CN114236581B - Beidou slope monitoring data post-processing method - Google Patents
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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
Technical Field
The invention relates to the field of big data, in particular to a Beidou side slope monitoring data post-processing method.
Background
Along with the construction of the Beidou system, the Beidou system is greatly popularized in side slope monitoring by 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 the errors to judge the state of a slope, the deformation of the slope is slow deformation under the common condition, sudden landslide may occur under extreme weather such as rainstorm, typhoon or earthquake, and therefore slope monitoring needs to be provided with both slow deformation high-precision monitoring and sudden deformation sudden monitoring.
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 calculation carries out unified calculation on data of the receiver for a period of time to obtain the base length from the monitoring point to a reference station, then the adjustment algorithm is utilized to carry out adjustment processing on the data of the monitoring point to obtain a high-precision displacement result of each monitoring point, and the real-time dynamic rtk in the prior art has low calculation precision and stability and can not 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, eliminating 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, performing periodic error correction on the average result to obtain a corrected periodic result, and taking the corrected periodic result 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.
Note that the monitoring 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 latest D days of the period result of the point is queried in step S5, and there are D × 24/h results. The accuracy of error correction is higher along with the increase of the number of days of the time interval result, but the accuracy improvement is limited after the number of days is more than 7 days, and (D is more than or equal to 2 and less than or equal to 7) is taken in practical application in consideration of the calculation complexity, and if (D is more than or equal to 0 and less than 2), periodic error correction is not carried out.
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 are totally used as the time period result, respectively averaging the results of the corresponding time periods in the D segment historical result, using the average value as the periodic error correction value of the time period to totally obtain 24/h error values, searching the corresponding error values according to the time of the average value result, and subtracting the periodic error values from the current average value result to obtain the 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:
wherein theta is a stability index of a historical result; k is the slope of the historical result; k M Is a slope threshold, K M 10/(24/h); p is the current time period result; m is a historical result reference value; STD is the error in the historical result; f is the weight of the error ratio; n is the current of the counterA value; n is a radical of max Is the maximum value of the counter; and c is the counter weight.
In the above formula
When c is equal to 0, the compound is,
when c is 1; when | P-M |>When 16 STD, f is 0, when | P-M ≦ 16 STD, f is 1; when theta is more than or equal to 1, theta is equal to 1;
wherein the historical result reference value M is the average value of the latest 24-hour data in the historical results of D days.
And calculating the slope K of the historical result by using a least square method, wherein the slope K is used for judging whether the historical monitoring result is stable or not.
And the error STD in the historical result is the error in the historical result of D days, and is used for judging whether the result of the current time period is mutated or not.
Wherein, 3 STD < | X-M | in the above formula represents that P is an abnormal result; the value taking method for dividing the abnormal result into the current value n of the counter under three conditions according to the difference value of P and M is as follows:
further, the stable smoothing calculation post-processing result calculation formula is as follows:
P'＝P*θ+M*(1-θ)；
where P' 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 which is a novel self-adaptive smoothing algorithm, so that on one hand, high-frequency errors can be removed, on the other hand, the displacement of the current result can be kept, 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 side 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 side 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 interval 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.
Note that the monitoring 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 gross error result is removed using a triple medium error method on the segment result.
Preferably, the latest D days of the period result of the point is queried in step S5, and there are a total of D × 24/h results. The accuracy of error correction is higher along with the increase of the number of days of the time interval result, but the accuracy improvement is limited after the number of days is more than 7 days, and (D is more than or equal to 2 and less than or equal to 7) is taken in practical application in consideration of the calculation complexity, and if (D is more than or equal to 0 and less than 2), periodic error correction is not carried out.
Specifically, the periodic error correction method in step S5 is as follows:
subtracting the average value of the period result from the period result, segmenting according to the number of days, dividing the period result into D segments on the assumption that the period result has D days, respectively averaging the results of the corresponding period in the D segment historical results, taking the average value as the periodic error correction value of the period to obtain 24/h error values in total, searching the corresponding error value according to the time of the average value result, and subtracting the periodic error value from the current average value result to obtain the corrected 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 stabilization mode is suitable for long-term monitoring of more stable slopes.
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:
wherein K is the slope of the historical result; k M Is a slope threshold, K M 10/(24/h); p is the current time interval result; n is a radical of hydrogen max Is the maximum value of the counter; n is the current value of the counter; f is the weight of the error ratio; c is the counter weight; theta is a stability index of a historical result; m is a historical result reference value; STD is the error in the historical result.
In the above formula, when
When c is 0; when the temperature is higher than the set temperature
When c is 1; when | P-M |>When 16 STD, f is 0, when | P-M ≦ 16 STD, f is 1; theta is more than or equal to 1，θ＝1；
Wherein the historical result reference value M is the average value of the latest 24-hour data in the historical results of D days.
Wherein the historical result slope is calculated using a least squares method.
Wherein, STD is the error in the D calendar history result.
The value method of the current value n of the counter is as follows:
in the above formula, 3 × STD < | P-M | indicates that P is an abnormal result, and the abnormal result is divided into three cases according to the difference between X and M, each case having a different influence on n.
Further, the final data calculation formula of the post-processing method is as follows:
P'＝P*θ+M*(1-θ)；
the above scheme was applied to the following specific experimental cases:
the data from 2021-09-15 to 2021-09-24 were processed 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. averaging the result set in the 30-minute time period to obtain an average result;
5. and carrying out periodic error correction on the time interval result after the gross error is eliminated, wherein the specific correction method comprises the following steps:
(1) the historical results of the last 5 days of the point are queried, and the total number of results is 240.
(2) Subtracting the average value of the time interval result from each time interval result, segmenting the time interval result according to the number of days, dividing the time interval result into 5 segments in total, respectively averaging the time interval results of the corresponding time points in the 5 segments of time interval 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 current time period results in (120.1,115.3,134.8), 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); if the error model value is subtracted from the current result, the corrected result is P ═ 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;
calculate median error for 5 days of historical results: STD ═ (1.3, 1.5, 2.7);
calculating the slope of the curve of the 5-day history result by using a least square method, wherein the slopes in three directions are K (0.009,0.015 and 0.012), and the slope threshold is 0.1, N max Taking 3, | X-M | (1.8,3.3,3.5), | X-M | ≦ 3 ≦ STD, so f ═ 1, c ═ 0;
7. and (3) calculating a post-processing result:
P'＝P*θ+M*(1-θ)＝(116.4,124.6,122.3)。
as shown in fig. 3, a graph comparing the error between the post-processing result in the X direction and 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 both the precision and the stability are 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 (8)
1. A Beidou side slope monitoring data post-processing method is characterized by comprising 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: an adaptive smoothing algorithm, specifically a stable smoothing algorithm is configured in configuration information; the stability smoothing algorithm introduces stability indexes into post-processing result calculation, and the historical result stability index calculation formula is as follows:
wherein theta is a stability index of a historical result; k is the slope of the historical result; k M Is a slope threshold; p is the current time period result; m is a historical result reference value; STD is the error in the historical result; f is the weight of the error ratio; n is the current value of the counter; n is a radical of hydrogen max Is the maximum value of the counter; c is the counter weight;
the value method of the current value n of the counter is as follows:
the counter counts abnormal values in the historical result;
step S7: and (4) performing post-processing, specifically, calculating by using a stable smoothing algorithm to obtain final data.
2. The Beidou side slope monitoring data post-processing method according to claim 1, characterized in that 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 is further configured with a dynamic smoothing algorithm according to an application scenario;
the dynamic smoothing algorithm is used for monitoring scenes needing to rapidly reflect the displacement of the monitored object;
the stability smoothing algorithm is used for long-term monitoring of relatively stable monitored objects.
6. The Beidou side slope monitoring data post-processing method according to claim 5, characterized in that a Kalman filtering algorithm is adopted for the dynamic smoothing algorithm to output results.
7. The Beidou side slope monitoring data post-processing method according to claim 6, characterized in that the historical result slope K is calculated by a least square method.
8. The Beidou side slope monitoring data post-processing method according to claim 7, wherein in the step S7, a calculation formula of post-processing is as follows:
P′＝P*θ+M*(1-θ)；
where P' is the final data.
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