CN115792974A - GNSS deformation monitoring result quality evaluation method - Google Patents
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Abstract
The invention provides a GNSS deformation monitoring result quality evaluation method, which comprises the following steps: s1, selecting a single evaluation index and acquiring each index value, wherein the evaluation index comprises original data quality, data resolving characteristic information, a baseline distance and observation duration; s2, constructing a comprehensive evaluation index; s21, homotrending indexes; s22, dimensionless of indexes; s23, determining an index weight coefficient by using the variation coefficient; and S3, calculating a comprehensive evaluation index value, and solving the technical problem that the evaluation method in the prior art cannot effectively represent the quality of the GNSS resolving result.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a GNSS deformation monitoring result quality evaluation method.
Background
China has broad breadth, complex and various landforms, and huge property loss caused by natural geological disasters all year around the country, so that the construction of an efficient geological disaster monitoring and forecasting system is very important. In deformation monitoring, particularly for monitoring deformation such as landslide and dams, the GNSS displays incomparable advantages of other measuring means by the characteristics of simple operation, all-weather observation, no need of communication between stations and the like. However, for a satellite navigation system, the precise and reliable positioning result and the sufficient observation redundancy are inseparable, in actual observation, a survey station is often limited by an observation environment, the quality of GNSS data is poor, and the rough difference condition of a resolving result is easy to occur. The quality of the GNSS calculation result is highly related to the quality of the GNSS original data and is closely connected with the GNSS calculation algorithm, the quality of the monitoring result is accurately evaluated by combining the quality analysis of the GNSS original observation data and utilizing methods such as machine learning and mathematical modeling based on the characteristics of the GNSS calculation result data, and the usability of the calculation result can be effectively judged and judged by a user. The GNSS resolving result quality influencing factors are complex, and mainly include the following: 1) Inaccurate base station coordinates or unstable base station site selection. If the base station coordinates are inaccurate, the base line is deviated in the dimension and direction, and the base station is unstable, so that the relative displacement of the monitoring station and the base station is superposed, and the real displacement of the monitoring body cannot be identified. 2) The whole-cycle ambiguity of part of the satellite cannot be completely fixed due to short monitoring time caused by equipment or human factors. When the ambiguity of the whole satellite can not be fixed, the fixed solution of the monitoring station in the corresponding monitoring time period can not be obtained, and the monitoring precision is seriously influenced. 3) In the observation period, due to environmental shielding or loss of lock of the satellite due to other reasons, cycle slip of the satellite in a certain period is serious in the monitoring period, and when the cycle slip is serious, cycle slip repair is incomplete during data preprocessing. 4) In the observation period, because the site selection of the monitoring station does not accord with the high-precision GNSS measurement standard, the external environment brings more serious multipath effect, and the correction number of the observation value is generally larger. 5) The tropospheric and ionospheric refraction caused by the harsh atmospheric environment during the observation period is too much affected. 6) The signal propagation path is influenced by external electromagnetic waves. The effective path of signal propagation is changed, and the interpretation of the signal, the ranging code and the reconstruction process of the carrier wave are indirectly influenced. 7) The receiver has poor quality of observed data due to low board card quality. This includes mainly the phase measurement accuracy of the receiver, the phase deviation of the receiver antenna, the clock accuracy of the receiver, etc. In order to more accurately capture the deformation information of the monitored object, the method for researching the GNSS resolving result quality assessment is very significant. The current commonly used baseline solution result quality evaluation indexes mainly comprise unit weight variance factors, root mean square errors, data deletion rates, ratio values, PDOPs and the like, however, GNSS solution is a complex process, and single index or simple combination of multiple indexes cannot effectively represent the GNSS solution result quality.
Disclosure of Invention
The invention aims to provide a GNSS deformation monitoring result quality evaluation method, which aims to solve the technical problem that the evaluation method in the prior art cannot effectively represent the quality of a GNSS resolving result.
A GNSS deformation monitoring result quality evaluation method is characterized by comprising the following steps: s1, selecting a single evaluation index and acquiring each index value, wherein the evaluation index comprises original data quality, data resolving characteristic information, a baseline distance and observation duration; s2, constructing a comprehensive evaluation index; s21, homotrending indexes; s22, dimensionless of indexes; s23, determining an index weight coefficient by using the variation coefficient; and S3, calculating a comprehensive evaluation index value.
Further, the dimensionless index in S22 is to process each index value after the trend is made according to the following formula (1):
in the formula, Q l The first index value after the index is subjected to non-dimensionalization; x is the number of l The index value of the item I after the homotrenization; min l Is the minimum value of the indicator of the item I; max (maximum of ten) l The maximum value of the indicator is the l;
the determination of the index weight coefficient in S23 is as shown in equation (2) (3):
wherein, CV is l Denotes the coefficient of variation, W l For weighting based on the coefficient of variation, σ and μ are the standard deviation operation and the mean operation, respectively.
Further, the expression for calculating the comprehensive evaluation index value T in S3 is shown in (4):
wherein, N l Is the number of the individual indexes, w i And Q i The weight and the dimensionless index of the ith index are respectively.
Further, the calculation of each index of the quality of the raw data in S1 includes:
-data integrity rate, comprising calculating single frequency data integrity rate and single system data integrity rate:
DI f the integrity rate of observed data at a single frequency point, n is the total number of theoretical satellites in an observation period, hf j The actual number, ef, of effective observed values of the jth satellite in an observation period on a certain frequency point j Theory of effective observed value of j-th satellite in observation period at certain frequency pointCounting; DI S Denotes the single system observed data integrity rate, hs j Indicating that all frequency points in the observation period of the jth satellite have effective observed value actual number, es j The theoretical number indicates that all frequency points in the observation period of the jth satellite have effective observation values;
-data cycle slip ratio, calculated as shown in equation (7):
in the formula, oslip represents the cycle slip ratio, obs have Represents the number of effective observed values, slip represents the number of cycle hops;
the average multipath error is calculated according to equation (8):
in the formula, MP i 、MP j For the calculated quantities containing multipath errors and integer ambiguities, p i 、ρ j Is pseudo-range observed quantity, unit m;
is a carrier phase observation, in units of m; f. of i 、f j Is frequency, in MHz;
calculating among a plurality of epochs without cycle slip according to a formula (9) to obtain an average multipath error:
wherein, the first and the second end of the pipe are connected with each other,
an estimate of multipath error at k frequencies for a satellite observed by the receiver; n is the number of epochs of the sliding window, and the default is 50;
the calculation amount of multipath error and integer ambiguity information on the frequency of the satellite observed by the receiver of the epoch t;
-average signal-to-noise ratio, calculated according to equation (10):
in the formula (I), the compound is shown in the specification,
the satellite average signal-to-noise ratio index statistic value is obtained; n is the total number of observation satellites; j is an observation satellite number; n is a radical of j The total number of observation epochs for satellite j; i is the observation epoch serial number of the satellite j;
is the signal-to-noise ratio observation of satellite j at epoch t, in dBHz.
According to the GNSS deformation monitoring result quality evaluation method provided by the invention, the observation data quality index and the GNSS calculation algorithm characteristic data are subjected to normalization operation, a variable coefficient weighting method is utilized, various indexes are linearly combined, the obtained new index is a comprehensive evaluation index, and the technical problem that the evaluation method in the prior art cannot effectively represent the GNSS calculation result quality is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a timing diagram of a monitoring and evaluation index of a measuring point provided in this embodiment;
FIG. 2 is a timing diagram of monitoring and evaluating indicators at the second measuring point provided in this embodiment;
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be apparent that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment provides a GNSS deformation monitoring result quality evaluation method. The quality of the GNSS resolving result is closely related to the quality of the GNSS original observation data and the GNSS resolving data information. In the embodiment, the influence of various factors on the result precision is comprehensively considered, and various indexes are linearly combined by using a coefficient of variation weighting method to evaluate the result quality of the GNSS. The method for evaluating the quality of the GNSS deformation monitoring result provided by the embodiment comprises the following steps:
s1, selecting a single evaluation index and obtaining each index value.
Through comparative research, 11 main evaluation items including three main types of indexes related to the original data quality, the data resolving characteristic information and the baseline distance and the observation duration are selected in the embodiment, as shown in table 1:
table 1 evaluation item table
S11, obtaining the quality-related index value of the original observation data
The GNSS resolving result quality is indistinguishable from the original observation data quality, key indexes representing the original observation data quality mainly comprise a data integrity rate, a data cycle slip ratio, an average multipath error, an average signal-to-noise ratio and the like, and a specific calculation method of each index comprises the following steps:
(1) Data integrity rate
The data integrity rate represents the ratio of the actual effective observation data quantity observed by the receiver to the theoretical observation data quantity in the observation period, the environment shielding condition is reflected, and the ratio is better if the ratio is larger. Respectively calculating the single-frequency data integrity rate and the single-system data integrity rate according to the following formulas (5) and (6):
DI f the integrity rate of observed data at a single frequency point, n is the total number of theoretical satellites in an observation period, hf j The actual number of effective observed values, ef, of the jth satellite in an observation period at a certain frequency point j The theoretical number of effective observed values of the jth satellite in an observation time period on a certain frequency point is obtained; DI S Denotes the single system observed data integrity rate, hs j Indicating that all frequency points in the observation period of the jth satellite have effective observed value actual number, es j And the theoretical number of all frequency points with effective observed values in the observation period of the jth satellite is represented.
(2) Cycle slip ratio of data
The data cycle slip ratio represents the ratio of the actual effective observed value number observed by the receiver to the data volume of the cycle slip in the observation period, reflects the average observed value number of the cycle slip, and represents the continuity condition of the satellite signal tracking by the equipment, and the larger the ratio is, the better the ratio is, as shown in formula (7):
(3) Averaging multipath errors
The multipath error refers to the ranging error introduced by a non-line-of-sight signal, and the multipath error of carrier phase measurement is smaller than 1/4 of the wavelength and far smaller than the pseudo-range multipath error. Therefore, the focus of the study is pseudo multipath error. The pseudo-range multi-path error is calculated according to equation (8):
in the formula, MP i 、MP j For the calculated quantities containing multipath errors and integer ambiguities, p i 、ρ j Is pseudo-range observed quantity, unit m;
is the carrier phase observation, in units of m; f. of i 、f j Is frequency in MHz.
For the same satellite, the combined ambiguity parameters are not changed under the condition of continuous observation and no cycle slip, and the average multipath error is obtained by calculating according to the formula (9) among a plurality of epochs without cycle slip.
Wherein the content of the first and second substances,
an estimate of multipath error at k frequencies for a satellite observed by the receiver; n is the number of epochs of the sliding window, and the default is 50;
the receiver observes the satellite over frequency with a computation containing multipath error and integer ambiguity information at epoch t.
(4) Average signal-to-noise ratio
And (3) calculating a statistic value of the average signal-to-noise ratio index according to the formula (10), wherein the larger the value is, the better the value is, and representing the condition of the signal intensity captured by equipment.
In the formula (I), the compound is shown in the specification,
for averaging over satellitesA signal-to-noise ratio index statistic; n is the total number of observation satellites; j is an observation satellite number; n is a radical of j The total number of observation epochs for satellite j; i is the observation epoch serial number of the satellite j;
is the signal-to-noise ratio observation of satellite j at epoch t, in dBHz.
And S12, acquiring data resolving characteristic information.
The quality of the GNSS solution result is also relevant to the data processing algorithm and the processing strategy. Different function models, random models and error correction models in the data processing algorithm are selected to cause result differences, and different data sampling intervals and the setting of the cut-to-height angle can cause the difference of the calculation results. The influence of the data processing algorithm and the strategy on the quality of the calculation result cannot be quantitatively analyzed, and the data calculation characteristic information such as a variance Ratio (Ratio value), a root mean square error (RMS), a data rejection rate, a positioning accuracy factor (PDOP) and the like is generally adopted for representation.
And S13, resolving the GNSS data baseline to obtain indexes such as baseline resolving results, baseline resolving characteristic information, baseline distance height difference and data duration.
The baseline distance and the data duration also affect the quality of the GNSS resolving result. The base line distance is long or the elevation difference between stations is large, the troposphere characteristic similarity at two ends of the base line is reduced, all troposphere delays are difficult to effectively eliminate in a differential mode, the influence on the positioning accuracy is large, and especially the influence on the elevation accuracy is large. In addition, different observation period lengths can also affect baseline resolution accuracy. In general, the longer the observation period, the higher the baseline solution accuracy, but when the observation environment is poor, the longer the observation period, the higher the baseline solution accuracy is not necessarily. Because the data quality of different observation periods in the same observation environment is different, when the data quality is better, the observation data in a shorter time can obtain a baseline solution result with higher precision; conversely, even if the observation time is long, if the observed data quality is poor for some of the time periods, the baseline solution may fail, mainly because the poor data quality of the data will cause cycle slip detection and repair to be erroneous, resulting in failure of the baseline solution.
And S2, constructing a comprehensive evaluation index.
Multiple indices cannot be simply combined because the trends and ranges of variation may be inconsistent between different indices. It needs to be normalized to make the value of the vector be planned to a certain interval to become a pure quantity. For example, [0,1],0 represents the optimum value, and 1 represents the worst value. The method comprises the following specific steps:
s21, the co-trend of indexes: in the comprehensive index system, if the data integrity rate is higher and the multipath value is lower, the observed data quality is better, so that the data integrity rate index needs to be converted into a very large index value in an inverse manner.
S22, dimensionless of indexes: each index value of the comprehensive evaluation must be a dimensionless value, and each index value after the homotrend is processed according to the formula (1):
in the formula, Q l The first index value after the index is subjected to non-dimensionalization; x is the number of l The index value of the item I after the homotrenization; min l Is the minimum value of the I index; max (maximum of ten) l The maximum value of the indicator is the l;
s23, determining an index weight coefficient: when different indexes are fused, weights are different, and therefore weighting operation needs to be performed on the indexes, and the embodiment adopts coefficient of variation weighting. The basic idea is that the index with larger coefficient of variation, namely the index which is difficult to realize, can reflect the difference of the evaluated units more, and therefore the weight of the index is also larger. The specific weighting process is shown in the formulas (2) and (3):
wherein CV is l Denotes the coefficient of variation, W l For weighting based on the coefficient of variation, σ and μ are the standard deviation operation and the mean operation, respectively, and the values are usually trained by using the measured data in the same scene.
And S3, calculating a comprehensive evaluation index value.
And linearly combining the root mean square error and the smoothness of each weighted parameter to obtain a comprehensive evaluation index value T:
wherein, N l The number of the single indicators is 11 in this embodiment. w is a i And Q i The weight and the dimensionless index of the ith index are respectively.
In order to verify the reliability of the method of the embodiment, the measured data is used for test verification. The test data is from a group of dam actual measurement data, and the total number of the test data is 2 GNSS monitoring points. The data calculation is specifically configured as follows: the test period is from 1 day 7 month in 2022 to 31 days 7 month in 2022, which is one month in total; the data sampling interval is 10s; participating in resolving a satellite system as GPS + BDS; the resolving arc length is 4h, and the time sequence of the residual error of the coordinates of the two measuring points, the sequence and the quality index is shown in figure 1.
As can be seen from FIG. 1, the coordinate sequences of the two measuring points are relatively stable as a whole, and no obvious displacement condition is found; jumping points appear in the second measuring point within a testing time period, the horizontal deviation exceeds 20mm, and the elevation deviation exceeds 40mm; assuming that there is no displacement between two measuring points in the test period, the accuracy statistics before and after gross error removal are shown in table 2.
TABLE 2 statistical indexes of precision of monitoring points (unit: mm)
As can be seen from Table 2, in the horizontal precision, the measuring point 1 is better than 2mm, and the measuring point 2 is better than 4mm; in the elevation precision, the elevation precision of the two measuring points is superior to 5mm; after the jumping points are eliminated at the second measuring point, certain promotion is realized in the N/E/U directions; as can be seen from fig. 2, when a jump occurs in the monitoring sequence, the quality index becomes significantly large, which indicates that the quality index can effectively feed back the jump situation of the measurement point, and provides a basis for using the monitoring data, thereby verifying that the quality evaluation index provided herein is a reliable GNSS quality evaluation index, and can serve in engineering practice.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and these modifications or substitutions do not depart from the spirit of the corresponding technical solutions of the embodiments of the present invention.
Claims (4)
1. A GNSS deformation monitoring result quality evaluation method is characterized by comprising the following steps:
s1, calculating a single evaluation index, wherein the evaluation index comprises original data quality, data resolving characteristic information, a base line distance and observation duration;
s2, constructing a comprehensive evaluation index;
s21, homotrending indexes;
s22, dimensionless of indexes;
s23, determining an index weight coefficient by using the variation coefficient;
and S3, calculating a comprehensive evaluation index value.
2. The method according to claim 1, wherein the non-dimensionalization of the indicator in S22 is performed on the homotrended indicator values according to the following formula (1):
in the formula, Q l The first index value after the index is subjected to non-dimensionalization; x is the number of l The first index value after homotrenization; min l Is the minimum value of the indicator of the item I; max (maximum of ten) l The maximum value of the indicator is the l;
the determination of the index weight coefficient in S23 is as shown in equation (2) (3):
wherein, CV is l Denotes the coefficient of variation, W l For weighting based on the coefficient of variation, σ and μ are the standard deviation operation and the mean operation, respectively.
3. The method for estimating the quality of the GNSS deformation monitoring result according to claim 1, wherein the expression for calculating the comprehensive estimation index value T in S3 is shown in (4):
wherein N is l Is the number of the individual indexes, w i And Q i The weight and the dimensionless index of the ith index are respectively.
4. The method as claimed in claim 1, wherein the calculating of the indicators of the raw data quality in S1 includes:
-data integrity rate, comprising calculating single frequency data integrity rate and single system data integrity rate:
DI f the integrity rate of observed data at a single frequency point, n is the total number of theoretical satellites in an observation period, hf j The actual number, ef, of effective observed values of the jth satellite in an observation period on a certain frequency point j The theoretical number of effective observed values of the jth satellite in an observation time period on a certain frequency point is obtained; DI S Represents the complete rate, hs, of the observation data of a single system j Indicating that all frequency points in the observation period of the jth satellite have effective observed value actual number, es j The theoretical number indicates that all frequency points in the observation period of the jth satellite have effective observation values;
-a data cycle slip ratio, calculated as shown in equation (7):
where oslip represents the cycle slip ratio, obs have The number of effective observed values is represented, and slip represents the number of cycle hops; the average multipath error is calculated according to equation (8):
in the formula, MP i 、MP j For the calculated quantities containing multipath errors and integer ambiguities, p i 、ρ j Is pseudo range observed quantity, unit m;
is the carrier phase observation, in units of m; f. of i 、f j Is frequency, in MHz;
calculating among a plurality of epochs without cycle slip according to a formula (9) to obtain an average multipath error:
wherein the content of the first and second substances,
an estimate of multipath error at k frequencies for the receiver observed for the satellite; n is the number of epochs of the sliding window, and the default is 50;
the calculation amount of multipath error and integer ambiguity information on the frequency of the satellite observed by the receiver of the epoch t;
-average signal-to-noise ratio, calculated according to equation (10):
in the formula (I), the compound is shown in the specification,
the satellite average signal-to-noise ratio index statistic value is obtained; n is the total number of observation satellites; j is an observation satellite number; n is a radical of j The total number of observation epochs for satellite j; i is the observation epoch serial number of the satellite j;
is the signal-to-noise ratio observation of satellite j at epoch t, in dBHz.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116990841A (en) * | 2023-06-25 | 2023-11-03 | 无锡卡尔曼导航技术有限公司南京技术中心 | GNSS deformation monitoring data quality control method, system and device |
| CN117055074A (en) * | 2023-10-13 | 2023-11-14 | 中国电子科技集团公司第十五研究所 | Relative precision comprehensive quantitative evaluation method, server and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116990841A (en) * | 2023-06-25 | 2023-11-03 | 无锡卡尔曼导航技术有限公司南京技术中心 | GNSS deformation monitoring data quality control method, system and device |
| CN116990841B (en) * | 2023-06-25 | 2024-01-23 | 无锡卡尔曼导航技术有限公司南京技术中心 | GNSS deformation monitoring data quality control method, system and device |
| CN117055074A (en) * | 2023-10-13 | 2023-11-14 | 中国电子科技集团公司第十五研究所 | Relative precision comprehensive quantitative evaluation method, server and storage medium |
| CN117055074B (en) * | 2023-10-13 | 2024-01-23 | 中国电子科技集团公司第十五研究所 | Relative precision comprehensive quantitative evaluation method, server and storage medium |
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