Geological disaster monitoring and early warning method and system
阅读说明：本技术 一种地质灾害监测预警方法和系统 (Geological disaster monitoring and early warning method and system ) 是由 朱翔 于 2019-12-02 设计创作，主要内容包括：本发明提供了一种地质灾害检测预警系统和系统,包括：飞行时间TOF相机根据接收到的监控数据采集指令采集所属监控区域的环境图像,生成三维点云数据,并将其与相机ID一起发送给监控服务器；监控服务器对三维点云数据进行去噪处理和特征数据提取,得到监控区域的地貌特征点云数据,并保存在监控区域ID对应的区域地貌特征数据列表中,然后将其与监控区域的基准地貌特征点云数据进行分析对比处理,得到监控区域的地貌形态变化数据；监控处理器确定监控区域的变化数据预警阈值,经判断监控区域的地貌形态变化数据超出变化数据预警阈值时,向监控区域ID对应的预警处理中心发送第一预警信息；第一预警信息包括：监控区域ID和地貌形态变化数据。(The invention provides a geological disaster detection early warning system and a geological disaster detection early warning system, which comprise: the method comprises the steps that a time of flight (TOF) camera collects an environment image of a monitored area according to a received monitoring data collecting instruction, generates three-dimensional point cloud data and sends the three-dimensional point cloud data and a camera ID to a monitoring server; the monitoring server carries out denoising processing and characteristic data extraction on the three-dimensional point cloud data to obtain landform characteristic point cloud data of a monitoring area, the landform characteristic point cloud data of the monitoring area are stored in an area landform characteristic data list corresponding to a monitoring area ID, and then the landform characteristic point cloud data and reference landform characteristic point cloud data of the monitoring area are analyzed and contrasted to obtain landform form change data of the monitoring area; the monitoring processor determines a change data early warning threshold value of the monitored area, and sends first early warning information to an early warning processing center corresponding to the ID of the monitored area when judging that the landform form change data of the monitored area exceeds the change data early warning threshold value; the first warning information includes: monitoring area ID and topographic variation data.)
1. The geological disaster monitoring and early warning method is characterized by comprising the following steps:
the method comprises the steps that a time of flight (TOF) camera collects an environment image of a monitored area according to a received monitoring data collection instruction to generate three-dimensional point cloud data;
the TOF camera sends the three-dimensional point cloud data and the camera ID to a monitoring server;
the monitoring server carries out denoising processing on the three-dimensional point cloud data to obtain denoised three-dimensional point cloud data;
the monitoring processor extracts feature data of the denoised three-dimensional point cloud data to obtain landform feature point cloud data of a monitored area, and stores the landform feature point cloud data in an area landform feature data list corresponding to the ID of the monitored area;
the monitoring processor analyzes and contrasts the landform characteristic point cloud data and the reference landform characteristic point cloud data of the monitoring area to obtain landform form change data of the monitoring area;
the monitoring processor searches a monitoring area control data list according to the ID of the monitoring area and determines a change data early warning threshold value of the monitoring area;
when the landform form change data of the monitored area exceeds the change data early warning threshold value, the monitoring processor sends first early warning information to an early warning processing center corresponding to the ID of the monitored area; the first warning information includes: the monitoring area ID and the geomorphic morphological change data.
2. The geological disaster monitoring and early warning method as claimed in claim 1, wherein before the TOF camera collects the environmental image of the monitored area according to the preset frequency, the geological disaster monitoring and early warning method further comprises:
and the monitoring processor receives a monitoring starting instruction input from the outside, generates a monitoring image data acquisition instruction according to the preset frequency and sends the monitoring image data acquisition instruction to the TOF camera.
3. The geological disaster monitoring and early warning method as claimed in claim 1, wherein the geological disaster monitoring and early warning method further comprises:
the monitoring processor acquires the point cloud data of the first continuous number of landform features stored in the regional landform feature data list;
performing trend analysis on the continuous first number of landform feature point cloud data to obtain regional landform feature prediction data; the regional topographic feature prediction data comprises a second amount of prediction data;
when the second amount of predicted data contains data exceeding the change data early warning threshold, the monitoring processor sends second early warning information to an early warning processing center corresponding to the monitored area ID; the second warning information includes: the monitoring area ID, the continuous first number of topographic feature point cloud data and the area topographic feature prediction data.
4. The geological disaster monitoring and early warning method as claimed in claim 3, wherein said method further comprises:
the early warning processing center analyzes the received second early warning information to obtain the ID of the monitoring area and the regional landform characteristic prediction data;
the early warning processing center determines early warning levels according to the regional landform feature prediction data and determines early warning regions according to the monitoring IDs;
and the early warning processing center generates an early warning prompt message according to the early warning level and the early warning area and sends the early warning prompt message to the user terminal in the early warning area.
5. The geological disaster monitoring and early warning method as claimed in claim 1, wherein the geological disaster monitoring and early warning method further comprises:
the monitoring processor acquires the point cloud data of the first continuous number of landform features stored in the regional landform feature data list;
performing trend analysis on the continuous first number of landform feature point cloud data to obtain regional landform feature change trend data; the regional topographic feature change trend data comprises a plurality of relative change rate data; each relative change rate data is obtained by calculation according to two adjacent landform feature point cloud data;
when the change trend of the relative change rate data is continuously increased, the monitoring processor sends second early warning information to an early warning processing center corresponding to the monitored area ID; the second warning information includes: the monitoring area ID, the continuous first number of the landform feature point cloud data and the area landform feature change trend data.
6. The geological disaster monitoring and early warning method as claimed in claim 4, wherein said method further comprises:
the early warning processing center analyzes the received second early warning information to obtain the ID of the monitoring area and the change trend data of the landform characteristics of the area;
the early warning processing center determines early warning levels according to the regional landform feature change trend data and determines early warning regions according to the monitoring IDs;
and the early warning processing center generates an early warning prompt message according to the early warning level and the early warning area and sends the early warning prompt message to the user terminal in the early warning area.
7. The geological disaster monitoring and early warning method as claimed in claim 1, wherein said method further comprises:
the early warning processing center analyzes the received first early warning information to obtain the landform morphological change data and the monitoring area ID;
the early warning processing center determines an early warning level according to the landform form change data; determining an early warning area according to the monitoring ID;
and the early warning processing center generates an early warning prompt message according to the early warning level and the early warning area and sends the early warning prompt message to the user terminal in the early warning area.
8. The geological disaster monitoring and early warning method as claimed in claim 4, 6 or 7, wherein the early warning processing center determines the early warning area according to the monitoring ID specifically as follows:
the early warning processing center searches the position of the monitoring area in a monitoring area information list according to the monitoring ID to obtain the central position data of the monitoring area corresponding to the monitoring ID;
and the early warning processing center determines an area with the central position data of the monitored area as the central radius and the radius smaller than or equal to the first radius as the early warning area according to the position data of the monitored area.
9. The utility model provides a geological disaster monitoring and early warning system which characterized in that, geological disaster monitoring and early warning system includes: the system comprises a line time TOF camera, a monitoring processor and an early warning processing center;
the time of flight TOF camera is used for acquiring an environment image of a monitoring area to which the TOF camera belongs according to a received monitoring data acquisition instruction and generating three-dimensional point cloud data;
the TOF camera is further used for sending the three-dimensional point cloud data and the camera ID to a monitoring server;
the monitoring server is used for denoising the three-dimensional point cloud data to obtain denoised three-dimensional point cloud data;
the monitoring processor is also used for extracting feature data of the denoised three-dimensional point cloud data to obtain landform feature point cloud data of a monitoring area and storing the landform feature point cloud data in an area landform feature data list corresponding to the ID of the monitoring area;
the monitoring processor is also used for analyzing and comparing the point cloud data of the landform characteristic with the point cloud data of the reference landform characteristic of the monitoring area to obtain landform form change data of the monitoring area;
the monitoring processor is also used for searching a monitoring area control data list according to the ID of the monitoring area and determining a change data early warning threshold value of the monitoring area;
when the landform form change data of the monitored area exceeds the change data early warning threshold, the monitoring processor is further used for sending first early warning information to an early warning processing center corresponding to the monitored area ID; the first warning information includes: the monitoring area ID and the geomorphic morphological change data.
10. The geological disaster monitoring and early warning system as claimed in claim 9, wherein said geological disaster monitoring and early warning system further comprises:
and the user terminal is used for receiving the early warning prompt message sent by the early warning processing center and displaying or broadcasting in voice.
Technical Field
The invention relates to the field of early warning prevention and control, in particular to a geological disaster monitoring and early warning method and system.
Background
Landslide and debris flow are common geological disasters, the debris flow is caused by heavy rain or rain to enable sand, soil and stones to reach water saturation and reach a liquefied state, and the liquefied sand, soil and stones flow to a low-lying position under the action of gravity, so that life and property of people are harmed. The landslide is caused by heavy rain or rain, is broken by a weak zone of the mountain and integrally slides down, so that the landslide can be a residual slope deposit in the quaternary period and can also be weathered bedrock. Landslide and debris flow are the most common geological disasters, which bring much inconvenience to the life of people and cause huge life and property loss. Therefore, detection and early warning of landslide and debris flow become important links for preventing and treating disasters and reducing loss of people and countries.
The existing landslide technology also acquires data in a manual measurement mode, and a monitoring mechanism invests a large amount of manpower to manually measure important areas to obtain mountain change data. Through the manual measurement mode, on one hand, the labor is consumed, and the investment amount is large; on the other hand, the accuracy of the measured data also has the influence of an artificial subjective measurement technology, for example, it is difficult to ensure that the measurement location is fixed every time through artificial measurement, which has a subjective influence on the measurement value and the calculation result. And moreover, data obtained by manual measurement are recorded manually, so that the measurement speed is low and the monitoring efficiency is low.
With the development of internet technology, people apply advanced information technology, communication technology, computer technology and the like to the field of prevention and control of geological disasters. Due to the particularity of the terrain conditions of the geological disaster area, the difficulty in acquiring data of the geological disaster area greatly limits the development of the geological disaster prevention and control automation, and especially at night, the acquisition of the data of the geological disaster area becomes an important reason for hindering the development of the geological disaster area.
Disclosure of Invention
Aiming at the defects Of the prior art, the embodiment Of the invention aims to provide a geological disaster monitoring and early warning method and system, wherein a Time Of Flight (TOF) camera which is installed at a fixed position in a monitoring area is used for collecting an environmental image Of the monitoring area according to a preset frequency, so as to generate three-dimensional point cloud data and send the three-dimensional point cloud data to a monitoring processor. The collection of the image data is not influenced by external illumination light, and the environmental image data of the monitored area can be collected even under the dark condition. The monitoring processor analyzes the received three-dimensional point cloud data to obtain the landform form change data of the monitored area, then judges according to the landform form change data of the monitored area, generates early warning information, sends the early warning information to the early warning processing center for further analysis, judges early warning level, generates early warning prompt information and sends the early warning prompt information to the user terminal.
In order to achieve the above object, in one aspect, the present invention provides a geological disaster monitoring and early warning method, including:
the method comprises the steps that a time of flight (TOF) camera collects an environment image of a monitored area according to a received monitoring data collection instruction to generate three-dimensional point cloud data;
the TOF camera sends the three-dimensional point cloud data and the camera ID to a monitoring server;
the monitoring server carries out denoising processing on the three-dimensional point cloud data to obtain denoised three-dimensional point cloud data;
the monitoring processor extracts feature data of the denoised three-dimensional point cloud data to obtain landform feature point cloud data of a monitored area, and stores the landform feature point cloud data in an area landform feature data list corresponding to the ID of the monitored area;
the monitoring processor analyzes and contrasts the landform characteristic point cloud data and the reference landform characteristic point cloud data of the monitoring area to obtain landform form change data of the monitoring area;
the monitoring processor searches a monitoring area control data list according to the ID of the monitoring area and determines a change data early warning threshold value of the monitoring area;
when the landform form change data of the monitored area exceeds the change data early warning threshold value, the monitoring processor sends first early warning information to an early warning processing center corresponding to the ID of the monitored area; the first warning information includes: the monitoring area ID and the geomorphic morphological change data.
Preferably, before the TOF camera acquires the environmental image of the monitored area according to a preset frequency, the geological disaster monitoring and early warning method further includes:
and the monitoring processor receives a monitoring starting instruction input from the outside, generates a monitoring image data acquisition instruction according to the preset frequency and sends the monitoring image data acquisition instruction to the TOF camera.
Preferably, the geological disaster monitoring and early warning method further comprises:
the monitoring processor acquires the point cloud data of the first continuous number of landform features stored in the regional landform feature data list;
performing trend analysis on the continuous first number of landform feature point cloud data to obtain regional landform feature prediction data; the regional topographic feature prediction data comprises a second amount of prediction data;
when the second amount of predicted data contains data exceeding the change data early warning threshold, the monitoring processor sends second early warning information to an early warning processing center corresponding to the monitored area ID; the second warning information includes: the monitoring area ID, the continuous first number of topographic feature point cloud data and the area topographic feature prediction data.
Further preferably, the method further comprises:
the early warning processing center analyzes the received second early warning information to obtain the ID of the monitoring area and the regional landform characteristic prediction data;
the early warning processing center determines early warning levels according to the regional landform feature prediction data and determines early warning regions according to the monitoring IDs;
and the early warning processing center generates an early warning prompt message according to the early warning level and the early warning area and sends the early warning prompt message to the user terminal in the early warning area.
Further preferably, the geological disaster monitoring and early warning method further includes:
the monitoring processor acquires the point cloud data of the first continuous number of landform features stored in the regional landform feature data list;
performing trend analysis on the continuous first number of landform feature point cloud data to obtain regional landform feature change trend data; the regional topographic feature change trend data comprises a plurality of relative change rate data; each relative change rate data is obtained by calculation according to two adjacent landform feature point cloud data;
when the change trend of the relative change rate data is continuously increased, the monitoring processor sends second early warning information to an early warning processing center corresponding to the monitored area ID; the second warning information includes: the monitoring area ID, the continuous first number of the landform feature point cloud data and the area landform feature change trend data.
Further preferably, the method further comprises:
the early warning processing center analyzes the received second early warning information to obtain the ID of the monitoring area and the change trend data of the landform characteristics of the area;
the early warning processing center determines early warning levels according to the regional landform feature change trend data and determines early warning regions according to the monitoring IDs;
and the early warning processing center generates an early warning prompt message according to the early warning level and the early warning area and sends the early warning prompt message to the user terminal in the early warning area.
Preferably, the method further comprises:
the early warning processing center analyzes the received first early warning information to obtain the landform morphological change data and the monitoring area ID;
the early warning processing center determines an early warning level according to the landform form change data; determining an early warning area according to the monitoring ID;
and the early warning processing center generates an early warning prompt message according to the early warning level and the early warning area and sends the early warning prompt message to the user terminal in the early warning area.
Further preferably, the determining, by the early warning processing center according to the monitoring ID, an early warning area specifically includes:
the early warning processing center searches the position of the monitoring area in a monitoring area information list according to the monitoring ID to obtain the central position data of the monitoring area corresponding to the monitoring ID;
and the early warning processing center determines an area with the central position data of the monitored area as the central radius and the radius smaller than or equal to the first radius as the early warning area according to the position data of the monitored area.
In another aspect, the present invention provides a geological disaster monitoring and early warning system, including: the system comprises a line time TOF camera, a monitoring processor and an early warning processing center;
the time of flight TOF camera is used for acquiring an environment image of a monitoring area to which the TOF camera belongs according to a received monitoring data acquisition instruction and generating three-dimensional point cloud data;
the TOF camera is further used for sending the three-dimensional point cloud data and the camera ID to a monitoring server;
the monitoring server is used for denoising the three-dimensional point cloud data to obtain denoised three-dimensional point cloud data;
the monitoring processor is also used for extracting feature data of the denoised three-dimensional point cloud data to obtain landform feature point cloud data of a monitoring area and storing the landform feature point cloud data in an area landform feature data list corresponding to the ID of the monitoring area;
the monitoring processor is also used for analyzing and comparing the point cloud data of the landform characteristic with the point cloud data of the reference landform characteristic of the monitoring area to obtain landform form change data of the monitoring area;
the monitoring processor is also used for searching a monitoring area control data list according to the ID of the monitoring area and determining a change data early warning threshold value of the monitoring area;
when the landform form change data of the monitored area exceeds the change data early warning threshold, the monitoring processor is further used for sending first early warning information to an early warning processing center corresponding to the monitored area ID; the first warning information includes: the monitoring area ID and the geomorphic morphological change data.
Preferably, the geological disaster monitoring and early warning system further comprises:
and the user terminal is used for receiving the early warning prompt message sent by the early warning processing center and displaying or broadcasting in voice.
According to the geological disaster monitoring and early warning method provided by the embodiment of the invention, by using the TOF camera, the environmental image acquisition of the environmental image by the TOF camera is not influenced by the environmental light according to the preset frequency, the three-dimensional point cloud data is generated and sent to the monitoring processor, the three-dimensional point cloud data acquired by the monitoring processor is analyzed to obtain the landform form change data, the relation between the landform form change data and the change data early warning threshold value is determined, and when the landform change data exceeds the change data early warning threshold value, the monitoring processor sends first early warning information to the early warning processing center corresponding to the monitoring area. And the early warning processing center performs grade judgment on the first early warning information according to the first early warning information, generates an early warning prompt message and sends the early warning prompt message to the user terminal in the early warning area so as to remind people of harm. The method provided by the embodiment of the invention can finish automatic monitoring and early warning of geological disasters, improves the accuracy of measurement, improves the timeliness of early warning, and greatly reduces the cost of manpower and material resources for manually acquiring data and processing data.
Drawings
Fig. 1 is a flowchart of a geological disaster monitoring and early warning method according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for performing early warning according to a plurality of continuous point cloud data of topographic features according to an embodiment of the present invention;
fig. 3 is a flowchart of a method for performing early warning according to a change trend of point cloud data of topographic features according to an embodiment of the present invention;
fig. 4 is a system block diagram of a geological disaster monitoring and early warning system provided in the embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be further noted that, for the convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
The invention discloses a geological disaster monitoring and early warning method, which is used for monitoring and early warning landslides and debris flows in a geological disaster area, and fig. 1 is a flow chart of the geological disaster monitoring and early warning method provided by the embodiment of the invention, and comprises the following steps as shown in the figure:
and 101, receiving a monitoring starting instruction input from the outside by a monitoring processor, generating a monitoring image data acquisition instruction according to a preset frequency, and sending the monitoring image data acquisition instruction to a TOF camera.
Specifically, after receiving a monitoring start instruction of an administrator user, the monitoring processor generates a monitoring image data acquisition instruction according to a preset frequency, and sends the monitoring image data acquisition instruction to the TOF camera. The TOF camera is installed at a fixed position in the monitoring area, for example, a mountain or a specially-arranged support rod of the installation equipment, etc. where the TOF camera can be fixed.
The preset frequency is evaluated according to the geological condition and the weather condition of the monitored area and then written into the monitoring processor. For example, geological conditions such as soil moisture, rock hardness and the like in the monitored area are measured and then determined after comprehensive evaluation is carried out in combination with weather conditions. Generally, the soil humidity is in a direct proportion relation with the preset frequency, and the rainfall is also in a direct proportion relation with the preset frequency. In one specific example of the embodiment of the present invention, the preset frequency is 6 times/day, that is, data is collected every 4 hours.
And 102, acquiring an environment image of a monitoring area to which the TOF camera belongs according to a received monitoring data acquisition instruction to generate three-dimensional point cloud data.
Specifically, the TOF camera receives a monitoring data acquisition instruction to shoot an environment image of a monitoring area, and the TOF camera analyzes and processes the acquired image environment information through a processing unit of the TOF camera to generate three-dimensional point cloud data.
The TOF camera adopted in the embodiment of the invention transmits the optical signal through the built-in laser emission module and acquires the distance field depth data of the three-dimensional scene through the built-in Complementary Metal Oxide Semiconductor (CMOS) pixel array, the imaging rate can reach hundreds of frames per second, and meanwhile, the TOF camera has a compact structure and low power consumption. The three-dimensional data acquisition mode for the target scene is as follows: TOF cameras use an amplitude modulated light source that actively illuminates the target scene and is coupled to an associated sensor that is locked onto each pixel of the same frequency. The emission light of the built-in laser emission and the reflected light emitted after the emission light irradiates on the scene object have phase shift, and multiple measurements are obtained by detecting different phase shift amounts between the emission light and the reflected light. The amplitude modulation of the built-in laser transmitter is in the modulation frequency interval of 10-100MH, while the frequency controls the TOF camera sensor depth range and depth resolution. Meanwhile, a processing unit of the TOF camera independently executes phase difference calculation on each pixel to obtain depth data of a target scene, the processing unit of the TOF camera analyzes and calculates the reflection intensity of the reflected light to obtain intensity data of the target scene, and the intensity data of the target scene is analyzed and processed by combining the acquired two-dimensional data to obtain three-dimensional point cloud data of the target scene.
In a specific example of the embodiment of the present invention, the TOF camera uses a solid-state laser or an LED array as a built-in laser transmitter that transmits light waves with a wavelength around 850 nm. The emitting light source is continuous square wave or sine wave obtained by continuous modulation. The TOF camera processing unit obtains intensity data by calculating phase angles of emitted light and reflected light in a plurality of sampling samples and distances of target objects, analyzing and calculating current intensity converted by reflected light intensity, and then performing fusion processing by combining two-dimensional image data obtained by the optical camera to obtain three-dimensional point cloud data of a target scene.
In the process of collecting the environment image of the monitored area, due to the fact that scene shooting is carried out through non-visible light actively emitted by the TOF camera, clear three-dimensional point cloud data of the environment image of the monitored area can be obtained even under the dark condition. Therefore, the method provided by the embodiment of the invention is also suitable for use in night or dark environment with poor lighting state or even without lighting.
Specifically, each TOF camera stores a camera ID, and the monitoring area ID of the monitoring area to which each TOF camera ID belongs corresponds to each other. The TOF camera sends the generated three-dimensional point cloud data to the monitoring server together with the monitoring area ID so that the monitoring server can determine which TOF camera acquired the three-dimensional point cloud data.
And 104, the monitoring server carries out denoising processing on the three-dimensional point cloud data to obtain denoised three-dimensional point cloud data.
Specifically, the monitoring server selects a specific filtering mode to filter the received three-dimensional point cloud data, and removes noise in the three-dimensional point cloud data. The three-dimensional point cloud data is subjected to filtering processing using, for example, the following method:
in the embodiment of the invention, the resolution of the TOF camera is M × N (M, N are all positive integers), so that one frame of three-dimensional point cloud data acquired by the TOF camera has M × N pixel points, and each pixel point further comprises X, Y, Z three-dimensional coordinate values. Wherein, the TOF camera is used for converting original depth data into required 3-dimensional point cloud data: firstly, carrying out preliminary correction and temperature calibration on original depth data; secondly, distortion correction processing is carried out on the image; thirdly, the depth image coordinate system (x0, y0, z0) is converted into a camera coordinate system (x1, y1, z1), and the depth information on the image is converted into a three-dimensional coordinate system with the camera as an origin; finally, the camera coordinate system (x1, y1, z1) is converted to the required world coordinate system (x2, y2, z2) and the camera coordinate system is converted to the coordinate system required by the project, i.e. the coordinate system of the final point cloud. The data values of the X axis and the Y axis represent plane coordinate positions of scene points, and the data value of the Z axis represents an acquired actual depth value of the acquired scene.
The monitoring processor converts the three-dimensional point cloud data into an mxnx3 matrix, with each row representing a pixel arranged in the time-of-flight sensor. By resetting the M × N × 3 matrix to an M × N matrix and expressing the value of each element in the reset matrix with a depth value, the three-dimensional point cloud data is converted into two-dimensional planar image data.
The monitoring processor calculates the depth value of each pixel point of the two-dimensional plane image data by adopting a 3 multiplied by 3 space filtering operator based on the three-dimensional point cloud, and calculates the depth difference between the pixel of the central point and the pixel around the central point. And comparing the depth difference with a preset global threshold, judging that the depth value measured by the pixel point is a noise point when the depth difference is greater than the preset global threshold, and filtering the pixel point in the corresponding three-dimensional point cloud data. Otherwise, the corresponding pixel points in the three-dimensional point cloud data are reserved. And processing to obtain the denoised three-dimensional point cloud data.
And 105, the monitoring processor extracts feature data of the denoised three-dimensional point cloud data to obtain landform feature point cloud data of the monitored area, and stores the landform feature point cloud data in an area landform feature data list corresponding to the monitored area ID.
Specifically, the monitoring processor performs edge detection on gray data of a two-dimensional image of the denoised three-dimensional point cloud data to obtain data of gray edge pixel points, corresponds the data of the gray edge pixel points with pixel points in the denoised three-dimensional point cloud data, and extracts the three-dimensional point cloud data corresponding to the gray edge data from the denoised three-dimensional point cloud data to obtain edge three-dimensional point cloud data, namely the landform feature point cloud data of the monitored area. And the monitoring processor searches the corresponding relation of the monitored area in the camera area corresponding list according to the camera ID and determines the monitored area ID corresponding to the camera ID. And the monitoring processor stores the topographic feature point cloud data in a regional topographic feature data list corresponding to the monitoring region ID.
And 106, analyzing and comparing the landform characteristic point cloud data with the reference landform characteristic point cloud data of the monitored area by the monitoring processor to obtain landform form change data of the monitored area.
Specifically, the monitoring processor stores reference geomorphic feature point cloud data of each monitoring area.
And the monitoring processor searches the reference data in the storage unit according to the ID of the monitoring area to obtain the reference landform feature point cloud data of the monitoring area. And the monitoring processor compares the depth data of each pixel point of the landform characteristic point cloud data with the depth data of each pixel point of the reference landform characteristic point cloud data, calculates the depth data difference of each pixel point, and performs weighting processing on each difference value to obtain landform form change data of the monitored area.
The source of the reference topographic feature point cloud data is that, when the method is implemented, a TOF camera of each monitoring area collects an environment image of the monitoring area for the first time to generate three-dimensional point cloud data, and a monitoring processor performs the processing of the
And step 107, the monitoring processor searches a monitoring area control data list according to the monitoring area ID and determines a change data early warning threshold value of the monitoring area.
Specifically, the monitoring processor searches for a transformed data early warning threshold corresponding to the monitoring area ID in the monitoring area control data list according to the monitoring area ID, that is, a transformed data early warning threshold of the monitoring area. The change data early warning threshold is obtained by counting the monitoring data of the monitoring area and comprehensively considering the geological characteristics of the monitoring area, such as rock hardness, soil water content and other factors, and is written into the storage device of the monitoring processor by an administrator.
And step 108, when the landform form change data of the monitored area exceeds the change data early warning threshold value, the monitoring processor sends first early warning information to an early warning processing center corresponding to the ID of the monitored area.
The first warning information includes: monitoring area ID and topographic variation data.
Specifically, when the topographic form change data of the monitored area exceeds the change data early warning threshold, the monitoring processor generates first early warning information according to the ID of the monitored area and the topographic form change data.
And step 109, analyzing the received first early warning information by the early warning processing center to obtain landform morphological change data and a monitoring area ID.
Specifically, the early warning processing center analyzes the landform morphological change data of each monitoring area to determine the early warning level of the monitoring area. According to the possible harm degree, spread range, influence, personnel and property loss and other conditions of an emergency, the emergency is divided into four levels of a special important level I, an important level II, a larger level III and a general level IV from high to low, and red early warning, orange early warning, yellow early warning and blue early warning are adopted to represent the emergency in sequence. The geomorphic form change data range defined for each level is a first geomorphic form change data range, a second geomorphic form change data range, a third geomorphic form change data range and a fourth geomorphic form change data range. The early warning areas and corresponding early warning levels satisfied according to the landform morphological change data d are as follows:
the first landform form change data range (D is more than 3 and less than or equal to D4) is a red early warning.
The second morphology change data range (D2 < D ≦ D3) is orange early warning.
The third landform form change data range (D is more than 1 and less than or equal to D2) is yellow early warning.
The fourth landform form change data range (D is more than 0 and less than or equal to D1) is blue early warning.
The determination of the early warning area is completed by the following steps:
firstly, the early warning processing center searches the position of the monitoring area in the monitoring area information list according to the monitoring ID to obtain the central position data of the monitoring area corresponding to the monitoring ID.
And then, the early warning processing center determines an area with the central position data of the monitored area as the central radius and the size of the first radius as an early warning area according to the position data of the monitored area.
And step 111, the early warning processing center generates an early warning prompt message according to the early warning level and the early warning area, and sends the early warning prompt message to the user terminal in the early warning area.
Specifically, each user terminal has a positioning function, can perform data interaction with the early warning processing center, and sends the positioned position data to the early warning processing center. And when the early warning processing center determines the early warning area, determining the user terminal with the position data in the early warning area range as an object for sending early warning prompt information according to the early warning area. And the early warning processing center generates an early warning prompt message according to the early warning level and the early warning area and sends the early warning prompt message to the user terminal in the early warning area. The early warning prompt message is used for prompting people in the early warning area, who are in the early warning range, in addition, geological disasters possibly occur in the area where the people are located, the danger level of the disasters is informed, and people are requested to make defense work.
In a preferred scheme, the embodiment of the invention also provides a method for analyzing the landform feature point cloud data obtained by multiple times of shooting and analysis according to the TOF camera of each monitoring area, predicting the possibility of disaster occurrence and giving out early warning under the condition of possible disaster occurrence. Fig. 2 is a flowchart of a method for performing early warning according to a plurality of continuous point cloud data of topographic features according to an embodiment of the present invention, as shown in the figure, the method includes the following steps:
Specifically, the monitoring processor analyzes and processes a first number of continuous point cloud data of the landform features stored in the landform data list at a preset time. In a specific example, the first number is a 10, and the predetermined time is 8:00 am. Then, the monitoring processor reads the last stored 10 topographic feature point cloud data in the regional topographic feature data list.
The regional topographic feature prediction data includes a second quantity of prediction data and a predicted time of occurrence of each prediction data.
Specifically, the monitoring processor performs pairwise comparison analysis on pairwise adjacent geomorphic feature point cloud data in the first number of geomorphic feature point cloud data by the analysis method in the
In a specific example, the first number is a equal to 10, 9 pieces of relative geomorphic feature change data are obtained, and an average value of the 9 pieces of relative geomorphic feature change data is calculated and used as a prediction increment value of the trend prediction. If the second number is B equal to 3, then only three prediction data are calculated, i.e. the first prediction data is obtained by adding one time of the prediction increment value to the prediction base value, the second prediction data is obtained by adding two times of the prediction increment value to the prediction base value, and the third prediction data is obtained by adding three times of the prediction increment value to the prediction base value, i.e. 3 prediction data. And meanwhile, determining the predicted occurrence time of the first predicted data according to the time and the preset frequency of the last stored geomorphic feature point cloud data in the third predicted data area geomorphic feature data list. If the time for finally storing the geomorphic feature point cloud data is 3: 00, the preset frequency is 6 times/day, that is, data is collected every 4 hours, then the predicted occurrence time of the first prediction data is 7:00 of the day, which is recorded as the predicted occurrence time, the predicted occurrence time of the second prediction data is 11:00 of the day, and the predicted occurrence time of the third prediction data is 15:00 of the day. The predicted occurrence time of each prediction data is also stored in the regional topographic feature prediction data.
In
The second warning information includes: monitoring an area ID, continuous first quantity of topographic feature point cloud data and area topographic feature prediction data.
Specifically, the monitoring processor determines whether the second amount of prediction data exceeds the change data early warning threshold one by one, and when the second amount of prediction data contains prediction data exceeding the change data early warning threshold, the monitoring processor generates second early warning information according to the first amount of continuous topographic feature point cloud data and the regional topographic feature prediction data of the monitored area ID, and sends the second early warning information to the early warning processing center corresponding to the monitored area ID.
And 240, analyzing the received second early warning information by the early warning processing center to obtain the ID of the monitored area and the prediction data of the regional landform characteristics.
And step 250, the early warning processing center determines early warning levels according to the regional landform feature prediction data and determines early warning regions according to the monitoring IDs.
Specifically, the early warning processing center determines the early warning level and the early warning area according to the method of
And step 260, the early warning processing center generates an early warning prompt message according to the early warning level and the early warning area, and sends the early warning prompt message to the user terminal in the early warning area.
Specifically, the early warning processing center generates an early warning prompt message according to the early warning level and the early warning area, the early warning prompt message is used for prompting people in the early warning area, and the predicted early warning data is likely to occur at the predicted occurrence time and needs people to perform defense work.
In another preferred scheme, the embodiment of the invention further provides a method for analyzing the change trend of the geomorphic feature point cloud data obtained by analyzing and shooting the geomorphic feature point cloud data for multiple times by the TOF camera in each monitoring area, predicting the possibility of disaster occurrence, and giving out an early warning under the condition that the disaster is possible to occur. Fig. 3 is a flowchart of a method for performing early warning according to a change trend of point cloud data of topographic features according to an embodiment of the present invention, as shown in the figure, the method includes the following steps:
in
Specifically, the monitoring processor analyzes and processes a first number of continuous point cloud data of the geomorphic features stored in the geomorphic data list at a preset time. In a specific example, the first number is a 5, and the preset time is 8: 00. Then, the monitoring processor reads the last stored 5 topographic feature point cloud data in the regional topographic feature data list.
And 320, performing trend analysis on the continuous point cloud data of the first number of the landform features to obtain regional landform feature change trend data.
The regional landform feature change trend data comprises a plurality of relative change rate data; and each relative change rate data is obtained by calculation according to the point cloud data of two adjacent landform features.
Specifically, the monitoring processor compares the depth data of each pixel point of each landform feature point cloud data in a first continuous number A of landform feature point cloud data with the depth data of each pixel point of the previous landform feature point cloud data, calculates the depth data difference of each pixel point, calculates the ratio of the depth data difference of each pixel point to a preset sampling time interval, records the ratio as a pixel change rate, and performs weighted average processing on each pixel change rate to obtain relative landform feature change data. A-1 pieces of relative geomorphic feature change data are obtained by calculating a first number a of continuous geomorphic feature point cloud data, in a specific example, the first number a is 4, and the 3 pieces of relative geomorphic feature change data obtained by calculation are denoted as k1, k2, and k3, which are relative change rate data. The predetermined sampling time interval is determined by a predetermined frequency, for example, 6 times/day, and the sampling time interval is 4 hours.
And 330, when the change trend of the relative change rate data is continuously increased, the monitoring processor sends second early warning information to the early warning processing center corresponding to the monitored area ID.
The second warning information includes: monitoring an area ID, continuous first amount of geomorphic feature point cloud data and area geomorphic feature change trend data.
Specifically, the trend of the relative change rate data is judged, in a specific embodiment, the relative change rate data is k1, k2, k3, and the magnitudes of k1, k2, k3 are compared:
and when k1 is larger than k2 is larger than k3, the change trend of the relative change rate data is continuously increased, and the monitoring processor generates second early warning information according to the monitoring area ID, the continuous first number of point cloud data of the topographic features and the change trend data of the topographic features and sends the second early warning information to the early warning processing center.
And 340, analyzing the received second early warning information by the early warning processing center to obtain the ID of the monitored area and the change trend data of the area landform characteristics.
Specifically, the early warning processing center compares the regional landform feature change trend data obtained by analyzing the second early warning information with a preset trend range, determines the early warning range, and determines the early warning region according to the monitoring region ID. According to the possible harm degree, spread range, influence, personnel and property loss and other conditions of an emergency, the emergency is divided into four levels of a special important level I, an important level II, a larger level III and a general level IV from high to low, and red early warning, orange early warning, yellow early warning and blue early warning are adopted to represent the emergency in sequence. The geomorphic form change data range defined for each level is a first early warning range, a second early warning range, a third early warning range and a fourth early warning range. The early warning range and the corresponding early warning level which satisfy k according to the last relative change rate data in the regional landform feature change trend data are as follows:
the first preset trend range (K3 is more than or equal to K4) is a red early warning.
The second preset trend range (K2 is more than or equal to K3) is an orange early warning.
The third preset trend range (K1 is more than or equal to K2) is yellow early warning.
The fourth preset trend range (K0 is more than or equal to K1) is a blue early warning.
In a specific example, the obtained relative change rate data are k1, k2 and k3, and the early warning processing center judges that k3 is compared with the preset trend range, and determines that the trend range is in a fourth preset trend range, so that the early warning level is blue early warning.
And step 360, the early warning processing center generates an early warning prompt message according to the early warning level and the early warning area, and sends the early warning prompt message to the user terminal in the early warning area.
Specifically, the early warning processing center searches an early warning trend template in an early warning prompt message template according to the early warning level, fills relevant data of the early warning trend template according to the early warning level and the early warning area to obtain an early warning prompt message, and sends the early warning prompt message to a user terminal in the early warning area to inform people in the early warning area that the recent change of the monitoring area is increased and the disaster needs to be defended.
The above is a complete implementation process of the geological disaster monitoring and early warning method provided by the embodiment of the invention.
The invention also provides a geological disaster prediction and early warning system for implementing the method, and fig. 4 is a system block diagram of a geological disaster monitoring and early warning system provided by the embodiment of the invention, and the system comprises: a time of flight TOF camera 1, a monitoring processor 2 and an early warning processing center 3.
The time of flight TOF camera 1 is arranged on any column, wall or upper body of each monitoring area where the camera can be erected, and is fixed after being arranged, and the arrangement position of the TOF camera cannot be changed easily, so that the acquired data at each time have accurate contrast. The monitoring processor 2 is installed in a machine room or the like near the monitoring area. The early warning processing center 3 is arranged in a safety area determined by investigation. The system of the geological disaster monitoring and early warning system further comprises a user terminal 4 which can be a mobile phone, a notebook computer, a desktop computer and other terminal equipment capable of receiving communication messages.
The time of flight TOF camera 1 is connected to the monitoring processor 2 by a wired or wireless communication connection. The monitoring processor 2 is connected with the early warning processing center 3 through a wired or wireless communication connection mode. The early warning processing center 3 is connected with the user terminal 4 through a wired or wireless communication connection mode.
The functions performed by the components in the system and the interactions between them are as described in the above method embodiments, and are not described in detail here.
According to the geological disaster monitoring and early warning method provided by the embodiment of the invention, by using the TOF camera, the environmental image acquisition of the environmental image by the TOF camera is not influenced by the environmental light according to the preset frequency, the three-dimensional point cloud data is generated and sent to the monitoring processor, the three-dimensional point cloud data acquired by the monitoring processor is analyzed to obtain the landform form change data, the relation between the landform form change data and the change data early warning threshold value is determined, and when the landform change data exceeds the change data early warning threshold value, the monitoring processor sends first early warning information to the early warning processing center corresponding to the monitoring area. And the early warning processing center performs grade judgment on the first early warning information according to the first early warning information, generates an early warning prompt message and sends the early warning prompt message to the user terminal in the early warning area so as to remind people of harm. The method provided by the embodiment of the invention can finish automatic monitoring and early warning of geological disasters, improves the accuracy of measurement, improves the timeliness of early warning, and greatly reduces the cost of manpower and material resources for manually acquiring data and processing data.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
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