CN111694021B - GNSS environment model-based single-station landslide deformation monitoring and early warning method - Google Patents
GNSS environment model-based single-station landslide deformation monitoring and early warning method Download PDFInfo
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Abstract
The invention discloses a GNSS environment model-based single-station landslide deformation monitoring and early warning method, which comprises the following steps: deploying terminal equipment, acquiring data, determining the initial position of a monitoring point, calculating environment modeling source data, modeling a sunday environment, solving a correlation coefficient and applying early warning. The monitoring and early warning method mainly utilizes the low-cost consumer GNSS positioning terminal to acquire data and perform environment modeling, early identifies the landslide acceleration state through the change trend of the correlation coefficient of the environment model and effectively early warns, so that the monitoring cost is greatly reduced, and a new thought and a new method are provided for the GNSS landslide monitoring and early warning.
Description
Technical Field
The invention belongs to the technical field of monitoring and early warning, and particularly relates to a method for monitoring and early warning of landslide deformation of a single station based on a GNSS environment model.
Background
China is one of the most serious countries in the world with geological landslide disasters. According to the incomplete statistics of the national geological disaster network, the direct economic loss is 27.7 billion yuan only from the occurrence of geological disasters 6181 in 2019 nationwide; the geological disaster 948 can be successfully forecasted, 2 million persons can be possibly injured, the direct economic loss is avoided by 8.3 hundred million yuan, and the disaster prevention and reduction effects are remarkable. And the landslide disaster loss accounts for 61.2 percent of the total disaster, and the implementation of efficient landslide monitoring and early warning has important practical significance for the disaster prevention and reduction work in China.
The GNSS technology has high-precision, all-time and all-weather monitoring capability in landslide monitoring and early warning, and is an important means for directly acquiring surface three-dimensional vector deformation at present. With the modernization and the gradual improvement of GNSS, currently, there are up to more than 110 global in-orbit GNSS satellites, which become an important means for global high-precision positioning and navigation services.
The GNSS positioning technology has been widely applied to various deformation monitoring fields, in particular to landslide disaster deformation monitoring, by virtue of its unique technical advantages, and mainly uses the RTK positioning technology. The cost of a single GNSS navigation positioning terminal which can be used for high-precision landslide monitoring is generally higher (more than 3 thousands), the cost of a common measurement type double-frequency receiving terminal is at least more than ten thousand yuan, the cost of a GNSS application terminal used for consumer-level positioning is less than one hundred yuan at present, and the positioning precision is low (more than 10m), so that the landslide monitoring and early warning requirements cannot be met.
Therefore, how to utilize a single GNSS positioning terminal with low cost to realize effective early warning of landslide deformation monitoring has important practical significance for geological disaster prevention and reduction work in China.
Disclosure of Invention
Aiming at the problems of high early warning cost, low success rate and the like of the existing GNSS landslide deformation monitoring technology, the invention provides a single-station landslide deformation monitoring and early warning method based on a GNSS environment model.
The technical scheme of the invention is as follows: a GNSS environment model-based single-station landslide deformation monitoring and early warning method comprises the following steps:
s1: deploying terminal equipment and performing data acquisition
The method comprises the steps that a GNSS application terminal is deployed on a monitoring point (landslide deformation characteristic point), and after the GNSS application terminal is started, original observation data of the monitoring point are collected through a 4G or 5G wireless communication network and the like;
s2: determining the initial position of a monitoring point
After the original observation data of several hours (>1 hour) are collected, downloading a real-time broadcast ephemeris file through an Internet IGS service center, and roughly estimating the approximate position of a monitoring point by using a pseudo-range single-point positioning technology;
s3: computing environment modeling source data
Based on the broadcast ephemeris, performing integrity check on the original observation data acquired by S1, calculating satellite azimuth angle and altitude angle information of each epoch through a receiver approximate coordinate, summarizing satellite sight line information, and outputting continuous observation arc-segment observation values, wherein the initial and final observation values of the arc-segment observation values are marked as head and tail points;
s4: sunday environment modeling
Performing gross error elimination on the head and tail point observed values output by the S3, constructing an observation equation by using a plurality of head and tail point observed values, selecting a least square criterion to solve a fitting coefficient in the equation by a fitting system, and establishing an environment model according to the fitting coefficient;
s5: correlation coefficient solving
Setting intervals by taking days as units, and jointly solving correlation coefficients in the environment models according to the corresponding environment models every day;
s6: early warning application
And setting a proper threshold value in practical application, comparing and judging the threshold value and the correlation coefficient calculated in the step S5, if the correlation coefficient is lower than the threshold value, carrying out early warning, and if not, continuing monitoring.
Further, the raw observation data in S1 includes raw pseudorange and ephemeris data.
Further, the GNSS application terminal in S1 is a consumer-grade single frequency GNSS.
Further, the integrity detection method of the original observation data in S3 includes: and comparing the raw observation data collected in the step S1 with the real-time broadcast ephemeris downloaded in the step S2 every 24 hours to determine whether the raw observation data correspond to the real-time broadcast ephemeris or not, so as to detect the integrity of the raw observation data.
Further, the method for constructing the environment model in S4 includes the following steps:
s41: and (2) constructing an observation equation for a plurality of head and tail point observation values by using a formula (1):
in the formula (1), n represents the fitting order, a i 、b i 、c i Denotes the fitting coefficient, e x Representing the measurement height angle, x representing e x A corresponding azimuth angle; n is 360/0.5, i is {0,0.5, …,360 };
s42: selecting a least square criterion to carry out fitting system solution on the observation equation to obtain a fitting coefficient, and establishing an environment model according to the fitting coefficient by taking 0.5 degrees as a step length from 0 to 360 degrees, as shown in a formula (2):
in equation (2), a 'model coefficient vector, e' i Representing the virtual satellite altitude using the model coefficients back-deducted, i ═ 0,0.5, …, 360.
Further, the specific method for solving the correlation coefficient in S5 is as follows:
to set an environmental model a 'of day 1' 0 Reference, environmental model a 'for day k every 24 hours' k And a' 0 Is solved as shown in equation (3):
wherein, Cov (a' k ,a′ 0 ) Is a' k And a' 0 Covariance, Var [ a' k ]Is a' k Variance of (1), Var [ a' 0 ]Is a' 0 The variance of (c).
The invention has the beneficial effects that: the monitoring and early warning method mainly utilizes the low-cost consumer GNSS positioning terminal to acquire data and perform environment modeling, early identifies the landslide acceleration state through the change trend of the correlation coefficient of the environment model and effectively early warns, so that the monitoring cost is greatly reduced, and a new thought and a new method are provided for the GNSS landslide monitoring and early warning.
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FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a view showing modeling results of GNSS environment at monitoring points for 19 consecutive weeks according to embodiment 2 of the present invention;
fig. 3 is a timing chart of three-dimensional position change and correlation coefficient of the monitoring points in embodiment 2 of the present invention.
Detailed Description
Example 1: in this embodiment, a single-station landslide deformation monitoring and early warning method based on a GNSS environment model is designed, and is mainly used for providing a new monitoring and early warning method based on a consumer-grade single-frequency GNSS, which is specifically described based on the method flowchart in fig. 1.
S1: deploying terminal equipment and performing data acquisition
The method comprises the steps that a GNSS application terminal is deployed on a monitoring point (landslide deformation characteristic point), and after the GNSS application terminal is started, original observation data of the monitoring point are collected through a 4G or 5G wireless communication network and the like, wherein the original observation data comprise original pseudo-range and ephemeris data; the GNSS application terminal can be a consumer-grade single-frequency GNSS, or can be a professional type with centimeter-level precision or a common measurement type with sub-meter-level precision, and the GNSS comprises one or more of BDS/GPS/GLONASS/GALILEO.
S2: determining the initial position of a monitoring point
After the original observation data of several hours (>1 hour) are collected, downloading a real-time broadcast ephemeris file through an Internet IGS service center, and roughly estimating the approximate position of a monitoring point by using a pseudo-range single-point positioning technology;
s3: computing environment modeling source data
On the basis of a broadcast ephemeris, comparing original observation data acquired in S1 with the real-time broadcast ephemeris downloaded in S2 every 24 hours to determine whether the original observation data are corresponding or not, performing integrity check on the original observation data acquired in S1, calculating satellite azimuth angle and altitude angle information of each epoch through a receiver approximate coordinate, summarizing satellite sight line information, and outputting continuous observation arc section observation values, wherein the initial observation value and the final observation value of the arc section observation values are marked as head and tail points;
s4: sunday environment modeling
Performing gross error elimination on the head and tail point observed values output by the S3, constructing an observation equation by using a plurality of head and tail point observed values, selecting a least square criterion to solve a fitting coefficient in the equation by a fitting system, and establishing an environment model according to the fitting coefficient;
the construction method of the environment model comprises the following steps:
s41: the formula (1) is utilized to construct an observation equation for a plurality of head and tail point observation values:
in the formula (1), n represents the fitting order, a i 、b i 、c i Denotes the fitting coefficient, e x Representing the measurement height angle, x representing e x A corresponding azimuth angle; n is 360/0.5, i is {0,0.5, …,360 };
s42: selecting a least square criterion to carry out fitting system solution on the observation equation to obtain a fitting coefficient, and establishing an environment model according to the fitting coefficient by taking 0.5 degrees as a step length from 0 to 360 degrees, as shown in a formula (2):
in equation (2), a 'model coefficient vector, e' i Representing the virtual satellite altitude using the model coefficients back-deducted, i ═ 0,0.5, …, 360.
S5: correlation coefficient solving
Setting intervals by taking days as units, and jointly solving correlation coefficients in the environment models according to the corresponding environment models every day;
the concrete method for solving the correlation coefficient comprises the following steps:
to set an environmental model a 'of day 1' 0 Reference, environmental model a 'for day k every 24 hours' k And a' 0 Is solved as shown in equation (3):
wherein, Cov (a' k ,a′ 0 ) Is a' k And a' 0 Covariance, Var [ a' k ]Is a' k Variance of (1), Var [ a' 0 ]Is a' 0 The variance of (c).
S6: early warning application
And setting a proper threshold value in practical application, comparing and judging the threshold value and the correlation coefficient calculated in the step S5, if the correlation coefficient is lower than the threshold value, carrying out early warning, and if not, continuing monitoring.
Example 2: in this embodiment, the method in embodiment 1 is used for actual measurement, data of a certain landslide monitoring point in a creep stage is selected for an experiment, the monitoring point gradually sinks along with the time, original observation data are collected and environment modeling is performed, the result is shown in fig. 2, the direction indicated by an arrow in fig. 2 represents the modeling result of 19 consecutive weeks from the 1 st week to the 19 th week, and it can be seen that the satellite height angle of the environment model in the same azimuth gradually increases along with the creep movement of the monitoring point.
In the time chart of the three-dimensional position change and the correlation coefficient of the monitoring point given in conjunction with fig. 3, the horizontal axis in fig. 3 represents the time axis, the left vertical axis represents the 3D vector displacement, and the right vertical axis represents the correlation coefficient. As can be seen from the displacement sequence, the monitoring point passes through a creep stage- > an acceleration stage- > and recovers the creep; the correlation coefficient is correspondingly restored to be stable from a stable stage- > an acceleration stage- >; therefore, it can be seen that the trend of the correlation coefficient can be consistent with the position change trend of the monitoring point, and therefore effective landslide early warning can be carried out through setting a reasonable threshold value.
Claims (6)
1. A GNSS environment model-based single-station landslide deformation monitoring and early warning method is characterized by comprising the following steps:
s1: deploying terminal equipment and performing data acquisition
The GNSS application terminal is deployed on a monitoring point, and the original observation data of the monitoring point is collected through a wireless communication network after the GNSS application terminal is started;
s2: determining the initial position of a monitoring point
After the original observation data are collected, downloading a real-time broadcast ephemeris file, and roughly estimating the rough position of the monitoring point by utilizing a pseudo-range single-point positioning technology;
s3: computing environment modeling source data
Based on the broadcast ephemeris, performing integrity check on the original observation data acquired by S1, calculating satellite azimuth angle and altitude angle information of each epoch through a receiver approximate coordinate, summarizing satellite sight line information, and outputting continuous observation arc-segment observation values, wherein the initial and final observation values of the arc-segment observation values are marked as head and tail points;
s4: sunday environment modeling
Performing gross error elimination on the head and tail point observed values output by the S3, constructing an observation equation by using a plurality of head and tail point observed values, selecting a least square criterion to solve a fitting coefficient in the equation by a fitting system, and establishing an environment model according to the fitting coefficient;
s5: correlation coefficient solving
Setting intervals by taking days as units, and jointly solving correlation coefficients in the environment models according to the corresponding environment models every day;
s6: early warning application
And setting a proper threshold value in practical application, comparing and judging the threshold value and the correlation coefficient calculated in the step S5, if the correlation coefficient is lower than the threshold value, carrying out early warning, and if not, continuing monitoring.
2. The GNSS environment model-based single-station landslide deformation monitoring and early warning method according to claim 1, wherein the raw observation data in S1 comprises raw pseudoranges and ephemeris data.
3. The GNSS environment model-based single-station landslide deformation monitoring and early warning method according to claim 1, wherein the GNSS application terminal in S1 is a consumer-grade single-frequency GNSS.
4. The GNSS environment model-based single-station landslide deformation monitoring and early warning method of claim 1, wherein the integrity detection method of the original observation data in S3 comprises: and comparing the raw observation data collected in the step S1 with the real-time broadcast ephemeris downloaded in the step S2 every 24 hours to determine whether the raw observation data correspond to the real-time broadcast ephemeris or not, so as to detect the integrity of the raw observation data.
5. The GNSS environment model-based single-station landslide deformation monitoring and early warning method according to claim 1, wherein the construction method of the environment model in S4 comprises the following steps:
s41: and (2) constructing an observation equation for a plurality of head and tail point observation values by using a formula (1):
in the formula (1), n represents the fitting order, a i 、b i 、c i Denotes the fitting coefficient, e x Representing the measurement height angle, x representing e x A corresponding azimuth angle; n is 360/0.5, i is {0,0.5, …,360 };
s42: selecting a least square criterion to carry out fitting system solution on the observation equation to obtain a fitting coefficient, and establishing an environment model according to the fitting coefficient by taking 0.5 degrees as a step length from 0 to 360 degrees, as shown in a formula (2):
in the formula (2), the first and second groups,
vector of model coefficients, e' i Representing the virtual satellite altitude using the model coefficients back-deducted, i ═ 0,0.5, …, 360.
6. The GNSS environment model-based single-station landslide deformation monitoring and early warning method as claimed in claim 1, wherein the specific method for solving the correlation coefficient in S5 is as follows:
to set an environmental model a 'of day 1' 0 Reference, environmental model a 'for day k every 24 hours' k And a' 0 Is solved as shown in equation (3):
wherein, Cov (a' k ,a′ 0 ) Is a' k And a' 0 Covariance, Var [ a' k ]Is a' k Variance of (1), Var [ a' 0 ]Is a' 0 The variance of (c).
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