CN114236581A - Beidou slope monitoring data post-processing method - Google Patents

Beidou slope monitoring data post-processing method Download PDF

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CN114236581A
CN114236581A CN202210188780.1A CN202210188780A CN114236581A CN 114236581 A CN114236581 A CN 114236581A CN 202210188780 A CN202210188780 A CN 202210188780A CN 114236581 A CN114236581 A CN 114236581A
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CN114236581B (en
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梁晓东
雷孟飞
汤金毅
龙兴
黎凯
张涛
周俊华
刘琴
刘�文
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Hunan Lianzhi Technology Co Ltd
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Abstract

The invention solves the problems of low precision and high delay of the Beidou data post-processing method in the prior art. The invention provides a Beidou slope monitoring data post-processing method, which realizes the resolving of Beidou original monitoring data, carries out gross error judgment and periodic error correction on the current resolving result according to the historical monitoring result of a monitoring point, and effectively corrects the periodic error in the monitoring result; the method also comprises a data smoothing algorithm, wherein the data smoothing algorithm is a novel self-adaptive smoothing algorithm, on one hand, high-frequency errors can be removed, on the other hand, the displacement of the current result can be reserved, and high-precision low-delay post-processing calculation is realized.

Description

Beidou slope monitoring data post-processing method
Technical Field
The invention relates to the field of big data, in particular to a Beidou slope monitoring data post-processing method.
Background
Under landslide disasters, the global economy can lose nearly billions of dollars per year, resulting in thousands of casualties. Our country has wide territorial breadth, extremely complex landform and geological conditions, so that our country becomes a country seriously affected by landslide disasters.
The landslide hazard occurrence distribution range is wide, the frequency is high, the density is large, according to incomplete statistics, seventy or more cities and four hundred and sixty or more counties in China are harmed and threatened potentially by the landslide hazard, and along with the construction of the Beidou system, the Beidou system is greatly popularized in slope monitoring due to the characteristics of high precision, all weather, simplicity and convenience in operation and the like.
Most errors in the Beidou data resolving process are eliminated, however, in slope monitoring projects, signal shielding is serious, satellite constellation distribution is uneven, long period errors and high frequency errors are obvious and cannot be effectively eliminated, the periodic errors and the high frequency errors bring great difficulty to slope monitoring, technicians are difficult to extract useful signals from all errors to judge the state of a slope, the deformation of the slope is slow deformation under the common condition, sudden landslide can occur under extreme weather such as rainstorm, typhoon or earthquake, and therefore slope monitoring needs to be provided with both high-precision monitoring of slow deformation and sudden monitoring of severe deformation.
At present, the Beidou calculation in slope monitoring mainly adopts real-time calculation and post-processing calculation, wherein the real-time calculation adopts a dynamic rtk algorithm and can obtain a monitoring result according to the single epoch data calculation of a receiver; the post-processing resolving is to perform unified resolving on data of the receiver for a period of time to obtain the length of a baseline from a monitoring point to a reference station, then perform adjustment processing on the data of the monitoring point by using an adjustment algorithm to obtain a high-precision displacement result of each monitoring point, and the real-time dynamic rtk in the prior art has low resolving precision and stability and cannot meet the requirements of slope monitoring and early warning; the post-processing calculation precision is high, but the traditional post-processing calculation delay is high, and sudden slope sliding cannot be quickly reflected.
Disclosure of Invention
In order to solve the above problems, the invention provides a post-processing method of a slope monitoring result aiming at the characteristics of a slope, which comprises the following steps:
step S1: acquiring original data, and acquiring observation data and ephemeris data of a monitoring point and a reference station;
step S2: resolving original data, specifically resolving the original data to obtain a time period result;
step S3: screening data, specifically, removing gross error results from the time interval results to obtain processed time interval results;
step S4: averaging the processed time period results to obtain an average result;
step S5: periodic error correction, specifically, periodic error correction is carried out on the average result to obtain a corrected periodic result, and the corrected periodic result is used as a historical result;
step S6: a self-adaptive smoothing algorithm, specifically a dynamic smoothing algorithm or a stable smoothing algorithm is configured in configuration information;
step S7: and (4) performing post-processing, specifically, calculating by using a dynamic smoothing algorithm or a stable smoothing algorithm to obtain final data.
The monitor point post-processing time interval is set to h hours.
Preferably, in step S2, the raw data solution employs a conventional baseline solution algorithm.
Preferably, in step S3, the coarse error result is eliminated by using a triple error method for the segment result.
Preferably, the step S5 inquires the nearest pointDResults of the day period, in total
Figure 300656DEST_PATH_IMAGE001
And (6) obtaining the result. The error correction precision is higher along with the increase of the time interval result days, but the precision is improved in a limited way after the number of days is more than 7 days, and the calculation complexity is considered, so that the error correction precision is obtained in practical application
Figure 93162DEST_PATH_IMAGE002
If, if
Figure 872899DEST_PATH_IMAGE003
Periodic error correction is not performed.
Specifically, the periodic error correction method in step S5 is as follows:
subtracting the average value of the time period result from the time period result, segmenting the time period result according to days, dividing the time period result into D segments on the assumption that D days of time period results are shared, respectively averaging the results of the corresponding time periods in the D segment historical results, taking the average value as the periodic error correction value of the time period, and obtaining the periodic error correction value of the time period
Figure 268109DEST_PATH_IMAGE004
And the error value is searched according to the time of the average value result, and the periodic error value is subtracted from the current average value result to obtain a corrected time period result.
Further, the configuration information configures dynamic smoothing calculation or stable smoothing calculation according to the application scenario.
In particular, dynamic smoothing is used in monitoring scenarios where the displacement of the monitored object needs to be reflected quickly, such as: scenes such as monitoring of a sliding slope, monitoring of bridge deviation rectification and the like are generated; the stable mode is suitable for long-term monitoring of a more stable slope.
Preferably, the dynamic smoothing calculation uses a kalman filter algorithm.
Preferably, stable smooth calculation introduces the historical result stability index into the final post-processing result calculation, and the stability of the monitoring result is improved.
Specifically, the stable smoothing algorithm needs to calculate a historical result stationarity index, the historical result stationarity index needs to be calculated by a slope ratio, a median error ratio and a counter ratio, wherein the slope ratio represents stationarity of a historical monitoring result, the median error ratio represents stability degree of a current result, the counter ratio represents reliability of the current abnormal result, and the larger the counter ratio is, the larger the reliability of the current result is.
Further, the historical result stationarity index calculation method comprises the following steps:
Figure 957847DEST_PATH_IMAGE005
；
wherein the content of the first and second substances,
Figure 780310DEST_PATH_IMAGE006
the stability index of the historical result is;
Figure 922709DEST_PATH_IMAGE007
is the historical result slope;
Figure 121609DEST_PATH_IMAGE008
in the form of a slope threshold value, the slope threshold value,
Figure 665854DEST_PATH_IMAGE009
；
Figure 659218DEST_PATH_IMAGE010
is the current time period result;
Figure 413547DEST_PATH_IMAGE011
is a historical result reference value;
Figure 776658DEST_PATH_IMAGE012
error in historical results;
Figure 300043DEST_PATH_IMAGE013
the error ratio is weighted;
Figure 605254DEST_PATH_IMAGE014
is the current value of the counter;
Figure 846879DEST_PATH_IMAGE015
is the maximum value of the counter;
Figure 122003DEST_PATH_IMAGE016
is the counter weight.
In the above formula
Figure 640840DEST_PATH_IMAGE017
When the temperature of the water is higher than the set temperature,
Figure 976006DEST_PATH_IMAGE018
，
Figure 704928DEST_PATH_IMAGE019
when the temperature of the water is higher than the set temperature,
Figure 783742DEST_PATH_IMAGE020
(ii) a When in use
Figure 157086DEST_PATH_IMAGE021
When the temperature of the water is higher than the set temperature,
Figure 928733DEST_PATH_IMAGE022
when is coming into contact with
Figure 879371DEST_PATH_IMAGE023
When the temperature of the water is higher than the set temperature,
Figure 761877DEST_PATH_IMAGE024
；
Figure 989727DEST_PATH_IMAGE025
when the temperature of the water is higher than the set temperature,
Figure 932275DEST_PATH_IMAGE026
；
wherein the historical result reference value
Figure 370210DEST_PATH_IMAGE027
The average of the last 24 hours of data in the historical results for D days was calculated.
Wherein the slope of the historical result is calculated by using a least square method
Figure 790827DEST_PATH_IMAGE028
And the method is used for judging whether the historical monitoring result is stable or not.
Wherein errors in historical results
Figure 138762DEST_PATH_IMAGE029
And D, errors in the historical results of days are used for judging whether mutation occurs in the results of the current time period.
Wherein, in the above formula
Figure 252212DEST_PATH_IMAGE030
Is shown by
Figure 177443DEST_PATH_IMAGE031
Is an abnormal result; according to
Figure 401751DEST_PATH_IMAGE031
And
Figure 338614DEST_PATH_IMAGE027
the difference value of (A) divides the abnormal result into the current value of the counter in three cases
Figure 622965DEST_PATH_IMAGE032
The value taking method comprises the following steps:
Figure 35491DEST_PATH_IMAGE033
。
further, the stable smoothing calculation post-processing result calculation formula is as follows:
Figure 63490DEST_PATH_IMAGE034
；
wherein the content of the first and second substances,
Figure 120439DEST_PATH_IMAGE035
is the final data.
The technical scheme of the invention has the following beneficial effects: the method realizes the resolving of the original Beidou monitoring data, performs gross error judgment and periodic error correction on the current resolving result according to the historical monitoring result of the monitoring point, and effectively corrects the periodic error in the monitoring result; the method also comprises a data smoothing algorithm, wherein the data smoothing algorithm is a novel self-adaptive smoothing algorithm, on one hand, high-frequency errors can be removed, on the other hand, the displacement of the current result can be reserved, and high-precision low-delay post-processing calculation is realized.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The invention will now be described in further detail with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a method for post-processing slope monitoring results;
FIG. 2 is a graph of a periodic error correction value model in the X direction;
FIG. 3 is a graph comparing the error in the X-direction post-processing results with the original results.
Detailed Description
Embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways, which are defined and covered by the claims.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, and the terms used herein in the specification of the present invention are for the purpose of describing particular embodiments only and are not intended to limit the present invention.
The invention provides a Beidou slope monitoring data post-processing method, which realizes high-precision low-delay post-processing of Beidou original monitoring data, and comprises the following specific implementation modes:
as shown in fig. 1, a Beidou slope monitoring data post-processing method includes the following steps:
step S1: acquiring original data, and acquiring observation data and ephemeris data of a monitoring point and a reference station;
step S2: resolving original data, specifically resolving the original data to obtain a time period result;
step S3: screening data, specifically, rejecting gross error results from the time interval results to obtain processed time interval results;
step S4: calculating an average result, specifically calculating an average value of the processed time period result to obtain an average result;
step S5: performing periodic error correction on the average result, specifically performing periodic error correction on the average result to obtain a corrected periodic result as a historical result;
step S6: and the adaptive smoothing algorithm is specifically a dynamic smoothing algorithm or a stable smoothing algorithm configured in the configuration information.
Step S7: and (4) performing post-processing, specifically, calculating by using a dynamic smoothing algorithm or a stable smoothing algorithm to obtain final data.
The monitor point post-processing time interval is set to h hours.
Preferably, in step S2, the raw data is solved using a conventional baseline solution algorithm.
Preferably, in step S3, the coarse error result is eliminated by using a triple error method for the segment result.
Preferably, the step S5 inquires the nearest pointDResults of the day period, in total
Figure 310112DEST_PATH_IMAGE001
And (6) obtaining the result. The error correction precision is higher along with the increase of the time interval result days, but the precision is improved in a limited way after the number of days is more than 7 days, and the calculation complexity is considered, so that the method should be used in practiceIn use take out
Figure 209935DEST_PATH_IMAGE002
If, if
Figure 651412DEST_PATH_IMAGE003
Periodic error correction is not performed.
Specifically, the periodic error correction method in step S5 is as follows:
subtracting the average value of the time period result from the time period result, segmenting the time period result according to days, dividing the time period result into D segments on the assumption that D days of time period results are shared, respectively averaging the results of the corresponding time periods in the D segment historical results, taking the average value as the periodic error correction value of the time period, and obtaining the periodic error correction value of the time period
Figure 953080DEST_PATH_IMAGE004
And the error value is searched according to the time of the average value result, and the periodic error value is subtracted from the current average value result to obtain a corrected time period result.
Further, the configuration information configures dynamic smoothing calculation or stable smoothing calculation according to the application scenario.
In particular, dynamic smoothing is used in monitoring scenarios where the displacement of the monitored object needs to be reflected quickly, such as: scenes such as monitoring of a sliding slope, monitoring of bridge deviation rectification and the like are generated; the stable mode is suitable for long-term monitoring of a more stable slope.
Preferably, the dynamic smoothing algorithm is a kalman filter algorithm.
Preferably, the stability smoothing algorithm introduces the stability index of the historical result into a post-processing method to calculate final data, and the stability of the monitoring result is improved.
It should be noted that the stable smoothing algorithm needs to calculate a historical result stationarity index, the historical result stationarity index needs to be calculated by a slope ratio, a median error ratio and a counter ratio, wherein the slope ratio represents stationarity of a historical monitoring result, the median error ratio represents stability degree of a current result, the counter ratio represents reliability of a current abnormal result, and the larger the counter ratio is, the larger the reliability of the current result is.
Further, the historical result stationarity index calculation method comprises the following steps:
Figure 313654DEST_PATH_IMAGE036
；
wherein the content of the first and second substances,
Figure 576139DEST_PATH_IMAGE007
is the historical result slope;
Figure 945941DEST_PATH_IMAGE008
in the form of a slope threshold value, the slope threshold value,
Figure 711903DEST_PATH_IMAGE009
；
Figure 508957DEST_PATH_IMAGE010
is the current time period result;
Figure 117793DEST_PATH_IMAGE015
is the maximum value of the counter;
Figure 190089DEST_PATH_IMAGE014
is the current value of the counter;
Figure 200771DEST_PATH_IMAGE013
the error ratio is weighted;
Figure 168727DEST_PATH_IMAGE016
is the counter weight;
Figure 264859DEST_PATH_IMAGE006
the stability index of the historical result is;
Figure 851829DEST_PATH_IMAGE037
is a historical result reference value;
Figure 717017DEST_PATH_IMAGE012
as errors in historical results。
In the above formula, when
Figure 855874DEST_PATH_IMAGE017
When the temperature of the water is higher than the set temperature,
Figure 439302DEST_PATH_IMAGE018
(ii) a When in use
Figure 564384DEST_PATH_IMAGE019
When the temperature of the water is higher than the set temperature,
Figure 549658DEST_PATH_IMAGE020
(ii) a When in use
Figure 859416DEST_PATH_IMAGE021
When the temperature of the water is higher than the set temperature,
Figure 805507DEST_PATH_IMAGE022
when is coming into contact with
Figure 858913DEST_PATH_IMAGE023
When the temperature of the water is higher than the set temperature,
Figure 698693DEST_PATH_IMAGE024
；
Figure 54719DEST_PATH_IMAGE025
when the temperature of the water is higher than the set temperature,
Figure 347160DEST_PATH_IMAGE026
；
wherein the historical result reference value
Figure 204258DEST_PATH_IMAGE027
The average of the last 24 hours of data in the historical results for D days was calculated.
Wherein the historical result slope is calculated using a least squares method.
Wherein the content of the first and second substances,
Figure 898544DEST_PATH_IMAGE029
and D, errors in the results of the calendar history.
Wherein the content of the first and second substances,current value of counter
Figure 425472DEST_PATH_IMAGE032
The value taking method comprises the following steps:
Figure 470788DEST_PATH_IMAGE033
；
in the above formula
Figure 865998DEST_PATH_IMAGE038
To represent
Figure 414791DEST_PATH_IMAGE039
For abnormal result, the abnormal result is divided into three cases according to the difference value between X and M, and each case is paired
Figure 502832DEST_PATH_IMAGE014
The influence of (c) is different.
Further, the final data calculation formula of the post-processing method is as follows:
Figure 645232DEST_PATH_IMAGE040
；
the above scheme was applied to the following specific experimental cases:
the data from 2021-09-15 to 2021-09-24 were selected for processing with a post-processing time interval of 0.5 hours.
1. Acquiring observation data and ephemeris data of a monitoring point and a reference station for 30 minutes as original data;
2. resolving the Beidou data by using a conventional baseline resolving algorithm to obtain a 30-minute time period result set of the monitoring points;
3. removing gross error results in a 30-minute time period result set by using a triple error method;
4. calculating an average value of the result set in the 30-minute time period to obtain an average value result;
5. and (3) carrying out periodic error correction on the time period result after the gross error is removed, wherein the specific correction method comprises the following steps:
(1) the historical results of the last 5 days of the point are inquired, and the total number of the results is 240.
(2) Subtracting the average value of the time period results from each time period result, segmenting the time period results according to the number of days, dividing the time period results into 5 segments in total, respectively averaging the time period results of the corresponding time points in the 5 segments of time period results, taking the average value as the periodic error correction value of the time point, and obtaining 48 error values in total, wherein the model curve of the periodic error correction value in the X direction is shown in FIG. 2.
(3) The result of the current time period is (120.1, 115.3, 134.8), and the result time is 2021-10-1000: 30: 00; the horizontal axis of the error model value corresponding to the time is 2, and the periodic error model value is found to be (5.2, -6.9, 9.7); subtracting the error model value from the current result, and obtaining the corrected result
Figure 594864DEST_PATH_IMAGE031
=（114.9，122.2，125.1）。
6. And (3) calculating a stationarity index:
calculate the average of the last 24 hours history: m = (116.7, 125.5, 121.6) as a reference value;
calculate median error for 5 days of historical results: STD = (1.3, 1.5, 2.7);
the slope of the 5-day history result curve is calculated by using a least square method, the slope in three directions is K = (0.009, 0.015 and 0.012), the threshold value of the slope is 0.1,
Figure 998164DEST_PATH_IMAGE041
taking out the step 3,
Figure 257107DEST_PATH_IMAGE042
，
Figure 11436DEST_PATH_IMAGE043
thus, therefore, it is
Figure 623814DEST_PATH_IMAGE044
；
Figure 147200DEST_PATH_IMAGE045
。
7. And (3) calculating a post-processing result:
Figure 577044DEST_PATH_IMAGE046
。
as shown in fig. 3, a graph comparing the error in the X-direction post-processing result with the error in the original result, where the solid line is the post-processing result of this embodiment and the dotted line is the error in the original result, it can be seen from the graph that the periodicity of the error in the original result is obvious, the processed result is stable, there is no obvious periodicity, and the accuracy and stability are both improved.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. The Beidou slope monitoring data post-processing method is characterized by comprising the following steps of:
step S1: acquiring original data, and acquiring observation data and ephemeris data of a monitoring point and a reference station;
step S2: resolving original data, specifically resolving the original data to obtain a time period result;
step S3: screening data, specifically, removing gross error results from the time interval results to obtain processed time interval results;
step S4: averaging the processed time period results to obtain an average result;
step S5: periodic error correction, specifically, periodic error correction is carried out on the average result to obtain a corrected periodic result, and the corrected periodic result is used as a historical result;
step S6: a self-adaptive smoothing algorithm, specifically a dynamic smoothing algorithm or a stable smoothing algorithm is configured in configuration information;
step S7: and (4) performing post-processing, specifically, calculating by using a dynamic smoothing algorithm or a stable smoothing algorithm to obtain final data.
2. The Beidou slope monitoring data post-processing method according to claim 1, wherein in the step S2, a conventional baseline solution algorithm is adopted for original data solution.
3. The Beidou slope monitoring data post-processing method according to claim 1, wherein in step S3, a triple error method is adopted for time interval result coarse difference elimination results.
4. The Beidou slope monitoring data post-processing method according to claim 1, wherein in the step S5, the periodic error correction method is as follows:
and subtracting the average value of the time period result from the time period result, segmenting according to the number of days, respectively averaging the results of the corresponding time periods in the time period result of the current day to be used as the periodic error correction value of the time period, subtracting the periodic error value from the average value of the current time period result to obtain a corrected time period result, and using the corrected time period result as a historical result.
5. The Beidou slope monitoring data post-processing method according to claim 1, wherein in the step S6, the configuration information configures dynamic smooth calculation or stable smooth calculation according to an application scene;
wherein, the dynamic smooth calculation is used for monitoring scenes which need to rapidly reflect the displacement of the monitored object;
the stability smoothing calculation is used for long-term monitoring of relatively stable monitored objects.
6. The Beidou slope monitoring data post-processing method according to claim 5, characterized in that the dynamic smoothing algorithm adopts Kalman filtering algorithm to output results.
7. The Beidou slope monitoring data post-processing method according to claim 5, characterized in that the stationarity index is introduced into post-processing result calculation by the stability smoothing algorithm, and the historical result stationarity index calculation formula is as follows:
Figure 765310DEST_PATH_IMAGE001
；
wherein the content of the first and second substances,
Figure 49661DEST_PATH_IMAGE002
the stability index of the historical result is;
Figure 462188DEST_PATH_IMAGE003
is the historical result slope;
Figure 427870DEST_PATH_IMAGE004
is a slope threshold;
Figure 609453DEST_PATH_IMAGE005
is the current time period result;
Figure 799126DEST_PATH_IMAGE006
is a historical result reference value;
Figure 698948DEST_PATH_IMAGE007
error in historical results;
Figure 265059DEST_PATH_IMAGE008
the error ratio is weighted;
Figure 504410DEST_PATH_IMAGE009
is the current value of the counter;
Figure 864985DEST_PATH_IMAGE010
is the maximum value of the counter;
Figure 252104DEST_PATH_IMAGE011
is the counter weight.
8. The Beidou slope monitoring data post-processing method according to claim 7, characterized in that the slope of the historical result is
Figure 559588DEST_PATH_IMAGE003
And calculating by using a least square method.
9. The Beidou slope monitoring data post-processing method according to claim 7, characterized in that the current value of the counter
Figure 450184DEST_PATH_IMAGE009
The value taking method comprises the following steps:
Figure 247239DEST_PATH_IMAGE012
。
10. the Beidou slope monitoring data post-processing method according to claim 9, wherein in the step S7, a calculation formula of post-processing is as follows:
Figure 856074DEST_PATH_IMAGE013
；
wherein the content of the first and second substances,
Figure 967250DEST_PATH_IMAGE014
is the final data.
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Citations (7)

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