技术领域Technical Field
本发明涉及输电线路施工安全监测技术领域，更具体地说，本发明涉及基于卫星遥感的高压输电线路施工安全监测系统及方法。The present invention relates to the technical field of power transmission line construction safety monitoring, and more specifically, to a high-voltage power transmission line construction safety monitoring system and method based on satellite remote sensing.
背景技术Background Art
高压输电线路施工安全监测是为了有效管理和控制施工过程中的安全风险，确保电力设施建设和运行的顺利进行。现代施工环境复杂，涉及大量的人员、设备和环境要素，安全问题直接关系到工作人员的生命安全和电力系统的稳定运行。因此，施工过程中的安全监测显得尤为重要。High-voltage transmission line construction safety monitoring is to effectively manage and control safety risks during the construction process and ensure the smooth construction and operation of power facilities. The modern construction environment is complex, involving a large number of personnel, equipment and environmental factors. Safety issues are directly related to the life safety of workers and the stable operation of the power system. Therefore, safety monitoring during the construction process is particularly important.
例如公告号为：CN116797012B的发明专利公告的一种基于数据分析的电力输电线路高压铁塔安全性能监测系统，通过获取目标高压铁塔的拉线牢固系数和拉线倾角符合系数，得到目标高压铁塔的抗倾倒指数，进而判断目标高压铁塔本身是否存在倾倒危险，进一步获取目标高压铁塔的电线拉力系数和风力影响综合指数，得到目标高压铁塔的外力强度指数，根据目标高压铁塔的抗倾倒指数和外力强度指数，进而判断目标高压铁塔在外力作用下是否存在倾倒危险，并进行相应处理，将高压铁塔同与其关联的物体进行综合分析，丰富高压铁塔安全性评估指标的多样性，从而提高高压铁塔安全性能监测结果的准确性和可靠性。For example, the invention patent with announcement number: CN116797012B announces a power transmission line high-voltage tower safety performance monitoring system based on data analysis. By obtaining the target high-voltage tower's guy wire firmness coefficient and guy wire inclination compliance coefficient, the target high-voltage tower's anti-toppling index is obtained, and then it is judged whether the target high-voltage tower itself is in danger of toppling. The target high-voltage tower's wire tension coefficient and wind impact comprehensive index are further obtained to obtain the target high-voltage tower's external force strength index. According to the target high-voltage tower's anti-toppling index and external force strength index, it is judged whether the target high-voltage tower is in danger of toppling under the action of external force, and corresponding processing is performed. The high-voltage tower is comprehensively analyzed with objects associated with it, and the diversity of high-voltage tower safety assessment indicators is enriched, thereby improving the accuracy and reliability of the high-voltage tower safety performance monitoring results.
例如公告号为：CN109193451A的发明专利公告的一种高压输电线路管理系统，用于对高压输电线路振动进行测量，并将振动测量结果发送至线路管理子系统，所述第二测量子系统用于对高压输电线路的电流进行测量，并将电流测量结果发送至线路管理子系统，所述线路管理子系统根据振动测量结果和电流测量结果对高压输电线路进行管理；本发明的有益效果为：提供了一种高压输电线路管理系统，从而实现了高压输电线路的有效管理。For example, the invention patent with announcement number CN109193451A announces a high-voltage transmission line management system, which is used to measure the vibration of the high-voltage transmission line and send the vibration measurement result to the line management subsystem. The second measurement subsystem is used to measure the current of the high-voltage transmission line and send the current measurement result to the line management subsystem. The line management subsystem manages the high-voltage transmission line according to the vibration measurement result and the current measurement result. The beneficial effect of the present invention is: a high-voltage transmission line management system is provided, thereby realizing the effective management of the high-voltage transmission line.
上述公开的技术方案中，至少存在如下技术问题：The above disclosed technical solutions have at least the following technical problems:
现有的监测系统有时无法提供实时的数据更新和分析，导致管理者不能及时发现和应对安全问题，尤其是在施工现场情况动态变化时存在实时性不足的情况，导致无法产生不必要的伤亡；Existing monitoring systems sometimes fail to provide real-time data updates and analysis, which results in managers being unable to detect and respond to safety issues in a timely manner, especially when conditions at the construction site change dynamically, resulting in unnecessary casualties.
卫星遥感虽然能够提供高分辨率的影像，但有时可能受天气、云层等因素影响，导致影像质量下降或者无法获取完整的数据，影响监测精度和准确性。Although satellite remote sensing can provide high-resolution images, it may sometimes be affected by factors such as weather and clouds, resulting in reduced image quality or inability to obtain complete data, affecting monitoring precision and accuracy.
针对上述问题，本发明提出一种解决方案。In view of the above problems, the present invention proposes a solution.
发明内容Summary of the invention
为了克服现有技术的上述缺陷，本发明的实施例提供基于卫星遥感的高压输电线路施工安全监测系统及方法，通过高压输电线路施工安全隐患监测，以解决上述背景技术中提出的问题。In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a high-voltage transmission line construction safety monitoring system and method based on satellite remote sensing, which solves the problems raised in the above-mentioned background technology by monitoring the safety hazards of high-voltage transmission line construction.
为实现上述目的，本发明提供如下技术方案：To achieve the above object, the present invention provides the following technical solutions:
基于卫星遥感的高压输电线路施工安全监测方法，包括如下步骤：采集高压输电线路施工时的遥感卫星图像数据集；将所述遥感卫星图像数据集进行清洗，得到标准的遥感卫星图像数据集；基于分割算法对遥感卫星图像数据集进行地物分类，得到多项隐患图像参数，所述多项隐患图像参数包括环境图像隐患参数、施工设计隐患图像参数以及人员异常行为隐患参数；将多项隐患图像参数分别进行特征提取，进行数据分析得到对应的隐患特征值；基于预先得到的基于卷积神经网络的施工隐患评估模型对所述隐患特征值进行数据分析，并根据分析结果对高压输电线路施工进行安全隐患监测。A high-voltage transmission line construction safety monitoring method based on satellite remote sensing comprises the following steps: collecting a remote sensing satellite image data set during the construction of the high-voltage transmission line; cleaning the remote sensing satellite image data set to obtain a standard remote sensing satellite image data set; classifying the remote sensing satellite image data set based on a segmentation algorithm to obtain a plurality of hidden danger image parameters, wherein the plurality of hidden danger image parameters include environmental image hidden danger parameters, construction design hidden danger image parameters and personnel abnormal behavior hidden danger parameters; extracting features from the plurality of hidden danger image parameters respectively, and performing data analysis to obtain corresponding hidden danger feature values; performing data analysis on the hidden danger feature values based on a pre-obtained construction hidden danger assessment model based on a convolutional neural network, and performing safety hidden danger monitoring on the high-voltage transmission line construction according to the analysis results.
在一个优选的实施方式中，所述遥感卫星图像数据集为频繁获取的施工现场遥感图像，并将施工现场遥感图像以时间序列的形式排列，所述遥感卫星图像数据集至少包括可见光谱图、红外波段图。In a preferred embodiment, the remote sensing satellite image data set is frequently acquired remote sensing images of the construction site, and the remote sensing images of the construction site are arranged in the form of a time series. The remote sensing satellite image data set at least includes a visible spectrum map and an infrared band map.
在一个优选的实施方式中，所述将所述遥感卫星图像数据集进行清洗，具体为：对遥感卫星图像数据集进行质量控制，将云层遮挡、完整性不足以及清晰度不足的图像进行剔除；对质量控制后的遥感卫星图像数据集内的遥感图像与预先获取的同一区域的高分辨率参考历史图像进行比较，得到对比差异数据，基于局部窗口法调整对比差异数据，对遥感图像进行几何校正，所述对比差异数据包括图像的位置和图像的角度；将校正后的遥感卫星图像数据集内的多个视角的同一区域的遥感图像的辐射亮度值进行比较，分析大气对地物反射的影响值，并基于大气对地物反射的影响值对遥感卫星图像数据集进行大气校正。In a preferred embodiment, the remote sensing satellite image data set is cleaned, specifically: quality control is performed on the remote sensing satellite image data set, and images with cloud occlusion, insufficient integrity, and insufficient clarity are eliminated; the remote sensing images in the remote sensing satellite image data set after quality control are compared with high-resolution reference historical images of the same area acquired in advance to obtain contrast difference data, the contrast difference data is adjusted based on a local window method, and the remote sensing images are geometrically corrected, wherein the contrast difference data includes the position and angle of the image; the radiation brightness values of the remote sensing images of the same area from multiple perspectives in the corrected remote sensing satellite image data set are compared, the influence of the atmosphere on the reflection of the ground objects is analyzed, and the remote sensing satellite image data set is atmospherically corrected based on the influence of the atmosphere on the reflection of the ground objects.
在一个优选的实施方式中，所述基于分割算法对遥感卫星图像数据集进行地物分类，具体为：检测图像中的边缘信息，然后根据边缘进行图像区域分割；将分割后的图像区域基于图像统计特征和聚类算法对图像进行自动的非监督分类，得到多项隐患图像参数。In a preferred embodiment, the segmentation algorithm is used to classify objects in the remote sensing satellite image data set, specifically: detecting edge information in the image, and then segmenting the image area according to the edges; automatically performing unsupervised classification of the segmented image areas based on image statistical features and clustering algorithms to obtain multiple hidden danger image parameters.
在一个优选的实施方式中，所述隐患特征值包括环境隐患特征值、施工设计隐患值、人员异常行为隐患特征值。In a preferred embodiment, the hidden danger characteristic values include environmental hidden danger characteristic values, construction design hidden danger values, and personnel abnormal behavior hidden danger characteristic values.
在一个优选的实施方式中，所述根据分析结果对高压输电线路施工进行安全隐患监测，具体为：基于预先得到的基于卷积神经网络的施工隐患评估模型对所述隐患特征值进行数据分析，生成施工隐患评估值；将施工隐患评估值与预设的隐患阈值做比较，当施工隐患评估值大于预设的隐患阈值时，发出警报。In a preferred embodiment, the safety hazard monitoring of the high-voltage transmission line construction is performed based on the analysis results, specifically: based on the pre-obtained construction hazard assessment model based on convolutional neural network, the hazard characteristic value is subjected to data analysis to generate a construction hazard assessment value; the construction hazard assessment value is compared with a preset hazard threshold value, and when the construction hazard assessment value is greater than the preset hazard threshold value, an alarm is issued.
在一个优选的实施方式中，所述环境隐患特征值的具体获取方法如下：通过边缘密度和边缘分布的比值表示地形复杂指数；根据图像分割结果，计算每个分割区域内的植被覆盖占比；将植被覆盖占比结合预先获取的边缘数量进行数据分析，计算植被覆盖率；将地形复杂指数和植被覆盖率进行加权求和计算环境隐患特征值。In a preferred embodiment, the specific method for obtaining the environmental hazard characteristic value is as follows: the terrain complexity index is represented by the ratio of edge density and edge distribution; based on the image segmentation result, the vegetation coverage ratio in each segmented area is calculated; the vegetation coverage ratio is combined with the pre-acquired edge number for data analysis to calculate the vegetation coverage rate; the terrain complexity index and the vegetation coverage rate are weighted and summed to calculate the environmental hazard characteristic value.
在一个优选的实施方式中，所述施工设计隐患值的具体获取方法如下：基于图像分割和轮廓检测技术，识别高压输电线路施工区域内的道路布局情况，作为施工设计隐患值，具体为；通过边缘检测算法获取图像边缘信息；根据边缘信息，计算出施工道路的总长度；使用目标检测算法分析施工区域内设备的位置和类型，并结合设备的位置和类型计算设备分布的密度；通过边缘检测技术确定施工区域的边界，计算出施工区域的边界长度；结合施工区域的边界长度、设备分布的密度以及施工道路的总长度加权计算出施工设计隐患值。In a preferred embodiment, the specific method for obtaining the construction design hazard value is as follows: based on image segmentation and contour detection technology, the road layout in the high-voltage transmission line construction area is identified as the construction design hazard value, specifically: image edge information is obtained through an edge detection algorithm; based on the edge information, the total length of the construction road is calculated; the target detection algorithm is used to analyze the location and type of equipment in the construction area, and the density of equipment distribution is calculated based on the location and type of equipment; the boundary of the construction area is determined through edge detection technology, and the boundary length of the construction area is calculated; the construction design hazard value is weightedly calculated based on the boundary length of the construction area, the density of equipment distribution and the total length of the construction road.
在一个优选的实施方式中，所述人员异常行为隐患特征值的具体获取方法如下：基于员工行为准则和实时添加，获取人员异常行为集，所述人员异常行为集包括各种违反了规定的人员行为、移动路径以及异常人员位置；对人员异常行为集内的各种异常行为赋予预设的隐患分数；基于人员异常行为集和对应隐患分数的构建回归模型，生成人员异常行为隐患特征值。In a preferred embodiment, the specific method for obtaining the hidden danger characteristic value of abnormal behavior of personnel is as follows: based on the employee code of conduct and real-time addition, a set of abnormal behavior of personnel is obtained, and the set of abnormal behavior of personnel includes various behaviors of personnel that violate regulations, movement paths, and abnormal personnel positions; various abnormal behaviors in the set of abnormal behavior of personnel are assigned preset hidden danger scores; a regression model is constructed based on the set of abnormal behavior of personnel and the corresponding hidden danger scores to generate hidden danger characteristic values of abnormal behavior of personnel.
一种基于卫星遥感的高压输电线路施工安全监测系统，包括图像采集模块、图像预处理模块、隐患区分模块、隐患分析模块和隐患监测模块，模块之间存在关联；图像采集模块，用于采集高压输电线路施工时的遥感卫星图像数据集；图像预处理模块，用于将所述遥感卫星图像数据集进行清洗，得到标准的遥感卫星图像数据集；隐患区分模块，用于基于分割算法对遥感卫星图像数据集进行地物分类，得到多项隐患图像参数，所述多项隐患图像参数包括环境图像隐患参数、施工设计隐患图像参数以及人员异常行为隐患参数；隐患分析模块，用于将多项隐患图像参数分别进行特征提取，进行数据分析得到对应的隐患特征值；隐患监测模块，用于基于预先得到的基于卷积神经网络的施工隐患评估模型对所述隐患特征值进行数据分析，并根据分析结果对高压输电线路施工进行安全隐患监测。A high-voltage transmission line construction safety monitoring system based on satellite remote sensing comprises an image acquisition module, an image preprocessing module, a hidden danger distinguishing module, a hidden danger analysis module and a hidden danger monitoring module, wherein the modules are associated with each other; the image acquisition module is used to acquire a remote sensing satellite image data set during the construction of the high-voltage transmission line; the image preprocessing module is used to clean the remote sensing satellite image data set to obtain a standard remote sensing satellite image data set; the hidden danger distinguishing module is used to classify the remote sensing satellite image data set based on a segmentation algorithm to obtain a plurality of hidden danger image parameters, wherein the plurality of hidden danger image parameters include environmental image hidden danger parameters, construction design hidden danger image parameters and personnel abnormal behavior hidden danger parameters; the hidden danger analysis module is used to extract features of the plurality of hidden danger image parameters respectively, and perform data analysis to obtain corresponding hidden danger feature values; the hidden danger monitoring module is used to perform data analysis on the hidden danger feature values based on a pre-obtained construction hidden danger assessment model based on a convolutional neural network, and perform safety hidden danger monitoring on the construction of the high-voltage transmission line according to the analysis results.
本发明基于卫星遥感的高压输电线路施工安全监测系统及方法的技术效果和优点：Technical effects and advantages of the high-voltage transmission line construction safety monitoring system and method based on satellite remote sensing of the present invention:
1.本发明通过频繁获取高压输电线路施工现场的遥感卫星图像数据集，能够实时捕捉施工过程中的动态变化和环境条件，有效监测施工进度和设施变化；时间序列排列的遥感图像数据集提供了连续的演变视角，对监测安全隐患演变过程至关重要，增强了信息量和分析可能性。1. The present invention can capture the dynamic changes and environmental conditions in the construction process in real time and effectively monitor the construction progress and facility changes by frequently acquiring remote sensing satellite image datasets of high-voltage transmission line construction sites; the remote sensing image datasets arranged in time series provide a continuous evolution perspective, which is crucial for monitoring the evolution of safety hazards and enhances the amount of information and analysis possibilities.
2.本发明通过将多项隐患图像参数进行特征提取和数据分析，得到对应的隐患特征值，包括环境隐患特征值、施工设计隐患值和人员异常行为隐患特征值；环境隐患特征值通过遥感卫星图像提取，包括地形复杂性指标和植被覆盖率，能客观反映施工现场的地形难度和生态环境情况，为安全管理提供重要的地理信息支持。2. The present invention extracts features and performs data analysis on multiple hidden danger image parameters to obtain corresponding hidden danger characteristic values, including environmental hidden danger characteristic values, construction design hidden danger values and personnel abnormal behavior hidden danger characteristic values; the environmental hidden danger characteristic values are extracted through remote sensing satellite images, including terrain complexity indicators and vegetation coverage rate, which can objectively reflect the terrain difficulty and ecological environment conditions of the construction site, and provide important geographic information support for safety management.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明基于卫星遥感的高压输电线路施工安全监测方法的结构示意图。FIG1 is a schematic diagram of the structure of a high-voltage transmission line construction safety monitoring method based on satellite remote sensing according to the present invention.
图2为本发明基于卫星遥感的高压输电线路施工安全监测系统的结构示意图。FIG2 is a schematic diagram of the structure of a high-voltage transmission line construction safety monitoring system based on satellite remote sensing according to the present invention.
具体实施方式DETAILED DESCRIPTION
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整的描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
实施例1，图1给出了本发明基于卫星遥感的高压输电线路施工安全监测方法，包括如下步骤：Embodiment 1, FIG1 shows a high-voltage transmission line construction safety monitoring method based on satellite remote sensing of the present invention, comprising the following steps:
S1，采集高压输电线路施工时的遥感卫星图像数据集。S1, a remote sensing satellite image dataset collected during the construction of high-voltage transmission lines.
本实施例中，所述遥感卫星图像数据集为频繁获取的施工现场遥感图像，并将施工现场遥感图像以时间序列的形式排列，所述遥感卫星图像数据集至少包括可见光谱图、红外波段图。In this embodiment, the remote sensing satellite image data set is frequently acquired remote sensing images of the construction site, and the remote sensing images of the construction site are arranged in the form of a time series. The remote sensing satellite image data set at least includes a visible spectrum map and an infrared band map.
需要说明的是，施工现场的情况常常是动态变化的，随着施工进展、天气条件和环境因素的变化，需要频繁获取遥感图像以捕捉这些变化。只有频繁获取的图像，才能真实反映施工现场的实际状态和变化过程。It should be noted that the situation at the construction site is often changing dynamically. As the construction progresses, weather conditions and environmental factors change, remote sensing images need to be frequently acquired to capture these changes. Only images acquired frequently can truly reflect the actual status and change process of the construction site.
将遥感图像按时间序列排列，可以形成连续的图像序列，显示施工过程中的演变和变化；这种时间序列数据有助于监测施工进度、设施变化以及可能的安全隐患演变过程，提供了更多的信息和分析的可能性。Arranging remote sensing images in time series can form a continuous image sequence that shows the evolution and changes in the construction process; this time series data helps monitor construction progress, facility changes, and the evolution of possible safety hazards, providing more information and analysis possibilities.
可见光谱图像提供了人眼可见的图像信息，可以直观地显示出施工现场的景观、设施和人员活动等情况；这些图像通常具有较高的空间分辨率，能够清晰地显示细节。Visible spectrum images provide image information visible to the human eye and can intuitively display the landscape, facilities, and personnel activities at the construction site; these images usually have a high spatial resolution and can clearly display details.
红外波段图像能够提供额外的信息，如地表温度分布、建筑物和设备的热量分布等；这对于监测设备状态、环境温度变化以及可能的热点问题（如设备过热）具有重要意义。Infrared band images can provide additional information, such as surface temperature distribution, heat distribution of buildings and equipment, etc.; this is important for monitoring equipment status, ambient temperature changes, and possible hot spot problems (such as equipment overheating).
S2，将所述遥感卫星图像数据集进行清洗，得到标准的遥感卫星图像数据集。S2, cleaning the remote sensing satellite image dataset to obtain a standard remote sensing satellite image dataset.
所述将所述遥感卫星图像数据集进行清洗，具体为：The cleaning of the remote sensing satellite image data set is specifically as follows:
对遥感卫星图像数据集进行质量控制，将云层遮挡、完整性不足以及清晰度不足的图像进行剔除；Perform quality control on remote sensing satellite image data sets and remove images that are obscured by clouds, lack integrity, or lack clarity;
对质量控制后的遥感卫星图像数据集内的遥感图像与预先获取的同一区域的高分辨率参考历史图像进行比较，得到对比差异数据，基于局部窗口法调整对比差异数据，对遥感图像进行几何校正，所述对比差异数据包括图像的位置和图像的角度；Compare the remote sensing images in the remote sensing satellite image data set after quality control with the high-resolution reference historical images of the same area acquired in advance to obtain contrast difference data, adjust the contrast difference data based on the local window method, and perform geometric correction on the remote sensing images, wherein the contrast difference data includes the position and angle of the image;
将校正后的遥感卫星图像数据集内的多个视角的同一区域的遥感图像的辐射亮度值进行比较，分析大气对地物反射的影响值，并基于大气对地物反射的影响值对遥感卫星图像数据集进行大气校正。The radiance brightness values of remote sensing images of the same area from multiple perspectives in the corrected remote sensing satellite image dataset are compared, the influence of the atmosphere on the reflection of ground objects is analyzed, and atmospheric correction is performed on the remote sensing satellite image dataset based on the influence of the atmosphere on the reflection of ground objects.
需要说明的是，对遥感卫星图像数据集进行质量控制包括几个关键步骤。首先，通过自动或人工的方式检测和识别图像中存在云层遮挡的部分，这些部分会影响地物表面的可见性和反射率，因此需要将受影响的图像剔除；其次，评估图像的完整性，确保图像没有由于传输或接收过程中的损坏而导致的数据缺失或图像部分丢失的问题，从而保证后续处理的数据完整性；最后，评估图像的清晰度，检查图像的分辨率和细节程度，确保图像足够清晰以便于后续的几何校正和大气校正处理；对于那些不符合质量标准的图像，会被移除或标记，以确保最终的遥感卫星图像数据集质量和可用性。It should be noted that quality control of remote sensing satellite image datasets includes several key steps. First, detect and identify the parts of the image that are blocked by clouds through automatic or manual methods. These parts will affect the visibility and reflectivity of the surface of the object, so the affected images need to be removed; second, evaluate the integrity of the image to ensure that there is no data loss or partial loss of the image due to damage during transmission or reception, so as to ensure the integrity of the data for subsequent processing; finally, evaluate the clarity of the image, check the resolution and detail of the image, and ensure that the image is clear enough for subsequent geometric correction and atmospheric correction processing; those images that do not meet the quality standards will be removed or marked to ensure the quality and availability of the final remote sensing satellite image dataset.
需要说明的是，对质量控制后的遥感卫星图像数据集进行进一步处理时，首先将这些图像与预先获取的同一区域的高分辨率参考历史图像进行比较；通过比较分析，可以得到图像之间的对比差异数据，这些差异数据反映了图像在位置和角度上的偏移或变化。然后，利用局部窗口法对这些对比差异数据进行调整，即根据局部区域内的差异特征来优化图像的位置和角度。这一步骤旨在确保遥感图像与高分辨率参考图像在几何上的一致性，以便后续的精确地物提取和分析；通过这样的过程，能够有效地进行遥感图像的几何校正，确保最终的数据集在空间位置和视角上的精确性和一致性。It should be noted that when further processing the remote sensing satellite image data set after quality control, these images are first compared with the high-resolution reference historical images of the same area acquired in advance; through comparative analysis, the contrast difference data between the images can be obtained, and these difference data reflect the displacement or change of the image in position and angle. Then, these contrast difference data are adjusted using the local window method, that is, the position and angle of the image are optimized according to the difference characteristics in the local area. This step is to ensure the geometric consistency of the remote sensing image and the high-resolution reference image for subsequent accurate object extraction and analysis; through such a process, the geometric correction of the remote sensing image can be effectively performed to ensure the accuracy and consistency of the final data set in spatial position and perspective.
需要说明的是，校正后的遥感卫星图像数据集内包含多个视角的同一区域遥感图像，接下来的步骤是比较这些图像的辐射亮度值；通过分析不同视角下的图像亮度差异，可以识别和量化大气对地物反射的影响；大气影响包括大气吸收和散射，会导致不同视角下同一地物的辐射亮度不同；基于这些分析结果，可以计算和应用大气校正因子，以调整每幅图像的辐射亮度值，从而消除或减小大气影响，使得遥感卫星图像数据集在反映地物真实反射特性时更加准确和一致；这一过程确保了遥感数据在后续地物分类、变化检测和环境监测等应用中的可靠性和精度。It should be noted that the corrected remote sensing satellite image dataset contains remote sensing images of the same area from multiple perspectives. The next step is to compare the radiance values of these images. By analyzing the differences in image brightness from different perspectives, the impact of the atmosphere on the reflectance of ground objects can be identified and quantified. Atmospheric influences include atmospheric absorption and scattering, which will cause different radiances of the same ground object from different perspectives. Based on these analysis results, the atmospheric correction factor can be calculated and applied to adjust the radiance value of each image, thereby eliminating or reducing the atmospheric impact, making the remote sensing satellite image dataset more accurate and consistent in reflecting the true reflectance characteristics of the ground objects. This process ensures the reliability and accuracy of remote sensing data in subsequent applications such as ground object classification, change detection and environmental monitoring.
所述对比差异数据的具体计算公式如下：The specific calculation formula of the comparison difference data is as follows:
式中，为图像在位置（x，y）的差异，为参考图像在（x，y）位置的比较值，为遥感卫星图像在（x，y）位置的比较值。In the formula, is the difference of the image at position (x, y), is the comparison value of the reference image at position (x, y), is the comparison value of the remote sensing satellite image at the (x, y) position.
需要说明的是，比较值包括但不限于亮度、像素值、饱和度。It should be noted that the comparison value includes but is not limited to brightness, pixel value, and saturation.
所述大气对地物反射的影响值的具体计算公式为：The specific calculation formula of the influence of the atmosphere on the reflection of the ground object is:
式中，为大气对地物反射的影响值，为遥感图像辐射亮度，为大气透射率，为大气的辐射亮度，为卫星接收到的辐射亮度，为经过大气校正后的遥感图像波长、为入射角度、为视角编号即为同一区域的遥感卫星图像编号。 In the formula, is the influence of atmosphere on the reflection of ground objects, is the remote sensing image radiance, is the atmospheric transmittance, is the radiance of the atmosphere, is the radiance received by the satellite, is the wavelength of the remote sensing image after atmospheric correction, is the incident angle, The viewing angle number is the remote sensing satellite image number of the same area.
S3，基于分割算法对遥感卫星图像数据集进行地物分类，得到多项隐患图像参数，所述多项隐患图像参数包括环境图像隐患参数、施工设计隐患图像参数以及人员异常行为隐患参数。S3, classifying the objects in the remote sensing satellite image data set based on the segmentation algorithm to obtain a plurality of hidden danger image parameters, wherein the plurality of hidden danger image parameters include environmental image hidden danger parameters, construction design hidden danger image parameters and personnel abnormal behavior hidden danger parameters.
所述基于分割算法对遥感卫星图像数据集进行地物分类，具体为：The classification of objects in the remote sensing satellite image data set based on the segmentation algorithm is specifically as follows:
检测图像中的边缘信息，然后根据边缘进行图像区域分割；Detect edge information in the image and then segment the image region based on the edges;
将分割后的图像区域基于图像统计特征和聚类算法对图像进行自动的非监督分类，得到多项隐患图像参数。The segmented image areas are automatically classified in an unsupervised manner based on image statistical features and clustering algorithms to obtain multiple hidden danger image parameters.
需要说明的是，边缘检测算法被应用于遥感卫星图像的处理中，以提取有关高压输电线路施工安全的隐患参数；首先，通过选择适当的边缘检测算法（如Sobel算子或Canny边缘检测），对遥感图像进行处理，准确地识别出地形边缘和物体轮廓。然后，利用这些边缘信息，可以评估环境图像的地形复杂性，识别施工设计中的道路布局和设备安置情况，并检测出任何可能存在的人员异常行为；这些分析结果有助于实现对高压输电线路施工过程中潜在安全隐患的及时监测和管理。It should be noted that edge detection algorithms are applied to the processing of remote sensing satellite images to extract parameters related to the hidden dangers of high-voltage transmission line construction safety. First, by selecting an appropriate edge detection algorithm (such as Sobel operator or Canny edge detection), the remote sensing image is processed to accurately identify the terrain edges and object contours. Then, using this edge information, the terrain complexity of the environmental image can be evaluated, the road layout and equipment placement in the construction design can be identified, and any possible abnormal behavior of personnel can be detected. These analysis results help to achieve timely monitoring and management of potential safety hazards during the construction of high-voltage transmission lines.
需要说明的是，分割后的遥感卫星图像区域通过图像统计特征和聚类算法进行自动的非监督分类，以获取与高压输电线路施工安全相关的多项隐患图像参数；首先，利用提取的图像统计特征如颜色、纹理和形状等，对每个分割区域进行描述和表示；随后，采用聚类算法如K均值聚类或自组织映射（SOM），将相似特征的区域分组为类别，从而识别出具有相似隐患特征的地物区域；通过这一过程，可以自动地获取环境图像隐患参数、施工设计隐患图像参数以及人员异常行为隐患参数，为高压输电线路施工安全监测提供深入的图像分析和数据支持。It should be noted that the segmented remote sensing satellite image areas are automatically unsupervisedly classified through image statistical features and clustering algorithms to obtain multiple hidden danger image parameters related to the safety of high-voltage transmission line construction; first, each segmented area is described and represented using the extracted image statistical features such as color, texture and shape; then, clustering algorithms such as K-means clustering or self-organizing map (SOM) are used to group areas with similar features into categories, thereby identifying areas of land objects with similar hidden danger features; through this process, environmental image hidden danger parameters, construction design hidden danger image parameters and personnel abnormal behavior hidden danger parameters can be automatically obtained, providing in-depth image analysis and data support for high-voltage transmission line construction safety monitoring.
需要说明的是，施工设计隐患图像参数：根据边缘检测结果，可以识别出施工道路的轮廓和布局，进而评估施工设计的合理性和可操作性；例如，通过检测到的边缘确定施工区域的边界，分析道路的通行性和施工设备的布置情况。It should be noted that the construction design hidden danger image parameters are: based on the edge detection results, the outline and layout of the construction road can be identified, and then the rationality and operability of the construction design can be evaluated; for example, the boundaries of the construction area can be determined through the detected edges, and the road passability and the layout of the construction equipment can be analyzed.
人员异常行为隐患参数：边缘检测还可以用于检测图像中的异常行为，如工作人员的位置或移动路径；通过跟踪边缘和形状变化，可以检测出人员是否进入禁止区域或是否执行了异常的操作。Personnel abnormal behavior hidden danger parameters: Edge detection can also be used to detect abnormal behavior in images, such as the position or movement path of staff; by tracking edge and shape changes, it can be detected whether a person enters a prohibited area or performs an abnormal operation.
环境图像隐患参数：可以通过检测到的边缘信息来估计环境中的地形复杂度或植被密度；例如，通过计算边缘的密度和分布来评估地形的复杂性，或者通过边缘的形状和密度来估算植被的覆盖程度。Environmental image hidden danger parameters: The terrain complexity or vegetation density in the environment can be estimated through the detected edge information; for example, the complexity of the terrain can be evaluated by calculating the density and distribution of the edges, or the coverage of vegetation can be estimated by the shape and density of the edges.
进一步的，本步骤结合了边缘检测、图像分割、图像统计特征提取和聚类算法，具有以下优势：Furthermore, this step combines edge detection, image segmentation, image statistical feature extraction and clustering algorithm, which has the following advantages:
精确性和准确性：通过边缘检测算法准确提取地物边缘和物体轮廓，结合图像分割和聚类算法，能够精细化地分类遥感图像中的地物，获取多项隐患图像参数；Precision and accuracy: The edge detection algorithm can accurately extract the edge and contour of the object. Combined with the image segmentation and clustering algorithms, it can finely classify the objects in the remote sensing image and obtain multiple hidden danger image parameters;
自动化处理：采用非监督学习的方法进行地物分类，不需要人工干预，提高了处理效率并减少了人为误差；Automated processing: It uses unsupervised learning methods to classify objects, without the need for human intervention, which improves processing efficiency and reduces human errors;
综合分析：通过分析环境图像隐患参数、施工设计隐患图像参数和人员异常行为隐患参数，能够全面评估施工现场的安全状况和潜在风险；Comprehensive analysis: By analyzing environmental image hidden danger parameters, construction design hidden danger image parameters and abnormal behavior hidden danger parameters of personnel, the safety status and potential risks of the construction site can be comprehensively evaluated;
实时监测：基于遥感卫星图像数据集进行持续监测，可以及时发现和响应施工现场的变化和安全隐患，提高了施工安全管理的响应速度和效果；Real-time monitoring: Continuous monitoring based on remote sensing satellite image data sets can promptly detect and respond to changes and safety hazards at the construction site, improving the response speed and effectiveness of construction safety management;
科学决策支持：为相关决策者提供了科学的数据支持，帮助优化施工安全管理策略和环境监测措施，提升了整体管理效率和安全性。Scientific decision-making support: Provides scientific data support to relevant decision makers, helps optimize construction safety management strategies and environmental monitoring measures, and improves overall management efficiency and safety.
本实施通过频繁获取高压输电线路施工现场的遥感卫星图像数据集，能够实时捕捉施工过程中的动态变化和环境条件，有效监测施工进度和设施变化；时间序列排列的遥感图像数据集提供了连续的演变视角，对监测安全隐患演变过程至关重要，增强了信息量和分析可能性。This implementation can capture the dynamic changes and environmental conditions during the construction process in real time and effectively monitor the construction progress and facility changes by frequently acquiring remote sensing satellite image datasets of high-voltage transmission line construction sites. The remote sensing image datasets arranged in time series provide a continuous evolution perspective, which is crucial for monitoring the evolution of safety hazards and enhances the amount of information and analysis possibilities.
实施例2，S4，将多项隐患图像参数分别进行特征提取，进行数据分析得到对应的隐患特征值。In Example 2, S4, feature extraction is performed on multiple hidden danger image parameters respectively, and data analysis is performed to obtain corresponding hidden danger feature values.
所述隐患特征值包括环境隐患特征值、施工设计隐患值、人员异常行为隐患特征值。The hidden danger characteristic values include environmental hidden danger characteristic values, construction design hidden danger values, and personnel abnormal behavior hidden danger characteristic values.
环境隐患特征值是从遥感卫星图像中提取的重要信息，主要包括地形复杂性指标和植被覆盖率等；地形复杂性指标通过边缘检测和区域分析来评估地表的起伏和复杂程度，反映了施工过程中可能遇到的地形难度和安全风险。植被覆盖率则基于图像分割和边缘分析，估算植被密度和分布，有助于评估施工区域的生态环境状况和潜在的生物多样性影响；综合分析这些特征值可以帮助识别环境中的潜在风险区域，为工程施工安全管理提供重要的地理信息支持。Environmental hazard characteristic values are important information extracted from remote sensing satellite images, mainly including terrain complexity index and vegetation coverage rate. The terrain complexity index evaluates the undulation and complexity of the surface through edge detection and regional analysis, reflecting the terrain difficulty and safety risks that may be encountered during the construction process. Vegetation coverage rate estimates vegetation density and distribution based on image segmentation and edge analysis, which helps to evaluate the ecological environment status and potential biodiversity impacts of the construction area. Comprehensive analysis of these characteristic values can help identify potential risk areas in the environment and provide important geographic information support for engineering construction safety management.
环境隐患特征值对监测高压输电线路的安全隐患具有以下优点：Environmental hazard characteristic values have the following advantages for monitoring safety hazards of high-voltage transmission lines:
客观性和全面性： 环境隐患特征值通过遥感技术获取，能够客观地反映施工现场的地形复杂性和植被覆盖情况，避免了主观评估可能带来的误差和偏差；Objectivity and comprehensiveness: The environmental hazard characteristic values are obtained through remote sensing technology, which can objectively reflect the terrain complexity and vegetation coverage of the construction site, avoiding the errors and deviations that may be caused by subjective evaluation;
实时性和动态性： 基于遥感卫星图像的获取频率，可以实现对施工现场的实时监测。随着施工进程和环境条件的变化，环境隐患特征值能够及时更新和反映地物的变化状态；Real-time and dynamic: Based on the acquisition frequency of remote sensing satellite images, real-time monitoring of the construction site can be achieved. As the construction progress and environmental conditions change, the environmental hazard characteristic values can be updated in time to reflect the changing status of the ground objects;
高效性和经济性： 遥感技术能够迅速获取大范围的地理信息数据，节约了人力和时间成本；通过自动化处理和分析，可以快速提取出环境隐患特征值，为安全管理和决策提供及时支持；Efficiency and economy: Remote sensing technology can quickly obtain geographic information data over a wide range, saving manpower and time costs; through automated processing and analysis, it can quickly extract characteristic values of environmental hazards, providing timely support for safety management and decision-making;
多维信息： 环境隐患特征值不仅包括地形复杂性和植被覆盖率等基础信息，还可以结合其它地理、气象和环境因素进行综合分析，提供更多维度的数据支持，全面评估施工现场的安全风险；Multi-dimensional information: Environmental hazard characteristic values include not only basic information such as terrain complexity and vegetation coverage, but can also be combined with other geographical, meteorological and environmental factors for comprehensive analysis, providing more dimensional data support to comprehensively assess the safety risks of construction sites;
智能化应用： 结合现代地理信息系统（GIS）和人工智能技术，可以实现对环境隐患特征值的智能分析和预测，进一步优化施工安全管理策略和措施，提升安全管理的科学性和精准性；Intelligent application: Combining modern geographic information system (GIS) and artificial intelligence technology, it can realize intelligent analysis and prediction of environmental hazard characteristic values, further optimize construction safety management strategies and measures, and improve the scientificity and accuracy of safety management;
综上所述，环境隐患特征值作为遥感技术在高压输电线路安全监测中的应用，不仅提高了监测的全面性和精确性，还有效支持了安全管理的决策和措施制定。In summary, the application of environmental hazard characteristic values as remote sensing technology in the safety monitoring of high-voltage transmission lines not only improves the comprehensiveness and accuracy of monitoring, but also effectively supports the decision-making and formulation of safety management measures.
本实施例中，环境隐患特征值的具体获取方法如下：In this embodiment, the specific method for obtaining the environmental hazard characteristic value is as follows:
通过边缘密度和边缘分布的比值表示地形复杂指数；The terrain complexity index is expressed by the ratio of edge density and edge distribution;
根据图像分割结果，计算每个分割区域内的植被覆盖占比；According to the image segmentation results, calculate the vegetation coverage ratio in each segmented area;
将植被覆盖占比结合预先获取的边缘数量进行数据分析，计算植被覆盖率；The vegetation coverage ratio is combined with the pre-acquired edge quantity for data analysis to calculate the vegetation coverage rate;
将地形复杂指数和植被覆盖率进行加权求和计算环境隐患特征值。The environmental hazard characteristic value is calculated by taking the weighted sum of the terrain complexity index and vegetation coverage.
所述环境隐患特征值的具体计算公式如下：The specific calculation formula of the environmental hazard characteristic value is as follows:
式中，为环境隐患特征值，N为图像中占用的边缘像素数目，A为图像的面积，为图像中存在边缘像素的数量，为表示图像的总像素数目，为每个区域内的植被覆盖占比，sl为预先获取的参考边缘数量。In the formula, is the environmental hazard characteristic value, N is the number of edge pixels occupied in the image, A is the area of the image, is the number of edge pixels in the image, is the total number of pixels representing the image, is the percentage of vegetation coverage in each area, and sl is the number of reference edges obtained in advance.
根据上述公式可知，地形复杂度指数和植被覆盖率越高，环境隐患特征值也会相应增加，表示环境中地形更为复杂且植被覆盖较多，这可能意味着更高的潜在安全隐患。According to the above formula, the higher the terrain complexity index and vegetation coverage, the higher the environmental hazard characteristic value will be, indicating that the terrain in the environment is more complex and the vegetation coverage is more, which may mean higher potential safety hazards.
施工设计隐患值是通过对高压输电线路施工区域内的道路布局、设备分布以及施工区域边界等因素进行综合分析和计算得出的指标；通过边缘检测和目标检测技术，识别施工现场中道路的总长度或面积、设备的分布密度以及施工区域的边界长度或面积，然后应用加权系数对这些因素进行综合评估；施工设计隐患值的增加可能暗示着施工设计中存在道路布局不合理、设备分布不均匀或施工区域边界复杂等潜在安全隐患，为监测和管理高压输电线路施工过程中的安全问题提供了定量的评估依据。The construction design hazard value is an indicator obtained by comprehensive analysis and calculation of factors such as road layout, equipment distribution and construction area boundaries in the high-voltage transmission line construction area; through edge detection and target detection technology, the total length or area of roads in the construction site, the distribution density of equipment and the boundary length or area of the construction area are identified, and then these factors are comprehensively evaluated using weighted coefficients; an increase in the construction design hazard value may indicate potential safety hazards in the construction design, such as unreasonable road layout, uneven equipment distribution or complex construction area boundaries, which provides a quantitative assessment basis for monitoring and managing safety issues in the construction process of high-voltage transmission lines.
施工设计隐患值对监测高压输电线路的安全隐患具有以下优点：The construction design hidden danger value has the following advantages in monitoring the safety hazards of high-voltage transmission lines:
定量评估施工设计合理性：通过计算施工区域内道路总长度或面积、设备分布密度以及边界长度或面积等参数，可以量化评估施工设计的合理性；这有助于发现施工过程中可能存在的设计缺陷或不合理布局，及时调整施工计划以减少潜在安全风险；Quantitative evaluation of construction design rationality: By calculating parameters such as the total length or area of roads in the construction area, equipment distribution density, and boundary length or area, the rationality of the construction design can be quantitatively evaluated; this helps to discover possible design defects or unreasonable layouts during the construction process, and to adjust the construction plan in a timely manner to reduce potential safety risks;
提前预警潜在安全隐患：施工设计隐患值的增加可能暗示施工区域中道路布局不合理、设备分布不均匀或边界复杂等问题，这些都是可能导致事故和安全风险的因素；通过早期识别这些隐患，可以及时采取措施以减少事故发生的可能性；Early warning of potential safety hazards: An increase in the construction design hazard value may indicate problems such as unreasonable road layout, uneven equipment distribution, or complex boundaries in the construction area, which are factors that may lead to accidents and safety risks. By identifying these hazards early, timely measures can be taken to reduce the possibility of accidents.
数据驱动决策支持：施工设计隐患值提供了基于数据的量化分析结果，为监管部门、施工管理者和安全专家提供了决策支持；他们可以根据这些评估结果制定更有效的安全管理策略和应对措施，以确保施工过程的安全性和可控性；Data-driven decision support: The construction design hazard value provides quantitative analysis results based on data, providing decision support for regulatory authorities, construction managers and safety experts; they can formulate more effective safety management strategies and response measures based on these evaluation results to ensure the safety and controllability of the construction process;
持续监测和改进：施工设计隐患值作为一个定量指标，可以随着施工进程的推进进行持续监测和跟踪；通过比较不同时间点的数值变化，可以评估施工设计改进的效果，并及时调整监管和管理措施，以提高安全性和效率；Continuous monitoring and improvement: As a quantitative indicator, the construction design hazard value can be continuously monitored and tracked as the construction progresses. By comparing the changes in values at different time points, the effect of construction design improvements can be evaluated, and supervision and management measures can be adjusted in a timely manner to improve safety and efficiency.
综合性评估安全风险：将施工设计隐患值与其他隐患参数如环境隐患特征值和人员异常行为隐患值结合分析，能够全面评估高压输电线路施工过程中的安全风险，提高预警和应对能力，保障输电线路建设的顺利进行和安全运行。Comprehensive assessment of safety risks: Combining the construction design hazard values with other hazard parameters such as environmental hazard characteristic values and abnormal personnel behavior hazard values can comprehensively assess the safety risks in the construction process of high-voltage transmission lines, improve early warning and response capabilities, and ensure the smooth progress and safe operation of transmission line construction.
所述施工设计隐患值的具体获取方法如下：The specific method for obtaining the construction design hidden danger value is as follows:
基于图像分割和轮廓检测技术，识别高压输电线路施工区域内的道路布局情况，作为施工设计隐患值，具体为；Based on image segmentation and contour detection technology, the road layout in the construction area of the high-voltage transmission line is identified as the construction design hazard value, specifically:
通过边缘检测算法获取图像边缘信息；Obtain image edge information through edge detection algorithm;
根据边缘信息，计算出施工道路的总长度；Based on the edge information, the total length of the construction road is calculated;
使用目标检测算法分析施工区域内设备的位置和类型，并结合设备的位置和类型计算设备分布的密度；Use target detection algorithms to analyze the location and type of equipment in the construction area, and calculate the density of equipment distribution based on the location and type of equipment;
通过边缘检测技术确定施工区域的边界；计算出施工区域的边界长度；Determine the boundary of the construction area through edge detection technology; calculate the boundary length of the construction area;
结合施工区域的边界长度、设备分布的密度以及施工道路的总长度加权计算出施工设计隐患值。The construction design hazard value is calculated by weighting the boundary length of the construction area, the density of equipment distribution and the total length of the construction road.
人员异常行为隐患特征值指标对于监测高压输电线路的安全具有重要意义。通过分析和计算图像中检测到的人员活动的位置、移动轨迹及其与安全限制区域的接触情况，可以量化评估潜在的安全风险；高数值的人员异常行为隐患特征值可能暗示着不当行为或违反安全规定的可能性增加，这些信息可以帮助监管部门和施工管理者及时采取措施，以减少安全事故的发生可能性，提高工地的安全管理水平。The characteristic value index of hidden dangers of abnormal behavior of personnel is of great significance for monitoring the safety of high-voltage transmission lines. By analyzing and calculating the location, movement trajectory and contact with the safety restricted area of the personnel activities detected in the image, the potential safety risks can be quantitatively evaluated; high values of the characteristic value of hidden dangers of abnormal behavior of personnel may indicate an increased possibility of improper behavior or violation of safety regulations. This information can help regulatory authorities and construction managers take timely measures to reduce the possibility of safety accidents and improve the safety management level of the construction site.
人员异常行为隐患特征值对监测高压输电线路的安全隐患具有以下优点：The characteristic value of abnormal behavior hazards of personnel has the following advantages in monitoring the safety hazards of high-voltage transmission lines:
及时发现异常行为：能够通过图像分析快速检测到工作人员可能的违规行为或不安全操作，如进入禁止区域或执行异常操作；Timely detection of abnormal behavior: Through image analysis, possible violations or unsafe operations of staff can be quickly detected, such as entering prohibited areas or performing abnormal operations;
提高安全管理效率：有效识别和监测施工现场的人员活动，帮助管理人员及时采取必要的安全措施和管理手段，减少安全事故发生的可能性；Improve safety management efficiency: effectively identify and monitor personnel activities at the construction site, help managers take necessary safety measures and management methods in a timely manner, and reduce the possibility of safety accidents;
辅助安全培训和教育：通过分析异常行为数据，可以针对性地进行安全培训和教育，提高工作人员对安全规定和操作规程的遵守度；Assisted safety training and education: By analyzing abnormal behavior data, targeted safety training and education can be carried out to improve staff's compliance with safety regulations and operating procedures;
预防事故发生：通过预警和监控，避免潜在的危险情况和不安全行为，从而有效减少安全事故的发生，保障高压输电线路施工的顺利进行和设备的安全运行。Prevent accidents: Avoid potential dangerous situations and unsafe behaviors through early warning and monitoring, thereby effectively reducing the occurrence of safety accidents and ensuring the smooth progress of high-voltage transmission line construction and the safe operation of equipment.
所述人员异常行为隐患特征值的具体获取方法如下：The specific method for obtaining the characteristic value of the hidden danger of abnormal behavior of personnel is as follows:
基于员工行为准则和实时添加，获取人员异常行为集，所述人员异常行为集包括各种违反了规定的人员行为、移动路径以及异常人员位置；Based on the employee behavior code and real-time addition, a set of abnormal personnel behaviors is obtained, wherein the set of abnormal personnel behaviors includes various personnel behaviors that violate regulations, movement paths, and abnormal personnel positions;
对人员异常行为集内的各种异常行为赋予预设的隐患分数；Assign preset hidden danger scores to various abnormal behaviors in the personnel abnormal behavior set;
基于人员异常行为集和对应隐患分数的构建回归模型，生成人员异常行为隐患特征值。A regression model is constructed based on the set of abnormal behaviors of personnel and the corresponding hidden danger scores to generate the characteristic values of hidden dangers of abnormal behaviors of personnel.
所述人员异常行为隐患特征值的具体计算公式如下：The specific calculation formula of the hidden danger characteristic value of abnormal behavior of personnel is as follows:
式中，为人员异常行为隐患特征值，……为异常行为对应的预设的隐患分数，……为异常行为在所有人员行为的占比，为异常行为的类型，n为正整数，为异常行为的编号。In the formula, is the hidden danger characteristic value of abnormal behavior of personnel, … is the preset hidden danger score corresponding to the abnormal behavior, … is the proportion of abnormal behaviors in all personnel behaviors, is the type of abnormal behavior, n is a positive integer, and is the number of the abnormal behavior.
由计算公式可知，高隐患的异常行为越多，占比越大，则人员异常行为隐患特征值越大，表明施工安全隐患越大。It can be seen from the calculation formula that the more abnormal behaviors with high hidden dangers there are and the larger their proportion is, the larger the hidden danger characteristic value of abnormal behavior of personnel is, indicating that the construction safety hazard is greater.
S5，基于预先得到的基于卷积神经网络的施工隐患评估模型对所述隐患特征值进行数据分析，并根据分析结果对高压输电线路施工进行安全隐患监测。S5, performing data analysis on the hidden danger characteristic values based on the pre-obtained construction hidden danger assessment model based on the convolutional neural network, and monitoring the safety hidden dangers of the high-voltage transmission line construction according to the analysis results.
本实施例中，根据分析结果对高压输电线路施工进行安全隐患监测，具体为：In this embodiment, the safety hazard monitoring of the high-voltage transmission line construction is performed according to the analysis results, specifically:
基于预先得到的基于卷积神经网络的施工隐患评估模型对所述隐患特征值进行数据分析，生成施工隐患评估值；Performing data analysis on the hidden danger characteristic values based on a pre-obtained construction hidden danger assessment model based on a convolutional neural network to generate a construction hidden danger assessment value;
将施工隐患评估值与预设的隐患阈值做比较，当施工隐患评估值大于预设的隐患阈值时，发出警报。The construction hazard assessment value is compared with the preset hazard threshold. When the construction hazard assessment value is greater than the preset hazard threshold, an alarm is issued.
本实施例中，预先得到的基于卷积神经网络的施工隐患评估模型具体计算公式为：In this embodiment, the specific calculation formula of the construction hazard assessment model based on the convolutional neural network obtained in advance is:
式中，为施工隐患评估值，为环境隐患特征值的预设比例系数，为环境隐患特征值，为施工设计隐患值的预设比例系数，为施工设计隐患值，为人员异常行为隐患特征值的预设比例系数，为人员异常行为隐患特征值。In the formula, is the construction hazard assessment value, is the preset proportional coefficient of the environmental hazard characteristic value, is the characteristic value of environmental hazard, It is the preset proportional coefficient of the construction design hidden danger value. Design hazard value for construction. is the preset proportional coefficient of the characteristic value of the hidden danger of abnormal behavior of personnel, It is the hidden danger characteristic value of abnormal behavior of personnel.
由施工隐患评估值的计算公式可知，当施工隐患评估值的表现值越大，则施工过程的安全隐患越大。It can be seen from the calculation formula of the construction hazard assessment value that the greater the performance value of the construction hazard assessment value, the greater the safety hazard in the construction process.
本实施例通过将多项隐患图像参数进行特征提取和数据分析，得到对应的隐患特征值，包括环境隐患特征值、施工设计隐患值和人员异常行为隐患特征值；环境隐患特征值通过遥感卫星图像提取，包括地形复杂性指标和植被覆盖率，能客观反映施工现场的地形难度和生态环境情况，为安全管理提供重要的地理信息支持。This embodiment obtains corresponding hidden danger feature values by performing feature extraction and data analysis on multiple hidden danger image parameters, including environmental hidden danger feature values, construction design hidden danger values and personnel abnormal behavior hidden danger feature values; environmental hidden danger feature values are extracted through remote sensing satellite images, including terrain complexity indicators and vegetation coverage rate, which can objectively reflect the terrain difficulty and ecological environment conditions of the construction site, and provide important geographic information support for safety management.
实施例3，如图2提供的一种基于卫星遥感的高压输电线路施工安全监测系统，包括图像采集模块、图像预处理模块、隐患区分模块、隐患分析模块和隐患监测模块，模块之间存在关联；Embodiment 3, as shown in FIG2 , a high-voltage transmission line construction safety monitoring system based on satellite remote sensing includes an image acquisition module, an image preprocessing module, a hidden danger distinguishing module, a hidden danger analyzing module and a hidden danger monitoring module, and there are associations between the modules;
图像采集模块，用于采集高压输电线路施工时的遥感卫星图像数据集；Image acquisition module, used to collect remote sensing satellite image data sets during the construction of high-voltage transmission lines;
图像预处理模块，用于将所述遥感卫星图像数据集进行清洗，得到标准的遥感卫星图像数据集；An image preprocessing module is used to clean the remote sensing satellite image data set to obtain a standard remote sensing satellite image data set;
隐患区分模块，用于基于分割算法对遥感卫星图像数据集进行地物分类，得到多项隐患图像参数，所述多项隐患图像参数包括环境图像隐患参数、施工设计隐患图像参数以及人员异常行为隐患参数；A hidden danger differentiation module is used to classify objects in a remote sensing satellite image data set based on a segmentation algorithm to obtain multiple hidden danger image parameters, including environmental image hidden danger parameters, construction design hidden danger image parameters, and personnel abnormal behavior hidden danger parameters;
隐患分析模块，用于将多项隐患图像参数分别进行特征提取，进行数据分析得到对应的隐患特征值；The hidden danger analysis module is used to extract features of multiple hidden danger image parameters respectively, and perform data analysis to obtain corresponding hidden danger feature values;
隐患监测模块，用于基于预先得到的基于卷积神经网络的施工隐患评估模型对所述隐患特征值进行数据分析，并根据分析结果对高压输电线路施工进行安全隐患监测。The hidden danger monitoring module is used to perform data analysis on the hidden danger characteristic values based on a pre-obtained construction hidden danger assessment model based on a convolutional neural network, and to monitor safety hidden dangers in the construction of high-voltage transmission lines according to the analysis results.
上述公式均是去量纲取其数值计算，公式是由采集大量数据进行软件模拟得到最近真实情况的一个公式，公式中的预设参数由本领域的技术人员根据实际情况进行设置。The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.
上述实施例，可以全部或部分地通过软件、硬件、固件或其他任意组合来实现。当使用软件实现时，上述实施例可以全部或部分地以计算机程序产品的形式实现。The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的模块及算法步骤，能够以电子硬件，或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
另外，在本申请各个实施例中的各功能模块可以集成在一个处理模块中，也可以是各个模块单独物理存在，也可以两个或两个以上模块集成在一个模块中。In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
最后：以上所述仅为本发明的优选实施例而已，并不用于限制本发明，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411039189.5ACN118570665B (en) | 2024-07-31 | 2024-07-31 | High-voltage transmission line construction safety monitoring system and method based on satellite remote sensing |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202411039189.5ACN118570665B (en) | 2024-07-31 | 2024-07-31 | High-voltage transmission line construction safety monitoring system and method based on satellite remote sensing |
| Publication Number | Publication Date |
|---|---|
| CN118570665Atrue CN118570665A (en) | 2024-08-30 |
| CN118570665B CN118570665B (en) | 2024-11-22 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202411039189.5AActiveCN118570665B (en) | 2024-07-31 | 2024-07-31 | High-voltage transmission line construction safety monitoring system and method based on satellite remote sensing |
| Country | Link |
|---|---|
| CN (1) | CN118570665B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119250537A (en)* | 2024-11-29 | 2025-01-03 | 国网江苏省电力有限公司电力科学研究院 | A method for line fault diagnosis based on satellite and meteorological information |
| CN119444810A (en)* | 2025-01-13 | 2025-02-14 | 国网江苏省电力有限公司电力科学研究院 | A method and system for analyzing the full life cycle of hidden dangers |
| CN119672557A (en)* | 2024-12-09 | 2025-03-21 | 北京华联电力工程咨询有限公司 | A method for assisting the construction layout of environmental protection projects based on satellite real-scene three-dimensional model |
| CN120375220A (en)* | 2025-06-26 | 2025-07-25 | 北京华联电力工程咨询有限公司 | Construction site environment state monitoring method based on satellite images and unmanned aerial vehicle data |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102298147A (en)* | 2011-05-27 | 2011-12-28 | 国网电力科学研究院 | Super-high/ultra-high voltage power transmission line radar remote sensing monitoring method |
| CN107657260A (en)* | 2017-10-09 | 2018-02-02 | 济南大学 | The detection method of ground hidden danger in a kind of transmission line of electricity surrounding enviroment |
| US20230360390A1 (en)* | 2022-05-09 | 2023-11-09 | Zhejiang University | Transmission line defect identification method based on saliency map and semantic-embedded feature pyramid |
| US20230386199A1 (en)* | 2022-05-26 | 2023-11-30 | International Business Machines Corporation | Automated hazard recognition using multiparameter analysis of aerial imagery |
| CN117612098A (en)* | 2023-11-28 | 2024-02-27 | 云南电网有限责任公司电力科学研究院 | Method, device, equipment and storage medium for detecting hidden trouble points of power transmission line |
| WO2024089479A1 (en)* | 2023-07-17 | 2024-05-02 | Alizadeh Majid | Method and system for early recognition of accident-prone individuals through neural-based safety in the jobs involved extreme safety hazards |
| US20240216731A1 (en)* | 2021-09-10 | 2024-07-04 | Electric Power Research Institute Of Guangdong Power Grid Co., Ltd. | Three-dimensional wildfire monitoring and control method and system for power transmission corridor |
| KR102688965B1 (en)* | 2023-01-31 | 2024-07-29 | 서길원 | Monitoring system of railroad and method for control of that |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102298147A (en)* | 2011-05-27 | 2011-12-28 | 国网电力科学研究院 | Super-high/ultra-high voltage power transmission line radar remote sensing monitoring method |
| CN107657260A (en)* | 2017-10-09 | 2018-02-02 | 济南大学 | The detection method of ground hidden danger in a kind of transmission line of electricity surrounding enviroment |
| US20240216731A1 (en)* | 2021-09-10 | 2024-07-04 | Electric Power Research Institute Of Guangdong Power Grid Co., Ltd. | Three-dimensional wildfire monitoring and control method and system for power transmission corridor |
| US20230360390A1 (en)* | 2022-05-09 | 2023-11-09 | Zhejiang University | Transmission line defect identification method based on saliency map and semantic-embedded feature pyramid |
| US20230386199A1 (en)* | 2022-05-26 | 2023-11-30 | International Business Machines Corporation | Automated hazard recognition using multiparameter analysis of aerial imagery |
| KR102688965B1 (en)* | 2023-01-31 | 2024-07-29 | 서길원 | Monitoring system of railroad and method for control of that |
| WO2024089479A1 (en)* | 2023-07-17 | 2024-05-02 | Alizadeh Majid | Method and system for early recognition of accident-prone individuals through neural-based safety in the jobs involved extreme safety hazards |
| CN117612098A (en)* | 2023-11-28 | 2024-02-27 | 云南电网有限责任公司电力科学研究院 | Method, device, equipment and storage medium for detecting hidden trouble points of power transmission line |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119250537A (en)* | 2024-11-29 | 2025-01-03 | 国网江苏省电力有限公司电力科学研究院 | A method for line fault diagnosis based on satellite and meteorological information |
| CN119672557A (en)* | 2024-12-09 | 2025-03-21 | 北京华联电力工程咨询有限公司 | A method for assisting the construction layout of environmental protection projects based on satellite real-scene three-dimensional model |
| CN119444810A (en)* | 2025-01-13 | 2025-02-14 | 国网江苏省电力有限公司电力科学研究院 | A method and system for analyzing the full life cycle of hidden dangers |
| CN120375220A (en)* | 2025-06-26 | 2025-07-25 | 北京华联电力工程咨询有限公司 | Construction site environment state monitoring method based on satellite images and unmanned aerial vehicle data |
| Publication number | Publication date |
|---|---|
| CN118570665B (en) | 2024-11-22 |
| Publication | Publication Date | Title |
|---|---|---|
| CN118570665A (en) | High-voltage transmission line construction safety monitoring system and method based on satellite remote sensing | |
| US10729117B2 (en) | Pest monitoring method based on machine vision | |
| CN117933825A (en) | Forest protection restoration ecological effect evaluation method based on multispectral unmanned aerial vehicle | |
| CN118887767B (en) | Forest fire early warning method, system and equipment based on satellite Internet of things | |
| CN111931559A (en) | A Classification Method of Tree Species in Transmission Line Corridor Area | |
| CN117636129A (en) | A recognition method for electric power construction supervision images | |
| CN118397452A (en) | Ecological bearing capacity estimation method based on multi-source remote sensing data | |
| CN111091069A (en) | A power grid target detection method and system guided by blind image quality assessment | |
| CN119963933B (en) | Risk classification and early warning method and system for garden plants based on artificial intelligence | |
| WO2025107839A1 (en) | Method and apparatus for drawing distributed map of ice covering-prone regions of power grid | |
| CN119164894A (en) | A method, device, medium and product for crop monitoring based on multispectral | |
| CN118429821A (en) | Flood disaster range extraction method | |
| CN117523397A (en) | A method for identifying building facade hazards based on image recognition | |
| dos Santos et al. | Deep learning applied to equipment detection on flat roofs in images captured by UAV | |
| CN119355840A (en) | An intelligent early warning system for road meteorological environment monitoring | |
| CN120126003A (en) | Green plant disease identification and early warning management system and method | |
| CN118736451A (en) | A photoelectric detection method for power transmission lines | |
| CN120375314A (en) | Intelligent pavement disease diagnosis method based on image recognition | |
| CN119339535A (en) | Fire monitoring method and fire monitoring device | |
| CN117726959B (en) | UAV power line safety inspection system and method based on intelligent image recognition | |
| CN118736421A (en) | Tree nematode disease identification method, device and electronic device based on deep learning | |
| CN118586648A (en) | A GIS-based national land space planning method, system and device | |
| CN118155125A (en) | Workshop hoisting operation safety production monitoring method and device, electronic equipment and medium | |
| CN119599528B (en) | Hidden danger evaluation method for power transmission channel | |
| CN119324575B (en) | Application method and system of electrical equipment fire control monitoring system in mountain photovoltaic power station |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |