CN118276035A - Synchronous monitoring method for soil-vegetation-atmosphere vertical structure of local area - Google Patents
Synchronous monitoring method for soil-vegetation-atmosphere vertical structure of local area Download PDFInfo
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Abstract
The invention discloses a synchronous monitoring method for a soil-vegetation-atmosphere vertical structure in a local area, which relates to the technical field of synchronous monitoring for the soil-vegetation-atmosphere vertical structure and comprises the following steps: lidar causes laser pulses to be transmitted to the vegetation covered surface by exciting the laser pulses, which once they reach the surface will interact with objects on the surface; the laser radar receives the returned laser pulse reflected signal, records the time difference between the laser pulse emission and the laser pulse reception, and records the intensity of the reflected signal. According to the invention, potential precision potential abnormal hidden dangers can be identified by analyzing vegetation structure information provided by the laser radar, the problem that vegetation monitoring precision is possibly affected can be found in time, abnormal information is transmitted to the mobile terminal, remote monitoring and response are realized, different levels of warning are provided through the distinction of continuous potential abnormal dangers, unstable potential abnormal dangers and sudden potential abnormal signals, and a decision maker can quickly take appropriate measures.
Description
Technical Field
The invention relates to the technical field of synchronous monitoring of soil-vegetation-atmosphere vertical structures, in particular to a method for synchronously monitoring soil-vegetation-atmosphere vertical structures in local areas.
Background
The soil-vegetation-atmosphere vertical structure synchronous monitoring in local area is a comprehensive environment monitoring method, and aims to deeply understand and analyze the vertical structures of soil, vegetation and atmosphere in specific area and their interrelationships. Such monitoring methods typically involve multidisciplinary knowledge and various technological means to comprehensively and systematically study changes and interactions of the earth's surface system.
Specifically, this monitoring process involves the following aspects: soil monitoring involves monitoring soil properties such as soil humidity, temperature, texture, water content, organic matter content, and the like. Variations in these parameters can reveal the moisture status of the soil, nutrient content, and other factors related to vegetation growth. Vegetation monitoring includes monitoring vegetation, such as vegetation coverage, vegetation type, plant physiology, etc. The health and change in vegetation may reflect the stability and ecological balance of the ecosystem. Atmospheric monitoring involves various parameters of the atmosphere, such as air temperature, humidity, wind speed, air pressure, etc. These parameters may provide information about weather conditions, including precipitation, weather events, etc., and the effect on vegetation and soil may be analyzed by this link. Vertical structure synchronous monitoring means synchronous observation of changes in soil, vegetation and atmosphere in time and space. By monitoring these three layers simultaneously, the interaction and feedback mechanisms between them can be better understood.
In soil-vegetation-atmosphere vertical structure synchronous monitoring in local areas, vegetation monitoring often utilizes lidar technology. Lidar can provide high accuracy three-dimensional vegetation structure information by emitting a laser beam toward vegetation and measuring the reflected return signal. Its functions include measuring vegetation height, canopy structure, vertical distribution, etc. and providing reliable data for deep understanding of vegetation ecological features, biomass and growth state. The high resolution of the laser radar in the vertical direction makes the laser radar an important tool for monitoring the vertical structure and the spatial distribution of vegetation, and provides precious information support for the fields of ecology, environmental science, resource management and the like.
The prior art has the following defects:
when vegetation is monitored by the lidar, if the accuracy of the lidar when providing three-dimensional vegetation structure information is abnormal, incorrect three-dimensional representation of the vegetation structure may lead to incorrect interpretation of the ecosystem, for example, if the lidar incorrectly measures vegetation height, understanding of vegetation growth state, biomass and type composition may be misled, and thus evaluation of the health and ecological balance of the ecosystem is affected.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a synchronous monitoring method for a soil-vegetation-atmosphere vertical structure in a local area, potential abnormal hidden danger of precision can be identified by analyzing vegetation structure information provided by a laser radar, the problem that the vegetation monitoring precision is possibly affected can be found in time, abnormal information is transmitted to a mobile terminal, remote monitoring and response are realized, different levels of warning are provided by continuously distinguishing abnormal hidden danger, unstable abnormal hidden danger and sudden abnormal hidden danger signals, and a decision maker can quickly take appropriate measures to solve the problems in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions: the soil-vegetation-atmosphere vertical structure synchronous monitoring method for the local area comprises the following steps:
lidar causes laser pulses to be transmitted to the vegetation covered surface by exciting the laser pulses, which once they reach the surface will interact with objects on the surface;
The laser radar receives the returned laser pulse reflected signal, records the time difference between the laser pulse emission and the laser pulse reception, records the intensity of the reflected signal, and calculates the distance from each reflected point to the laser radar by utilizing the relation between the flight time and the light speed;
Generating point cloud data by the laser radar according to the distance measurement and the reflection intensity, and establishing a three-dimensional model of vegetation by utilizing the point cloud data;
acquiring emission characteristic information and attitude and direction control information when the laser radar provides three-dimensional vegetation structure information, comprehensively analyzing the emission characteristic information and the attitude and direction control information after acquiring the emission characteristic information and the attitude and direction control information, generating vegetation information providing precision, and monitoring the precision when the laser radar provides the three-dimensional vegetation structure information in real time through the vegetation information providing precision;
When the laser radar is monitored to provide three-dimensional vegetation structure information and provide precision and have abnormal hidden danger, comprehensively analyzing the abnormal hidden danger state of the laser radar, generating different types of abnormal hidden danger prompts, transmitting the different types of abnormal hidden danger prompts to the mobile terminal, and generating early warning prompts through the mobile terminal.
Preferably, the emission characteristic information when the laser radar provides the three-dimensional vegetation structure information comprises pulse repetition frequency and laser wavelength, after the acquisition, the pulse repetition frequency and the laser wavelength are respectively processed to generate a pulse repetition frequency abnormal hiding coefficient and a laser wavelength unstable coefficient, the gesture and direction control information when the laser radar provides the three-dimensional vegetation structure information comprises scanning angle control, and after the acquisition, the scanning angle control deviation coefficient is generated after the scanning angle control processing.
Preferably, the logic for acquiring the pulse repetition frequency anomaly concealment coefficients is as follows:
Acquiring an optimal pulse repetition frequency range when the laser radar provides three-dimensional vegetation structure information, and calibrating the optimal pulse repetition frequency range as PRF min～PRFmax;
Acquiring real-time pulse repetition frequency in T time when the laser radar provides three-dimensional vegetation structure information, and representing the real-time pulse repetition frequency with a function PRF (T) according to a time sequence;
Calculating an abnormal hiding coefficient of pulse repetition frequency, wherein the calculated expression is as follows: Where PRF Frequency of represents a pulse repetition frequency anomaly concealment coefficient, [ T a,tb ] represents a period in which the real-time pulse repetition frequency is greater than the maximum value PRF max of the optimal pulse repetition frequency range in T time when the laser radar provides three-dimensional vegetation structure information, T a＜tb,[tc,td ] represents a period in which the real-time pulse repetition frequency is less than the minimum value PRF min of the optimal pulse repetition frequency range in T time when the laser radar provides three-dimensional vegetation structure information, and T c＜td.
Preferably, the logic for obtaining the laser wavelength instability coefficients is as follows:
Acquiring real-time Laser wavelengths at different moments in T time when the Laser radar provides three-dimensional vegetation structure information, and calibrating the real-time Laser wavelengths as lasers Wavelength of y, wherein y represents sequential numbers of the real-time Laser wavelengths at different moments in T time when the Laser radar provides the three-dimensional vegetation structure information, and y=1, 2, 3, 4, … …, p and p are positive integers;
Calculating a laser wavelength instability coefficient, wherein the calculated expression is as follows: Wherein, laser Wavelength of represents the unstable coefficient of Laser wavelength, and p represents the total number of real-time Laser wavelengths acquired in the time T when the Laser radar provides three-dimensional vegetation structure information.
Preferably, the logic for obtaining the scan angle control deviation coefficient is as follows:
Acquiring an actual horizontal scanning angle and an actual vertical scanning angle when scanning angle control is performed in a time T when the laser radar provides three-dimensional vegetation structure information, simultaneously acquiring an expected horizontal scanning angle and an expected vertical scanning angle corresponding to the actual horizontal scanning angle and the actual vertical scanning angle, calibrating the actual horizontal scanning angle and the actual vertical scanning angle as Scan Angle of k and Scan Angle of v respectively, calibrating the expected horizontal scanning angle and the expected vertical scanning angle as Scan Anticipation of k and Scan Anticipation of v respectively, K represents numbers of the actual horizontal scanning angle generated when the laser radar provides three-dimensional vegetation structure information and the expected horizontal scanning angle corresponding to the actual horizontal scanning angle in the time T when the laser radar provides three-dimensional vegetation structure information, k=1, 2, 3, 4, … …, K is a positive integer, V represents numbers of the actual vertical scanning angle generated when the laser radar provides three-dimensional vegetation structure information and the expected vertical scanning angle corresponding to the actual vertical scanning angle in the time T when v=1, 2, 3, 4, … … and V are positive integers;
calculating a scanning angle control deviation coefficient, wherein the calculated expression is as follows: Where Scan Angle of represents the Scan angle control bias coefficient, scan Anticipation of k represents the desired horizontal Scan angle, and Scan Anticipation of v represents the desired vertical Scan angle.
Preferably, after acquiring a pulse repetition frequency abnormal hiding coefficient PRF Frequency of , a Laser wavelength unstable coefficient Laser Wavelength of and a Scan angle control deviation coefficient Scan Angle of generated in a time T when the Laser radar provides three-dimensional vegetation structure information, constructing a data analysis model by the pulse repetition frequency abnormal hiding coefficient PRF Frequency of , the Laser wavelength unstable coefficient Laser Wavelength of and the Scan angle control deviation coefficient Scan Angle of , and generating vegetation information providing precision Acc Information processing system according to the formula:
wherein r 1、r2、r3 is a preset proportionality coefficient of the pulse repetition frequency anomaly hiding coefficient PRF Frequency of , the Laser wavelength instability coefficient Laser Wavelength of and the scanning angle control deviation coefficient Scan Angle of respectively, and r 1、r2、r3 is larger than 0.
Preferably, when the abnormal hidden danger exists in the providing precision when the laser radar provides the three-dimensional vegetation structure information is monitored, a plurality of vegetation information providing precision establishment analysis sets generated when the laser radar subsequently provides the three-dimensional vegetation structure information are obtained, and the analysis sets are calibrated to be Z, then Z= { Acc Information processing system f }, f represents the number of the vegetation information providing precision in the analysis sets, and f=1, 2, 3, 4, … …, u and u are positive integers.
Preferably, the vegetation information providing precision discrete degree and the vegetation information providing precision average value are obtained through the vegetation information providing precision in the analysis set, and the vegetation information providing precision discrete degree and the vegetation information providing precision average value are respectively compared with a preset discrete degree reference threshold value and a preset vegetation information providing precision reference threshold value for analysis, and the comparison analysis results are as follows:
If the vegetation information providing precision average value is smaller than or equal to the vegetation information providing precision reference threshold value and the vegetation information providing precision discrete degree is smaller than or equal to the discrete degree reference threshold value, generating a continuous abnormal hidden danger signal, transmitting the signal to a mobile terminal, and sending a continuous abnormal hidden danger prompt through the mobile terminal;
If the vegetation information providing precision average value is smaller than or equal to the vegetation information providing precision reference threshold value and the vegetation information providing precision discrete degree is larger than the discrete degree reference threshold value, generating an unstable potential abnormal hazard signal, transmitting the signal to a mobile terminal, and sending an unstable potential abnormal hazard prompt through the mobile terminal;
If the vegetation information providing precision average value is larger than the vegetation information providing precision reference threshold value and the vegetation information providing precision discrete degree is smaller than the discrete degree reference threshold value, generating a sudden abnormal hidden danger signal, transmitting the signal to the mobile terminal, and not sending out an abnormal hidden danger prompt through the mobile terminal.
In the technical scheme, the invention has the technical effects and advantages that:
According to the invention, potential precision potential abnormal hidden dangers can be identified by analyzing vegetation structure information provided by the laser radar, so that the problem that vegetation monitoring precision is possibly affected can be found in time, the abnormal information is transmitted to the mobile terminal, remote monitoring and response are realized, different levels of warning are provided through the distinction of continuous potential abnormal dangers, unstable potential abnormal dangers and sudden potential abnormal signals, and a decision maker can quickly take appropriate measures;
The invention provides the quantitative index for the overall performance of the vegetation monitoring precision by comprehensively evaluating the precision average value and the discrete degree of the vegetation information, which is helpful for a decision maker to more comprehensively understand the reliability of the vegetation monitoring data, avoids misleading ecological system management decisions based on inaccurate data, generates hidden danger signals of three different types of continuous abnormality, unstable abnormality and sudden abnormality according to the comparison result of the precision average value and the discrete degree of the vegetation information, and is helpful for further guiding the decision maker to formulate coping strategies under different situations and improves the recognition and processing capacity of abnormal situations.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those skilled in the art.
FIG. 1 is a flow chart of a method for simultaneous monitoring of soil-vegetation-atmosphere vertical structures in a localized area according to the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The invention provides a method for synchronously monitoring soil-vegetation-atmosphere vertical structures in a local area as shown in fig. 1, which comprises the following steps:
lidar causes laser pulses to be transmitted to the vegetation covered surface by exciting the laser pulses, which once they reach the surface will interact with objects on the surface;
the laser pulse reaches the surface, a portion of the laser energy is reflected by the vegetation surface and a portion is absorbed or penetrated.
The laser radar receives the returned laser pulse reflected signal, records the time difference between the laser pulse emission and the laser pulse reception, records the intensity of the reflected signal, and calculates the distance from each reflected point to the laser radar by utilizing the relation between the flight time and the light speed;
The returned laser pulse reflected signals include reflected signals from the earth's surface, vegetation surface and vegetation interior;
Calculating the distance from the reflection point measured by the laser radar to the laser radar is accomplished by measuring the flight time of the laser pulse, and comprises the following specific steps:
The lidar measures the time elapsed from the laser emission to the laser reception, i.e. the time of flight, which is the time difference between the emission of the laser pulse from the lidar transmitter to the reception of the corresponding reflected signal by the lidar;
Using the value of the speed of light, typically measured at 299792458 meters per second, as a constant, the speed of light being the speed at which the laser pulse propagates in vacuum for converting time of flight into distance;
calculating the distance from each reflection point to the laser radar by using a formula by utilizing the relation between the flight time and the light speed, wherein the calculation formula is as follows: This is because the distance that the laser propagates in the time of flight is equal to the speed of light times half the time of flight.
Generating point cloud data by the laser radar according to the distance measurement and the reflection intensity, and establishing a three-dimensional model of vegetation by utilizing the point cloud data;
the points in the point cloud data represent scattering points on the vegetation surface and in the vegetation, a three-dimensional model of the vegetation is established by utilizing the point cloud data, the three-dimensional model comprises a vertical structure, height distribution, canopy density and the like of the vegetation, and key information such as biomass, height, density and the like of the vegetation can be extracted by processing the point cloud data.
Acquiring emission characteristic information and attitude and direction control information when the laser radar provides three-dimensional vegetation structure information, comprehensively analyzing the emission characteristic information and the attitude and direction control information after acquiring the emission characteristic information and the attitude and direction control information, generating vegetation information providing precision, and monitoring the precision when the laser radar provides the three-dimensional vegetation structure information in real time through the vegetation information providing precision;
The method comprises the steps that when the laser radar provides three-dimensional vegetation structure information, emission characteristic information comprises pulse repetition frequency and laser wavelength, after the acquisition, abnormal hiding coefficients of the pulse repetition frequency and unstable coefficients of the laser wavelength are generated after the pulse repetition frequency and the laser wavelength are respectively processed, gesture and direction control information comprises scanning angle control when the laser radar provides the three-dimensional vegetation structure information, and after the acquisition, scanning angle control deviation coefficients are generated after the scanning angle control processing.
When vegetation is monitored by the laser radar, the laser radar provides three-dimensional vegetation structural information, and the accuracy provided for the three-dimensional vegetation structural information is reduced due to the fact that the pulse repetition frequency is larger or smaller, and the following aspects are particularly shown:
Point cloud density and resolution problems:
The pulse repetition frequency is large: too high a frequency may cause too dense sampling points, so that the distance between adjacent points becomes large, and thus details of vegetation structures cannot be captured, and the spatial resolution of data is reduced.
The pulse repetition frequency is small: the too low frequency may cause insufficient sampling points, so that the expression of the vegetation structure is not complete enough, the actual vegetation distribution and form are difficult to accurately reflect, and the comprehensiveness of the data is reduced.
Dynamic effects and change monitoring problems:
The pulse repetition frequency is large: at high frequencies, the time interval between successive laser pulses is short, which may result in the inability to capture dynamic changes in objects, such as the movement of wind leaves, in a rapidly changing vegetation environment.
The pulse repetition frequency is small: the laser pulse interval at low frequency is larger, and may not meet the real-time monitoring requirement for fast-changing scenes, resulting in lower timeliness of the data.
Vegetation density and structural complexity issues:
The pulse repetition frequency is large: under dense vegetation coverage, the high frequency may cause too dense point clouds, which are difficult to correctly distinguish and express different vegetation levels, reducing the accuracy of vegetation structures.
The pulse repetition frequency is small: for complex vegetation structures, the low frequency may not capture enough data points, resulting in missing certain features of the vegetation, affecting the integrity of the vegetation structure.
The shadowing effect is exacerbated: high pulse repetition frequencies may exacerbate shadowing effects of adjacent vegetation structures such that vegetation in certain areas may not be accurately measured. Conversely, lower frequencies may not be suitable for areas where vegetation is dense, resulting in structures where all vegetation cannot be captured.
The logic for acquiring the pulse repetition frequency anomaly concealment coefficients is as follows:
Acquiring an optimal pulse repetition frequency range when the laser radar provides three-dimensional vegetation structure information, and calibrating the optimal pulse repetition frequency range as PRF min～PRFmax;
It should be noted that, the field test is performed in the monitored area, the data acquisition is performed by using the laser radar devices with different pulse repetition frequencies, the field test can directly simulate the monitored environment, provide performance evaluation in the actual scene, and determine the pulse repetition frequency range most suitable for the area by comparing the vegetation structure information obtained under different frequencies, the optimal pulse repetition frequency range when the laser radar provides the three-dimensional vegetation structure information is not particularly limited here, and can be adjusted according to the actual test condition;
Acquiring real-time pulse repetition frequency in T time when the laser radar provides three-dimensional vegetation structure information, and representing the real-time pulse repetition frequency with a function PRF (T) according to a time sequence;
It should be noted that, a special external frequency measuring instrument, such as a frequency meter or an oscilloscope, is used to connect the external frequency measuring instrument to the pulse transmitting pin of the laser radar, and these instruments can measure the frequency of laser pulse transmission in real time, so as to provide actual pulse repetition frequency information;
Calculating an abnormal hiding coefficient of pulse repetition frequency, wherein the calculated expression is as follows: Wherein PRF Frequency of represents a pulse repetition frequency anomaly concealment coefficient, [ T a,tb ] represents a period in which the real-time pulse repetition frequency is greater than the maximum value PRF max of the optimal pulse repetition frequency range in T time when the laser radar provides three-dimensional vegetation structure information, T a＜tb,[tc,td ] represents a period in which the real-time pulse repetition frequency is less than the minimum value PRF min of the optimal pulse repetition frequency range in T time when the laser radar provides three-dimensional vegetation structure information, T c＜td;
The calculation expression of the pulse repetition frequency abnormal hiding coefficient shows that the larger the expression value of the pulse repetition frequency abnormal hiding coefficient generated in the time T when the laser radar provides the three-dimensional vegetation structure information is, the larger the hidden danger that the accuracy of the laser radar provides the three-dimensional vegetation structure information is, and otherwise, the smaller the hidden danger that the accuracy of the laser radar provides the three-dimensional vegetation structure information is.
When vegetation is monitored by the laser radar, the poor stability of the laser wavelength when the laser radar provides three-dimensional vegetation structure information may cause the reduction of the accuracy provided by the three-dimensional vegetation structure information, and the following is a specific analysis reason:
Reflection characteristic difference: vegetation has different reflection characteristics for light with different wavelengths, and the instability of the laser wavelength can cause the change of spectral information reflected by different times or different vegetation types. Such variations can make interpretation and analysis of vegetation structures difficult, affecting monitoring accuracy.
Classification of vegetation types is difficult: the response of different vegetation types to the laser wavelength is greatly different, and the instability of the laser wavelength can lead to difficulty in classifying vegetation types. For example, assessment of vegetation health typically utilizes the near infrared band, which if the laser wavelength is not stable over this band, will affect accurate assessment of vegetation health.
Height measurement error: the laser radar obtains vegetation height information by measuring the flight time of the laser pulse. The instability of the wavelength may cause fluctuations in the ranging accuracy, thereby introducing a height measurement error. This is critical for accurate vegetation structure monitoring, especially when detailed measurements of terrain heights are made.
Spectral information distortion: lidar vegetation monitoring typically uses relatively long wavelength near infrared light that is sensitive to vegetation structure and health. Instability of the laser wavelength may cause distortion of the spectral information, thereby affecting accurate estimation of vegetation parameters, such as chlorophyll content, etc.
Remote sensing data consistency problem: the wavelength stability of lidar is critical to the consistency of the remote sensing data, especially when comparing multi-temporal or multi-sensor data. If the wavelength is unstable, data acquired by different time periods or different sensors may be inconsistent, and the comparability of the data and the reliability of analysis results are affected.
The logic for obtaining the laser wavelength instability coefficient is as follows:
Acquiring real-time Laser wavelengths at different moments in T time when the Laser radar provides three-dimensional vegetation structure information, and calibrating the real-time Laser wavelengths as lasers Wavelength of y, wherein y represents sequential numbers of the real-time Laser wavelengths at different moments in T time when the Laser radar provides the three-dimensional vegetation structure information, and y=1, 2, 3, 4, … …, p and p are positive integers;
It should be noted that, the wavelength of the laser beam can be directly measured by using a professional spectrometer, such a spectrometer can analyze the laser spectrum to determine the specific wavelength of the laser, and the wavelength of the laser can be monitored and measured in real time by aligning the spectrometer to the laser beam;
Calculating a laser wavelength instability coefficient, wherein the calculated expression is as follows: Wherein, laser Wavelength of represents the unstable coefficient of Laser wavelength, and p represents the total number of real-time Laser wavelengths acquired in the time T when the Laser radar provides three-dimensional vegetation structure information.
According to the calculation expression of the laser wavelength instability coefficient, the larger the expression value of the laser wavelength instability coefficient generated in the time T when the laser radar provides the three-dimensional vegetation structure information, the greater the hidden danger that the accuracy is abnormal when the laser radar provides the three-dimensional vegetation structure information, and the smaller the hidden danger that the accuracy is abnormal when the laser radar provides the three-dimensional vegetation structure information.
When vegetation is monitored by the laser radar, the deviation between the actual scanning angle and the expected scanning angle may affect the accuracy of the three-dimensional vegetation structure information provided by the laser radar, and the following is a specific analysis reason:
Height measurement error: deviations in the scan angle may lead to height measurement errors. Lidar calculates vegetation height information by measuring the time of flight of a laser pulse. If the scan angle deviates from the expected, the time of flight measurement may be inaccurate, resulting in an error in the vegetation height.
Horizontal position offset: the deviation of the scanning angle may cause a positional shift of the laser spot in the horizontal direction. This can have an impact on the ground's positional information, resulting in inaccurate information on the horizontal distribution of vegetation structures.
The density of the point cloud is uneven: deviations in the scan angle may result in an uneven distribution of the density of the laser spot cloud in the vertical direction. Some regions may be overscan while other regions may be underestimated. Such uneven distribution can affect the overall capture of vegetation structures.
Data registration problem: the scan angle deviation may cause data registration problems between different scan positions. Data acquired at multiple times or positions need to be registered, and deviation of scanning angles may increase difficulty of registration, and affect data consistency at different time points or positions.
Surface property change: the change in the scan angle may cause a change in the angle of illumination of the target surface by the laser spot, thereby affecting the spectral characteristics of the laser spot reflection. The method has influence on the material and color information of the vegetation surface, and further influence the interpretation and analysis of vegetation structures.
The logic for obtaining the scanning angle control deviation coefficient is as follows:
Acquiring an actual horizontal scanning angle and an actual vertical scanning angle when scanning angle control is performed in a time T when the laser radar provides three-dimensional vegetation structure information, simultaneously acquiring an expected horizontal scanning angle and an expected vertical scanning angle corresponding to the actual horizontal scanning angle and the actual vertical scanning angle, calibrating the actual horizontal scanning angle and the actual vertical scanning angle as Scan Angle of k and Scan Angle of v respectively, calibrating the expected horizontal scanning angle and the expected vertical scanning angle as Scan Anticipation of k and Scan Anticipation of v respectively, K represents numbers of the actual horizontal scanning angle generated when the laser radar provides three-dimensional vegetation structure information and the expected horizontal scanning angle corresponding to the actual horizontal scanning angle in the time T when the laser radar provides three-dimensional vegetation structure information, k=1, 2, 3, 4, … …, K is a positive integer, V represents numbers of the actual vertical scanning angle generated when the laser radar provides three-dimensional vegetation structure information and the expected vertical scanning angle corresponding to the actual vertical scanning angle in the time T when v=1, 2, 3, 4, … … and V are positive integers;
it should be noted that, the lidar device is equipped with a built-in sensor system for monitoring the direction of the laser beam, these sensors may include a gyroscope, an Inertial Measurement Unit (IMU), etc., the gyroscope may be used to measure the rotation angle of the device, and the IMU may provide the tilt and direction information of the device, and the actual horizontal scan angle and the actual vertical scan angle may be calculated in combination with the data of these sensors;
calculating a scanning angle control deviation coefficient, wherein the calculated expression is as follows: Where Scan Angle of represents the Scan angle control bias coefficient, scan Anticipation of k represents the desired horizontal Scan angle, and Scan Anticipation of v represents the desired vertical Scan angle.
The calculation expression of the scanning angle control deviation coefficient shows that the larger the expression value of the scanning angle control deviation coefficient generated in the time T when the laser radar provides the three-dimensional vegetation structure information, the larger the hidden danger that the accuracy is abnormal when the laser radar provides the three-dimensional vegetation structure information is, and otherwise, the smaller the hidden danger that the accuracy is abnormal when the laser radar provides the three-dimensional vegetation structure information is.
After acquiring a pulse repetition frequency abnormal hiding coefficient PRF Frequency of , a Laser wavelength unstable coefficient Laser Wavelength of and a scanning angle control deviation coefficient Scan Angle of which are generated in a time T when the Laser radar provides three-dimensional vegetation structure information, constructing a data analysis model by the pulse repetition frequency abnormal hiding coefficient PRF Frequency of , the Laser wavelength unstable coefficient Laser Wavelength of and the scanning angle control deviation coefficient Scan Angle of , and generating vegetation information providing precision Acc Information processing system according to the following formula:
Wherein r 1、r2、r3 is a preset proportionality coefficient of a pulse repetition frequency abnormality hiding coefficient PRF Frequency of , a Laser wavelength instability coefficient Laser Wavelength of and a scanning angle control deviation coefficient Scan Angle of , and r 1、r2、r3 is larger than 0;
According to a calculation formula, the smaller the pulse repetition frequency abnormality hiding coefficient generated in the T time when the laser radar provides the three-dimensional vegetation structure information, the smaller the laser wavelength instability coefficient and the smaller the scanning angle control deviation coefficient, namely the larger the expression value of the vegetation information providing precision Acc Information processing system generated in the T time when the laser radar provides the three-dimensional vegetation structure information, the smaller the hidden danger that the precision is abnormal when the laser radar provides the three-dimensional vegetation structure information is indicated, and the larger the hidden danger that the precision is abnormal when the laser radar provides the three-dimensional vegetation structure information is indicated.
When the condition that the laser radar provides three-dimensional vegetation structure information and provides precision and potential abnormality exists is monitored, comprehensively analyzing the potential abnormality state of the laser radar, generating different types of potential abnormality prompts, transmitting the different types of potential abnormality prompts to a mobile terminal, and generating early warning prompts through the mobile terminal;
When abnormal hidden danger exists in the providing precision when the laser radar provides the three-dimensional vegetation structure information is monitored, a plurality of vegetation information providing precision establishment analysis sets generated when the laser radar subsequently provides the three-dimensional vegetation structure information are obtained, and the analysis sets are marked as Z, then Z= { Acc Information processing system f }, f represents the number of the vegetation information providing precision in the analysis sets, and f=1, 2, 3, 4, … …, u and u are positive integers;
calculating a vegetation information providing precision discrete degree and a vegetation information providing precision average value through the vegetation information providing precision in the analysis set, and respectively comparing the vegetation information providing precision discrete degree and the vegetation information providing precision average value with a preset discrete degree reference threshold value and a preset vegetation information providing precision reference threshold value to obtain the following comparison analysis results:
If the vegetation information providing precision average value is smaller than or equal to the vegetation information providing precision reference threshold value and the vegetation information providing precision discrete degree is smaller than or equal to the discrete degree reference threshold value, generating a continuous abnormal hidden danger signal, transmitting the signal to a mobile terminal, and sending a continuous abnormal hidden danger prompt through the mobile terminal;
If the vegetation information providing precision average value is smaller than or equal to the vegetation information providing precision reference threshold value and the vegetation information providing precision discrete degree is larger than the discrete degree reference threshold value, generating an unstable potential abnormal hazard signal, transmitting the signal to a mobile terminal, and sending an unstable potential abnormal hazard prompt through the mobile terminal;
If the vegetation information providing precision average value is larger than the vegetation information providing precision reference threshold value and the vegetation information providing precision discrete degree is smaller than the discrete degree reference threshold value, generating a sudden abnormal hidden danger signal, transmitting the signal to the mobile terminal, and not sending out an abnormal hidden danger prompt through the mobile terminal.
According to the invention, potential precision potential abnormal hidden dangers can be identified by analyzing vegetation structure information provided by the laser radar, so that the problem that vegetation monitoring precision is possibly affected can be found in time, the abnormal information is transmitted to the mobile terminal, remote monitoring and response are realized, different levels of warning are provided through the distinction of continuous potential abnormal dangers, unstable potential abnormal dangers and sudden potential abnormal signals, and a decision maker can quickly take appropriate measures;
The invention provides the quantitative index for the overall performance of the vegetation monitoring precision by comprehensively evaluating the precision average value and the discrete degree of the vegetation information, which is helpful for a decision maker to more comprehensively understand the reliability of the vegetation monitoring data, avoids misleading ecological system management decisions based on inaccurate data, generates hidden danger signals of three different types of continuous abnormality, unstable abnormality and sudden abnormality according to the comparison result of the precision average value and the discrete degree of the vegetation information, and is helpful for further guiding the decision maker to formulate coping strategies under different situations and improves the recognition and processing capacity of abnormal situations.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
While certain exemplary embodiments of the present invention have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the invention, which is defined by the appended claims.
It is noted that relational terms such as first and second, and the like, if any, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. 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 application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. The method for synchronously monitoring the soil-vegetation-atmosphere vertical structure of the local area is characterized by comprising the following steps of:
lidar causes laser pulses to be transmitted to the vegetation covered surface by exciting the laser pulses, which once they reach the surface will interact with objects on the surface;
The laser radar receives the returned laser pulse reflected signal, records the time difference between the laser pulse emission and the laser pulse reception, records the intensity of the reflected signal, and calculates the distance from each reflected point to the laser radar by utilizing the relation between the flight time and the light speed;
Generating point cloud data by the laser radar according to the distance measurement and the reflection intensity, and establishing a three-dimensional model of vegetation by utilizing the point cloud data;
acquiring emission characteristic information and attitude and direction control information when the laser radar provides three-dimensional vegetation structure information, comprehensively analyzing the emission characteristic information and the attitude and direction control information after acquiring the emission characteristic information and the attitude and direction control information, generating vegetation information providing precision, and monitoring the precision when the laser radar provides the three-dimensional vegetation structure information in real time through the vegetation information providing precision;
When the laser radar is monitored to provide three-dimensional vegetation structure information and provide precision and have abnormal hidden danger, comprehensively analyzing the abnormal hidden danger state of the laser radar, generating different types of abnormal hidden danger prompts, transmitting the different types of abnormal hidden danger prompts to the mobile terminal, and generating early warning prompts through the mobile terminal.
2. The method according to claim 1, wherein the emission characteristic information of the laser radar when providing the three-dimensional vegetation structure information includes a pulse repetition frequency and a laser wavelength, the pulse repetition frequency and the laser wavelength are respectively processed after the acquisition to generate a pulse repetition frequency abnormal hiding coefficient and a laser wavelength unstable coefficient, the attitude and direction control information of the laser radar when providing the three-dimensional vegetation structure information includes a scan angle control, and the scan angle control deviation coefficient is generated after the scan angle control processing after the acquisition.
3. The method for simultaneous monitoring of soil-vegetation-atmosphere vertical structures in localized areas according to claim 2, wherein the logic for obtaining the pulse repetition frequency anomaly concealment coefficients is as follows:
Acquiring an optimal pulse repetition frequency range when the laser radar provides three-dimensional vegetation structure information, and calibrating the optimal pulse repetition frequency range as PRF min～PRFmax;
Acquiring real-time pulse repetition frequency in T time when the laser radar provides three-dimensional vegetation structure information, and representing the real-time pulse repetition frequency with a function PRF (T) according to a time sequence;
Calculating an abnormal hiding coefficient of pulse repetition frequency, wherein the calculated expression is as follows: Where PRF Frequency of represents a pulse repetition frequency anomaly concealment coefficient, [ T a,tb ] represents a period in which the real-time pulse repetition frequency is greater than the maximum value PRF max of the optimal pulse repetition frequency range in T time when the laser radar provides three-dimensional vegetation structure information, T a<tb,[tc,td ] represents a period in which the real-time pulse repetition frequency is less than the minimum value PRF min of the optimal pulse repetition frequency range in T time when the laser radar provides three-dimensional vegetation structure information, and T c<td.
4. A method for simultaneous monitoring of soil-vegetation-atmosphere vertical structures in a localized area according to claim 3 wherein the logic for obtaining the laser wavelength instability coefficients is as follows:
Acquiring real-time Laser wavelengths at different moments in T time when the Laser radar provides three-dimensional vegetation structure information, and calibrating the real-time Laser wavelengths as lasers Wavelength of y, wherein y represents sequential numbers of the real-time Laser wavelengths at different moments in T time when the Laser radar provides the three-dimensional vegetation structure information, and y=1, 2, 3, 4, … …, p and p are positive integers;
Calculating a laser wavelength instability coefficient, wherein the calculated expression is as follows: Wherein, laser Wavelength of represents the unstable coefficient of Laser wavelength, and p represents the total number of real-time Laser wavelengths acquired in the time T when the Laser radar provides three-dimensional vegetation structure information.
5. The method for simultaneous monitoring of soil-vegetation-atmosphere vertical structures in localized areas according to claim 4 wherein the logic for obtaining the scan angle control bias factor is as follows:
Acquiring an actual horizontal scanning angle and an actual vertical scanning angle when scanning angle control is performed in a time T when the laser radar provides three-dimensional vegetation structure information, simultaneously acquiring an expected horizontal scanning angle and an expected vertical scanning angle corresponding to the actual horizontal scanning angle and the actual vertical scanning angle, calibrating the actual horizontal scanning angle and the actual vertical scanning angle as Scan Angle of k and Scan Angle of v respectively, calibrating the expected horizontal scanning angle and the expected vertical scanning angle as Scan Anticipation of k and Scan Anticipation of v respectively, K represents numbers of the actual horizontal scanning angle generated when the laser radar provides three-dimensional vegetation structure information and the expected horizontal scanning angle corresponding to the actual horizontal scanning angle in the time T when the laser radar provides three-dimensional vegetation structure information, k=1, 2, 3, 4, … …, K is a positive integer, V represents numbers of the actual vertical scanning angle generated when the laser radar provides three-dimensional vegetation structure information and the expected vertical scanning angle corresponding to the actual vertical scanning angle in the time T when v=1, 2, 3, 4, … … and V are positive integers;
calculating a scanning angle control deviation coefficient, wherein the calculated expression is as follows: Where Scan Angle of represents the Scan angle control bias coefficient, scan Anticipation of k represents the desired horizontal Scan angle, and Scan Anticipation of v represents the desired vertical Scan angle.
6. The method for synchronously monitoring the soil-vegetation-atmosphere vertical structure in a local area according to claim 5, wherein after acquiring the pulse repetition frequency anomaly concealment coefficient PRF Frequency of , the Laser wavelength instability coefficient Laser Wavelength of and the Scan angle control deviation coefficient Scan Angle of generated in the time T when the Laser radar provides the three-dimensional vegetation structure information, constructing a data analysis model by the pulse repetition frequency anomaly concealment coefficient PRF Frequency of , the Laser wavelength instability coefficient Laser Wavelength of and the Scan angle control deviation coefficient Scan Angle of to generate vegetation information providing precision Acc Information processing system according to the following formula:
，
Wherein r 1、r2、r3 is a preset proportionality coefficient of the pulse repetition frequency anomaly hiding coefficient PRF Frequency of , the Laser wavelength instability coefficient Laser Wavelength of and the scanning angle control deviation coefficient Scan Angle of , and r 1、r2、r3 is larger than 0.
7. The method for synchronously monitoring the soil-vegetation-atmosphere vertical structure of a local area according to claim 6, wherein when the condition that the accuracy provided by the laser radar has abnormal hidden danger when the three-dimensional vegetation structure information is provided is monitored, a plurality of vegetation information providing accuracy establishment analysis sets generated when the three-dimensional vegetation structure information is subsequently provided by the laser radar are obtained, and the analysis sets are calibrated to be Z, then Z= { Acc Information processing system f }, f represents the number of vegetation information providing accuracy in the analysis sets, and f=1, 2, 3, 4, … …, u and u are positive integers.
8. The method according to claim 7, wherein the vegetation information providing precision discrete degree and the vegetation information providing precision average value are obtained by analyzing the vegetation information providing precision in the collection, and the vegetation information providing precision discrete degree and the vegetation information providing precision average value are compared with a preset discrete degree reference threshold value and a preset vegetation information providing precision reference threshold value, respectively, and the result of the comparison analysis is as follows:
If the vegetation information providing precision average value is smaller than or equal to the vegetation information providing precision reference threshold value and the vegetation information providing precision discrete degree is smaller than or equal to the discrete degree reference threshold value, generating a continuous abnormal hidden danger signal, transmitting the signal to a mobile terminal, and sending a continuous abnormal hidden danger prompt through the mobile terminal;
If the vegetation information providing precision average value is smaller than or equal to the vegetation information providing precision reference threshold value and the vegetation information providing precision discrete degree is larger than the discrete degree reference threshold value, generating an unstable potential abnormal hazard signal, transmitting the signal to a mobile terminal, and sending an unstable potential abnormal hazard prompt through the mobile terminal;
If the vegetation information providing precision average value is larger than the vegetation information providing precision reference threshold value and the vegetation information providing precision discrete degree is smaller than the discrete degree reference threshold value, generating a sudden abnormal hidden danger signal, transmitting the signal to the mobile terminal, and not sending out an abnormal hidden danger prompt through the mobile terminal.
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