基础上,在第二方面的方案中结合了地面站和采集盒,对该技术方案做了进一步的优化和限定。其中,考虑到一开始是没有收敛的对象检测模型的,因此,需要一定量的样本数据进行训练得到该对象检测模型,但是若无人机仅仅是自主的执行既定的飞行线路,进行获取待监测区域内包括监测目标的图像数据,则可能导致图像数据获取的准确度受到飞行线路的选择影响,而有所降低,不利于对象检测模型的快速收敛。因此,在上述实施例中,通过在训练对象检测模型的过程中,基于地面站观测到的无人机图传的实时画面,进行规划无人机的飞行线路,将可以控制无人机更加准确有效的获取包括监测目标的第三图像数据。需要说明的是,此处的第三图像数据和前文中的第一图像数据本质上都是图像数据,只是说此处是为了便于将无人机在两种不同飞行控制情况下获取的图像数据进行区分开来而做的描述。On the basis of the method for collecting geospatial events disclosed in the first aspect, the ground station and the collection box are combined in the scheme of the second aspect, and the technical scheme is further optimized and limited. Among them, considering that there is no converged object detection model at the beginning, a certain amount of sample data is required to train the object detection model. However, if the drone only autonomously executes the established flight route to obtain image data including the monitored target in the monitored area, the accuracy of image data acquisition may be affected by the selection of the flight route, which is reduced, which is not conducive to the rapid convergence of the object detection model. Therefore, in the above embodiment, by planning the flight route of the drone based on the real-time picture of the drone image transmission observed by the ground station during the training of the object detection model, the drone can be controlled to obtain the third image data including the monitored target more accurately and effectively. It should be noted that the third image data here and the first image data in the previous text are essentially image data, but it is said that this is a description made to facilitate the distinction between the image data obtained by the drone under two different flight control conditions.

另外,在云服务器训练得到了收敛的对象检测模型后,即可利用采集盒进行下载接收该对象检测模型,然后在采集盒端利用该对象检测模型对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,从而将目标对象检测的计算量分担到采集盒端,可以提升后续对监测目标的检测和跟踪等计算的实时性,不会因受到太多的网络波动的影响,而导致其时效性下降。In addition, after the cloud server has trained and obtained a converged object detection model, the acquisition box can be used to download and receive the object detection model, and then the object detection model can be used on the acquisition box to perform target object detection on the real-time images of the event area to be monitored newly collected by the drone, thereby sharing the computational workload of target object detection to the acquisition box. This can improve the real-time performance of subsequent detection and tracking calculations of the monitored targets, and will not be affected by too many network fluctuations, which will lead to a decrease in timeliness.

基于前述方案,在本发明的一些实现方式中,所述将基于所述空间数据生成的地理空间事件发送至云服务器时包括:基于预定的GIS事件模型将所述空间数据进行转换为符合OGC空间数据标准的数据(包括对该空间数据进行结构和格式上的转换),并将该数据存储至对应的存储器中和发送至云服务器。Based on the aforementioned scheme, in some implementations of the present invention, sending the geospatial events generated based on the spatial data to the cloud server includes: converting the spatial data into data that complies with the OGC spatial data standard based on a predetermined GIS event model (including converting the structure and format of the spatial data), storing the data in a corresponding memory and sending it to the cloud server.

实施例2Example 2

如图3所示,基于与实施例1相同的发明构思,本申请实施例提供了一种采集盒,其包括:As shown in FIG3 , based on the same inventive concept as in Example 1, the embodiment of the present application provides a collection box, which includes:

接收模块,被配置为:接收云服务器训练至收敛的对象检测模型,其中云服务器训练所述对象检测模型的步骤为:持续接收无人机在执行对应飞行线路中实时采集的包括监测目标的第四图像数据,并基于对应人员对该第四图像数据持续进行人工标注所得到的样本数据,进行训练和验证对应的对象检测模型,直至所述对象检测模型收敛为止;The receiving module is configured to: receive the object detection model trained to convergence by the cloud server, wherein the step of the cloud server training the object detection model is: continuously receiving the fourth image data including the monitoring target collected in real time by the drone during the execution of the corresponding flight route, and training and verifying the corresponding object detection model based on the sample data obtained by the corresponding personnel continuously manually annotating the fourth image data, until the object detection model converges;

处理模块,被配置为:利用接收的云服务器训练至收敛的对象检测模型,对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,以得到目标事件对应的空间数据;The processing module is configured to: use the received object detection model trained to convergence by the cloud server to perform target object detection on the real-time picture of the event area to be monitored newly collected by the drone to obtain spatial data corresponding to the target event;

发送模块,被配置为:将所述空间数据发送至云服务器和/或目标终端设备,以供云服务和/或目标终端设备进行空间分析。The sending module is configured to: send the spatial data to the cloud server and/or the target terminal device for the cloud service and/or the target terminal device to perform spatial analysis.

在实施例1公开的地理空间事件的采集方法的基础上,上述实施例2中,公开了一种采集盒,包括接收模块、处理模块和发送模型,从而用以接收云服务器训练至收敛的对象检测模型,并基于此对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,以得到目标事件对应的空间数据,然后发送给云服务器和/或目标终端设备,以供云服务器和/或目标终端设备进行空间分析用。其中,目标终端设备包括电脑、平板和手机等终端设备。Based on the method for collecting geospatial events disclosed in Example 1, in the above Example 2, a collection box is disclosed, including a receiving module, a processing module and a sending model, so as to receive the object detection model trained to convergence by the cloud server, and based on this, perform target object detection on the real-time picture of the event area to be monitored newly collected by the drone, so as to obtain the spatial data corresponding to the target event, and then send it to the cloud server and/or the target terminal device for the cloud server and/or the target terminal device to perform spatial analysis. Among them, the target terminal device includes terminal devices such as computers, tablets and mobile phones.

需要说明的是,此处的第四图像数据和前文中的第一图像数据、第三图像数据,本质上都是图像数据,只是说,此处在为了便于对采集盒的技术方案进行理解和区分,而将接收的无人机获取的图像数据进行区分开来而做的描述。It should be noted that the fourth image data here and the first image data and the third image data in the previous text are essentially image data. It is just that the description here is made to distinguish the image data obtained by the receiving drone in order to facilitate the understanding and distinction of the technical solution of the acquisition box.

另外,在本发明的一些实现方式中,所述采集盒包括:In addition, in some implementations of the present invention, the collection box includes:

接收模块,被配置为:接收云服务器训练至收敛的对象检测模型,其中云服务器训练所述对象检测模型的步骤为:执行循环过程直至满足预设条件;其中,所述预设条件为对象检测模型达到收敛,所述循环过程包括:基于地面站观测到的无人机图传的实时画面,规划无人机的飞行线路,并将无人机在该飞行线路持续采集的包括监测目标的第三图像数据,发送至云服务器中;基于所述第三图像数据进行人工标注,得到对应的样本数据,以在云服务器中进行训练和验证对应的对象检测模型。The receiving module is configured to: receive an object detection model trained to convergence by a cloud server, wherein the step of the cloud server training the object detection model is: executing a loop process until a preset condition is met; wherein the preset condition is that the object detection model reaches convergence, and the loop process includes: planning a flight route of the drone based on the real-time image transmitted by the drone observed by the ground station, and sending third image data including the monitored target continuously collected by the drone on the flight route to the cloud server; performing manual annotation based on the third image data to obtain corresponding sample data, so as to train and verify the corresponding object detection model in the cloud server.

处理模块,被配置为:利用接收的云服务器训练至收敛的对象检测模型,对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,以得到目标事件对应的空间数据。The processing module is configured to: use the received object detection model trained to convergence by the cloud server to perform target object detection on the real-time images of the event area to be monitored newly collected by the drone to obtain spatial data corresponding to the target event.

发送模块,被配置为:将所述空间数据发送至云服务器和/或目标终端设备,以供云服务和/或目标终端设备进行空间分析。The sending module is configured to: send the spatial data to the cloud server and/or the target terminal device for the cloud service and/or the target terminal device to perform spatial analysis.

上述采集盒的具体实现过程请参照实施例1中提供的一种地理空间事件的采集方法,在此不再赘述。For the specific implementation process of the above-mentioned collection box, please refer to the method for collecting geospatial events provided in Example 1, which will not be repeated here.

实施例3Example 3

请参阅图4,基于与实施例1的第一方面公开的地理空间事件的采集方法的相同发明构思,本申请实施例提供了一种地理空间事件的实时采集系统,其包括:Referring to FIG. 4 , based on the same inventive concept as the method for collecting geospatial events disclosed in the first aspect of Example 1, this embodiment of the present application provides a real-time collection system for geospatial events, which includes:

模型训练模块,被配置为:执行循环过程直至满足预设条件;其中,所述预设条件为对象检测模型达到收敛,所述循环过程包括:根据无人机图传的实时画面,控制该无人机持续采集包括监测目标的第一图像数据;根据所述第一图像数据进行人工标注,得到对应的样本数据;根据所述样本数据在云服务器中进行构建并训练对应的对象检测模型;The model training module is configured to: execute a loop process until a preset condition is met; wherein the preset condition is that the object detection model reaches convergence, and the loop process includes: controlling the drone to continuously collect first image data including the monitored target according to the real-time image transmitted by the drone; manually annotating the first image data to obtain corresponding sample data; and constructing and training a corresponding object detection model in a cloud server according to the sample data;

空间数据生成模块,被配置为:根据收敛的对象检测模型对第二图像数据进行计算推理,得到监测目标对应的空间数据,其中,第二图像数据为在对象检测模型收敛后,无人机采集得到的包括监测目标的图像数据;The spatial data generation module is configured to: perform calculation and reasoning on the second image data according to the converged object detection model to obtain spatial data corresponding to the monitoring target, wherein the second image data is image data including the monitoring target collected by the drone after the object detection model converges;

空间事件生成模块,被配置为:根据所述空间数据生成对应的地理空间事件。The spatial event generation module is configured to generate corresponding geographic spatial events according to the spatial data.

上述系统具体实现过程请参照实施例1中提供的一种地理空间事件的采集方法,在此不再赘述。For the specific implementation process of the above system, please refer to the method for collecting geospatial events provided in Example 1, which will not be repeated here.

基于与实施例1的第二方面公开的地理空间事件的采集方法的相同发明构思,如图5所示,本申请实施例还提供了一种地理空间事件的实时采集系统,其包括:无人机、采集盒、地面站和云服务器;Based on the same inventive concept as the method for collecting geospatial events disclosed in the second aspect of Example 1, as shown in FIG5 , the embodiment of the present application further provides a real-time collection system for geospatial events, which includes: a drone, a collection box, a ground station, and a cloud server;

所述无人机,用于接收地面站的控制信号,以在待监测事件区域执行对应的飞行线路,并将获取的实时画面传输至所述地面站进行显示,以及将采集的包括监测目标的图像数据发送至所述云服务;The drone is used to receive control signals from the ground station to execute a corresponding flight route in the event area to be monitored, transmit the acquired real-time images to the ground station for display, and send the collected image data including the monitored target to the cloud service;

所述地面站,用于接收并显示无人机获取的实时画面,并响应于预定操作指令,向无人机发送对应的控制信号;The ground station is used to receive and display the real-time images acquired by the UAV, and send corresponding control signals to the UAV in response to predetermined operation instructions;

所述采集盒,用于接收云服务器训练至收敛的对象检测模型,并基于该对象检测模型,对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,以得到对应的空间数据,并用以将所述空间数据发送至云服务器;The acquisition box is used to receive the object detection model trained to convergence by the cloud server, and based on the object detection model, perform target object detection on the real-time picture of the event area to be monitored newly acquired by the drone to obtain corresponding spatial data, and send the spatial data to the cloud server;

所述云服务器,用于持续接收无人机在执行对应飞行线路中实时采集的包括监测目标的第五图像数据,并基于对应人员对该第五图像数据持续进行人工标注所得到的样本数据,进行训练和验证对应的对象检测模型,直至所述对象检测模型收敛为止;以及用以将训练至收敛的对象检测模型发送至所述采集盒;还用于接收采集盒发送的空间数据,以进行空间分析。The cloud server is used to continuously receive fifth image data including monitoring targets collected in real time by the drone during the execution of the corresponding flight route, and to train and verify the corresponding object detection model based on sample data obtained by the corresponding personnel continuously manually annotating the fifth image data until the object detection model converges; and to send the object detection model trained to convergence to the acquisition box; and to receive spatial data sent by the acquisition box for spatial analysis.

如图5所示,上述地理空间事件的实时采集系统中包括无人机、采集盒、地面站和云服务器,其中,无人机用于获取包括监测目标的图像数据,然后操作人员可以在地面站进行实时控制无人机执行对应的飞行线路(不管是模型训练阶段,还是后期利用收敛的模型阶段,均可根据需要在地面站进行控制无人机执行对应的飞行线路),而云服务器用于对包括监测目标的图像数据进行接收,并在人工对该图像数据进行标注后,进行训练和验证对应的对象检测模型,直至模型收敛为止。接着,就可以利用采集盒下载接收的该收敛了的对象检测模型,对无人机接下来获取的包括监测目标的图像数据进行目标对象检测,以得到对应的空间数据,从而供云服务器进行空间分析用。As shown in FIG5 , the real-time collection system of the above-mentioned geospatial events includes a drone, a collection box, a ground station and a cloud server, wherein the drone is used to obtain image data including monitoring targets, and then the operator can control the drone in real time at the ground station to execute the corresponding flight route (whether it is the model training stage or the later stage of using the converged model, the drone can be controlled at the ground station to execute the corresponding flight route as needed), and the cloud server is used to receive the image data including the monitoring targets, and after manually annotating the image data, train and verify the corresponding object detection model until the model converges. Then, the collection box can be used to download the received converged object detection model, and the target object detection can be performed on the image data including the monitoring targets acquired by the drone next, so as to obtain the corresponding spatial data, so as to provide the cloud server with spatial analysis.

上述系统具体实现过程请参照实施例1中提供的一种地理空间事件的采集方法,在此不再赘述。For the specific implementation process of the above system, please refer to the method for collecting geospatial events provided in Example 1, which will not be repeated here.

实施例4Example 4

请参阅图6,本申请实施例提供了一种电子设备,该电子设备包括至少一个处理器101、至少一个存储器102和数据总线103;其中:处理器101与存储器102通过数据总线103完成相互间的通信;存储器102存储有可被处理器101执行的程序指令,处理器101调用程序指令以执行一种地理空间事件的采集方法。例如实现:Please refer to FIG6 , an embodiment of the present application provides an electronic device, which includes at least one processor 101, at least one memory 102 and a data bus 103; wherein: the processor 101 and the memory 102 communicate with each other through the data bus 103; the memory 102 stores program instructions that can be executed by the processor 101, and the processor 101 calls the program instructions to execute a method for collecting geospatial events. For example, the following is implemented:

执行循环过程直至满足预设条件;其中,上述预设条件为对象检测模型达到收敛,上述循环过程包括:根据无人机图传的实时画面,控制该无人机持续采集包括监测目标的第一图像数据;根据上述第一图像数据进行人工标注,得到对应的样本数据;根据上述样本数据在云服务器中进行构建并训练对应的对象检测模型。根据收敛的对象检测模型对第二图像数据进行计算推理,得到监测目标对应的空间数据,其中,第二图像数据为在对象检测模型收敛后,无人机采集得到的包括监测目标的图像数据。根据上述空间数据生成对应的地理空间事件。The loop process is executed until the preset conditions are met; wherein the preset conditions are that the object detection model reaches convergence, and the loop process includes: according to the real-time picture of the drone image transmission, the drone is controlled to continuously collect the first image data including the monitoring target; according to the first image data, manual annotation is performed to obtain the corresponding sample data; according to the sample data, the corresponding object detection model is constructed and trained in the cloud server. According to the converged object detection model, the second image data is calculated and inferred to obtain the spatial data corresponding to the monitoring target, wherein the second image data is the image data including the monitoring target collected by the drone after the object detection model converges. The corresponding geospatial event is generated according to the above spatial data.

或者实现:Or implement:

执行循环过程直至满足预设条件;其中,上述预设条件为对象检测模型收敛,上述循环过程包括:基于地面站观测到的无人机图传的实时画面,规划无人机的飞行线路,并将无人机在该飞行线路持续采集的包括监测目标的第三图像数据,发送至云服务器中;基于上述第三图像数据进行人工标注,得到对应的样本数据,以在云服务器中进行训练和验证对应的对象检测模型。采集盒在收到训练至收敛的对象检测模型后,利用该对象检测模型对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,得到对应的空间数据,并将基于上述空间数据生成的地理空间事件发送至云服务器,以供云服务进行空间分析。The loop process is executed until the preset conditions are met; wherein the preset conditions are that the object detection model converges, and the loop process includes: planning the flight route of the drone based on the real-time images of the drone image transmission observed by the ground station, and sending the third image data including the monitoring target continuously collected by the drone during the flight route to the cloud server; performing manual annotation based on the third image data to obtain the corresponding sample data to train and verify the corresponding object detection model in the cloud server. After receiving the object detection model trained to convergence, the acquisition box uses the object detection model to detect the target object on the real-time images of the event area to be monitored newly collected by the drone, obtain the corresponding spatial data, and send the geospatial events generated based on the above spatial data to the cloud server for the cloud service to perform spatial analysis.

其中,存储器102可以是但不限于,随机存取存储器(Random Access Memory,RAM),只读存储器(Read Only Memory,ROM),可编程只读存储器(Programmable Read-OnlyMemory,PROM),可擦除只读存储器(Erasable Programmable Read-Only Memory,EPROM),电可擦除只读存储器(Electric Erasable Programmable Read-Only Memory,EEPROM)等。Among them, the memory 102 can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc.

处理器101可以是一种集成电路芯片,具有信号处理能力。该处理器101可以是通用处理器,包括中央处理器(Central Processing Unit,CPU)、网络处理器(NetworkProcessor,NP)等;还可以是数字信号处理器(Digital Signal Processing,DSP)、专用集成电路(Application Specific Integrated Circuit,ASIC)、现场可编程门阵列(Field-Programmable Gate Array,FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。The processor 101 may be an integrated circuit chip with signal processing capability. The processor 101 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

可以理解,图6所示的结构仅为示意,电子设备还可包括比图6中所示更多或者更少的组件,或者具有与图6所示不同的配置。图6中所示的各组件可以采用硬件、软件或其组合实现。It is understood that the structure shown in Figure 6 is only for illustration, and the electronic device may also include more or fewer components than those shown in Figure 6, or have a different configuration than that shown in Figure 6. Each component shown in Figure 6 may be implemented by hardware, software, or a combination thereof.

实施例5Example 5

本发明提供一种计算机可读存储介质,其上存储有计算机程序,该计算机程序被处理器101执行时实现一种地理空间事件的采集方法。例如实现:The present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by the processor 101, a method for collecting geospatial events is implemented. For example, the following is implemented:

执行循环过程直至满足预设条件;其中,上述预设条件为对象检测模型达到收敛,上述循环过程包括:根据无人机图传的实时画面,控制该无人机持续采集包括监测目标的第一图像数据;根据上述第一图像数据进行人工标注,得到对应的样本数据;根据上述样本数据在云服务器中进行构建并训练对应的对象检测模型。根据收敛的对象检测模型对第二图像数据进行计算推理,得到监测目标对应的空间数据,其中,第二图像数据为在对象检测模型收敛后,无人机采集得到的包括监测目标的图像数据。根据上述空间数据生成对应的地理空间事件。The loop process is executed until the preset conditions are met; wherein the preset conditions are that the object detection model reaches convergence, and the loop process includes: according to the real-time picture of the drone image transmission, the drone is controlled to continuously collect the first image data including the monitoring target; according to the first image data, manual annotation is performed to obtain the corresponding sample data; according to the sample data, the corresponding object detection model is constructed and trained in the cloud server. According to the converged object detection model, the second image data is calculated and inferred to obtain the spatial data corresponding to the monitoring target, wherein the second image data is the image data including the monitoring target collected by the drone after the object detection model converges. The corresponding geospatial event is generated according to the above spatial data.

或者实现:Or implement:

执行循环过程直至满足预设条件;其中,上述预设条件为对象检测模型收敛,上述循环过程包括:基于地面站观测到的无人机图传的实时画面,规划无人机的飞行线路,并将无人机在该飞行线路持续采集的包括监测目标的第三图像数据,发送至云服务器中;基于上述第三图像数据进行人工标注,得到对应的样本数据,以在云服务器中进行训练和验证对应的对象检测模型。采集盒在收到训练至收敛的对象检测模型后,利用该对象检测模型对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,得到对应的空间数据,并将基于上述空间数据生成的地理空间事件发送至云服务器,以供云服务进行空间分析。The loop process is executed until the preset conditions are met; wherein the preset conditions are that the object detection model converges, and the loop process includes: planning the flight route of the drone based on the real-time images of the drone image transmission observed by the ground station, and sending the third image data including the monitoring target continuously collected by the drone during the flight route to the cloud server; performing manual annotation based on the third image data to obtain the corresponding sample data to train and verify the corresponding object detection model in the cloud server. After receiving the object detection model trained to convergence, the acquisition box uses the object detection model to detect the target object on the real-time images of the event area to be monitored newly collected by the drone, obtain the corresponding spatial data, and send the geospatial events generated based on the above spatial data to the cloud server for the cloud service to perform spatial analysis.

上述功能如果以软件功能模块的形式实现并作为独立的产品销售或使用时,可以存储在一个计算机可读取存储介质中。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或者该技术方案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一个存储介质中,包括若干指令用以使得一台计算机设备(可以是个人计算机,服务器,或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括:U盘、移动硬盘、只读存储器(ROM,Read-Only Memory)、随机存取存储器(RAM,Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。If the above functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

对于本领域技术人员而言,显然本申请不限于上述示范性实施例的细节,而且在不背离本申请的精神或基本特征的情况下,能够以其它的具体形式实现本申请。因此,无论从哪一点来看,均应将实施例看作是示范性的,而且是非限制性的,本申请的范围由所附权利要求而不是上述说明限定,因此旨在将落在权利要求的等同要件的含义和范围内的所有变化囊括在本申请内。不应将权利要求中的任何附图标记视为限制所涉及的权利要求。It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present application. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims (10)

1.一种地理空间事件的采集方法,其特征在于,包括以下步骤:1. A method for collecting geospatial events, characterized in that it comprises the following steps: 执行循环过程直至满足预设条件;其中,所述预设条件为对象检测模型达到收敛,所述循环过程包括:根据无人机图传的实时画面,控制该无人机持续采集包括监测目标的第一图像数据,其中所述监测目标为提前不既定的;根据所述第一图像数据进行人工标注,得到对应的样本数据;根据所述样本数据在云服务器中进行构建并训练对应的对象检测模型;The loop process is executed until a preset condition is met; wherein the preset condition is that the object detection model reaches convergence, and the loop process includes: according to the real-time picture transmitted by the drone, controlling the drone to continuously collect first image data including the monitoring target, wherein the monitoring target is not predetermined in advance; manually annotating according to the first image data to obtain corresponding sample data; and constructing and training the corresponding object detection model in the cloud server according to the sample data; 响应于对象检测模型的收敛,根据无人机最新图传的实时画面,重新规划并基于重新规划的飞行线路,控制该无人机执行对应的飞行线路;In response to the convergence of the object detection model, according to the latest real-time image transmitted by the drone, re-plan the flight route and control the drone to execute the corresponding flight route based on the re-planned flight route; 根据收敛的对象检测模型对第二图像数据进行计算推理,得到监测目标对应的空间数据,其中,第二图像数据为在对象检测模型收敛后,无人机采集得到的包括监测目标的图像数据;Performing computational inference on the second image data according to the converged object detection model to obtain spatial data corresponding to the monitoring target, wherein the second image data is image data including the monitoring target acquired by the drone after the object detection model converges; 根据所述空间数据生成对应的地理空间事件,其中所述地理空间事件是突发性的事件。A corresponding geospatial event is generated according to the spatial data, wherein the geospatial event is a sudden event. 2.如权利要求1所述的地理空间事件的采集方法,其特征在于,还包括:2. The method for collecting geospatial events according to claim 1, further comprising: 根据GIS空间分析算法对所述地理空间事件进行空间分析,得到对应的事件分析结果。The geographic space events are spatially analyzed according to the GIS spatial analysis algorithm to obtain corresponding event analysis results. 3.如权利要求1所述的地理空间事件的采集方法,其特征在于,所述空间数据包括几何形状信息、位置信息、事件标识、事件类别和时间信息中的至少一种。3. The method for collecting geospatial events as described in claim 1 is characterized in that the spatial data includes at least one of geometric shape information, location information, event identification, event category and time information. 4.一种地理空间事件的采集方法,其特征在于,包括以下步骤:4. A method for collecting geospatial events, characterized in that it comprises the following steps: 执行循环过程直至满足预设条件;其中,所述预设条件为对象检测模型收敛,所述循环过程包括:基于地面站观测到的无人机图传的实时画面,规划无人机的飞行线路,并将无人机在该飞行线路持续采集的包括监测目标的第三图像数据,发送至云服务器中,其中所述监测目标为提前不既定的;基于所述第三图像数据进行人工标注,得到对应的样本数据,以根据所述样本数据在云服务器中进行构建并训练对应的对象检测模型;The loop process is executed until a preset condition is met; wherein the preset condition is that the object detection model converges, and the loop process includes: planning the flight route of the drone based on the real-time image transmitted by the drone observed by the ground station, and sending the third image data including the monitoring target continuously collected by the drone on the flight route to the cloud server, wherein the monitoring target is not determined in advance; manually annotating the third image data to obtain corresponding sample data, so as to construct and train the corresponding object detection model in the cloud server according to the sample data; 响应于对象检测模型的收敛,从云服务器中将训练至收敛的对象检测模型下载至采集盒,并根据无人机最新图传的实时画面,重新规划并基于重新规划的飞行线路,控制该无人机执行对应的飞行线路;In response to the convergence of the object detection model, the object detection model trained to convergence is downloaded from the cloud server to the acquisition box, and according to the latest real-time image transmitted by the drone, the flight route is re-planned and based on the re-planned flight route, the drone is controlled to execute the corresponding flight route; 采集盒在收到训练至收敛的对象检测模型后,利用该对象检测模型对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,得到对应的空间数据,并将基于所述空间数据生成的地理空间事件发送至云服务器,以供云服务进行空间分析,其中所述地理空间事件是突发性的事件。After receiving the object detection model trained to convergence, the acquisition box uses the object detection model to detect target objects on the real-time images of the event area to be monitored newly acquired by the drone, obtains corresponding spatial data, and sends the geospatial events generated based on the spatial data to the cloud server for spatial analysis by the cloud service, where the geospatial events are sudden events. 5.如权利要求4所述的地理空间事件的采集方法,其特征在于,所述将基于所述空间数据生成的地理空间事件发送至云服务器时包括:5. The method for collecting geospatial events according to claim 4, wherein sending the geospatial events generated based on the spatial data to a cloud server comprises: 基于预定的GIS事件模型将所述空间数据进行转换为符合OGC空间数据标准的数据,并将该数据存储至对应的存储器中和发送至云服务器。The spatial data is converted into data that complies with the OGC spatial data standard based on a predetermined GIS event model, and the data is stored in a corresponding memory and sent to a cloud server. 6.一种采集盒,其特征在于,包括:6. A collection box, characterized in that it includes: 接收模块,被配置为:接收云服务器训练至收敛的对象检测模型,其中云服务器训练所述对象检测模型的步骤为:持续接收无人机在执行对应飞行线路中实时采集的包括监测目标的第四图像数据,并基于对应人员对该第四图像数据持续进行人工标注所得到的样本数据,在云服务器中进行构建并训练对应的对象检测模型,直至所述对象检测模型收敛为止;其中所述监测目标为提前不既定的;The receiving module is configured to: receive an object detection model trained to convergence by a cloud server, wherein the step of the cloud server training the object detection model is: continuously receiving fourth image data including a monitoring target collected in real time by the drone during the execution of a corresponding flight route, and based on sample data obtained by continuous manual annotation of the fourth image data by corresponding personnel, construct and train a corresponding object detection model in the cloud server until the object detection model converges; wherein the monitoring target is not determined in advance; 处理模块,被配置为:利用接收的云服务器训练至收敛的对象检测模型,对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,以得到目标事件对应的空间数据;其中,无人机新采集得到的待监测事件区域的实时画面是操作人员响应于对象检测模型的收敛,根据无人机最新图传的实时画面,重新规划并基于重新规划的飞行线路,控制该无人机执行对应的飞行线路时实时采集的画面;The processing module is configured to: use the received object detection model trained to convergence by the cloud server to perform target object detection on the real-time picture of the event area to be monitored newly acquired by the drone to obtain spatial data corresponding to the target event; wherein the real-time picture of the event area to be monitored newly acquired by the drone is the picture collected in real time when the operator responds to the convergence of the object detection model, re-plans the flight route according to the latest real-time picture transmitted by the drone, and controls the drone to execute the corresponding flight route based on the re-planned flight route; 发送模块,被配置为:将所述空间数据发送至云服务器和/或目标终端设备,用以根据空间数据生成突发性的地理空间事件,以供云服务和/或目标终端设备进行空间分析。The sending module is configured to: send the spatial data to the cloud server and/or the target terminal device to generate sudden geospatial events based on the spatial data for the cloud service and/or the target terminal device to perform spatial analysis. 7.一种地理空间事件的实时采集系统,其特征在于,包括:7. A real-time collection system for geospatial events, comprising: 模型训练模块,被配置为:执行循环过程直至满足预设条件;其中,所述预设条件为对象检测模型达到收敛,所述循环过程包括:根据无人机图传的实时画面,控制该无人机持续采集包括监测目标的第一图像数据,其中所述监测目标为提前不既定的;根据所述第一图像数据进行人工标注,得到对应的样本数据;根据所述样本数据在云服务器中进行构建并训练对应的对象检测模型;The model training module is configured to: execute a loop process until a preset condition is met; wherein the preset condition is that the object detection model reaches convergence, and the loop process includes: controlling the drone to continuously collect first image data including a monitoring target according to the real-time image transmitted by the drone, wherein the monitoring target is not predetermined in advance; manually annotating the first image data to obtain corresponding sample data; and constructing and training a corresponding object detection model in a cloud server according to the sample data; 路线规划模块,被配置为:响应于对象检测模型的收敛,根据无人机最新图传的实时画面,重新规划并基于重新规划的飞行线路,控制该无人机执行对应的飞行线路;The route planning module is configured to: in response to the convergence of the object detection model, re-plan the flight route according to the latest real-time image transmitted by the drone, and control the drone to execute the corresponding flight route based on the re-planned flight route; 空间数据生成模块,被配置为:根据收敛的对象检测模型对第二图像数据进行计算推理,得到监测目标对应的空间数据,其中,第二图像数据为在对象检测模型收敛后,无人机采集得到的包括监测目标的图像数据;The spatial data generation module is configured to: perform calculation and reasoning on the second image data according to the converged object detection model to obtain spatial data corresponding to the monitoring target, wherein the second image data is image data including the monitoring target collected by the drone after the object detection model converges; 空间事件生成模块,被配置为:根据所述空间数据生成对应的地理空间事件,其中所述地理空间事件是突发性的事件。The spatial event generation module is configured to generate corresponding geographic spatial events according to the spatial data, wherein the geographic spatial events are sudden events. 8.一种地理空间事件的实时采集系统,其特征在于,包括无人机、采集盒、地面站和云服务器;8. A real-time collection system for geospatial events, characterized by comprising a drone, a collection box, a ground station and a cloud server; 所述无人机,用于接收地面站的控制信号,以在待监测事件区域执行对应的飞行线路,并将获取的实时画面传输至所述地面站进行显示,以及将采集的包括监测目标的图像数据发送至所述云服务,其中所述监测目标为提前不既定的;The drone is used to receive control signals from the ground station to execute a corresponding flight route in the event area to be monitored, transmit the acquired real-time images to the ground station for display, and send the collected image data including the monitoring target to the cloud service, wherein the monitoring target is not determined in advance; 所述地面站,用于接收并显示无人机获取的实时画面,并响应于预定操作指令,向无人机发送对应的控制信号;所述控制信号包括根据无人机获取的实时画面,控制无人机持续采集包括检测目标的图像数据的信号,还包括响应于对象检测模型的收敛,根据无人机最新图传的实时画面,重新规划并基于重新规划的飞行线路,控制该无人机执行对应的飞行线路的信号;The ground station is used to receive and display the real-time images acquired by the drone, and send corresponding control signals to the drone in response to predetermined operation instructions; the control signals include signals for controlling the drone to continuously collect image data including the detection target according to the real-time images acquired by the drone, and also include signals for controlling the drone to execute the corresponding flight route based on the newly-planned flight route and the latest real-time images transmitted by the drone in response to the convergence of the object detection model; 所述采集盒,用于接收云服务器训练至收敛的对象检测模型,并基于该对象检测模型,对无人机新采集得到的待监测事件区域的实时画面进行目标对象检测,以得到对应的空间数据,并用以将所述空间数据发送至云服务器;The acquisition box is used to receive the object detection model trained to convergence by the cloud server, and based on the object detection model, perform target object detection on the real-time picture of the event area to be monitored newly acquired by the drone to obtain corresponding spatial data, and send the spatial data to the cloud server; 所述云服务器,用于持续接收无人机在执行对应飞行线路中实时采集的包括监测目标的第五图像数据,并基于对应人员对该第五图像数据持续进行人工标注所得到的样本数据,在云服务器中进行构建并训练对应的对象检测模型,直至所述对象检测模型收敛为止;以及用以将训练至收敛的对象检测模型发送至所述采集盒;还用于接收采集盒发送的空间数据,以进行空间分析,所述空间数据是用以生成突发性的地理空间事件的数据。The cloud server is used to continuously receive fifth image data including monitoring targets collected in real time by the drone during the execution of the corresponding flight route, and to construct and train a corresponding object detection model in the cloud server based on sample data obtained by continuous manual annotation of the fifth image data by corresponding personnel until the object detection model converges; and to send the object detection model trained to convergence to the acquisition box; and to receive spatial data sent by the acquisition box for spatial analysis, wherein the spatial data is used to generate data of sudden geospatial events. 9.一种电子设备,其特征在于,包括至少一个处理器、至少一个存储器和数据总线;其中:所述处理器与所述存储器通过所述数据总线完成相互间的通信;所述存储器存储有被所述处理器执行的程序指令,所述处理器调用所述程序指令以执行如权利要求1-5任一项所述的方法。9. An electronic device, characterized in that it comprises at least one processor, at least one memory and a data bus; wherein: the processor and the memory communicate with each other through the data bus; the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the method as described in any one of claims 1-5. 10.一种计算机可读存储介质,其上存储有计算机程序,其特征在于,该计算机程序被处理器执行时实现如权利要求1-5中任一项所述的方法。10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method according to any one of claims 1 to 5 when executed by a processor.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112414405A (en) * 2020-10-22 2021-02-26 武汉大学 Unmanned aerial vehicle cluster flight path planning method considering emergency task of DSM
CN114373138A (en) * 2021-12-28 2022-04-19 北京交通大学 Full-automatic unmanned aerial vehicle inspection method and system for high-speed railway
CN114429594A (en) * 2022-01-26 2022-05-03 华北电力大学 Power transmission line typical target detection method and system based on unmanned aerial vehicle federal learning
US11565807B1 (en) * 2019-06-05 2023-01-31 Gal Zuckerman Systems and methods facilitating street-level interactions between flying drones and on-road vehicles
CN115880591A (en) * 2022-11-04 2023-03-31 宁波大学 A real-time object detection method based on UAV video stream
CN115905450A (en) * 2023-01-04 2023-04-04 深圳联和智慧科技有限公司 Unmanned aerial vehicle monitoring-based water quality abnormity tracing method and system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11565807B1 (en) * 2019-06-05 2023-01-31 Gal Zuckerman Systems and methods facilitating street-level interactions between flying drones and on-road vehicles
CN112414405A (en) * 2020-10-22 2021-02-26 武汉大学 Unmanned aerial vehicle cluster flight path planning method considering emergency task of DSM
CN114373138A (en) * 2021-12-28 2022-04-19 北京交通大学 Full-automatic unmanned aerial vehicle inspection method and system for high-speed railway
CN114429594A (en) * 2022-01-26 2022-05-03 华北电力大学 Power transmission line typical target detection method and system based on unmanned aerial vehicle federal learning
CN115880591A (en) * 2022-11-04 2023-03-31 宁波大学 A real-time object detection method based on UAV video stream
CN115905450A (en) * 2023-01-04 2023-04-04 深圳联和智慧科技有限公司 Unmanned aerial vehicle monitoring-based water quality abnormity tracing method and system

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